whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world

Dylan Collins; Joseph Lee; Niklas Bobrovitz; Constantinos Koshiaris; Alison Ward; Carl J. Heneghan

doi:10.12688/f1000research.9742.2

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Revised

whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world

[version 2; peer review: 3 approved]

Previously titled: Simple and adaptable R implementation of WHO/ISH cardiovascular risk charts for all epidemiological subregions of the world

Dylan Collins ¹, Joseph Lee¹, Niklas Bobrovitz¹, Constantinos Koshiaris¹, Alison Ward¹, Carl J. Heneghan¹

Dylan Collins ¹, Joseph Lee¹, [...] Niklas Bobrovitz¹, Constantinos Koshiaris¹, Alison Ward¹, Carl J. Heneghan¹

PUBLISHED 08 Mar 2017

Author details Author details

¹ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the RPackage gateway.

Abstract

The World Health Organisation and International Society of Hypertension (WHO/ISH) cardiovascular disease (CVD) risk assessment charts have been implemented in many low- and middle-income countries as part of the WHO Package of Essential Non-Communicable Disease (PEN) Interventions for Primary Health Care in Low-Resource settings. Evaluation of the WHO/ISH cardiovascular risk charts and their use is a key priority and since they only existed in paper or PDF formats, we developed an R implementation of the charts for all epidemiological subregions of the world. The main strengths of this implementation are that it is built in a free, open-source, coding language with simple syntax, can be downloaded from github as a package (“whoishRisk”), and can be used with a standard computer.

Keywords

WHO/ISH, Cardiovascular Risk Charts, Risk Score, R

Corresponding author: Dylan Collins

Competing interests: DC has received payment from the WHO for consulting work. AW and CH have received expenses and grant income from the WHO for projects related to CVD and Self Care in NCDs, and direct a WHO Collaborating Centre. CH also receives funding form the National Institute of Health Research (NIHR) School of Primary Care Research. JL, CK, and NB declare no competing interests.

Grant information: The WHO Collaborating Centre for Self Care paid for the open access publishing fees. No other funding was provided for this work.

Copyright: © 2017 Collins D 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: Collins D, Lee J, Bobrovitz N et al. whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world [version 2; peer review: 3 approved]. F1000Research 2017, 5:2522 (https://doi.org/10.12688/f1000research.9742.2) First published: 14 Oct 2016, 5:2522 (https://doi.org/10.12688/f1000research.9742.1) Latest published: 08 Mar 2017, 5:2522 (https://doi.org/10.12688/f1000research.9742.2)

Revised Amendments from Version 1

The main update of this version is the development of an R package using the previously reported R code. The package, called whoishRisk, can be downloaded directly from github as described in the paper. The benefit of this is that users now do not need to load the R code or any dependency files in the work space; instead they must download the whoishRisk package and load it from the library.

The major update to the code itself is the addition of warning messages when parameters passed to the WHO_ISH_Risk() function appear out of range.

The title was changed accordingly to reflect the development of the whoishRisk package. Figure 1 was removed as it was no longer relevant, and table 2 was added to show the new WHO/ISH Risk warning messages.

See the authors' detailed response to the review by Raivo Kolde
See the authors' detailed response to the review by Maria Suarez-Diez
See the authors' detailed response to the review by Scott A. Chamberlain

Introduction

Cardiovascular disease (CVD) is the leading cause of death worldwide, including in many low-and-middle income countries (LMIC)^1,2. Preventing CVD is therefore a worldwide priority and the World Health Organisation (WHO) is coordinating a global strategy for LMIC to systematically prevent CVD in primary care³.

In 2007 the WHO and the International Society of Hypertension (ISH) published the WHO/ISH CVD risk charts for all WHO epidemiological subregions of the world⁴. These charts are to be used as part of the WHO’s Package of Essential NCD (PEN) Interventions for Primary Health Care in Low-Resource Settings in jurisdictions that do not have their own population-derived risk assessment algorithms. While these charts are a good resource for many health systems, little is known about their validity⁵. Therefore, it is important that jurisdictions that implement these charts conduct operational research and attempt to validate and optimise them for their setting.

Two paper-based versions of WHO/ISH charts are available for each subregion: one that requires measured total cholesterol and one that does not. The latter was made available for use in settings with limited access to laboratory testing or where the cost of cholesterol testing is prohibitive. Both charts require information on age, gender, diabetes status, smoking status, and systolic blood pressure to stratify people into one of five risk categories of 10-year risk of a fatal or non-fatal CVD event. Further instructions for their use have been published, including the definition and classification of the fourteen epidemiological sub-regions³.

Through our experience collaborating with LMIC with the implementation of WHO PEN, we identified a common need for an open-source tool to facilitate the implementation of WHO/ISH risk charts and operational research of WHO PEN at a population level. We therefore developed an R package called whoishRisk, which we describe here and make available to researchers in LMIC. R is a statistical computing language and environment which is open source and freely available to anyone⁶.

Methods

Extraction of WHO/ISH cardiovascular risk charts

We extracted all versions of the paper-based WHO/ISH CVD risk charts by hand into a standardized Microsoft Excel template, independently and in duplicate. We compared the duplicate extractions and calculated Cohen’s kappa coefficient for inter-rater reliability, using the irr package in R⁷. Discrepancies were reviewed by the same two extractors and resolved by referring to the original paper chart.

Development of the WHO/ISH risk function

One author wrote the initial code for the WHO/ISH risk function in R and created the whoishRisk package (DC). This was reviewed and adapted by a second author experienced in the R language (CK). Two additional authors (JL, NB), new to the R language, reviewed the code to ensure the syntax was comprehensible.

Validation

A MatLab implementation of WHO/ISH risk charts for epidemiological subregion SEAR D had been previously reported⁸. We used Octave (www.gnu.org/software/octave/) version 8.3.2 to calculate the SEAR D WHO/ISH risk score for every possible combination of risk factors using the previously reported MatLab implementation, and compared the percent agreement to the risk scores generated by our R package, whoishRisk.

Results

whoishRisk Package

The whoishRisk package can be downloaded and installed directly from github using the install_github() command in the devtools package, with the argument “DylanRJCollins/whoishRisk”⁹. The package contains a single function, WHO_ISH_Risk(), which calculates the WHO/ISH CVD risk score for any epidemiological subregion of the world based on the parameter values passed to it.

Extraction of WHO/ISH cardiovascular risk charts

All WHO/ISH risk charts were extracted by hand into a single comma delimited file (Dataset 1). The first six columns specify the risk factor values, and the last 14 columns specify the corresponding risk category for a given subregion. whoishRisk uses these data internally to calculate the WHO/ISH risk score. Cohen’s kappa for initial agreement between the independent extractors was 0.97, indicating excellent agreement. All remaining discrepancies were resolved by consensus.

