Nocturnal Hypertension and Attenuated Nocturnal Blood Pressure Dipping is Common in Pediatric Lupus

J. Fallon Campbell; Sarah J. Swartz; Scott E. Wenderfer

doi:10.12688/f1000research.6532.1

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Research Article

Nocturnal Hypertension and Attenuated Nocturnal Blood Pressure Dipping is Common in Pediatric Lupus

[version 1; peer review: 2 approved, 1 approved with reservations]

J. Fallon Campbell¹, Sarah J. Swartz¹, Scott E. Wenderfer¹

PUBLISHED 23 Jun 2015

Author details Author details

¹ Department of Pediatrics, Renal Section, Baylor College of Medicine, Houston, TX, 77030, USA

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Lupus nephritis and neuropsychiatric lupus collection.

Abstract

Hypertension is an important manifestation of systemic lupus erythematosus (SLE) but reports of prevalence vary between 20-70% in published reports of adult and pediatric patients. For both children and adults with SLE, the clinical diagnosis and management of hypertension has traditionally been based on guidelines developed for the general population. In clinical trials, the criteria used for defining participants with hypertension are mostly undefined. As a first step towards formally assessing the blood pressure (BP) patterns of children diagnosed with SLE, 24-hr ambulatory BP monitoring data was analyzed on clinic patients who presented with prehypertension or stage I hypertension. In this pediatric SLE cohort, 20% met daytime criteria for a diagnosis of hypertension. Patterns of BP elevation varied widely with white coat, masked, isolated systolic, and diastolic nocturnal hypertension all identified. Nocturnal hypertension was detected in 60% and attenuated nocturnal BP dipping in 90% of both hypertensive and normotensive SLE patients. In SLE patients, the median nighttime systolic and diastolic loads were 25% and 15.5% compared with median daily loads of 12.5% and 11.5%. Daytime and nighttime systolic and diastolic BP load and nocturnal dipping was compared to a control population consisting of 85 non-SLE patients under 21 years old with prehypertension or stage 1 hypertension presenting to hypertension clinic. Median systolic BP dipped 5.3 mmHg in SLE patients compared to 11.9 mmHg in non-lupus (p-value = 0.001). Median diastolic BP dipped 12.9 mmHg versus 18.5 mmHg in non-lupus (p-value = 0.003). Patterns of BP dysregulation in pediatric SLE merit further exploration. Children with or without SLE displaying prehypertensive or stage 1 casual BP measurements had similar rates of hypertension by ambulatory BP monitoring. However, regardless of BP diagnosis, and independent of kidney involvement, there was an increased proportion with attenuated nocturnal dipping and nocturnal hypertension in SLE patients.

Keywords

Hypertension, Blood pressure, SLE, Lupus, Pediatric, ABPM, White-coat, Prehypertension

Corresponding author: Scott E. Wenderfer

Competing interests: No competing interests were disclosed.

Grant information: This study was funded in part by a Pediatric Pilot Award program, granted to SEW by the Department of Pediatrics at Baylor College of Medicine.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2015 Campbell JF et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

How to cite: Campbell JF, Swartz SJ and Wenderfer SE. Nocturnal Hypertension and Attenuated Nocturnal Blood Pressure Dipping is Common in Pediatric Lupus [version 1; peer review: 2 approved, 1 approved with reservations]. F1000Research 2015, 4:164 (https://doi.org/10.12688/f1000research.6532.1) First published: 23 Jun 2015, 4:164 (https://doi.org/10.12688/f1000research.6532.1) Latest published: 23 Nov 2015, 4:164 (https://doi.org/10.12688/f1000research.6532.2)

Introduction

Ambulatory blood pressure monitoring (ABPM) is preferred to casual clinic blood pressure (BP) monitoring in the diagnosis of hypertension (HTN). There are many shortfalls of casual BP readings, including the white coat effect, observer bias/measurement error, regression to the mean with repeated measurements, and variability of blood pressure over time (Flynn, 2011). Additionally, the published normative values for casual BP are based on the auscultation method, yet many clinic measurements are taken with oscillometric devices (Woroniecki & Flynn, 2005). Perhaps the most clinically relevant shortfall is the limited outcome data regarding casual BP measurements and end-organ damage or cardiovascular risk.

There is good evidence supporting the utility of ABPM findings in the prediction of cardiovascular outcomes, both in adults and children (Belsha et al., 1998; Lurbe et al., 2004; Pickering et al., 2006; Singh et al., 2013; Sorof et al., 2002). ABPM can account for the white coat effect as well as measurement and observer errors. Where casual BP measurements account for the magnitude of BP at single points in time, ABPM can define BP loads which measure the proportion of BPs that exceed a defined cutoff, typically the 95^th percentile as defined by normative data, over a 24-hr period. Therefore ABPM provides a better appreciation of BP trends which can account for the dynamic nature of BP (such as circadian rhythms, nocturnal BP dipping) (Flynn, 2011).

Although ABPM is a valuable piece of the HTN evaluation, there are potential barriers to its widespread utilization related to both financial and clinical considerations. Insurance companies offer limited reimbursement for ABPM placement and interpretation (Swartz et al., 2008). Although there is vast evidence for normative values in adults; there is more limited normative data for ABPM interpretation in children. The current normative values are based on approximately 950 healthy children with limited variability in ethnicity/race (Wühl et al., 2002). Despite these obstacles, ABPM is considered the gold standard for diagnosis of HTN in both adults and pediatrics. It is useful in predicting cardiovascular risk related to HTN but is also helpful in assessing BP in special pediatric populations such as obesity, sickle cell disease, chronic kidney disease, end stage renal disease, and diabetes (Flynn, 2011). There is limited research on the use of ABPM in the pediatric systemic lupus erythematosus (SLE) population (Canpolat et al., 2013), though HTN occurs in 20–70% of these patients (Bogdanovic et al., 2004; Brunner et al., 2002; Lau et al., 2006; Ruggiero et al., 2013). There is also limited data regarding the management of HTN in the SLE population, with management traditionally based on guidelines developed for the general population (Tselios et al., 2014).

Cardiovascular disease is a leading cause of mortality in adults with SLE and though there are many non-traditional risk factors including altered renal function, impaired endothelial function, chronic inflammation, and an activated renin-angiotensin system (RAS) (Gustafsson et al., 2012; Kiani et al., 2008; Knight & Kaplan, 2013; Pieretti et al., 2007), HTN is still an important risk factor (Contreras et al., 2005; Ginzler et al., 1993; Petrin et al., 1993; Yang et al., 1994). Therefore the use of standardized definitions to define HTN in SLE patients is crucial in better understanding cardiovascular risk and preventing adverse outcomes. Although isolated nocturnal HTN is not considered sufficient for a diagnosis for systemic hypertension in the general population, it is known to associate with increased risk of cardiac outcomes (Yee, 2015). Additionally, attenuated nocturnal dipping, even in the setting of normal 24-hr BP, was noted to be an independent predictor of cardiovascular mortality in a large prospective cohort study in Japan (Ohkubo et al., 2002). Hence, it is also important to use ABPMs to characterize blood pressure patterns in SLE patients so that specific guidelines for ABPM interpretation can be established for this population.

Methods

Study population

BP patterns of the 10 SLE study participants (demographics summarised in Table 1) recruited from a single center were retrospectively reviewed using data from 24-hr ambulatory BP monitoring tests performed between February 2012 and April 2013. ABPM was routinely ordered only on SLE patients seen in a multispecialty pediatric lupus clinic when they presented without kidney disease (non-renal lupus or nephritis in remission) and with casual BP measurements in prehypertensive or stage 1 hypertensive range (National High BP Education Program Working Group on High BP in Children and Adolescents, 2004). The inclusion criteria for the SLE cohort were: (1) diagnosis of SLE by American College of Rheumatology (ACR) criteria, (2) age < 21 years, and (3) ABPM performed. There were 85 patients in the non-SLE cohort (43% female, race and ethnicity unknown). The inclusion criteria for the non-SLE cohort included: (1) age < 21 years and (2) ABPM performed. Exclusion criteria for both cohorts: (1) ABPM uninterpretable due to incomplete/missing data, (2) casual BP measurements all < 90th or all > 99th percentile for age, gender, and height, +5 mmHg (3) end stage renal disease, or (4) kidney transplant recipient. The mean age at ABPM for the non-SLE cohort was 12.4 ± 0.4 years, and the mean BMI was 25.1 ± 0.8 kg/m² using the Mosteller formula. The study protocol was reviewed and approved by the Institutional Review Board for Baylor College of Medicine (H-32061).

Table 1. Pediatric SLE Cohort Demographics.

Patient Characteristics	Values (n=10)
Age at ABPM (years)	14.6 ± 0.3
Age at SLE Diagnosis (years)	12.4 ± 1.0
BMI (kg/m²)	25.1 ± 1.1
SLE ACR criteria met	5.8 ± 0.4
Gender (% female)	9 (90)
Race
African American (%)	4 (40)
Hispanic (%)	3 (30)
Caucasian (%)	3 (30)

Laboratory Findings (within 3 months of ABPM)
eGFR (ml/min/1.73 m²)	133 ± 6.2
C3 (mg/dL)	83.4 ± 9.5
Sed Rate (mm/hr)	58.7 ± 16.2
Hgb (g/dL)	11.8 ± 0.5
Elevated CRP (%)	1 (10)
Proteinuria (%)	2 (20)
Positive ANA (%)	10 (100)
Positive Anti-DS DNA Antibody (%)	9 (90)
Positive Anti-phospholipid Antibody (%)	9 (90)

Medications (at time of ABPM)
Prednisone dose (mg/kg/day)	0.313 ± 0.07
IV corticosteroids (%)	3 (30)
Hydroxychloroquine (%)	9 (90)
Mycophenolate mofetile (%)	3 (30)
Methotrexate (%)	1 (10)
Azathioprine (%)	1 (10)
Aspirin (%)	7 (70)
ACE inhibitor (%)	1 (10)

Participants. As this study was a retrospective chart review, there were no dropouts.

Sample size. As this was a pilot study, the sample size was not determined by formal power analysis. There were 11 consecutive SLE patients and 100 consecutive non-SLE patients with ABPM data identified, but 1 and 15 patients, respectively did not qualify based on the inclusion and exclusion criteria.

Blinding. All interpretation of ABPM data was performed at the time of clinical testing, prior to inception of the study. There was no formal blinding of ABPM data during the data analysis or sensitivity analysis phases.

Ambulatory Blood Pressure Monitoring

24-hr ABPM was performed using SpaceLabs 90217-1Q or 90217A-1 equipment. SpaceLabs Medical Software (version 90219) was used to evaluate the BP patterns. BP measurements were automatically measured every 20 minutes during the daytime and every 30 minutes during the nighttime over a 24-hr period. Mean diastolic and systolic BPs were calculated for both daytime and nighttime periods and compared to normative data for mean BPs based on age and gender (Wühl et al., 2002). BP loads were calculated for both diastolic and systolic BP, reflecting the percentage of BP measurements above the 95^th percentile for gender and age. Blood pressure loads >25% were considered abnormal (Urbina et al., 2008). Additionally, nocturnal dipping of BP was defined as the difference between daytime and nighttime BPs. Nocturnal dipping <10% was considered abnormal (Urbina et al., 2008). American Heart Association (AHA) definitions were used to define normal blood pressure, masked, white-coat and sustained HTN (Flynn, 2011).

Clinical data

Demographic and clinical data was collected from medical records for the SLE cohort. Demographic data included race and age at diagnosis. Clinical data (from within 3 months of ABPM date) included height, weight, BMI, laboratory results, eGFR using the Schwartz formula (Schwartz et al., 2009), presence of proteinuria, medications at the time of ABPM, echocardiogram findings including left ventricular hypertrophy (LVH), left ventricular mass index (LVMI), and relative wall thickness (RWT), ACR criteria for SLE, and SLEDAI score (Systemic Lupus Erythematosus Disease Activity Index). Demographic information for the non-SLE cohort was obtained from the SpaceLabs software, including age at the time of ABPM, gender, height, and weight.

Statistical analysis

Statistical analysis was performed using SigmaPlot software (version 11.0). Patient characteristics and ABPM findings were analyzed using descriptive statistics (medians, and intra-quartile ranges). Fisher exact and Wilcoxon rank sum tests were used to characterize patient demographics and BP patterns. Statistical significance was defined as p-value ≤0.05 (two tailed).