dm	gdr	smk	age	sbp	chl	refv	AFR_D	AFR_E	AMR_A	AMR_B	AMR_D	EMR_B	EMR_D	EUR_A	EUR_B	EUR_C	SEAR_B	SEAR_D	WPR_B	WPR_A
1	0	1	40	120	4	400111204	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	1	40	120	5	400111205	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	1	40	120	6	400111206	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	1	40	120	7	400111207	<10%	<10%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	1	40	120	8	400111208	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%	<10%	<10%	<10%	10% to <20%	<10%	20% to <30%	30% to <40%	<10%	<10%
1	0	1	40	140	4	400111404	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	1	40	140	5	400111405	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%	<10%
1	0	1	40	140	6	400111406	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	1	40	140	7	400111407	10% to <20%	20% to <30%	20% to <30%	20% to <30%	10% to <20%	<10%	10% to <20%	<10%	10% to <20%	<10%	30% to <40%	30% to <40%	10% to <20%	10% to <20%
1	0	1	40	140	8	400111408	20% to <30%	20% to <30%	>=40%	30% to <40%	30% to <40%	10% to <20%	20% to <30%	10% to <20%	30% to <40%	20% to <30%	>=40%	>=40%	30% to <40%	10% to <20%
1	0	1	40	160	4	400111604	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	10% to <20%	20% to <30%	<10%	<10%
1	0	1	40	160	5	400111605	10% to <20%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	10% to <20%	10% to <20%	<10%	10% to <20%	<10%	30% to <40%	20% to <30%	20% to <30%	10% to <20%
1	0	1	40	160	6	400111606	20% to <30%	20% to <30%	30% to <40%	>=40%	30% to <40%	20% to <30%	20% to <30%	<10%	20% to <30%	10% to <20%	>=40%	>=40%	20% to <30%	20% to <30%
1	0	1	40	160	7	400111607	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%	10% to <20%	>=40%	20% to <30%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	40	160	8	400111608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	40	180	4	400111804	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%	10% to <20%	30% to <40%	20% to <30%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	40	180	5	400111805	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%
1	0	1	40	180	6	400111806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	40	180	7	400111807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	40	180	8	400111808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	50	120	4	500111204	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	1	50	120	5	500111205	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	1	50	120	6	500111206	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	1	50	120	7	500111207	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	10% to <20%	20% to <30%	<10%	<10%
1	0	1	50	120	8	500111208	10% to <20%	10% to <20%	20% to <30%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	10% to <20%	20% to <30%	>=40%	10% to <20%	<10%
1	0	1	50	140	4	500111404	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%
1	0	1	50	140	5	500111405	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	1	50	140	6	500111406	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	<10%	20% to <30%	30% to <40%	10% to <20%	<10%
1	0	1	50	140	7	500111407	20% to <30%	20% to <30%	30% to <40%	20% to <30%	20% to <30%	10% to <20%	20% to <30%	<10%	20% to <30%	10% to <20%	30% to <40%	>=40%	20% to <30%	10% to <20%
1	0	1	50	140	8	500111408	20% to <30%	20% to <30%	>=40%	30% to <40%	>=40%	30% to <40%	30% to <40%	10% to <20%	30% to <40%	30% to <40%	>=40%	>=40%	30% to <40%	20% to <30%
1	0	1	50	160	4	500111604	10% to <20%	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	<10%	20% to <30%	20% to <30%	10% to <20%	10% to <20%
1	0	1	50	160	5	500111605	10% to <20%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	20% to <30%	20% to <30%	<10%	10% to <20%	10% to <20%	30% to <40%	30% to <40%	20% to <30%	10% to <20%
1	0	1	50	160	6	500111606	20% to <30%	20% to <30%	>=40%	>=40%	30% to <40%	30% to <40%	30% to <40%	10% to <20%	20% to <30%	10% to <20%	>=40%	>=40%	30% to <40%	20% to <30%
1	0	1	50	160	7	500111607	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	50	160	8	500111608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	50	180	4	500111804	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	50	180	5	500111805	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	50	180	6	500111806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	50	180	7	500111807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	50	180	8	500111808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	60	120	4	600111204	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%
1	0	1	60	120	5	600111205	10% to <20%	<10%	<10%	<10%	<10%	20% to <30%	10% to <20%	<10%	<10%	<10%	10% to <20%	20% to <30%	<10%	<10%
1	0	1	60	120	6	600111206	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	20% to <30%	20% to <30%	<10%	10% to <20%	<10%	20% to <30%	30% to <40%	10% to <20%	<10%
1	0	1	60	120	7	600111207	20% to <30%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	30% to <40%	30% to <40%	<10%	10% to <20%	10% to <20%	30% to <40%	>=40%	10% to <20%	10% to <20%
1	0	1	60	120	8	600111208	30% to <40%	10% to <20%	20% to <30%	20% to <30%	30% to <40%	>=40%	>=40%	10% to <20%	20% to <30%	20% to <30%	30% to <40%	>=40%	20% to <30%	10% to <20%
1	0	1	60	140	4	600111404	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	10% to <20%	<10%	10% to <20%	<10%	10% to <20%	30% to <40%	10% to <20%	<10%
1	0	1	60	140	5	600111405	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	30% to <40%	20% to <30%	<10%	10% to <20%	10% to <20%	20% to <30%	>=40%	10% to <20%	10% to <20%
1	0	1	60	140	6	600111406	20% to <30%	20% to <30%	20% to <30%	10% to <20%	20% to <30%	>=40%	30% to <40%	10% to <20%	20% to <30%	10% to <20%	30% to <40%	>=40%	20% to <30%	10% to <20%
1	0	1	60	140	7	600111407	30% to <40%	20% to <30%	30% to <40%	20% to <30%	30% to <40%	>=40%	>=40%	10% to <20%	20% to <30%	20% to <30%	>=40%	>=40%	20% to <30%	10% to <20%
1	0	1	60	140	8	600111408	30% to <40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	20% to <30%
1	0	1	60	160	4	600111604	20% to <30%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	>=40%	30% to <40%	10% to <20%	10% to <20%	10% to <20%	30% to <40%	>=40%	20% to <30%	10% to <20%
1	0	1	60	160	5	600111605	30% to <40%	20% to <30%	30% to <40%	20% to <30%	30% to <40%	>=40%	>=40%	10% to <20%	20% to <30%	20% to <30%	>=40%	>=40%	30% to <40%	20% to <30%
1	0	1	60	160	6	600111606	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	30% to <40%	20% to <30%	>=40%	>=40%	>=40%	20% to <30%
1	0	1	60	160	7	600111607	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	60	160	8	600111608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	60	180	4	600111804	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	60	180	5	600111805	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	60	180	6	600111806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	60	180	7	600111807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	60	180	8	600111808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	120	4	700111204	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%
1	0	1	70	120	5	700111205	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%	30% to <40%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	30% to <40%	10% to <20%	10% to <20%
1	0	1	70	120	6	700111206	20% to <30%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	30% to <40%	30% to <40%	10% to <20%	10% to <20%	20% to <30%	20% to <30%	>=40%	20% to <30%	10% to <20%
1	0	1	70	120	7	700111207	20% to <30%	10% to <20%	30% to <40%	20% to <30%	30% to <40%	>=40%	>=40%	20% to <30%	20% to <30%	20% to <30%	30% to <40%	>=40%	20% to <30%	20% to <30%
1	0	1	70	120	8	700111208	30% to <40%	30% to <40%	30% to <40%	30% to <40%	30% to <40%	>=40%	>=40%	20% to <30%	30% to <40%	30% to <40%	30% to <40%	>=40%	20% to <30%	20% to <30%
1	0	1	70	140	4	700111404	20% to <30%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	30% to <40%	20% to <30%	10% to <20%	10% to <20%	20% to <30%	20% to <30%	>=40%	20% to <30%	10% to <20%
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1	0	1	70	140	7	700111407	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	70	140	8	700111408	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	160	4	700111604	30% to <40%	20% to <30%	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%	20% to <30%	30% to <40%	30% to <40%	>=40%	30% to <40%	20% to <30%
1	0	1	70	160	5	700111605	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	1	70	160	6	700111606	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	160	7	700111607	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	160	8	700111608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	180	4	700111804	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	180	5	700111805	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	180	6	700111806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	180	7	700111807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	1	70	180	8	700111808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	0	40	120	4	400101204	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	120	5	400101205	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	120	6	400101206	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	120	7	400101207	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	120	8	400101208	<10%	<10%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	20% to <30%	20% to <30%	<10%	<10%
1	0	0	40	140	4	400101404	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	140	5	400101405	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	140	6	400101406	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%	<10%
1	0	0	40	140	7	400101407	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	20% to <30%	10% to <20%	<10%	<10%
1	0	0	40	140	8	400101408	20% to <30%	10% to <20%	30% to <40%	30% to <40%	20% to <30%	10% to <20%	10% to <20%	<10%	20% to <30%	10% to <20%	20% to <30%	>=40%	20% to <30%	10% to <20%
1	0	0	40	160	4	400101604	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	40	160	5	400101605	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	0	40	160	6	400101606	10% to <20%	10% to <20%	20% to <30%	20% to <30%	10% to <20%	<10%	10% to <20%	<10%	10% to <20%	<10%	30% to <40%	20% to <30%	10% to <20%	<10%
1	0	0	40	160	7	400101607	20% to <30%	20% to <30%	30% to <40%	30% to <40%	20% to <30%	10% to <20%	10% to <20%	<10%	20% to <30%	10% to <20%	>=40%	>=40%	20% to <30%	10% to <20%