Results

Patient and clinical characteristics

The study population consisted of ten patients, all of whom met ACR diagnostic criteria for SLE (Patient demographics in Table 1). At the time of ABPM, mean age of the SLE cohort was 14.6 years. The mean BMI was 25.1 kg/m². Nine patients were female. Three SLE patients were Hispanic, three were Caucasian, and four were African American. The non-SLE control population consisted of 85 age- and BMI-matched pediatric patients with casual BP measurements between 90–99^th percentile without kidney disease or diabetes.

Case #	BP Diagnosis	ABPM Date	Age at ABPM	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Attenuated Dipping	Nocturnal HTN	Wake Sleep Method	Adequate #/% Readings		Age at SLE Diagnosis	SLE Vintage	Race	Gender	Height	Weight	BMI	eGFR (Schwartz formula)	Proteinuria	Hgb	ESR	CRP	total Cholesterol	LDL	HDL	Triglycerides	C3	ANA	Anti-double stranded DNA Ab	Anti-phospholipid antibodies	Steroid dose with IV pulse	Oral Prednisone (mg/kg/day)	AZA	MMF	HCQ	MTX	RTX	ACEI/ARB	aspirin	LVH on echo	LVMI on echo	RWT on echo	Malar Rash	Discoid Rash	Photo-sensitivity	Mouth Sores	Serositis	Arthritis	Renal disease	Neurologic disease	Hematologic disease	Antinuclear antibody	Immunologic disease	# SLEACR Criteria	OSA based on sleep study
1	hypertension	2/4/2012	14	68%	60%	131	82	60%	50%	131	83	100%	100%	128	79	2.3%	4.9%	Yes	Yes	reported	Yes	Based on 95%ile	14	0	African American	Female	1.57	53	21.5	153	Yes	9.1	121	Normal	N/A	N/A	N/A	N/A	47	1280	Yes	Yes	0	0.79	No	No	Yes	No	No	No	No	No	39.2	0.52	Yes	No	Yes	No	Yes	Yes	Yes	No	Yes	Yes	Yes	8	No
2	masked HTN	8/21/2012	14	6%	61%	116	78	0%	61%	120	83	12%	62%	111	70	7.6%	15.7%	Yes	Yes	reported	Yes	Based on 95%ile	5	9	African American	Male	1.45	47	22.4	169	No	13.1	5	Normal	N/A	N/A	N/A	N/A	106	1280	Yes	Yes	0	0.11	No	Yes	Yes	No	No	No	Yes	N/A	N/A	N/A	Yes	No	No	Yes	Yes	Yes	No	No	Yes	Yes	Yes	7	No
3	normotension	2/19/2013	17	18%	6%	122	68	12%	8%	126	72	36%	0%	113	57	10.4%	20.9%	No	Yes	reported	Yes	Based on 95%ile	14	3	African American	Female	1.53	73	31.2	114	No	9.9	79	High	N/A	N/A	N/A	N/A	44	1280	Yes	Yes	x1, 3 weeks prior	0.14	No	Yes	Yes	No	No	No	Yes	N/A	N/A	N/A	No	No	No	No	Yes	Yes	Yes	No	Yes	Yes	Yes	6	No
4	normotension	3/13/2012	15	20%	19%	113	67	9%	15%	116	71	35%	23%	109	62	6.1%	12.7%	Yes	Yes	reported	Yes	Based on 95%ile	14	1	Hispanic	Female	1.58	62	24.8	132	No	12.7	19	Normal	N/A	N/A	N/A	N/A	126	640	Yes	Yes	weekly	0.49	No	No	No	No	No	No	No	N/A	N/A	N/A	No	No	Yes	Yes	No	No	Yes	No	Yes	Yes	Yes	6	No
5	normotension	4/17/2012	15	19%	17%	115	69	0%	3%	113	70	64%	50%	119	68	-5.3%	2.9%	Yes	Yes	default	Yes	Based on 95%ile	14	1	Hispanic	Female	1.54	67	28.3	109	No	13.6	N/A	N/A	174	79	44	253	50	1280	Yes	Yes	0	0.46	No	No	Yes	No	No	No	Yes	N/A	N/A	N/A	Yes	No	No	No	No	Yes	Yes	Yes	No	Yes	Yes	6	No
6	normotension	5/23/2012	15	7%	3%	109	63	0%	3%	110	68	15%	4%	109	58	1.0%	14.8%	Yes	No	reported	Yes	Based on 95%ile	15	0	Caucasian	Female	1.63	76	28.6	137	Yes	10.3	122	Normal	116	66	14	261	90	1280	Yes	No	weekly	0.41	No	Yes	Yes	No	No	No	No	No	35.25	0.40	No	No	No	Yes	No	No	Yes	No	Yes	Yes	Yes	5	No
7	normotension	8/21/2012	14	0%	0%	101	54	0%	0%	103	58	0%	0%	97	48	5.9%	17.3%	Yes	No	reported	Yes	Based on 95%ile	8	6	Caucasian	Female	1.44	41	19.8	131	No	11.4	N/A	Normal	N/A	N/A	N/A	N/A	75	1280	Yes	Yes	0	0.09	No	No	Yes	No	No	No	Yes	N/A	N/A	N/A	Yes	No	Yes	Yes	No	Yes	No	No	No	Yes	Yes	6	No
8	normotension	3/13/2012	14	26%	25%	118	71	12%	17%	119	73	63%	44%	115	67	3.4%	8.3%	Yes	Yes	default	Yes	Based on 95%ile	13	1	Caucasian	Female	1.6	64	25.0	114	No	13.3	25	Normal	N/A	N/A	N/A	N/A	86	1280	Yes	Yes	0	0.31	Yes	No	Yes	No	No	Yes	Yes	N/A	N/A	N/A	Yes	No	No	No	No	Yes	Yes	No	Yes	Yes	Yes	6	No
9	white coat	1/31/2013	13	3%	6%	106	60	0%	4%	109	62	8%	8%	101	55	7.4%	11.3%	Yes	No	default	No	Based on 95%ile	12	1	African American	Female	1.59	65	25.7	129	No	12.6	40	Normal	N/A	N/A	N/A	N/A	106	1280	Yes	Yes	0	0.09	No	No	Yes	No	No	No	Yes	N/A	N/A	N/A	No	No	No	No	No	Yes	No	No	Yes	Yes	Yes	4	No
10	normotension	4/12/2013	15	2%	4%	106	59	0%	3%	107	62	8%	8%	102	54	4.7%	13.0%	Yes	No	reported	Yes	Based on 95%ile	15	0	Hispanic	Female	1.63	63	23.7	142	No	11.9	N/A	N/A	N/A	N/A	N/A	N/A	104	1280	No	No	0	0.24	No	No	Yes	Yes	No	No	Yes	No	42.86	0.39	No	No	No	Yes	No	Yes	No	No	No	Yes	Yes	4	No

Dataset 1.Raw ABPM Data for pSLE Cohort.

File contains the coded ambulatory blood pressure monitoring data for the pediatric SLE cohort abstracted from Space Labs software, using the default 95th percentile cutoff to distinguish normal versus high BP values. ABPM data was matched to demographic, clinical, and laboratory data abstracted from the electronic medical record. N/A (not available) indicates that data was sought but testing was not performed. BMI = body mass index, eGFR = estimated glomerular filtration rate, Hgb = hemoglobin, ESR = erythrocyte sedimentation rate, CRP = Creactive protein, LDL = low density lipoprotein, HDL = high density lipoprotein, ANA = anti-nuclear antibody, AZA = azathioprine, MTX = methotrexate, RTX = rituximab, ACEI = Angiotensin-converting enzyme inhibitor, ARB = angiotensin receptor blocker, LVH = left bentricular hypertrophy, LVMI = left ventricular mass index, RWT = right wall thickness, OSA = obstructive sleep apnea (Dataset 1: Campbell et al., 2015a).