1	0	0	40	160	8	400101608	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	30% to <40%	10% to <20%	>=40%	20% to <30%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	40	180	4	400101804	10% to <20%	20% to <30%	20% to <30%	30% to <40%	20% to <30%	10% to <20%	20% to <30%	<10%	20% to <30%	10% to <20%	30% to <40%	30% to <40%	20% to <30%	10% to <20%
1	0	0	40	180	5	400101805	20% to <30%	30% to <40%	30% to <40%	>=40%	30% to <40%	20% to <30%	30% to <40%	10% to <20%	30% to <40%	20% to <30%	>=40%	>=40%	>=40%	20% to <30%
1	0	0	40	180	6	400101806	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	10% to <20%	>=40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	40	180	7	400101807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	0	40	180	8	400101808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
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1	0	0	50	120	5	500101205	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	50	120	6	500101206	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	50	120	7	500101207	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	0	50	120	8	500101208	<10%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	10% to <20%	<10%	20% to <30%	20% to <30%	<10%	<10%
1	0	0	50	140	4	500101404	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	50	140	5	500101405	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	0	0	50	140	6	500101406	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	0	50	140	7	500101407	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	<10%	20% to <30%	20% to <30%	10% to <20%	<10%
1	0	0	50	140	8	500101408	20% to <30%	20% to <30%	30% to <40%	30% to <40%	20% to <30%	20% to <30%	20% to <30%	10% to <20%	20% to <30%	10% to <20%	30% to <40%	>=40%	20% to <30%	10% to <20%
1	0	0	50	160	4	500101604	<10%	<10%	<10%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%
1	0	0	50	160	5	500101605	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	<10%	10% to <20%	20% to <30%	10% to <20%	<10%
1	0	0	50	160	6	500101606	10% to <20%	10% to <20%	20% to <30%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	10% to <20%	30% to <40%	30% to <40%	10% to <20%	10% to <20%
1	0	0	50	160	7	500101607	20% to <30%	20% to <30%	30% to <40%	30% to <40%	20% to <30%	20% to <30%	20% to <30%	10% to <20%	20% to <30%	10% to <20%	>=40%	>=40%	20% to <30%	10% to <20%
1	0	0	50	160	8	500101608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	50	180	4	500101804	20% to <30%	20% to <30%	20% to <30%	30% to <40%	30% to <40%	20% to <30%	20% to <30%	10% to <20%	20% to <30%	20% to <30%	30% to <40%	30% to <40%	30% to <40%	20% to <30%
1	0	0	50	180	5	500101805	30% to <40%	30% to <40%	30% to <40%	>=40%	>=40%	30% to <40%	>=40%	10% to <20%	30% to <40%	20% to <30%	>=40%	>=40%	>=40%	20% to <30%
1	0	0	50	180	6	500101806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	50	180	7	500101807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
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1	0	0	60	120	6	600101206	<10%	<10%	10% to <20%	<10%	<10%	10% to <20%	10% to <20%	<10%	<10%	10% to <20%	<10%	10% to <20%	<10%	<10%
1	0	0	60	120	7	600101207	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	<10%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	10% to <20%	<10%
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1	0	0	60	140	8	600101408	30% to <40%	20% to <30%	>=40%	30% to <40%	30% to <40%	>=40%	>=40%	20% to <30%	30% to <40%	30% to <40%	30% to <40%	>=40%	30% to <40%	20% to <30%
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1	0	0	60	160	6	600101606	20% to <30%	20% to <30%	>=40%	30% to <40%	20% to <30%	>=40%	>=40%	20% to <30%	20% to <30%	30% to <40%	30% to <40%	>=40%	30% to <40%	20% to <30%
1	0	0	60	160	7	600101607	30% to <40%	30% to <40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	20% to <30%	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	60	160	8	600101608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
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1	0	0	70	120	7	700101207	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%	30% to <40%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	30% to <40%	10% to <20%	10% to <20%
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1	0	0	70	140	8	700101408	30% to <40%	30% to <40%	>=40%	30% to <40%	30% to <40%	>=40%	>=40%	20% to <30%	30% to <40%	30% to <40%	>=40%	>=40%	30% to <40%	20% to <30%
1	0	0	70	160	4	700101604	20% to <30%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	30% to <40%	20% to <30%	10% to <20%	10% to <20%	20% to <30%	20% to <30%	30% to <40%	20% to <30%	10% to <20%
1	0	0	70	160	5	700101605	20% to <30%	20% to <30%	30% to <40%	20% to <30%	30% to <40%	>=40%	30% to <40%	20% to <30%	20% to <30%	20% to <30%	30% to <40%	>=40%	20% to <30%	20% to <30%
1	0	0	70	160	6	700101606	30% to <40%	20% to <30%	>=40%	30% to <40%	30% to <40%	>=40%	>=40%	20% to <30%	20% to <30%	30% to <40%	30% to <40%	>=40%	30% to <40%	20% to <30%
1	0	0	70	160	7	700101607	>=40%	30% to <40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	30% to <40%	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	70	160	8	700101608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	0	70	180	4	700101804	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%
1	0	0	70	180	5	700101805	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	0	70	180	6	700101806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	0	70	180	7	700101807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	0	0	70	180	8	700101808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	40	120	4	401111204	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	40	120	5	401111205	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	40	120	6	401111206	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	40	120	7	401111207	<10%	<10%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%
1	1	1	40	120	8	401111208	10% to <20%	10% to <20%	30% to <40%	20% to <30%	<10%	<10%	10% to <20%	<10%	10% to <20%	10% to <20%	20% to <30%	30% to <40%	10% to <20%	10% to <20%
1	1	1	40	140	4	401111404	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	40	140	5	401111405	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	40	140	6	401111406	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%
1	1	1	40	140	7	401111407	10% to <20%	20% to <30%	30% to <40%	10% to <20%	<10%	<10%	10% to <20%	<10%	10% to <20%	10% to <20%	10% to <20%	30% to <40%	10% to <20%	10% to <20%
1	1	1	40	140	8	401111408	20% to <30%	>=40%	>=40%	>=40%	20% to <30%	10% to <20%	20% to <30%	10% to <20%	30% to <40%	30% to <40%	>=40%	>=40%	20% to <30%	20% to <30%
1	1	1	40	160	4	401111604	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	10% to <20%	<10%	<10%	<10%	10% to <20%	10% to <20%	10% to <20%	<10%
1	1	1	40	160	5	401111605	10% to <20%	20% to <30%	20% to <30%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	20% to <30%	10% to <20%
1	1	1	40	160	6	401111606	30% to <40%	>=40%	30% to <40%	30% to <40%	20% to <30%	10% to <20%	20% to <30%	<10%	20% to <30%	10% to <20%	20% to <30%	>=40%	30% to <40%	20% to <30%
1	1	1	40	160	7	401111607	>=40%	>=40%	>=40%	>=40%	30% to <40%	20% to <30%	30% to <40%	10% to <20%	30% to <40%	30% to <40%	>=40%	>=40%	30% to <40%	30% to <40%
1	1	1	40	160	8	401111608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	40	180	4	401111804	>=40%	>=40%	30% to <40%	>=40%	>=40%	30% to <40%	>=40%	10% to <20%	30% to <40%	30% to <40%	>=40%	>=40%	>=40%	30% to <40%
1	1	1	40	180	5	401111805	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	10% to <20%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	40	180	6	401111806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	40	180	7	401111807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	40	180	8	401111808	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	50	120	4	501111204	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	50	120	5	501111205	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	50	120	6	501111206	<10%	<10%	10% to <20%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	10% to <20%	<10%	<10%
1	1	1	50	120	7	501111207	10% to <20%	10% to <20%	20% to <30%	10% to <20%	<10%	<10%	10% to <20%	<10%	<10%	<10%	10% to <20%	20% to <30%	<10%	<10%
1	1	1	50	120	8	501111208	20% to <30%	10% to <20%	>=40%	20% to <30%	10% to <20%	10% to <20%	20% to <30%	<10%	20% to <30%	20% to <30%	20% to <30%	>=40%	10% to <20%	10% to <20%
1	1	1	50	140	4	501111404	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%	<10%
1	1	1	50	140	5	501111405	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	<10%	10% to <20%	<10%	<10%	<10%	<10%	10% to <20%	10% to <20%	<10%
1	1	1	50	140	6	501111406	10% to <20%	20% to <30%	20% to <30%	10% to <20%	10% to <20%	10% to <20%	10% to <20%	<10%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	10% to <20%	10% to <20%
1	1	1	50	140	7	501111407	20% to <30%	30% to <40%	>=40%	20% to <30%	10% to <20%	10% to <20%	20% to <30%	<10%	20% to <30%	20% to <30%	20% to <30%	>=40%	20% to <30%	10% to <20%
1	1	1	50	140	8	501111408	30% to <40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%	>=40%	20% to <30%	>=40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%
1	1	1	50	160	4	501111604	10% to <20%	10% to <20%	20% to <30%	20% to <30%	10% to <20%	10% to <20%	20% to <30%	<10%	10% to <20%	10% to <20%	10% to <20%	20% to <30%	20% to <30%	10% to <20%
1	1	1	50	160	5	501111605	20% to <30%	30% to <40%	30% to <40%	30% to <40%	20% to <30%	20% to <30%	30% to <40%	10% to <20%	20% to <30%	20% to <30%	20% to <30%	30% to <40%	30% to <40%	20% to <30%
1	1	1	50	160	6	501111606	30% to <40%	>=40%	>=40%	>=40%	30% to <40%	30% to <40%	>=40%	10% to <20%	30% to <40%	30% to <40%	30% to <40%	>=40%	>=40%	30% to <40%
1	1	1	50	160	7	501111607	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	50	160	8	501111608	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	50	180	4	501111804	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	20% to <30%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	50	180	5	501111805	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	30% to <40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	50	180	6	501111806	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
1	1	1	50	180	7	501111807	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%	>=40%
This is a portion of the data; to view all the data, please download the file.

Dataset 1.WHO_ISH_Scores.csv.

CSV file of all parameter combinations and corresponding WHO/ISH risk scores used internally by WHO_ISH_Risk() to match parameter values passed to it to their respective risk score.

Development of the WHO_ISH_Risk() Function

whoishRisk contains a single function, called WHO_ISH_Risk(), which calculates the WHO/ISH risk score for any epidemiological subregion. The function code is reported herein (Dataset 2).

Dataset 2.R code for the WHO_ISH_Risk() function of the whoishRisk package.

WHO_ISH_Risk() requires seven parameters: age, gender, smoking status, diabetes status, systolic blood pressure, total cholesterol, and the appropriate WHO epidemiological subregion. The function format in the workspace is: WHO_ISH_Risk(age, gdr, smk, sbp, dm, chl, subregion). These parameters and their abbreviations are summarised in Table 1. No default values are specified for any parameter.

Table 1. Description of the WHO_ISH_Risk() function parameters.

Function parameter	Full parameter name	Parameter class	Parameter values
age	Age	numeric	Continuous (years)
gdr	Gender	numeric	Dichotomous (0=female; 1=male)
smk	Smoking	numeric	Dichotomous (0=non-smoker; 1=smoker)
sbp	Systolic blood pressure	numeric	Continuous (mmHg)
dm	Diabetes mellitus status	numeric	Dichotomous (0=not diabetic; 1=diabetic)
chl	Total cholesterol	numeric	Continuous (mmol/ L); 0=unknown cholesterol
subregion	WHO epidemiological subregion	character	“AFR_D”, “AFR_E”, “AMR_A”, “AMR_B”, “AMR_D”,“EMR_B”, “EMR_D”, “EUR_A”, “EUR_B”, EUR_C”, “SEAR_B”, “SEAR_ D”, “WPR_A”, “WPR_B”

Internally, WHO_ISH_Risk() requires access to the comma delimited file named “WHO_ISH_Scores.csv”, which it calls automatically from within the package but is also included herein (Dataset 1).

The WHO_ISH_Risk() function creates an internal data frame of the risk factor values passed to it. Parameters can be single values or vectors of equal length. It then categorises the continuous parameters age, systolic blood pressure, and total cholesterol. Age and systolic blood pressure were categorised according to WHO guidance¹⁰. Total cholesterol was categorised into one of the five possible categories (<=4,5,6,7 and >=8 mmol/L) according to common clinical practice, rounding up from 0.5 to the nearest integer.

Internally, a unique identification code is generated corresponding to the combinations of risk factors for each individual. This code is matched to a reference code from the “WHO_ISH_Scores.csv” file. The function stores the risk scores in a data frame that includes the risk factors, and ultimately returns a vector containing the risk scores. The output of the function is one of five different character strings, corresponding to the five different WHO/ISH risk categories: “<10%”, “10 to <20%”, “20 to <30%”, “30 to <40%”, >=40%”.

Warning messages are included when parameters appear out of range. These messages, their conditions, and their intended interpretation are described in Table 2. Out of range continuous parameters (age, systolic blood pressure, total cholesterol) are non-fatal and a risk score will be generated with a warning message. Out of range dichotomous variables (gender, smoking status, diabetes status) are fatal errors and the output (NA) will be generated with a warning.

Table 2. WHO_ISH_Risk() warning messages, their conditions, and intended interpretation.

Condition of Warning	Warning Message	Interpretation of Warning
age < 19	"At least one age is 18 or younger"	WHO/ISH risk scores are meant for use in adults. At least one age value is equal to 18 or younger. Check for errors in age values.
age >100	"At least one age is greater than 100"	Age values greater than 100 may indicate a data error. Check to ensure age values over 100 are correct.
gdr > 1	"Gender must be equal to 0 or 1"	A value for gender other than 0 or 1 is included. Check gender values.
smk > 1	"Smoking must be equal to 0 or 1"	A value for smoking other than 0 or 1 is included. Check smoking values.
sbp < 90	"At least one systolic blood pressure is below 90 mmHg"	SBP values below 90 mmHg may indicate a data error. Check to ensure values under 90 mmHg are correct.
sbp > 250	"At least one systolic blood pressure is over 250 mmHg"	SBP values over 250 mmHg may indicate a data error. Check to ensure values over 250 are correct.
dm > 1	"Diabetes status must be equal to 0 or 1"	A value for diabetes status other than 0 or 1 is included. Check diabetes status values.
tc > 10	"At least one total cholesterol is greater than 10 mmol/L. Ensure all values are in units of mmol/L"	Total cholesterol values over 10 mmol/L may indicate a data error. Check to ensure values over 10 mmol/L are correct and in units of mmol/L.

Worked example

whoishRisk can be installed in one step using install_github() from the devtools package⁹. We have included a worked example of how to install whoishRisk and use the WHO_ISH_Risk() function to calculate the risk score for five individuals.

#Step 1: Install whoishRisk package
> library(devtools)
> install_github("DylanRJCollins/whoishRisk")
#Step 2: Load whoishRisk package into workspace
> library(whoishRisk)
#Step 3: Load risk factor data
> Age <- c(40, 87, 65, 53, 71) #Age in years
> Gender <- c(0,0,0,1,1) #0=female, 1=male
> Smoking <- c(1,1,0,1,0) #0=non-smoker 1=smoker
> Systolic_Blood_Pressure <- c(129, 157, 134, 189, 141) #SBP in mmHg
> Diabetes <- c(1,1,1,0,1) #0=not diabetic 1=diabetic
> Total_Cholesterol <- c(0, 5.1, 4.5, 0, 8.3) #Total cholesterol (mmol/L, 0=unknown cholesterol)
#Step 4: Pass the risk factor vectors to the WHO_ISH_Risk() function, and set subregion equal to the name of the appropriate epidemiological subregion (e.g. “EMR_B”). This will return a vector of WHO/ISH risk scores.
> WHO_ISH_Risk(Age, Gender, Smoking, Systolic_Blood_Pressure, Diabetes, Total_Cholesterol,"EMR_B")
[1] "<10%" ">=40%" "<10%" ">=40%" ">=40%"

Validation

Comparison with the published MatLab implementation of the SEAR D risk charts⁸ showed 100% agreement with our R implementation, for all possible combinations of risk factors.