Case #	Age	Diagnosis	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Nocturnal HTN	Attenuated?	Wake Sleep Method	Low Success	# Readings	% sucessful	Height	Weight	BMI	Gender
1	11	Hypertension	54%	44%	127	77	67%	56%	135	84	14%	7%	109	58	19.30%	31.00%	No	No	default	No	57	92	1.60	67	26.2	M
2	13	Hypertension	23%	39%	124	73	27%	38%	128	75	7%	33%	112	64	12.60%	14.70%	Yes	No	reported	No	56	90	1.66	54	19.6	M
3	14	White Coat	7%	7%	110	57	0%	6%	110	59	30%	10%	111	52	-0.90%	11.90%	Yes	Yes	reported	Yes	45	32	1.81	110	33.6	M
5	16	White Coat	16%	8%	122	65	17%	11%	130	70	14%	0%	106	54	18.50%	22.90%	No	No	reported	No	49	77	1.75	76	24.8	M
6	9	normotension	9%	11%	111	68	8%	13%	116	74	11%	6%	102	57	12.10%	23.00%	No	No	reported	No	57	92	1.43	54	26.4	F
7	17	White Coat	10%	15%	122	74	0%	14%	124	79	33%	17%	118	65	4.90%	17.80%	Yes	Yes	reported	Yes	41	65	1.92	85	23.1	M
8	16	White Coat	2%	5%	115	66	3%	8%	120	71	0%	0%	103	55	14.20%	22.60%	No	No	reported	No	56	92	1.72	59	19.9	F
9	12	hypertension	26%	4%	120	60	31%	3%	127	64	10%	10%	104	49	18.20%	23.50%	No	No	reported	Yes	46	61	1.59	63	24.9	M
10	5	normotension	2%	32%	108	72	3%	22%	113	76	0%	53%	98	65	13.30%	14.50%	Yes	No	reported	No	53	46	1.11	20	16.2	M
11	19	White Coat	2%	7%	117	78	0%	11%	123	83	6%	0%	107	69	13.10%	16.90%	No	No	reported	No	55	89	1.52	50	21.6	F
13	15	White Coat	9%	21%	116	66	9%	27%	129	75	8%	12%	102	56	21.00%	25.40%	No	No	reported	No	58	42	1.71	58	19.8	M
14	17	Hypertension	42%	37%	130	81	44%	53%	139	92	40%	16%	120	69	13.70%	25.00%	Yes	No	reported	No	57	93	1.54	41	17.3	M
15	11	White Coat	14%	15%	106	64	18%	15%	111	67	0%	15%	94	57	15.40%	15.00%	No	No	reported	No	52	84	1.55	85	35.4	F
16	17	normotension	12%	2%	121	68	13%	3%	127	75	8%	0%	108	54	15.00%	28.00%	No	No	reported	No	51	77	1.83	83	24.8	M
17	11	normotension	30%	16%	116	69	12%	3%	114	68	80%	53%	120	71	-5.20%	-4.40%	Yes	Yes	default	No	57	63	1.73	70	23.4	M
18	15	normotension	14%	14%	123	66	7%	0%	125	68	25%	38%	120	64	4.10%	5.90%	Yes	Yes	default	Yes	44	72	1.82	92	27.8	M
19	12	normotension	12%	35%	113	70	5%	27%	114	70	27%	53%	110	71	3.60%	-1.40%	Yes	Yes	default	No	52	84	1.64	98	36.4	M
20	16	White Coat	0%	4%	112	60	0%	3%	117	64	0%	8%	103	53	12.00%	17.20%	No	No	reported	No	49	79	1.82	67	20.2	M
21	11	normotension	11%	6%	113	63	9%	5%	116	66	15%	10%	105	57	9.50%	13.70%	No	Yes	reported	No	128	91	1.53	71	30.3	F
22	9	Hypertension	32%	20%	116	68	36%	21%	123	74	26%	17%	108	60	12.20%	19.00%	No	No	reported	No	56	90	1.53	63	26.9	M
23	9	Hypertension	44%	28%	119	71	42%	22%	122	74	48%	38%	115	65	5.80%	12.20%	Yes	Yes	reported	No	57	83	1.39	51	26.4	F
24	6	normotension	31%	6%	114	67	25%	3%	117	71	47%	13%	108	61	7.70%	14.10%	Yes	Yes	reported	No	51	82	1.27	25	15.5	F
25	16	White Coat	2%	7%	115	60	0%	7%	123	67	6%	6%	105	50	14.70%	25.40%	No	No	reported	Yes	44	59	1.81	161	49.1	M
26	10	normotension	31%	45%	117	74	21%	17%	121	76	41%	70%	113	72	6.70%	5.30%	Yes	Yes	reported	No	51	82	1.47	74	34.2	M
27	16	White Coat	5%	16%	119	67	3%	9%	126	71	8%	33%	108	59	14.30%	17.00%	Yes	No	reported	Yes	44	70	1.71	80	27.4	M
28	11	Hypertension	67%	32%	128	73	76%	36%	136	79	40%	20%	111	60	18.40%	24.10%	Yes	No	reported	No	57	90	1.58	55	22.0	M
29	6	normotension	0%	4%	99	63	0%	3%	101	67	0%	6%	94	56	7.00%	16.50%	No	Yes	reported	No	50	60	1.21	31	21.2	F
30	15	normotension	2%	11%	116	67	0%	8%	120	73	7%	20%	108	56	10.10%	23.30%	No	No	reported	No	55	86	1.69	66	23.1	M
31	16	hypertension	15%	26%	116	72	18%	30%	122	79	7%	13%	104	58	14.80%	26.60%	No	No	default	No	55	89	1.52	72	31.2	F
32	5	Hypertension	70%	71%	126	85	90%	88%	142	100	32%	41%	105	65	26.10%	35.00%	Yes	No	default	No	63	95	1.01	16	15.7	F
33	6	normotension	6%	6%	102	65	10%	10%	106	70	0%	0%	94	54	11.40%	22.90%	No	No	reported	Yes	47	61	1.14	19	14.6	F
34	7	White Coat	5%	5%	102	59	0%	5%	106	64	9%	5%	99	55	6.70%	14.10%	No	Yes	reported	Yes	42	66	1.28	32	19.5	F
35	15	hypertension	14%	23%	112	71	19%	32%	121	78	5%	5%	96	56	20.70%	28.30%	No	No	reported	No	56	88	1.59	50	19.8	F
37	16	Hypertension	71%	24%	133	70	80%	30%	141	77	47%	7%	114	55	19.20%	28.60%	Yes	No	reported	No	59	94	1.69	68	23.8	F
38	13	Hypertension	38%	2%	122	59	45%	3%	134	68	25%	0%	107	48	20.20%	29.50%	No	No	reported	Yes	45	70	1.75	84	27.4	M
39	6	Hypertension	57%	77%	121	86	66%	89%	126	92	31%	44%	112	71	11.20%	22.90%	Yes	No	reported	No	60	94	1.19	22	15.5	F
42	8	hypertension	29%	12%	115	66	30%	13%	119	70	28%	11%	108	61	9.30%	12.90%	No	Yes	default	Yes	41	65	1.43	67	32.8	M
43	6	Hypertension	44%	33%	118	73	50%	42%	125	79	35%	18%	108	63	13.60%	20.30%	Yes	No	reported	Yes	43	43	1.17	33	24.1	F
44	8	normotension	10%	2%	109	61	13%	3%	113	65	0%	0%	96	46	15.10%	29.30%	No	No	reported	No	49	74	1.42	55	27.3	F
45	8	Hypertension	36%	11%	118	67	39%	13%	120	71	20%	0%	106	50	11.70%	29.60%	No	No	reported	Yes	28	36	1.28	29	17.7	F
46	16	Hypertension	22%	33%	129	73	21%	40%	134	80	27%	13%	119	57	11.20%	28.80%	No	No	reported	No	58	59	1.74	67	22.1	M
48	13	Hypertension	86%	57%	136	78	90%	54%	141	82	77%	65%	126	71	10.70%	13.50%	Yes	No	reported	No	58	91	1.59	62	24.5	F
49	16	White Coat	2%	5%	115	64	2%	5%	120	69	0%	6%	105	56	12.60%	18.90%	No	No	default	No	62	100	1.80	103	31.8	M
50	8	Hypertension	65%	45%	123	76	65%	47%	128	82	67%	40%	114	67	11.00%	18.30%	Yes	No	reported	Yes	49	71	1.40	44	22.4	F
51	6	hypertension	69%	21%	117	64	50%	20%	118	72	79%	21%	116	59	1.70%	18.10%	Yes	Yes	reported	Yes	29	46	1.18	23	16.5	F
52	5	white coat	3%	6%	108	63	4%	4%	112	66	0%	8%	100	58	10.80%	12.20%	No	No	reported	Yes	36	47	1.19	22	15.5	F
53	13	hypertension	38%	23%	127	69	52%	28%	138	77	16%	16%	112	59	18.90%	23.40%	No	No	reported	Yes	48	61	1.60	69	27.0	M
54	17	white coat	2%	2%	114	70	2%	2%	120	77	0%	0%	101	55	15.90%	28.60%	No	No	default	No	56	90	1.65	53	19.5	F
56	17	hypertension	47%	26%	132	78	50%	38%	139	86	40%	0%	118	60	15.20%	30.30%	Yes	No	reported	No	47	77	1.65	72	26.4	F
57	14	normotension	10%	30%	120	72	3%	23%	121	74	30%	50%	119	67	1.70%	9.50%	Yes	Yes	default	Yes	40	60	1.62	104	39.6	M
58	13	white coat	4%	16%	104	64	4%	12%	112	73	6%	20%	100	60	10.80%	17.90%	No	No	reported	No	74	70	1.62	84	32.0	F
60	16	white coat	4%	6%	115	63	2%	5%	119	67	7%	7%	105	55	11.80%	18.00%	No	No	reported	No	55	89	1.79	72	22.5	M
62	16	normotension	9%	30%	122	75	9%	23%	125	78	8%	54%	113	66	9.60%	15.40%	Yes	Yes	reported	No	57	92	1.78	60	18.9	M
64	18	hypertension	49%	33%	130	77	44%	39%	133	81	63%	19%	124	70	6.80%	13.60%	Yes	Yes	default	No	55	82	1.78	115	36.3	M
66	11	hypertension	29%	33%	117	72	34%	30%	122	77	19%	38%	110	64	9.90%	16.90%	Yes	Yes	reported	Yes	49	42	1.54	41	17.3	F
67	14	hypertension	25%	64%	117	78	21%	62%	124	85	30%	67%	110	72	11.30%	15.30%	Yes	No	reported	No	56	81	1.60	68	26.6	F
68	6	normotension	12%	22%	107	69	8%	8%	106	69	20%	50%	108	70	-1.80%	-1.40%	Yes	Yes	reported	No	60	97	1.23	20	13.2	F
69	16	hypertension	35%	43%	134	77	34%	45%	142	84	39%	39%	119	64	16.20%	23.90%	Yes	No	default	No	51	82	1.74	112	37.0	M
71	14	hypertension	59%	23%	127	69	66%	27%	134	75	40%	13%	114	56	15.00%	25.40%	Yes	No	reported	No	56	89	1.70	66	22.8	F
72	8	hypertension	53%	23%	121	69	32%	16%	122	70	87%	33%	119	66	2.50%	5.80%	Yes	Yes	reported	Yes	40	65	1.37	49	26.1	M
73	9	white coat	5%	2%	106	59	4%	2%	110	63	6%	0%	97	51	11.90%	19.10%	No	No	reported	No	64	93	1.38	47	24.7	F
75	12	hypertension	54%	26%	128	70	59%	32%	134	77	39%	8%	115	53	14.20%	31.20%	Yes	No	reported	No	57	86	1.68	86	30.5	M
77	9	white coat	20%	2%	114	60	21%	3%	121	66	18%	0%	105	52	13.30%	21.30%	No	No	reported	No	46	79	1.36	47	25.4	M
78	8	normotension	6%	25%	104	68	10%	26%	108	73	0%	24%	99	61	8.00%	17.00%	No	Yes	default	No	48	79	1.40	19	9.7	M
79	17	hypertension	39%	12%	134	72	44%	18%	145	80	28%	0%	117	59	19.40%	26.30%	No	No	reported	No	52	37	1.67	75	26.9	M
80	17	white coat	23%	2%	122	64	25%	3%	130	69	15%	0%	102	52	21.60%	24.70%	No	No	reported	No	53	85	1.88	119	33.7	M
81	10	hypertension	57%	25%	124	70	61%	37%	130	79	50%	0%	114	54	12.40%	31.70%	Yes	No	reported	No	56	86	1.52	38	16.4	F
82	16	white coat	2%	5%	112	63	4%	7%	118	69	0%	0%	102	51	13.60%	26.10%	No	No	reported	Yes	41	67	1.67	81	29.0	M
83	13	normotension	13%	7%	120	67	13%	8%	127	72	13%	6%	108	56	15.00%	22.30%	No	No	reported	No	55	90	1.52	42	18.2	M
84	16	normotension	39%	22%	134	68	29%	14%	135	71	56%	33%	131	64	3.00%	9.90%	Yes	Yes	reported	Yes	23	37	1.76	116	37.4	M
85	14	hypertension	29%	31%	128	71	18%	32%	132	76	50%	29%	122	62	7.60%	18.50%	Yes	Yes	reported	Yes	42	68	1.75	132	43.1	M
86	9	normotension	3%	7%	102	62	0%	6%	104	66	8%	8%	97	57	6.80%	13.70%	No	Yes	reported	Yes	29	47	1.26	28	17.6	F
87	16	White Coat	4%	21%	119	70	2%	12%	126	75	7%	50%	107	62	15.10%	17.40%	Yes	No	reported	No	56	89	1.72	96	32.4	M
88	17	White Coat	9%	11%	123	77	7%	12%	127	81	20%	10%	111	64	12.60%	21.00%	No	No	reported	No	53	78	1.70	58	20.1	M
89	8	White Coat	30%	22%	114	70	24%	7%	118	74	44%	56%	106	63	10.20%	14.90%	Yes	No	reported	No	60	97	1.42	43	21.3	F
90	14	hypertension	19%	44%	120	73	24%	57%	130	81	6%	18%	103	58	20.80%	28.40%	No	No	reported	No	54	90	1.77	100	31.9	M
91	9	White Coat	7%	9%	109	64	5%	13%	114	70	11%	0%	101	54	11.50%	22.90%	No	No	reported	No	57	89	1.38	46	24.2	F
92	12	hypertension	67%	39%	138	77	67%	44%	141	82	67%	27%	130	68	7.90%	17.10%	Yes	Yes	default	No	54	76	1.67	59	21.2	M
93	13	White Coat	18%	21%	116	71	20%	17%	119	74	9%	36%	106	62	11.00%	16.30%	Yes	No	reported	No	57	84	1.64	75	27.9	F
94	15	hypertension	32%	3%	120	65	38%	4%	125	69	14%	0%	109	56	12.90%	18.90%	No	No	reported	Yes	31	50	1.88	146	41.3	F
95	14	white coat	7%	2%	120	62	7%	0%	124	66	8%	8%	110	54	11.30%	18.20%	No	No	default	Yes	41	73	1.78	77	24.3	M
96	15	White Coat	7%	7%	119	66	9%	7%	128	69	0%	7%	100	59	21.90%	14.50%	No	No	reported	No	60	95	1.67	63	22.6	M
97	11	White Coat	18%	11%	115	64	17%	3%	122	69	19%	19%	109	60	10.70%	13.10%	No	No	reported	No	56	89	1.41	41	20.6	M
98	15	white coat	7%	7%	116	64	0%	5%	117	70	27%	13%	113	55	3.50%	21.50%	No	Yes	reported	No	54	79	1.77	76	24.3	M

Dataset 2.Raw ABPM Data for non-SLE Cohort.

File contains the coded ambulatory blood pressure monitoring data and matched demographic data for the non-SLE pediatric cohort abstracted from Space Labs software, using the default 95th percentile cutoff to distinguish normal versus high BP values. Age is in years, BMI = body mass index (Dataset 2: Campbell et al., 2015b).

All of the SLE patients were antinuclear antibody (ANA) positive, nine were anti-double stranded DNA (anti-dsDNA) antibody positive, and nine tested positive for anti-phospholipid antibodies (aPL). Three SLE patients had echocardiograms and none had sonographic evidence of left ventricular hypertrophy based on adult criteria (LVMI 35.3, 39.2, and 42.9 g/m^2.7, with RWT of 0.40, 0.52, and 0.39). Two patients had proteinuria based on the SLE ACR definition (>0.5 g/day) at the time of ABPM. Among the seven patients with lab values available, the mean ESR was elevated at 58.71 mm/hr (normal <20) and the mean C3 was slightly low at 83.4 mg/dL (normal 90–200).