Discussion

To our knowledge, this is the first publically available R implementation of WHO/ISH CVD risk charts for all WHO epidemiological subregions of the world. Our package, whoishRisk, may be used for analysis of cardiovascular risk when electronic patient data is available. The code will automatically apply WHO/ISH risk scores to patients based on age, gender, systolic blood pressure, smoking status, diabetes status, total cholesterol, and epidemiological subregion. This code could be used, for example, during a pilot implementation of WHO PEN to audit the accuracy of risk assessment by comparing documented risk scores to actual risk scores calculated using this tool. We have provided a complete worked example.

While WHO PEN guidance specifies the range of systolic blood pressure values for each systolic blood pressure category, it provides no such guidance for categorising total cholesterol. Based on our opinion and clinical experience, and on a previously published implementation in MatLab⁸, we chose to categorise total cholesterol by rounding up at 0.5 to the next integer.

The “WHO_ISH_Scores.csv” file is provided herein for transparency and to promote collaboration and cross validation. While the risk score values it stores are returned to the workspace as characters (e.g. “10 to <20%”), a user could simply convert these to class numeric or factor. We chose to return them as character strings that are identical to the patient charts in order to produce a literal implementation of the risk charts.

Conclusion

We created an R package called whoishRisk to be used for the calculation of WHO/ISH CVD risk charts for all WHO epidemiological subregions of the world. It contains a single function, WHO_ISH_Risk(), that requires seven parameters: age, gender, systolic blood pressure, smoking status, diabetes status, total cholesterol, and epidemiological subregion. whoishRisk can be used to quickly calculate WHO/ISH risk scores from routinely collected electronic patient data and therefore aid in the implementation and evaluation of these risk charts in low-resource settings.

Data and software availability

F1000Research: Dataset 1. CSV file of all parameter combinations and corresponding WHO/ISH risk scores used internally by WHO_ISH_Risk() to match parameter values passed to it to their respective risk score 10.5256/f1000research.9742.d153375¹¹

F1000Research: Dataset 2. R code for the WHO_ISH_Risk() function of the whoishRisk package, 10.5256/f1000research.9742.d153376¹²

Author contributions

DC conceived of the idea, extracted data, wrote the initial code, and manuscript. JL and NB extracted data and contributed to writing the manuscript. CK reviewed and adapted the code and contributed to writing the manuscript. AW and CH reviewed and contributed to writing the manuscript.

Competing interests

DC has received payment from the WHO for consulting work. AW and CH have received expenses and grant income from the WHO for projects related to CVD and Self Care in NCDs, and direct a WHO Collaborating Centre. CH also receives funding form the National Institute of Health Research (NIHR) School of Primary Care Research. JL, CK, and NB declare no competing interests.

Grant information

The WHO Collaborating Centre for Self-Care paid for the open access publishing fees. No other funding was provided for this work.

F1000 recommended

References

1. Mathers CD, Loncar D: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006; 3(11): e442. PubMed Abstract | Publisher Full Text | Free Full Text
2. WHO: Noncommunicable Diseases, Fact Sheet. 2015. Reference Source
3. WHO: Package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. Geneva, Switzerland; 2010. Reference Source
4. Mendis S, Lindholm LH, Mancia G, et al.: World Health Organization (WHO) and International Society of Hypertension (ISH) risk prediction charts: assessment of cardiovascular risk for prevention and control of cardiovascular disease in low and middle-income countries. J Hypertens. 2007; 25(8): 1578–82. PubMed Abstract | Publisher Full Text
5. Cooney MT, Dudina A, D'Agostino R, et al.: Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation. 2010; 122(3): 300–10. PubMed Abstract | Publisher Full Text
6. R Core Team: R: A language and environment for statistical computing. 2016. Reference Source
7. Gamer M, Lemon J, Fellows I, et al.: irr: Various Coefficients of Interrater Reliability and Agreement. 2012. Reference Source
8. Raghu A, Praveen D, Peiris D, et al.: Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India. PLoS One. 2015; 10(8): e0133618. PubMed Abstract | Publisher Full Text | Free Full Text
9. Wickham H, Chang W: devtools: Tools to Make Developing R Packages Easier. 2016. Reference Source
10. WHO/ISH: World Health organization/International Society of Hypertension (WHO/ISH) risk prediction charts. 2007. Reference Source
11. Collins D, Lee J, Bobrovitz N, et al.: Dataset 1 in: whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world. F1000Research. 2017. Data Source
12. Collins D, Lee J, Bobrovitz N, et al.: Dataset 2 in: whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world. F1000Research. 2017. Data Source

Comments on this article Comments (2)

Version 2

VERSION 2 PUBLISHED 08 Mar 2017

Revised

Comment

Version 1

VERSION 1 PUBLISHED 14 Oct 2016

Discussion is closed on this version, please comment on the latest version above.

Author Response 20 Oct 2016

Dylan Collins, University of British Columbia, Canada

20 Oct 2016

Author Response

Dear Rajarshi Guha,

Thank you for reading our work. I've created a plain text format version of Dataset 2 which is the dataset that contains the R code and ... Continue reading Dear Rajarshi Guha,

Thank you for reading our work. I've created a plain text format version of Dataset 2 which is the dataset that contains the R code and worked example.

It can be found on my github repository here. Should you have any difficulty downloading from github or any further questions please let me know.

Every Best,

Dylan
Dear Rajarshi Guha,

Thank you for reading our work. I've created a plain text format version of Dataset 2 which is the dataset that contains the R code and worked example.

It can be found on my github repository here. Should you have any difficulty downloading from github or any further questions please let me know.

Every Best,

Dylan
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Reader Comment 18 Oct 2016

Rajarshi Guha, National Institutes of Health, USA

18 Oct 2016

Reader Comment

Please make the R code available in plain text format.
Competing Interests: No competing interests were disclosed.
Please make the R code available in plain text format.
Please make the R code available in plain text format.
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Author details Author details

¹ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK

Competing interests

Grant information

The WHO Collaborating Centre for Self Care paid for the open access publishing fees. No other funding was provided for this work.

Article Versions (2)

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Published: 08 Mar 2017, 5:2522

https://doi.org/10.12688/f1000research.9742.2

version 1

Published: 14 Oct 2016, 5:2522

https://doi.org/10.12688/f1000research.9742.1

© 2017 Collins D 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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Collins D, Lee J, Bobrovitz N et al. whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world [version 2; peer review: 3 approved]. F1000Research 2017, 5:2522 (https://doi.org/10.12688/f1000research.9742.2)

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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 approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 2

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PUBLISHED 08 Mar 2017

Revised

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Reviewer Report 14 Mar 2017

Scott A. Chamberlain, rOpenSci project, University of California, Berkeley, Berkeley, CA, USA

Approved

https://doi.org/10.5256/f1000research.11940.r20822

I'm mostly satisfied with the author's changes, with a few minor changes.

Thank you for making an R package. I downloaded, and checked the package, with a few observations:

In the package use

I'm mostly satisfied with the author's changes, with a few minor changes.