All SLE patients were prescribed prednisone at the time of ABPM with a mean dose of 0.31 ± 0.08 mg/kg/day. Three patients also received intravenous (IV) pulse steroids within the 3 months prior to ABPM. Two received weekly doses of IV solumedrol (30mg/kg/dose) and the third received a one-time dose in the week prior to ABPM placement. At the time of ABPM, only one was prescribed an ACE-inhibitor, and none were prescribed diuretics or any other anti-hypertensive. No one was treated with rituximab within the 2 years prior to the ABPM, though one patient did receive Rituximab following ABPM. The SLE patients met a median of six SLE ACR criteria for diagnosis of SLE. Five patients met criteria for malar rash; three met criteria for photosensitivity; five had mouth sores; three experienced serositis; eight had arthritis; six had renal involvement; seven met hematologic criteria; nine met immunologic criteria; and one met neurologic criteria. No patients were noted with a documented discoid rash.

ABPM findings

SLE patients tended to have lower daytime systolic blood pressure (SBP) and diastolic blood pressure (DBP) loads and higher nighttime systolic BP loads as compared to the non-SLE patients, and the decreased median SBP load was statistically significant (Table 2). The SLE cohort also showed a significantly higher rate of attenuated nocturnal dipping in both SBP and DBP, when compared to the non-SLE cohort (Figure 1). Ninety percent of SLE patients had attenuated nocturnal dipping compared to only 26% of non-SLE patients. SLE patients also had a higher rate of nocturnal HTN, whether in isolation or in conjunction with daytime HTN.

Table 2. Higher rates of Attenuated Systolic and Diastolic BP Dipping and Nocturnal Hypertension in Children with SLE.

Median Load * (%)	SLE	Non-SLE	p-value
24 hr SBP	12.5	18	0.22
24 hr DBP	11.5	16	0.45
Daytime SBP	0	18	0.01
Daytime DBP	6	13	0.25
Nighttime SBP	25	18	0.36
Nighttime DBP	15.5	13	0.59

Median BP Dipping (%)	SLE	Non-SLE	p-value
Systolic	5.3	11.9	0.001
Diastolic	12.9	18.5	0.003

*BP Diagnosis (%)**	SLE	Non-SLE	p-value
Daytime and Nighttime Hypertension	20	29	0.49
Daytime Hypertension (± nighttime)	20	42	0.10
Daytime Hypertension only	0	17	0.20
Nighttime Hypertension (± daytime)	60	39	0.51
Nighttime Hypertension only	40	21	0.45
Normal Daytime and Nighttime BP	40	30	0.74
Attenuated Nocturnal Dipping	90	26	< 0.001

* based on definition of elevated BP as exceeding the 95th%tile for age and gender (Wuhl, 2002)

Figure 1. Attenuated BP dipping and trends toward less daytime and more nighttime HTN in pediatric SLE.

Box plots indicate median, 10th, 25th, 75th, and 90th percentile data, based on Wilcoxon Analysis. Dots indicate the outliers. Differences in %SBP dipping (p = 0.001) and %DBP dipping (p = 0.003) and daytime SBP load (p = 0.01) were statistically significant whereas differences in nighttime SBP load (p = 0.36) and DBP load (p = 0.59) failed to reach significance.

Specifically, only two SLE patients met ABPM criteria for both daytime and nighttime HTN; however four additional patients had isolated nocturnal HTN with normal daytime BPs. Nine of the SLE patients had attenuated nocturnal dipping, regardless of HTN diagnosis. Of the nine SLE patients with attenuated nocturnal dipping, two had proteinuria at the time of ABPM. The one patient with normal dipping had no proteinuria.

Relationships between ABPM findings and clinical characteristics

There were no statistically significant associations between most laboratory measures (complement 3 (C3), ANA, anti-dsDNA antibodies, aPL antibodies) and nocturnal HTN or attenuated nocturnal dipping. The two patients without aPL antibodies did not have nocturnal HTN, though they did have attenuated dipping (Table 3). The one patient who had normal nocturnal dipping was African American, had the highest BMI, low C3 levels, a SLEDAI score of 4, received a dose of pulse steroid within 3 weeks of ABPM, and met more than six SLE ACR criteria. She did have nocturnal and masked HTN. There were no obvious associations between ABPM findings and the presence of specific historical ACR criteria for SLE; however, this study is underpowered to perform formal statistical analysis. Of the six SLE patients who historically met diagnostic criteria for kidney disease, five had nocturnal HTN, while only one of the four patients without a history of nephritis had nocturnal HTN. Moreover, five of the six with a history of nephritis and all four of the non-renal SLE patients had attenuated nocturnal dipping.

Table 3. Individual ABPM and Clinical Laboratory Data for Pediatric SLE Cohort*.

#	BP Diagnosis	Age at ABPM	Wake SBP load	Wake DBP load	Sleep SBP load	Sleep DBP load	SBP Dip	DBP Dip	Attenuated Dipping	Nocturnal HTN	BMI	eGFR	C3	DNA Ab	aPL Ab	SLEDAI Score	# ACR criteria met
1	HTN	14	60%	50%	100%	100%	2%	5%	Yes	Yes	22	153	47	Yes	Yes	15	8
2	masked HTN	14	0%	61%	12%	62%	8%	16%	Yes	Yes	22	169	106	Yes	Yes	4	7
3	normotension	17	12%	8%	36%	0%	10%	21%	No	Yes	31	114	44	Yes	Yes	4	6
4	normotension	15	9%	15%	35%	23%	6%	13%	Yes	Yes	25	132	126	Yes	Yes	4	6
5	normotension	15	0%	3%	64%	50%	-5%	3%	Yes	Yes	28	109	50	Yes	Yes	10	6
6	normotension	15	0%	3%	15%	4%	1%	15%	Yes	No	29	137	90	Yes	No	4	5
7	normotension	14	0%	0%	0%	0%	6%	17%	Yes	No	20	131	75	Yes	Yes	8	6
8	normotension	14	12%	17%	63%	44%	3%	8%	Yes	Yes	25	114	86	Yes	Yes	8	6
10	normotension	15	0%	3%	8%	8%	5%	13%	Yes	No	24	142	104	No	No	6	4
9	white coat	13	0%	4%	8%	8%	7%	11%	Yes	No	26	129	106	Yes	Yes	4	4

* based on definition of elevated BP as exceeding the 95th%tile for age and gender (Wuhl, 2002)

There was also no significant difference in attenuated nocturnal dipping between SLE patients who received pulse corticosteroids within 3 months of ABPM and those who had not. Of the three patients who received pulse steroids, two had nocturnal HTN and one did not. Finally, there were no statistically significant association between use of specific immunosuppressive medication usage (azathioprine, mycophenolate mofetile, hydroxychloroquine, methotrexate) and either nocturnal HTN or attenuated nocturnal dipping. The patient who was on an ACE inhibitor at the time of ABPM did have attenuated nocturnal dipping and nocturnal HTN.

Attenuated dipping was not associated with disease duration. The patient with the longest SLE vintage (9 years from SLE diagnosis to time of ABPM) had both nocturnal HTN and attenuated nocturnal dipping, but the three patients with the shortest disease duration (<1 year from SLE diagnosis to time of ABPM) all had attenuated dipping.

The two SLE patients with daytime HTN also had nocturnal HTN and attenuated nocturnal dipping. They were both African American and one was the only male in the cohort. One patient’s disease duration was 9 years whereas the other was diagnosed in the past year. The patient with disease duration of < 1 year was on a higher oral steroid dose and had proteinuria and low C3 level. They both met more than six ACR diagnostic criteria.

All four of the patients with normal nocturnal BPs still had attenuated nocturnal dipping. Three of these patients’ disease duration was ≤ 1 year while the fourth was 6 years. Their SLEDAI scores ranged from 4–8 at the time of ABPM ± 10 days.

Sensitivity analysis

To determine whether disease-specific BP parameters might be influenced by the thresholds used to define hypertension during analysis of the ABPM data, a sensitivity analysis was performed. Since all patients in our pediatric SLE cohort lacked active nephritis and heart disease, a 95% cutoff was used to distinguish normal versus high BP. To test whether using a 90% cutoff would alter the results, all SLE patient ABPM data was re-interpreted. The data was also reanalyzed using BP loads of >30% (per institutional protocol) rather than 25% (Urbina et al., 2008) to define HTN. In addition, to test if the quality of the ABPM data affected the findings, the comparison between cohorts was repeated after ABPM tests were discarded if either <75% of attempted BP measurements were successful, <50 total measurements were successful, or both. Finally, since 90% of the SLE cohort was female, comparisons were made to the non-SLE controls after eliminating ABPM data from males in the non-SLE cohort. Results showed that decrease in prevalence of daytime SBP load in the SLE cohort lost significance using a 90% cutoff, whereas the increase in incidence of nocturnal HTN became significant using BP loads >30% to define hypertension (Figure 2). The attenuation of nighttime BP dipping in the SLE cohort and all other ABPM findings were not significantly altered by any of the changes.

Figure 2. No major effects of methods for ABPM interpretation on results in SLE cohort.

Boxes represent the range of medians (left) or means (right) obtained from sensitivity analysis. Analyses were repeated comparing non-SLE to SLE cohort, using either 90th or 95th percentiles, and 25% or 30% load, in the definition for hypertension, and by restricting dataset to only include ABPM findings when success rates of measurements were >75%, when >50 total successful measurements were recorded, or both. Ranges of p-values are indicated.