Thank you for making an R package. I downloaded, and checked the package, with a few observations:

In the package use utils::read.csv instead of just read.csv - you probably saw the warning if you've run R CMD CHECK or devtools::check()
The package has no examples. I'd strongly suggest including examples.
The package has no tests. I'd strongly suggest including tests (though if time is limited, at least include examples).
The authors note that the package now gives warnings when input values are out of range - I'd strongly suggest to document what the correct ranges are for each parameter so users don't have to guess
In addition, if there are sensible default values I'd use those - if applicable - may not be applicable.
Urge authors to document what type of value is expected for each parameter, e.g., age (numeric), subregion (character)

Sincerely,
Scott Chamberlain

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 10 Mar 2017

Maria Suarez-Diez, Laboratory of Systems and Synthetic Biology, Wageningen University and Research (WUR), Wageningen, The Netherlands

Approved

https://doi.org/10.5256/f1000research.11940.r20821

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Reviewer Report 09 Mar 2017

Raivo Kolde, Philips Research North America, Cambridge, MA, USA

Approved

https://doi.org/10.5256/f1000research.11940.r20823

All my main concerns were satisfied with the update. I was able to install and run the package. I guess ... Continue reading

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Reviewer Report 28 Oct 2016

Maria Suarez-Diez, Laboratory of Systems and Synthetic Biology, Wageningen University and Research (WUR), Wageningen, The Netherlands

Approved with Reservations

https://doi.org/10.5256/f1000research.10503.r17004

The authors present an R script and an additional datafile to calculate cardiovascular risk using the Who/ISH risk assessment charts. Previous implementations required the use of MatLab and I believe an R implementation can be an useful addition to the field.

The authors intended to design a code requiring little R expertise. I think the authors have only partly succeeded in accomplishing this goal and the code still needs to be improved.

As mentioned by the other reviewers, RTF is far from an optimal format to distribute the code. The authors should present their code as a package hosted in some repository. Currently the RTF file contains the function definition, data loading and working example in a single view. When creating the package the authors should clearly separate the code from the documentation and provide a working example. If the authors intent this tool to be used and possibly modified by users with little R expertise then a separate file with detailed instruction should be provided.

The authors state that users can modify the WHO_ISH_Scores.csv file to represent other risk categories. That would require explanation of the content of the file. Although the used abbreviations gdr for gender might seem obvious, they should be explained. Also when downloading the file from F1000, the file name is changed and appears as “5ae9107XXX..._WHO_ISH_Scores.csv” so that the reading fails. This could be fixed by including the file as a dataset in the corresponding package.

When data from multiple patients are provided (as in the example) the output is a factor. I think it would help the users match input and output values is some tabular format or some patient identifier is provided in the output.

If any of the input parameters are missing (NA values) the code outputs “NA” but no information regarding the missing values is provided. It might be helpful if the code were to report which value was actually missing.

Fig. 1 shows the code and contains mainly information on how some continuous variables (age, cholesterol and systolic blood pressure are discretized). I think a more efficient way of conveying this information is by including it in Table 1.

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 08 Mar 2017

Dylan Collins, University of British Columbia, Canada

08 Mar 2017

Author Response
Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:
- We created an R package including all documentation files which
... Continue reading
Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:

We created an R package including all documentation files which can be downloaded from github

We no longer suggest users to change the “WHO_ISH_Scores.csv” files and we have updated the file name hosted by F1000 to be “WHO_ISH_Scores.csv”. This file, as suggested, is internal to the package and while we report it here is not necessary to download.

We have added a series of warning messages to help explain outputs of “NA” and to help catch errors in the input values (e.g. out of range parameters)

We have described how continuous variables are discretized in the main text

Every Best,

Dylan Collins
Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:

We created an R package including all documentation files which can be downloaded from github

We no longer suggest users to change the “WHO_ISH_Scores.csv” files and we have updated the file name hosted by F1000 to be “WHO_ISH_Scores.csv”. This file, as suggested, is internal to the package and while we report it here is not necessary to download.

We have added a series of warning messages to help explain outputs of “NA” and to help catch errors in the input values (e.g. out of range parameters)

We have described how continuous variables are discretized in the main text

Every Best,

Dylan Collins
Competing Interests: No competing interests were disclosed. Close
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Author Response 08 Mar 2017

Dylan Collins, University of British Columbia, Canada

08 Mar 2017

Author Response
Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:
- We created an R package including all documentation files which
... Continue reading
Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:

We created an R package including all documentation files which can be downloaded from github

We no longer suggest users to change the “WHO_ISH_Scores.csv” files and we have updated the file name hosted by F1000 to be “WHO_ISH_Scores.csv”. This file, as suggested, is internal to the package and while we report it here is not necessary to download.

We have added a series of warning messages to help explain outputs of “NA” and to help catch errors in the input values (e.g. out of range parameters)

We have described how continuous variables are discretized in the main text

Every Best,

Dylan Collins
Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:

We created an R package including all documentation files which can be downloaded from github

We no longer suggest users to change the “WHO_ISH_Scores.csv” files and we have updated the file name hosted by F1000 to be “WHO_ISH_Scores.csv”. This file, as suggested, is internal to the package and while we report it here is not necessary to download.

We have added a series of warning messages to help explain outputs of “NA” and to help catch errors in the input values (e.g. out of range parameters)

We have described how continuous variables are discretized in the main text

Every Best,

Dylan Collins
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 25 Oct 2016

Scott A. Chamberlain, rOpenSci project, University of California, Berkeley, Berkeley, CA, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.10503.r17006

The authors describe some R code for helping to calculate cardiovascular risk scores. I think the code needs significant work.

The code is in an .rtf file. This is very bad software practice. The authors

The authors describe some R code for helping to calculate cardiovascular risk scores. I think the code needs significant work.

The code is in an .rtf file. This is very bad software practice. The authors should at the very very least put the code in a file with .R extension.
Ideally the code should be put in an R package. It is relatively straight-forward to make an R package these days. See http://r-pkgs.had.co.nz/ for help. This makes it easy to add documentation (of which there is currently essentially none for the current function included in the manuscript), tests, etc. Looks like the authors have a Github repository (https://github.com/DylanRJCollins/WHO_ISH_R_Implementation) - this could be made into an R package.
Another benefit of making a package, is that R packages have a way of including datasets in them. The csv file is small enough that you can include it in the package if that's appropriate for the use case here. If the `WHO_ISH_Scores.csv` dataset is not likely to change, or not likely to change very often, they can include it in the package.
The dataset `WHO_ISH_Scores.csv` has two columns that are probably row names that should be removed. If they aren't data, remove them.
The dataset `WHO_ISH_Scores.csv`: the data is not in a tidy format that is easy to work with. There shouldn't be numeric data combined with symbols (e.g. >=40%). If possible, I urge authors to find a way to make these into numeric columns while retaining the same information. However, it may be that it's too difficult to separate numeric values from the greater than / less than symbols etc.
R packages should be cited by reference. e.g. "using the irr package (version 0.84)". It is good they cite the version, but put a reference in in your references. Run `citation("irr")` in an R session to get a reference for it.
"We used RStudio (version 0.99.489) to compare the duplicate extractions ...": Rstudio is just an IDE. Say that you used R, not RStudio. It's fine to cite RStudio, but also cite R. Run `citation()` in an R session to get the citation for R.
Octave is an open source language, and thus the authors should share the Octave code.
"[...] we intentionally sought to use simple syntax in the base package" - the authors script is in fact not a package, but if they do make a package this wording is good.
In the Conclusion: "We created a simple R implementation of WHO/ISH CVD risk charts for all WHO epidemiological subregions of the world, requiring only the base R package." - refer to this as "base R", not "the base R package".
If the dataset "WHO_ISH_Scores.csv" can be modified, thus different versions of it may be used by the user, it makes more sense to pass in the dataset as a parameter, instead of hard coding reading in the data in the function.
I urge authors to include a license with their R package (to be created) - and to submit the package to CRAN so it's easy for all to install.

Code comments:

I'm not sure the authors meant to do this, but the use of single ampersand in the `ifelse` statements (e.g., `ifelse(df$age > 17 & df$age < 50, 40, df$age)`) means that they get a vector of logicals (e..g., `df$age > 17 & df$age < 50` evaluates to `TRUE FALSE FALSE FALSE FALSE`) using their example, when I think what they want is a single logical return value. If so, use double ampersand instead: `&&` instead of `&`.
I'm pretty sure, but have not tested thoroughly, that the long series of if statements in the section "Match the look up value with the reference value" can be replaced with just this: `ref[[subregion]][match(df$luv, ref$refv)]`.

Competing Interests: No competing interests were disclosed.

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Author Response 08 Mar 2017

Dylan Collins, University of British Columbia, Canada

08 Mar 2017

Author Response
Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.
- We have created an R
... Continue reading
Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.

We have created an R package which can be downloaded from github

We provide the code from the R package as a .R file

We have included the “WHO_ISH_Scores.csv” data file in the R package

We tidied the ”WHO_ISH_Scores.csv” files but have kept the risk scores as character strings to retain a literal implementation of the risk charts, as described in the updated manuscript

We have cited R packages and R as suggested

The MatLab code used in Octave has previously been published as cited in the manuscript and is freely available

We no longer suggest users to change the “WHO_ISH_Scores.csv” files

We have included a GPL-3 license for the R Package

Every Best,

Dylan Collins
Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.

We have created an R package which can be downloaded from github

We provide the code from the R package as a .R file

We have included the “WHO_ISH_Scores.csv” data file in the R package

We tidied the ”WHO_ISH_Scores.csv” files but have kept the risk scores as character strings to retain a literal implementation of the risk charts, as described in the updated manuscript

We have cited R packages and R as suggested

The MatLab code used in Octave has previously been published as cited in the manuscript and is freely available

We no longer suggest users to change the “WHO_ISH_Scores.csv” files

We have included a GPL-3 license for the R Package

Every Best,

Dylan Collins
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 08 Mar 2017

Dylan Collins, University of British Columbia, Canada

08 Mar 2017

Author Response
Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.
- We have created an R
... Continue reading
Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.

We have created an R package which can be downloaded from github

We provide the code from the R package as a .R file

We have included the “WHO_ISH_Scores.csv” data file in the R package

We tidied the ”WHO_ISH_Scores.csv” files but have kept the risk scores as character strings to retain a literal implementation of the risk charts, as described in the updated manuscript

We have cited R packages and R as suggested

The MatLab code used in Octave has previously been published as cited in the manuscript and is freely available

We no longer suggest users to change the “WHO_ISH_Scores.csv” files

We have included a GPL-3 license for the R Package

Every Best,

Dylan Collins
Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.

We have created an R package which can be downloaded from github

We provide the code from the R package as a .R file

We have included the “WHO_ISH_Scores.csv” data file in the R package

We tidied the ”WHO_ISH_Scores.csv” files but have kept the risk scores as character strings to retain a literal implementation of the risk charts, as described in the updated manuscript

We have cited R packages and R as suggested

The MatLab code used in Octave has previously been published as cited in the manuscript and is freely available

We no longer suggest users to change the “WHO_ISH_Scores.csv” files

We have included a GPL-3 license for the R Package

Every Best,

Dylan Collins
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 20 Oct 2016

Raivo Kolde, Philips Research North America, Cambridge, MA, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.10503.r17007

The paper describes a software tool for calculation of WHO/ISH cardiovascular risk scores for different epidemiological subregions of the world. This could be a useful piece of software for researchers working with cardiovascular epidemiological data and make the practices for calculating such scores more reproducible over multiple studies. However, in the present form I found the paper to be severely lacking in both implementation and substance.

First, the code should not be distributed as an RTF file. R packages are well accepted standard for reproducibly distributing R code that also forces to provide some rudimentary documentation and examples. As an additional benefit, hosting the package in a repository like CRAN, Bioconductor or even Github, makes it easy to install and update the program. There is no reason to not follow this convention for this particular project, thus, the code should be converted into package format and hosted in one of the above-mentioned services to be any use to the community.

The argument of the code in the present form being simply adjustable is not valid. One has to spend quite a bit of time understanding what is happening in the code, especially when not an R expert. For the code to fulfill its purpose it has to be rewritten in a way that adjustable parts are presented to the user as function parameters (with reasonable defaults). This way users have to know even less R to use the code effectively.

Also, having a figure depicting the code is uncommon to say the least and not useful in any way. It would make much more sense to include a worked out example to the text, where you would describe a common use case of your R package.

To enhance usefulness of the package and make it more universally applicable it would be good to see some more risk prediction models like Framingham and SCORE added to the software. This would make it easy to compare the validity of different scores over a study population.

Competing Interests: No competing interests were disclosed.

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Author Response 08 Mar 2017

Dylan Collins, University of British Columbia, Canada

08 Mar 2017

Author Response
Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:
- As suggested, we created an R package which
... Continue reading
Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:

As suggested, we created an R package which can be downloaded directly from github

We no longer advocate for the code to be adjustable and through the development of a package have made it simpler to use

We removed the figure of the code, and have added a worked example in the text

The main difference between scores like Framingham, SCORE, and QRISK2 is that they have underlying cox model equations which can be implemented in R, whereas WHO/ISH risk charts do not – hence the rationale for the development of this package. While the additional of further risk scores was outside the scope of this work, we welcome collaborators who might want to contribute in this respect.

Every Best,

Dylan Collins
Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:

As suggested, we created an R package which can be downloaded directly from github

We no longer advocate for the code to be adjustable and through the development of a package have made it simpler to use

We removed the figure of the code, and have added a worked example in the text

The main difference between scores like Framingham, SCORE, and QRISK2 is that they have underlying cox model equations which can be implemented in R, whereas WHO/ISH risk charts do not – hence the rationale for the development of this package. While the additional of further risk scores was outside the scope of this work, we welcome collaborators who might want to contribute in this respect.

Every Best,

Dylan Collins
Competing Interests: No competing interests were disclosed. Close
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Author Response 08 Mar 2017

Dylan Collins, University of British Columbia, Canada

08 Mar 2017

Author Response
Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:
- As suggested, we created an R package which
... Continue reading
Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:

As suggested, we created an R package which can be downloaded directly from github

We no longer advocate for the code to be adjustable and through the development of a package have made it simpler to use

We removed the figure of the code, and have added a worked example in the text

The main difference between scores like Framingham, SCORE, and QRISK2 is that they have underlying cox model equations which can be implemented in R, whereas WHO/ISH risk charts do not – hence the rationale for the development of this package. While the additional of further risk scores was outside the scope of this work, we welcome collaborators who might want to contribute in this respect.

Every Best,

Dylan Collins
Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:

As suggested, we created an R package which can be downloaded directly from github

We no longer advocate for the code to be adjustable and through the development of a package have made it simpler to use

We removed the figure of the code, and have added a worked example in the text

The main difference between scores like Framingham, SCORE, and QRISK2 is that they have underlying cox model equations which can be implemented in R, whereas WHO/ISH risk charts do not – hence the rationale for the development of this package. While the additional of further risk scores was outside the scope of this work, we welcome collaborators who might want to contribute in this respect.

Every Best,

Dylan Collins
Competing Interests: No competing interests were disclosed. Close
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Comments on this article Comments (2)

Version 2

VERSION 2 PUBLISHED 08 Mar 2017

Revised

Comment

Version 1

VERSION 1 PUBLISHED 14 Oct 2016

Discussion is closed on this version, please comment on the latest version above.

Author Response 20 Oct 2016

Dylan Collins, University of British Columbia, Canada

20 Oct 2016

Author Response

Dear Rajarshi Guha,

Thank you for reading our work. I've created a plain text format version of Dataset 2 which is the dataset that contains the R code and ... Continue reading Dear Rajarshi Guha,

Thank you for reading our work. I've created a plain text format version of Dataset 2 which is the dataset that contains the R code and worked example.

It can be found on my github repository here. Should you have any difficulty downloading from github or any further questions please let me know.

Every Best,

Dylan
Dear Rajarshi Guha,

Thank you for reading our work. I've created a plain text format version of Dataset 2 which is the dataset that contains the R code and worked example.

It can be found on my github repository here. Should you have any difficulty downloading from github or any further questions please let me know.