Case #	Age	Diagnosis	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Nocturnal HTN	Attenuated?	Wake Sleep Method	Low Success	# Readings	% sucessful
1	11	Hypertension	54%	44%	127	77	67%	56%	135	84	14%	7%	109	58	19.3%	31.0%	No	No	default	No	57	92
2	13	Hypertension	23%	39%	124	73	27%	38%	128	75	7%	33%	112	64	12.6%	14.7%	Yes	No	reported	No	56	90
6	9	normotension	9%	11%	111	68	8%	13%	116	74	11%	6%	102	57	12.1%	23.0%	No	No	reported	No	57	92
8	16	White Coat	2%	5%	115	66	3%	8%	120	71	0%	0%	103	55	14.2%	22.6%	No	No	reported	No	56	92
10	5	normotension	2%	32%	108	72	3%	22%	113	76	0%	53%	98	65	13.3%	14.5%	Yes	No	reported	No	53	46
11	19	White Coat	2%	7%	117	78	0%	11%	123	83	6%	0%	107	69	13.1%	16.9%	No	No	reported	No	55	89
13	15	White Coat	9%	21%	116	66	9%	27%	129	75	8%	12%	102	56	21.0%	25.4%	No	No	reported	No	58	42
14	17	Hypertension	42%	37%	130	81	44%	53%	139	92	40%	16%	120	69	13.7%	25.0%	Yes	No	reported	No	57	93
15	11	White Coat	14%	15%	106	64	18%	15%	111	67	0%	15%	94	57	15.4%	15.0%	No	No	reported	No	52	84
16	17	normotension	12%	2%	121	68	13%	3%	127	75	8%	0%	108	54	15.0%	28.0%	No	No	reported	No	51	77
17	11	normotension	30%	16%	116	69	12%	3%	114	68	80%	53%	120	71	-5.2%	-4.4%	Yes	Yes	default	No	57	63
19	12	normotension	12%	35%	113	70	5%	27%	114	70	27%	53%	110	71	3.6%	-1.4%	Yes	Yes	default	No	52	84
21	11	normotension	11%	6%	113	63	9%	5%	116	66	15%	10%	105	57	9.5%	13.7%	No	Yes	reported	No	128	91
22	9	Hypertension	32%	20%	116	68	36%	21%	123	74	26%	17%	108	60	12.2%	19.0%	No	No	reported	No	56	90
23	9	Hypertension	44%	28%	119	71	42%	22%	122	74	48%	38%	115	65	5.8%	12.2%	Yes	Yes	reported	No	57	83
24	6	normotension	31%	6%	114	67	25%	3%	117	71	47%	13%	108	61	7.7%	14.1%	Yes	Yes	reported	No	51	82
26	10	normotension	31%	45%	117	74	21%	17%	121	76	41%	70%	113	72	6.7%	5.3%	Yes	Yes	reported	No	51	82
28	11	Hypertension	67%	32%	128	73	76%	36%	136	79	40%	20%	111	60	18.4%	24.1%	Yes	No	reported	No	57	90
29	6	normotension	0%	4%	99	63	0%	3%	101	67	0%	6%	94	56	7.0%	16.5%	No	Yes	reported	No	50	60
30	15	normotension	2%	11%	116	67	0%	8%	120	73	7%	20%	108	56	10.1%	23.3%	No	No	reported	No	55	86
31	16	hypertension	15%	26%	116	72	18%	30%	122	79	7%	13%	104	58	14.8%	26.6%	No	No	default	No	55	89
32	5	Hypertension	70%	71%	126	85	90%	88%	142	100	32%	41%	105	65	26.1%	35.0%	Yes	No	default	No	63	95
35	15	hypertension	14%	23%	112	71	19%	32%	121	78	5%	5%	96	56	20.7%	28.3%	No	No	reported	No	56	88
37	16	Hypertension	71%	24%	133	70	80%	30%	141	77	47%	7%	114	55	19.2%	28.6%	Yes	No	reported	No	59	94
39	6	Hypertension	57%	77%	121	86	66%	89%	126	92	31%	44%	112	71	11.2%	22.9%	Yes	No	reported	No	60	94
46	16	Hypertension	22%	33%	129	73	21%	40%	134	80	27%	13%	119	57	11.2%	28.8%	No	No	reported	No	58	59
48	13	Hypertension	86%	57%	136	78	90%	54%	141	82	77%	65%	126	71	10.7%	13.5%	Yes	No	reported	No	58	91
49	16	White Coat	2%	5%	115	64	2%	5%	120	69	0%	6%	105	56	12.6%	18.9%	No	No	default	No	62	100
54	17	white coat	2%	2%	114	70	2%	2%	120	77	0%	0%	101	55	15.9%	28.6%	No	No	default	No	56	90
58	13	white coat	4%	16%	104	64	4%	12%	112	73	6%	20%	100	60	10.8%	17.9%	No	No	reported	No	74	70
60	16	white coat	4%	6%	115	63	2%	5%	119	67	7%	7%	105	55	11.8%	18.0%	No	No	reported	No	55	89
62	16	normotension	9%	30%	122	75	9%	23%	125	78	8%	54%	113	66	9.6%	15.4%	Yes	Yes	reported	No	57	92
64	18	hypertension	49%	33%	130	77	44%	39%	133	81	63%	19%	124	70	6.8%	13.6%	Yes	Yes	default	No	55	82
67	14	hypertension	25%	64%	117	78	21%	62%	124	85	30%	67%	110	72	11.3%	15.3%	Yes	No	reported	No	56	81
68	6	normotension	12%	22%	107	69	8%	8%	106	69	20%	50%	108	70	-1.8%	-1.4%	Yes	Yes	reported	No	60	97
69	16	hypertension	35%	43%	134	77	34%	45%	142	84	39%	39%	119	64	16.2%	23.9%	Yes	No	default	No	51	82
71	14	hypertension	59%	23%	127	69	66%	27%	134	75	40%	13%	114	56	15.0%	25.4%	Yes	No	reported	No	56	89
73	9	white coat	5%	2%	106	59	4%	2%	110	63	6%	0%	97	51	11.9%	19.1%	No	No	reported	No	64	93
75	12	hypertension	54%	26%	128	70	59%	32%	134	77	39%	8%	115	53	14.2%	31.2%	Yes	No	reported	No	57	86
79	17	hypertension	39%	12%	134	72	44%	18%	145	80	28%	0%	117	59	19.4%	26.3%	No	No	reported	No	52	37
80	17	white coat	23%	2%	122	64	25%	3%	130	69	15%	0%	102	52	21.6%	24.7%	No	No	reported	No	53	85
81	10	hypertension	57%	25%	124	70	61%	37%	130	79	50%	0%	114	54	12.4%	31.7%	Yes	No	reported	No	56	86
83	13	normotension	13%	7%	120	67	13%	8%	127	72	13%	6%	108	56	15.0%	22.3%	No	No	reported	No	55	90
87	16	White Coat	4%	21%	119	70	2%	12%	126	75	7%	50%	107	62	15.1%	17.4%	Yes	No	reported	No	56	89
88	17	White Coat	9%	11%	123	77	7%	12%	127	81	20%	10%	111	64	12.6%	21.0%	No	No	reported	No	53	78
89	8	White Coat	30%	22%	114	70	24%	7%	118	74	44%	56%	106	63	10.2%	14.9%	Yes	No	reported	No	60	97
90	14	hypertension	19%	44%	120	73	24%	57%	130	81	6%	18%	103	58	20.8%	28.4%	No	No	reported	No	54	90
91	9	White Coat	7%	9%	109	64	5%	13%	114	70	11%	0%	101	54	11.5%	22.9%	No	No	reported	No	57	89
92	12	hypertension	67%	39%	138	77	67%	44%	141	82	67%	27%	130	68	7.9%	17.1%	Yes	Yes	default	No	54	76
93	13	White Coat	18%	21%	116	71	20%	17%	119	74	9%	36%	106	62	11.0%	16.3%	Yes	No	reported	No	57	84
96	15	White Coat	7%	7%	119	66	9%	7%	128	69	0%	7%	100	59	21.9%	14.5%	No	No	reported	No	60	95
97	11	White Coat	18%	11%	115	64	17%	3%	122	69	19%	19%	109	60	10.7%	13.1%	No	No	reported	No	56	89
98	15	white coat	7%	7%	116	64	0%	5%	117	70	27%	13%	113	55	3.5%	21.5%	No	Yes	reported	No	54	79
99	15	hypertension	49%	51%	128	78	41%	43%	130	81	73%	73%	121	70	7.0%	13.6%	Yes	Yes	reported	No	59	77

Mean			25.8%	23.9%			26.2%	24.1%			24.0%	22.7%			12.5%	19.5%
Stdev			22.7%	18.2%			25.7%	21.2%			21.7%	21.6%			5.7%	8.2%
SEM			3.1%	2.5%			3.5%	2.9%			3.0%	2.9%			0.8%	1.1%
p value

Median			18.0%	21.5%			18.5%	17.5%			17.0%	14.0%			12.3%	19.1%
25th%			7.0%	7.0%			5.0%	7.0%			7.0%	6.0%			9.9%	14.6%
75th%			42.5%	33.5%			42.5%	37.3%			40.0%	39.5%			15.2%	25.4%

Dataset 3.Raw Data: Sensitivity Analysis: non-SLE greater than 50 successful readings.

File contains the coded ambulatory blood pressure monitoring data and matched demographic data for the subset of the non-SLE pediatric cohort meeting a more stringent criterion of ABPM data quality, in particular the successful completion of 50 or more BP measurements within the 24-hour monitoring period. ABPM data was abstracted from Space Labs software, using a 90th percentile cutoff to distinguish normal versus high BP values. Age is in years, BMI = body mass index (Dataset 3: Campbell et al., 2015c).

Case #	Age	Diagnosis	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Nocturnal HTN	Attenuated?	Wake Sleep Method	Low Success	# Readings	% sucessful
1	11	Hypertension	54%	44%	127	77	67%	56%	135	84	14%	7%	109	58	19.3%	31.0%	No	No	default	No	57	92
2	13	Hypertension	23%	39%	124	73	27%	38%	128	75	7%	33%	112	64	12.6%	14.7%	Yes	No	reported	No	56	90
5	16	White Coat	16%	8%	122	65	17%	11%	130	70	14%	0%	106	54	18.5%	22.9%	No	No	reported	No	49	77
6	9	normotension	9%	11%	111	68	8%	13%	116	74	11%	6%	102	57	12.1%	23.0%	No	No	reported	No	57	92
8	16	White Coat	2%	5%	115	66	3%	8%	120	71	0%	0%	103	55	14.2%	22.6%	No	No	reported	No	56	92
11	19	White Coat	2%	7%	117	78	0%	11%	123	83	6%	0%	107	69	13.1%	16.9%	No	No	reported	No	55	89
14	17	Hypertension	42%	37%	130	81	44%	53%	139	92	40%	16%	120	69	13.7%	25.0%	Yes	No	reported	No	57	93
15	11	White Coat	14%	15%	106	64	18%	15%	111	67	0%	15%	94	57	15.4%	15.0%	No	No	reported	No	52	84
16	17	normotension	12%	2%	121	68	13%	3%	127	75	8%	0%	108	54	15.0%	28.0%	No	No	reported	No	51	77
19	12	normotension	12%	35%	113	70	5%	27%	114	70	27%	53%	110	71	3.6%	-1.4%	Yes	Yes	default	No	52	84
20	16	White Coat	0%	4%	112	60	0%	3%	117	64	0%	8%	103	53	12.0%	17.2%	No	No	reported	No	49	79
21	11	normotension	11%	6%	113	63	9%	5%	116	66	15%	10%	105	57	9.5%	13.7%	No	Yes	reported	No	128	91
22	9	Hypertension	32%	20%	116	68	36%	21%	123	74	26%	17%	108	60	12.2%	19.0%	No	No	reported	No	56	90
23	9	Hypertension	44%	28%	119	71	42%	22%	122	74	48%	38%	115	65	5.8%	12.2%	Yes	Yes	reported	No	57	83
24	6	normotension	31%	6%	114	67	25%	3%	117	71	47%	13%	108	61	7.7%	14.1%	Yes	Yes	reported	No	51	82
26	10	normotension	31%	45%	117	74	21%	17%	121	76	41%	70%	113	72	6.7%	5.3%	Yes	Yes	reported	No	51	82
28	11	Hypertension	67%	32%	128	73	76%	36%	136	79	40%	20%	111	60	18.4%	24.1%	Yes	No	reported	No	57	90
30	15	normotension	2%	11%	116	67	0%	8%	120	73	7%	20%	108	56	10.1%	23.3%	No	No	reported	No	55	86
31	16	hypertension	15%	26%	116	72	18%	30%	122	79	7%	13%	104	58	14.8%	26.6%	No	No	default	No	55	89
32	5	Hypertension	70%	71%	126	85	90%	88%	142	100	32%	41%	105	65	26.1%	35.0%	Yes	No	default	No	63	95
35	15	hypertension	14%	23%	112	71	19%	32%	121	78	5%	5%	96	56	20.7%	28.3%	No	No	reported	No	56	88
37	16	Hypertension	71%	24%	133	70	80%	30%	141	77	47%	7%	114	55	19.2%	28.6%	Yes	No	reported	No	59	94
39	6	Hypertension	57%	77%	121	86	66%	89%	126	92	31%	44%	112	71	11.2%	22.9%	Yes	No	reported	No	60	94
48	13	Hypertension	86%	57%	136	78	90%	54%	141	82	77%	65%	126	71	10.7%	13.5%	Yes	No	reported	No	58	91
49	16	White Coat	2%	5%	115	64	2%	5%	120	69	0%	6%	105	56	12.6%	18.9%	No	No	default	No	62	100
54	17	white coat	2%	2%	114	70	2%	2%	120	77	0%	0%	101	55	15.9%	28.6%	No	No	default	No	56	90
56	17	hypertension	47%	26%	132	78	50%	38%	139	86	40%	0%	118	60	15.2%	30.3%	Yes	No	reported	No	47	77
60	16	white coat	4%	6%	115	63	2%	5%	119	67	7%	7%	105	55	11.8%	18.0%	No	No	reported	No	55	89
62	16	normotension	9%	30%	122	75	9%	23%	125	78	8%	54%	113	66	9.6%	15.4%	Yes	Yes	reported	No	57	92
64	18	hypertension	49%	33%	130	77	44%	39%	133	81	63%	19%	124	70	6.8%	13.6%	Yes	Yes	default	No	55	82
67	14	hypertension	25%	64%	117	78	21%	62%	124	85	30%	67%	110	72	11.3%	15.3%	Yes	No	reported	No	56	81
68	6	normotension	12%	22%	107	69	8%	8%	106	69	20%	50%	108	70	-1.8%	-1.4%	Yes	Yes	reported	No	60	97
69	16	hypertension	35%	43%	134	77	34%	45%	142	84	39%	39%	119	64	16.2%	23.9%	Yes	No	default	No	51	82
71	14	hypertension	59%	23%	127	69	66%	27%	134	75	40%	13%	114	56	15.0%	25.4%	Yes	No	reported	No	56	89
73	9	white coat	5%	2%	106	59	4%	2%	110	63	6%	0%	97	51	11.9%	19.1%	No	No	reported	No	64	93
75	12	hypertension	54%	26%	128	70	59%	32%	134	77	39%	8%	115	53	14.2%	31.2%	Yes	No	reported	No	57	86
77	9	white coat	20%	2%	114	60	21%	3%	121	66	18%	0%	105	52	13.3%	21.3%	No	No	reported	No	46	79
78	8	normotension	6%	25%	104	68	10%	26%	108	73	0%	24%	99	61	8.0%	17.0%	No	Yes	default	No	48	79
80	17	white coat	23%	2%	122	64	25%	3%	130	69	15%	0%	102	52	21.6%	24.7%	No	No	reported	No	53	85
81	10	hypertension	57%	25%	124	70	61%	37%	130	79	50%	0%	114	54	12.4%	31.7%	Yes	No	reported	No	56	86
83	13	normotension	13%	7%	120	67	13%	8%	127	72	13%	6%	108	56	15.0%	22.3%	No	No	reported	No	55	90
87	16	White Coat	4%	21%	119	70	2%	12%	126	75	7%	50%	107	62	15.1%	17.4%	Yes	No	reported	No	56	89
88	17	White Coat	9%	11%	123	77	7%	12%	127	81	20%	10%	111	64	12.6%	21.0%	No	No	reported	No	53	78
89	8	White Coat	30%	22%	114	70	24%	7%	118	74	44%	56%	106	63	10.2%	14.9%	Yes	No	reported	No	60	97
90	14	hypertension	19%	44%	120	73	24%	57%	130	81	6%	18%	103	58	20.8%	28.4%	No	No	reported	No	54	90
91	9	White Coat	7%	9%	109	64	5%	13%	114	70	11%	0%	101	54	11.5%	22.9%	No	No	reported	No	57	89
92	12	hypertension	67%	39%	138	77	67%	44%	141	82	67%	27%	130	68	7.9%	17.1%	Yes	Yes	default	No	54	76
93	13	White Coat	18%	21%	116	71	20%	17%	119	74	9%	36%	106	62	11.0%	16.3%	Yes	No	reported	No	57	84
96	15	White Coat	7%	7%	119	66	9%	7%	128	69	0%	7%	100	59	21.9%	14.5%	No	No	reported	No	60	95
97	11	White Coat	18%	11%	115	64	17%	3%	122	69	19%	19%	109	60	10.7%	13.1%	No	No	reported	No	56	89
98	15	white coat	7%	7%	116	64	0%	5%	117	70	27%	13%	113	55	3.5%	21.5%	No	Yes	reported	No	54	79
99	15	hypertension	49%	51%	128	78	41%	43%	130	81	73%	73%	121	70	7.0%	13.6%	Yes	Yes	reported	No	59	77

Mean			26.4%	23.4%			27.3%	24.2%			23.5%	21.2%			12.8%	20.0%
Stdev			22.9%	18.7%			25.9%	21.6%			20.6%	21.5%			5.1%	7.6%
SEM			3.2%	2.6%			3.6%	3.0%			2.9%	3.0%			0.7%	1.1%
p value

Median			18.0%	22.0%			19.5%	17.0%			16.5%	13.0%			12.5%	2010.0%
25th%			7.5%	7.0%			7.3%	7.0%			7.0%	6.0%			10.1%	14.9%

Dataset 4.Raw Data: Sensitivity Analysis: non-SLE, greater than 75 percent successful.

File contains the coded ambulatory blood pressure monitoring data and matched demographic data for the subset of the pediatric SLE cohort meeting a more stringent criterion of ABPM data quality, in particular the successful completion of 75% or greater of the total attempted BP measurements within the 24-hour monitoring period. ABPM data was abstracted from Space Labs software, using a 90th percentile cutoff to distinguish normal versus high BP values. Age is in years, BMI = body mass index (Dataset 4: Campbell et al., 2015d).

Case #	Age	HTN	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Nocturnal HTN	Attenuated?	Wake Sleep method	Low Success	# Readings	% successful
1	14	Hypertension	77%	70%	131	82	71%	63%	131	83	100%	100%	128	79	2.3%	4.9%	Yes	Yes	reported	No	60	98	Based on 90%ile
2	14	Hypertension	13%	76%	116	78	7%	75%	120	83	19%	77%	111	70	7.6%	15.7%	Yes	Yes	reported	No	54	72	Based on 90%ile
4	15	normotension	25%	27%	113	67	9%	21%	116	71	46%	35%	109	62	6.1%	12.7%	Yes	Yes	reported	No	59	70	Based on 90%ile
6	15	normotension	19%	5%	109	63	0%	3%	110	68	41%	7%	109	58	1.0%	14.8%	Yes	Yes	reported	No	58	84	Based on 90%ile
7	14	normotension	4%	0%	101	54	4%	0%	103	58	4%	0%	97	48	5.9%	17.3%	No	Yes	reported	No	51	81	Based on 90%ile
8	14	normotension	33%	33%	118	71	17%	20%	119	73	75%	69%	115	67	3.4%	8.3%	Yes	Yes	default	No	57	92	Based on 90%ile
3	17	normotension	24%	11%	122	68	20%	14%	126	72	36%	0%	113	57	10.4%	20.9%	Yes	No	reported	No	63	91	Based on 90%ile

Mean			27.9%	31.7%			18.3%	27.9%			45.9%	41.1%			5.2%	13.5%
Stdev			23.6%	30.5%			24.3%	29.3%			32.6%	41.0%			3.3%	5.4%
SEM			8.9%	11.5%			9.2%	11.1%			12.3%	15.5%			1.2%	2.1%
p value			0.8197	0.3279			0.4431	0.6694			0.0219	0.0651			0.0019	0.0661

Median			24.0%	27.0%			9.0%	19.5%			41.0%	35.0%			5.9%	14.8%
25th%			13.0%	5.0%			4.0%	3.0%			19.0%	0.0%			2.3%	8.3%
75th%			33.0%	70.0%			20.0%	63.0%			75.0%	77.0%			7.6%	17.3%
Mann Whitney U Statistic			163.00	175.00			149.50	188.50			111.00	155.00			40.00	94.00

Dataset 5.Raw Data: Sensitivity Analysis: pSLE greater than 50 successful readings.

File contains the coded ambulatory blood pressure monitoring data and matched demographic data for the subset of the pediatric SLE cohort meeting a more stringent criterion of ABPM data quality, in particular the successful completion of 50 or more BP measurements within the 24-hour monitoring period. ABPM data was abstracted from Space Labs software, using a 90th percentile cutoff to distinguish normal versus high BP values. Age is in years, BMI = body mass index (Dataset 5: Campbell et al., 2015e).

Case #	Age	HTN	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Nocturnal HTN	Attenuated?	Wake Sleep method	Low Success	# Readings	% successful
1	14	Hypertension	77%	70%	131	82	71%	63%	131	83	100%	100%	128	79	2.3%	4.9%	Yes	Yes	reported	No	60	98	Based on 90%ile
5	15	normotension	26%	21%	115	69	3%	3%	113	70	79%	64%	119	68	-5.3%	2.9%	Yes	Yes	default	No	47	76	Based on 90%ile
6	15	normotension	19%	5%	109	63	0%	3%	110	68	41%	7%	109	58	1.0%	14.8%	Yes	Yes	reported	No	58	84	Based on 90%ile
7	14	normotension	4%	0%	101	54	4%	0%	103	58	4%	0%	97	48	5.9%	17.3%	No	Yes	reported	No	51	81	Based on 90%ile
8	14	normotension	33%	33%	118	71	17%	20%	119	73	75%	69%	115	67	3.4%	8.3%	Yes	Yes	default	No	57	92	Based on 90%ile
10	15	normotension	2%	4%	106	59	0%	3%	107	62	8%	8%	102	54	4.7%	13.0%	No	Yes	reported	No	49	80	Based on 90%ile
3	17	normotension	24%	11%	122	68	20%	14%	126	72	36%	0%	113	57	10.4%	20.9%	Yes	No	reported	No	63	91	Based on 90%ile

Mean			26.4%	20.6%			16.4%	15.1%			49.0%	35.4%			3.2%	11.7%
Stdev			25.0%	24.6%			25.4%	22.3%			36.8%	41.2%			4.8%	6.6%
SEM			9.5%	9.3%			9.6%	8.4%			13.9%	15.6%			1.8%	2.5%
p value			0.9995	0.7152			0.2986	0.2994			0.0075	0.1533			0.0000	0.0085

Median			24.0%	11.0%			4.0%	3.0%			41.0%	7.7%			3.4%	13.0%
25th%			4.0%	4.0%			0.0%	2.8%			7.7%	0.0%			1.0%	4.9%
75th%			33.0%	33.0%			20.0%	19.5%			79.0%	68.8%			5.9%	17.3%
Mann Whitney U Statistic			176.00	145.50			118.50	113.00			107.50	167.00			24.00	70.00

Dataset 6.Raw Data: Sensitivity Analysis: pSLE greater than 75 percent successful.

Case #	Age	BMI	Gender	HTN	SBP load	DBP load	SBP Average	DBP Average	Wake SBP load	Wake DBP load	Wake SBP Average	Wake DBP Average	Sleep SBP load	Sleep DBP load	Sleep SBP Average	Sleep DBP Average	SBP Dip	DBP Dip	Nocturnal HTN	Attenuated?	Wake Sleep method	Low Success	# Readings	% successful
1	14	21.50	1	Hypertension	77%	70%	131	82	71%	63%	131	83	100%	100%	128	79	2.3%	4.9%	Yes	Yes	reported	No	60	98	Based on 90%ile
2	14	22.35	2	Hypertension	13%	76%	116	78	7%	75%	120	83	19%	77%	111	70	7.6%	15.7%	Yes	Yes	reported	No	54	72	Based on 90%ile
3	17	31.18	1	normotension	24%	11%	122	68	20%	14%	126	72	36%	0%	113	57	10.4%	20.9%	Yes	No	reported	No	63	91	Based on 90%ile
4	15	24.84	1	normotension	25%	27%	113	67	9%	21%	116	71	46%	35%	109	62	6.1%	12.7%	Yes	Yes	reported	No	59	70	Based on 90%ile
5	15	28.25	1	normotension	26%	21%	115	69	3%	3%	113	70	79%	64%	119	68	-5.3%	2.9%	Yes	Yes	default	No	47	76	Based on 90%ile
6	15	28.60	1	normotension	19%	5%	109	63	0%	3%	110	68	41%	7%	109	58	1.0%	14.8%	Yes	Yes	reported	No	58	84	Based on 90%ile
7	14	19.77	1	normotension	4%	0%	101	54	4%	0%	103	58	4%	0%	97	48	5.9%	17.3%	No	Yes	reported	No	51	81	Based on 90%ile
8	14	25.00	1	normotension	33%	33%	118	71	17%	20%	119	73	75%	69%	115	67	3.4%	8.3%	Yes	Yes	default	No	57	92	Based on 90%ile
9	13	25.71	1	white coat	3%	6%	106	60	0%	4%	109	62	8%	8%	101	55	7.4%	11.3%	No	Yes	default	Yes	35	51	Based on 90%ile
10	15	23.71	1	normotension	2%	4%	106	59	0%	3%	107	62	8%	8%	102	54	4.7%	13.0%	No	Yes	reported	No	49	80	Based on 90%ile

Dataset 7.Raw Data: Sensitivity Analysis: pSLE using 90th percentile.

File contains the coded ambulatory blood pressure monitoring data and matched demographic data for the entire pediatric SLE cohort abstracted from Space Labs software, using a looser 90th percentile cutoff to distinguish normal versus high BP values. The 90th percentile cutoff is commonly used to distinguish normal versus high BP values from casual BP measurements in populations at high risk for cardiovascular events, such as in individuals with congestive heart failure, diabetes, and chronic kidney disease. Age is in years, BMI = body mass index (Dataset 7: Campbell et al., 2015g).

Discussion/Conclusions

This study illustrates the potential benefit for further investigation of ABPM use in characterizing BP patterns in SLE patients. Our results show that pediatric SLE patients have a very high rate of attenuated nocturnal SBP and DBP dipping. This was associated with higher rates of nocturnal HTN (whether isolated or in conjunction with daytime HTN), though with standard ABPM-based definitions, this was not statistically significant. A previous study of subclinical cardiovascular disease in pediatric SLE patients reported similar findings with 14 of 21 patients having attenuated nocturnal dipping and higher nocturnal BPs when compared to daytime BPs (Canpolat et al., 2013). However the prior study’s primary focus was cardiovascular risk. There was no control group, and the relationships between ABPM findings and clinical characteristics tested were limited to echocardiogram findings. The small number of studies using ABPMs to characterize BPs in other pediatric chronic illnesses, such as sickle cell disease, has been revealing. This is the first study to investigate the relationship between BP characteristics on ABPM and clinical characteristics in the pediatric SLE population.

SLE patients are at increased risk for death and cardiovascular disease is a leading cause of mortality in this population. This is related to both traditional and non-traditional risk factors in adult patients (Knight & Kaplan, 2013). In a study of 94 adults with SLE, correlations were noted between intima-medial thickness and clinical disease activity scores (Oryoji et al., 2013). Similarly, in a separate study of 64 adults with SLE and nephritis in complete remission, 53% were hypertensive (Shaharir et al., 2015), and the risk factors identified included disease duration (odds ratio (OR) 1.06), longer duration interval to achieving remission (OR 1.10), and the number of disease relapses (OR 2.5). There were no associations between histological classes of nephritis, body mass index, or waist circumference. A study of 51 children with SLE also demonstrated that functional and morphological cardiovascular changes were independent of traditional risk factors such as daytime HTN, hypertriglyceridemia, diabetes, and chronic kidney disease (Sozeri et al., 2013). In SLE, these changes in arterial stiffness, intima-media thickness, and LV mass (Canpolat et al., 2013; Oryoji et al., 2013; Sozeri et al., 2013) are likely to be almost entirely secondary to non-traditional factors, such as disease-related mechanisms like enhanced apoptosis, aPL antibodies, circulating immune complexes, and vasculitis.

Therefore it is important to understand the BP characteristics of these patients, particularly the nocturnal BP patterns, as our study shows that they differ from the general population. There was no effect on this altered ABPM blood pressure pattern in our cohort attributable to medication usage, complement cascade activation and hypocomplementemia, or titers of ANA, aPL antibodies, or anti-dsDNA antibodies. Future studies of ABPM testing in SLE populations can be designed to further assess clinical parameters such as degrees of systemic inflammation, interferon versus neutrophil signatures, or endothelial cell dysfunction, in order to try to understand the possible mechanisms for elevated nocturnal BPs and attenuated nocturnal dipping in SLE patients.

Based on our study, the duration of disease did not seem to play a role in the attenuated dipping, as this pattern was seen even within the first year after SLE diagnosis. Since 90% of SLE patients had attenuated dipping, compared with only 60% of patients meeting criteria for diagnosis of nocturnal hypertension, one might conclude that attenuated nocturnal BP dipping is an earlier change that progresses to nocturnal HTN in the setting of SLE. However, the SLE patient without attenuated dipping did have nocturnal hypertension. Therefore, it is more likely that the disease process in SLE leads to cardiovascular changes sufficient to cause elevated nighttime BP very early in the disease course. If nocturnal HTN or attenuated BP dipping turn out to be pathogenic in SLE, then monitoring for HTN solely with casual daytime clinic measurements may postpone possible interventions that could potentially reduce the increased cardiovascular risk faced by these patients.

One limitation of this study is the small number of patients in the SLE group and the resulting low power. Several findings trended toward significance and might become statistically significant with a larger study population. Although there were a limited number of statistically significant findings, strict inclusion of only SLE patients with prehypertension or stage 1 hypertension, without active nephritis, and most without anti-hypertensive medication, provided for a valid comparison between children with SLE and non-lupus controls. Our study does show that nocturnal HTN and attenuated nocturnal dipping do occur more frequently in pediatric SLE patients than in the non-SLE population. Further research is warranted regarding the association of these findings with other clinical characteristics.

Data availability

F1000Research: Dataset 1. Raw ABPM Data for pSLE Cohort, 10.5256/f1000research.6532.d49239 (Campbell et al., 2015a).

F1000Research: Dataset 2. Raw ABPM Data for non-SLE Cohort, 10.5256/f1000research.6532.d49240 (Campbell et al., 2015b).

F1000Research: Dataset 3. Raw Data: Sensitivity Analysis: non-SLE greater than 50 successful readings, 10.5256/f1000research.6532.d49241 (Campbell et al., 2015c).

F1000Research: Dataset 4. Raw Data: sensitivity analysis: non-SLE, greater than 75 percent successful, 10.5256/f1000research.6532.d49242 (Campbell et al., 2015d).

F1000Research: Dataset 5. Raw Data: Sensitivity Analysis: pSLE greater than 50 successful readings, 10.5256/f1000research.6532.d49257 (Campbell et al., 2015e).

F1000Research: Dataset 6. Raw Data: Sensitivity analysis: pSLE greater than 75 percent successful, 10.5256/f1000research.6532.d49258 (Campbell et al., 2015f).

F1000Research: Dataset 7. Raw Data: Sensitivity analysis: pSLE using 90th percentile, 10.5256/f1000research.6532.d49259 (Campbell et al., 2015g).

Consent

A waiver of consent was obtained from the Institutional Review Board for this study.

Author contributions

JFC and SEW conceived the study. JFC and SEW designed the experiments. JFC and SEW carried out the research. SJS contributed to the design of experiments and provided expertise in analysis of ambulatory BP monitoring data. JFC and SEW prepared the first draft of the manuscript. All authors were involved in the revision of the draft manuscript and have agreed to the final content.

Competing interests

No competing interests were disclosed.

Grant information

This study was funded in part by a Pediatric Pilot Award program, granted to SEW by the Department of Pediatrics at Baylor College of Medicine.

I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements

The authors would like to thank Dr. Michael Braun (BCM) for thoughtful discussions and assistance with reading ambulatory BP monitoring reports, Isenia Medina (BCM) for assistance with performing the ambulatory BP tests, and Debra Canter (BCM) for regulatory support; as well as Dr. Marietta De Guzman and all of the nurses and Pediatric Rheumatologists who see patients in the Texas Children’s Hospital Pediatric Lupus Clinic.

Faculty Opinions recommended

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¹ Department of Pediatrics, Renal Section, Baylor College of Medicine, Houston, TX, 77030, USA

Competing interests

No competing interests were disclosed.

Grant information

This study was funded in part by a Pediatric Pilot Award program, granted to SEW by the Department of Pediatrics at Baylor College of Medicine.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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https://doi.org/10.12688/f1000research.6532.2

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© 2015 Campbell JF et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

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Campbell JF, Swartz SJ and Wenderfer SE. Nocturnal Hypertension and Attenuated Nocturnal Blood Pressure Dipping is Common in Pediatric Lupus [version 1; peer review: 2 approved, 1 approved with reservations]. F1000Research 2015, 4:164 (https://doi.org/10.12688/f1000research.6532.1)

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Reviewer Report 16 Nov 2015

Coral Hanevold, Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA

Approved

https://doi.org/10.5256/f1000research.7012.r10968

Increased risk for cardiovascular disease is a significant cause of long term morbidity of SLE. In this small retrospective study the authors address one of the potential modifiable risk factors for CV disease in this population, hypertension. In this pilot study the authors focused on children and teens with SLE but no renal disease (or nephritis in remission) with prehypertension or stage 1 HTN who had been evaluated with an ABPM. Although the patient numbers are small, the study is strengthened by the use of the non-SLE control group.

With regard to the methods, findings in the SLE patients were compared to a control group comprised of children undergoing ABPM for evaluation of prehypertension or stage 1 hypertension who did not have SLE (though other details regarding this group are not available). This group was reported to be age matched though it is noted that the mean age of this group was 12.4 years as compared to 14.6 for the SLE group. The mean BMI for the 2 groups was well matched. Unfortunately data for the non-SLE group was limited to information on the ABPM report which was limited to age, sex, height, weight (with BMI calculated from these data). Thus it was not possible to compare race or ethnic group of the control group to the SLE group which was 40% African American and 30% Hispanic.

The authors found that the SLE patients demonstrated a higher prevalence of reduced dipping than the control group (90% vs 26% respectively). Additionally, 60% of SLE patients had nocturnal HTN as compared to 39% of the control group. Due to the small number of patients the authors were not able to demonstrate an association of findings on ABPM with clinical factors such as steroid exposure, disease activity, proteinuria, echocardiograms, etc. Of note all patients with nocturnal hypertension met historical criteria for nephritis even though not active at time of the study. It is possible that these SLE patients had other risk factors for hypertension not addressed here such as positive family history or elevated BMI. Perhaps the authors could comment on the latter point at least. These factors are important given the high prevalence of African American and Hispanic children in their SLE population. Data also showed that SLE patients had a statistically significant lower daytime systolic load as compared to controls while only 50% had elevated nighttime systolic loads and 60% had nocturnal hypertension. Thus it may be that the low daytime systolic load contributed to the finding of attenuated dipping in some of the SLE cohort.

The authors performed various sensitivity analyses that involved using a lower threshold (90^th percentile) to define hypertension, use of 30% as cutoff for abnormal load, more stringent requirements for adequacy, and limited non-SLE group to females. These data are somewhat confusing and the authors could consider deleting this section.

The study reported here highlights the need for scrutiny of BPs in SLE patients without active nephritis in whom mild elevations in the office might be attributed to white coat hypertension. Additionally the high prevalence of abnormalities of nocturnal pressures, often occurring in isolation, indicates that this method of evaluation (ABPM) is crucial in the evaluation of this population. These findings should be the basis for further study in a larger group of patients. The study has significant limitations as mentioned above but the findings are compelling and thus warrant reporting.

Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report 03 Nov 2015

Joseph Flynn, Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.7012.r10969

The authors present the results of a small retrospective case series of children with systemic lupus studied by Ambulatory Blood Pressure Monitoring (ABPM). Their major finding is that these patients have blunted nocturnal BP dipping, which has been associated with adverse cardiovascular risk. This is a novel finding that while needing to be confirmed in a larger, prospective study, could have major implications for the clinical management of children with SLE.

One issue that requires clarification is how the authors characterize the study population. In several places in the paper they state that the SLE patients don't have nephritis, but in reality, about half had a history of nephritis. The authors should restate the description of the SLE patients to clarify that these children were not currently being treated for active nephritis. There are actually some interesting differences in the children with and without a history of nephritis that could be explored in more detail - for example, the ABPM parameters in Table 2 could be further divided into groups of those with and without a history of nephritis. How would that division then affect the results of the other analyses performed?

Another major issue that requires greater exploration is the effect of prednisone treatment on BP dipping. All of the SLE patients were on prednisone and it is likely that this affected the dipping patterns. This needs to be explicitly acknowledged in the discussion, and some discussion of prior studies (if they exist) exploring the effect of corticosteroids on the circadian variation of BP needs to be added.

Minor suggestions:

add the number of subjects to the abstract (it only gives the number of non-SLE controls)
shorten the introduction by 50% (first 2 paragraphs and the first 2/3 of the third paragraph could be deleted)
I don't think the sensitivity analysis adds anything; this could probably be deleted as well
it is unlikely that specific laboratory findings would affect BP outcomes, could some of this information be removed as well?

Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Author Response 23 Nov 2015

Scott Wenderfer, Department of Pediatrics, Renal Section, Baylor College of Medicine, Houston, 77030, USA

23 Nov 2015

Author Response
The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments ... Continue reading
The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments and suggestions, as summarized in our response here.

One issue that requires clarification is the study population. In several places in the paper the authors state that the SLE patients don't have nephritis; but in reality, about half had a history of nephritis. The authors should restate the description of the SLE patients to clarify that these children were not currently being treated for active nephritis.
Done (modified the Study Population section of the Methods as well as in the Patient and Clinical Characteristics section of the Results)

There are actually some interesting differences in the children with and without a history of nephritis that could be explored in more detail - the ABPM parameters in Table 2 could be further divided into groups of those with and without a history of nephritis.
We thank the referee for this suggestion. Table 2 has been modified to sub-divide our pSLE cohort into the 4 non-renal pSLE patients and 6 patients with a history of lupus nephritis which was in remission and not being treated at the time of ABPM. The table now shows that nighttime SBP and DBP loads were higher in SLE patients with a history of nephritis than in non-renal lupus patients, even though the nephritis was in remission at time of ABPM. Moreover, it shows that all SLE patients with nocturnal HTN had a history of nephritis.

Another major issue that requires greater exploration is the effect of prednisone treatment on BP dipping.
We agree that the effect of pharmacologic therapy in patients with pSLE is a confounding factor in the interpretation of ABPM data in our cohort of patients, and we agree that there may indeed be an effect of the prednisone on the attenuated nocturnal dipping. We have added a paragraph to the discussion describing four studies that support this notion. Unfortunately, our cohort of patients is too small to distinguish between disease-specific and treatment-specific effects. Nonetheless, as long-term prednisone use in patients with SLE is nearly universal, our results emphasize the need to consider nocturnal HTN in the management of these patients.

Minor suggestion: add the number of subjects to the abstract.
Done (line 9 of the abstract)
The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments and suggestions, as summarized in our response here.

One issue that requires clarification is the study population. In several places in the paper the authors state that the SLE patients don't have nephritis; but in reality, about half had a history of nephritis. The authors should restate the description of the SLE patients to clarify that these children were not currently being treated for active nephritis.
Done (modified the Study Population section of the Methods as well as in the Patient and Clinical Characteristics section of the Results)

There are actually some interesting differences in the children with and without a history of nephritis that could be explored in more detail - the ABPM parameters in Table 2 could be further divided into groups of those with and without a history of nephritis.
We thank the referee for this suggestion. Table 2 has been modified to sub-divide our pSLE cohort into the 4 non-renal pSLE patients and 6 patients with a history of lupus nephritis which was in remission and not being treated at the time of ABPM. The table now shows that nighttime SBP and DBP loads were higher in SLE patients with a history of nephritis than in non-renal lupus patients, even though the nephritis was in remission at time of ABPM. Moreover, it shows that all SLE patients with nocturnal HTN had a history of nephritis.

Another major issue that requires greater exploration is the effect of prednisone treatment on BP dipping.
We agree that the effect of pharmacologic therapy in patients with pSLE is a confounding factor in the interpretation of ABPM data in our cohort of patients, and we agree that there may indeed be an effect of the prednisone on the attenuated nocturnal dipping. We have added a paragraph to the discussion describing four studies that support this notion. Unfortunately, our cohort of patients is too small to distinguish between disease-specific and treatment-specific effects. Nonetheless, as long-term prednisone use in patients with SLE is nearly universal, our results emphasize the need to consider nocturnal HTN in the management of these patients.

Minor suggestion: add the number of subjects to the abstract.
Done (line 9 of the abstract)
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Author Response 23 Nov 2015

Scott Wenderfer, Department of Pediatrics, Renal Section, Baylor College of Medicine, Houston, 77030, USA

23 Nov 2015

Author Response
The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments ... Continue reading
The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments and suggestions, as summarized in our response here.

One issue that requires clarification is the study population. In several places in the paper the authors state that the SLE patients don't have nephritis; but in reality, about half had a history of nephritis. The authors should restate the description of the SLE patients to clarify that these children were not currently being treated for active nephritis.
Done (modified the Study Population section of the Methods as well as in the Patient and Clinical Characteristics section of the Results)

There are actually some interesting differences in the children with and without a history of nephritis that could be explored in more detail - the ABPM parameters in Table 2 could be further divided into groups of those with and without a history of nephritis.
We thank the referee for this suggestion. Table 2 has been modified to sub-divide our pSLE cohort into the 4 non-renal pSLE patients and 6 patients with a history of lupus nephritis which was in remission and not being treated at the time of ABPM. The table now shows that nighttime SBP and DBP loads were higher in SLE patients with a history of nephritis than in non-renal lupus patients, even though the nephritis was in remission at time of ABPM. Moreover, it shows that all SLE patients with nocturnal HTN had a history of nephritis.

Another major issue that requires greater exploration is the effect of prednisone treatment on BP dipping.
We agree that the effect of pharmacologic therapy in patients with pSLE is a confounding factor in the interpretation of ABPM data in our cohort of patients, and we agree that there may indeed be an effect of the prednisone on the attenuated nocturnal dipping. We have added a paragraph to the discussion describing four studies that support this notion. Unfortunately, our cohort of patients is too small to distinguish between disease-specific and treatment-specific effects. Nonetheless, as long-term prednisone use in patients with SLE is nearly universal, our results emphasize the need to consider nocturnal HTN in the management of these patients.

Minor suggestion: add the number of subjects to the abstract.
Done (line 9 of the abstract)
The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments and suggestions, as summarized in our response here.

One issue that requires clarification is the study population. In several places in the paper the authors state that the SLE patients don't have nephritis; but in reality, about half had a history of nephritis. The authors should restate the description of the SLE patients to clarify that these children were not currently being treated for active nephritis.
Done (modified the Study Population section of the Methods as well as in the Patient and Clinical Characteristics section of the Results)

There are actually some interesting differences in the children with and without a history of nephritis that could be explored in more detail - the ABPM parameters in Table 2 could be further divided into groups of those with and without a history of nephritis.
We thank the referee for this suggestion. Table 2 has been modified to sub-divide our pSLE cohort into the 4 non-renal pSLE patients and 6 patients with a history of lupus nephritis which was in remission and not being treated at the time of ABPM. The table now shows that nighttime SBP and DBP loads were higher in SLE patients with a history of nephritis than in non-renal lupus patients, even though the nephritis was in remission at time of ABPM. Moreover, it shows that all SLE patients with nocturnal HTN had a history of nephritis.

Another major issue that requires greater exploration is the effect of prednisone treatment on BP dipping.
We agree that the effect of pharmacologic therapy in patients with pSLE is a confounding factor in the interpretation of ABPM data in our cohort of patients, and we agree that there may indeed be an effect of the prednisone on the attenuated nocturnal dipping. We have added a paragraph to the discussion describing four studies that support this notion. Unfortunately, our cohort of patients is too small to distinguish between disease-specific and treatment-specific effects. Nonetheless, as long-term prednisone use in patients with SLE is nearly universal, our results emphasize the need to consider nocturnal HTN in the management of these patients.

Minor suggestion: add the number of subjects to the abstract.
Done (line 9 of the abstract)
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Reviewer Report 17 Aug 2015

Joyce Popoola, Department of Renal Medicine and Transplantation, St George, London, UK

Approved

https://doi.org/10.5256/f1000research.7012.r9811

This is a retrospective study looking at nocturnal blood pressure dips in paediatric patients with lupus. It is descriptive in nature and looks at small numbers as it is a single centre review of a relatively rare condition in the paediatric population. Though the paper shows a statistically significant nocturnal dip in the systolic and diastolic blood pressure compared to the controls it is unable to dissect out demographics or other characteristics that could be used to explain this because of the small numbers. This is an important question as blood pressure will have an impact on long term effect of this condition and potential development of renal disease beyond lupus nephritis. The paper offers no opportunity for mechanistic explanations for the observations because of the retrospective nature and small numbers as recognised by the authors.

The statistics are also limited for the same reasons. I feel the paper should be published however to highlight the issue and also to provide a platform for the authors to raise awareness to the issue and enable a multi centre prospective study of the same question dissecting out demographics (age stratification, ethnicity), other co-morbidities (this would be more of an issue in an adult population) and to test out the mechanisms of this process potentially examining vascular resistance and flow mediated dilatation. It would also be of interest to see the effect of treatment on this phenomenon in a longitudinal fashion.

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Joyce Popoola, St George, London, UK
Joseph Flynn, Seattle Children's Hospital, Seattle, USA
Coral Hanevold, Seattle Children's Hospital, Seattle, USA
Rene VanDeVoorde, Cincinnati Children's Hospital Medical Center, Cincinnati, USA

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30 Nov 2015 | for Version 2

Rene VanDeVoorde, Division of Pediatric Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA

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Approved

This retrospective study looked at the ambulatory blood pressure monitoring (ABPM) patterns in a small number of pediatric patients with Lupus (SLE) but not nephritis compared to a larger cohort of patients with similar casual blood pressure findings but without SLE. The study design seems appropriate, however the lack of racial/ethnicity information in the comparison group makes it difficult to know if this comparison is entirely appropriate. The Lupus group had 70% African American or Hispanic patients, which are groups whose normal ABPM data are not known and may not be represented by the normative data by Wuhl. Potential matching by age, gender, and ethnicity may have lent more strength to this comparison.

However, the findings of reduced nocturnal dipping in 90% of the SLE group is fairly significant and definitely bears reporting. The authors are quite clear that their small numbers make it difficult to find statistical significant factors within the SLE cohort to explain these findings and that a larger study in this population is merited. The discussion about possible effect of corticosteroids, added in the revision, is one factor that certainly needs to be investigated further. Additionally, the presence of proteinuria in 20% of the SLE patients is somewhat curious as they may have some residual effects from their nephritis which would possibly also manifest with changes in their diurnal BP pattern. However, these are exactly some of the questions should be uncovered in the discussions of a "pilot" study such as this.

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16 Nov 2015 | for Version 1

Coral Hanevold, Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA

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Approved

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03 Nov 2015 | for Version 1

Joseph Flynn, Division of Nephrology, Seattle Children's Hospital, Seattle, WA, USA

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Approved With Reservations

add the number of subjects to the abstract (it only gives the number of non-SLE controls)
shorten the introduction by 50% (first 2 paragraphs and the first 2/3 of the third paragraph could be deleted)
I don't think the sensitivity analysis adds anything; this could probably be deleted as well
it is unlikely that specific laboratory findings would affect BP outcomes, could some of this information be removed as well?

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23 Nov 2015

Scott Wenderfer, Department of Pediatrics, Renal Section, Baylor College of Medicine, Houston, 77030, USA

The authors appreciate the detailed and thorough reviews performed by all of the referees, including Dr. Flynn. We have submitted a modified and improved version of the manuscript addressing the comments and suggestions, as summarized in our response here.

One issue that requires clarification is the study population. In several places in the paper the authors state that the SLE patients don't have nephritis; but in reality, about half had a history of nephritis. The authors should restate the description of the SLE patients to clarify that these children were not currently being treated for active nephritis.

Done (modified the Study Population section of the Methods as well as in the Patient and Clinical Characteristics section of the Results)

There are actually some interesting differences in the children with and without a history of nephritis that could be explored in more detail - the ABPM parameters in Table 2 could be further divided into groups of those with and without a history of nephritis.

We thank the referee for this suggestion. Table 2 has been modified to sub-divide our pSLE cohort into the 4 non-renal pSLE patients and 6 patients with a history of lupus nephritis which was in remission and not being treated at the time of ABPM. The table now shows that nighttime SBP and DBP loads were higher in SLE patients with a history of nephritis than in non-renal lupus patients, even though the nephritis was in remission at time of ABPM. Moreover, it shows that all SLE patients with nocturnal HTN had a history of nephritis.

Another major issue that requires greater exploration is the effect of prednisone treatment on BP dipping.

We agree that the effect of pharmacologic therapy in patients with pSLE is a confounding factor in the interpretation of ABPM data in our cohort of patients, and we agree that there may indeed be an effect of the prednisone on the attenuated nocturnal dipping. We have added a paragraph to the discussion describing four studies that support this notion. Unfortunately, our cohort of patients is too small to distinguish between disease-specific and treatment-specific effects. Nonetheless, as long-term prednisone use in patients with SLE is nearly universal, our results emphasize the need to consider nocturnal HTN in the management of these patients.

Minor suggestion: add the number of subjects to the abstract.

Done (line 9 of the abstract)

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17 Aug 2015 | for Version 1

Joyce Popoola, Department of Renal Medicine and Transplantation, St George, London, UK

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Approved

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Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Nocturnal Hypertension and Attenuated Nocturnal Blood Pressure Dipping is Common in Pediatric Lupus

Abstract

Keywords

Introduction

Methods

Study population

Table 1. Pediatric SLE Cohort Demographics.

Ambulatory Blood Pressure Monitoring

Clinical data

Statistical analysis

Results

Patient and clinical characteristics

ABPM findings

Table 2. Higher rates of Attenuated Systolic and Diastolic BP Dipping and Nocturnal Hypertension in Children with SLE.

Figure 1. Attenuated BP dipping and trends toward less daytime and more nighttime HTN in pediatric SLE.

Relationships between ABPM findings and clinical characteristics

Table 3. Individual ABPM and Clinical Laboratory Data for Pediatric SLE Cohort*.

Sensitivity analysis

Figure 2. No major effects of methods for ABPM interpretation on results in SLE cohort.

Discussion/Conclusions

Data availability

Consent

Author contributions

Competing interests

Grant information

Acknowledgements

References

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