Every Best,

Dylan
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Reader Comment 18 Oct 2016

Rajarshi Guha, National Institutes of Health, USA

18 Oct 2016

Reader Comment

Please make the R code available in plain text format.
Competing Interests: No competing interests were disclosed.
Please make the R code available in plain text format.
Please make the R code available in plain text format.
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Discussion is closed on this version, please comment on the latest version above.

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Raivo Kolde, Philips Research North America, Cambridge, USA
Scott A. Chamberlain, University of California, Berkeley, Berkeley, USA
Maria Suarez-Diez, Wageningen University and Research (WUR), Wageningen, The Netherlands

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14 Mar 2017 | for Version 2

Scott A. Chamberlain, rOpenSci project, University of California, Berkeley, Berkeley, CA, USA

23 Views Cite this report Responses(0)

Approved

I'm mostly satisfied with the author's changes, with a few minor changes.

Thank you for making an R package. I downloaded, and checked the package, with a few observations:

In the package use utils::read.csv instead of just read.csv - you probably saw the warning if you've run R CMD CHECK or devtools::check()
The package has no examples. I'd strongly suggest including examples.
The package has no tests. I'd strongly suggest including tests (though if time is limited, at least include examples).
The authors note that the package now gives warnings when input values are out of range - I'd strongly suggest to document what the correct ranges are for each parameter so users don't have to guess
In addition, if there are sensible default values I'd use those - if applicable - may not be applicable.
Urge authors to document what type of value is expected for each parameter, e.g., age (numeric), subregion (character)

Sincerely,
Scott Chamberlain

Competing Interests

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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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10 Mar 2017 | for Version 2

Maria Suarez-Diez, Laboratory of Systems and Synthetic Biology, Wageningen University and Research (WUR), Wageningen, The Netherlands

19 Views Cite this report Responses(0)

Approved

My main concerns have been addressed in this new version of the manuscript.

I think the developed package represents a useful addition to the field. I was able to install and run the package and indeed the package reports the absence of data points (NA).
Only a minor concern: within R, getting the citation 'citation("whoishRisk")' returns the names of the name of the package, the names of the authors and the R version. I recommend they include a reference to this work.

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23 Views

09 Mar 2017 | for Version 2

Raivo Kolde, Philips Research North America, Cambridge, MA, USA

23 Views Cite this report Responses(0)

Approved

All my main concerns were satisfied with the update. I was able to install and run the package. I guess from visibility standpoint it would be nice to include it to the CRAN database, but otherwise I approve.

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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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37 Views

28 Oct 2016 | for Version 1

Maria Suarez-Diez, Laboratory of Systems and Synthetic Biology, Wageningen University and Research (WUR), Wageningen, The Netherlands

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Approved With Reservations

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Author Response

08 Mar 2017

Dylan Collins, University of British Columbia, Canada

Dear Maria Suarez-Diez,

Thank you for your valuable comments. In response we have made the following changes:

We created an R package including all documentation files which can be downloaded from github
We no longer suggest users to change the “WHO_ISH_Scores.csv” files and we have updated the file name hosted by F1000 to be “WHO_ISH_Scores.csv”. This file, as suggested, is internal to the package and while we report it here is not necessary to download.
We have added a series of warning messages to help explain outputs of “NA” and to help catch errors in the input values (e.g. out of range parameters)
We have described how continuous variables are discretized in the main text

Every Best,

Dylan Collins

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

34 Views

25 Oct 2016 | for Version 1

Scott A. Chamberlain, rOpenSci project, University of California, Berkeley, Berkeley, CA, USA

34 Views Cite this report Responses(1)

Approved With Reservations

The authors describe some R code for helping to calculate cardiovascular risk scores. I think the code needs significant work.

The code is in an .rtf file. This is very bad software practice. The authors should at the very very least put the code in a file with .R extension.
Ideally the code should be put in an R package. It is relatively straight-forward to make an R package these days. See http://r-pkgs.had.co.nz/ for help. This makes it easy to add documentation (of which there is currently essentially none for the current function included in the manuscript), tests, etc. Looks like the authors have a Github repository (https://github.com/DylanRJCollins/WHO_ISH_R_Implementation) - this could be made into an R package.
Another benefit of making a package, is that R packages have a way of including datasets in them. The csv file is small enough that you can include it in the package if that's appropriate for the use case here. If the `WHO_ISH_Scores.csv` dataset is not likely to change, or not likely to change very often, they can include it in the package.
The dataset `WHO_ISH_Scores.csv` has two columns that are probably row names that should be removed. If they aren't data, remove them.
The dataset `WHO_ISH_Scores.csv`: the data is not in a tidy format that is easy to work with. There shouldn't be numeric data combined with symbols (e.g. >=40%). If possible, I urge authors to find a way to make these into numeric columns while retaining the same information. However, it may be that it's too difficult to separate numeric values from the greater than / less than symbols etc.
R packages should be cited by reference. e.g. "using the irr package (version 0.84)". It is good they cite the version, but put a reference in in your references. Run `citation("irr")` in an R session to get a reference for it.
"We used RStudio (version 0.99.489) to compare the duplicate extractions ...": Rstudio is just an IDE. Say that you used R, not RStudio. It's fine to cite RStudio, but also cite R. Run `citation()` in an R session to get the citation for R.
Octave is an open source language, and thus the authors should share the Octave code.
"[...] we intentionally sought to use simple syntax in the base package" - the authors script is in fact not a package, but if they do make a package this wording is good.
In the Conclusion: "We created a simple R implementation of WHO/ISH CVD risk charts for all WHO epidemiological subregions of the world, requiring only the base R package." - refer to this as "base R", not "the base R package".
If the dataset "WHO_ISH_Scores.csv" can be modified, thus different versions of it may be used by the user, it makes more sense to pass in the dataset as a parameter, instead of hard coding reading in the data in the function.
I urge authors to include a license with their R package (to be created) - and to submit the package to CRAN so it's easy for all to install.

Code comments:

I'm not sure the authors meant to do this, but the use of single ampersand in the `ifelse` statements (e.g., `ifelse(df$age > 17 & df$age < 50, 40, df$age)`) means that they get a vector of logicals (e..g., `df$age > 17 & df$age < 50` evaluates to `TRUE FALSE FALSE FALSE FALSE`) using their example, when I think what they want is a single logical return value. If so, use double ampersand instead: `&&` instead of `&`.
I'm pretty sure, but have not tested thoroughly, that the long series of if statements in the section "Match the look up value with the reference value" can be replaced with just this: `ref[[subregion]][match(df$luv, ref$refv)]`.

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Author Response

08 Mar 2017

Dylan Collins, University of British Columbia, Canada

Dear Scott A. Chamberlain,

Thank you for your thoughtful and considered feedback. We have responded in the updated version as detailed below.

We have created an R package which can be downloaded from github
We provide the code from the R package as a .R file
We have included the “WHO_ISH_Scores.csv” data file in the R package
We tidied the ”WHO_ISH_Scores.csv” files but have kept the risk scores as character strings to retain a literal implementation of the risk charts, as described in the updated manuscript
We have cited R packages and R as suggested
The MatLab code used in Octave has previously been published as cited in the manuscript and is freely available
We no longer suggest users to change the “WHO_ISH_Scores.csv” files
We have included a GPL-3 license for the R Package

Every Best,

Dylan Collins

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No competing interests were disclosed.

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Reviewer Report

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20 Oct 2016 | for Version 1

Raivo Kolde, Philips Research North America, Cambridge, MA, USA

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Approved With Reservations

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Author Response

08 Mar 2017

Dylan Collins, University of British Columbia, Canada

Dear Raivo Kolde,

Thank you for your insightful review and comments. In response we have made the following changes:

As suggested, we created an R package which can be downloaded directly from github
We no longer advocate for the code to be adjustable and through the development of a package have made it simpler to use
We removed the figure of the code, and have added a worked example in the text
The main difference between scores like Framingham, SCORE, and QRISK2 is that they have underlying cox model equations which can be implemented in R, whereas WHO/ISH risk charts do not – hence the rationale for the development of this package. While the additional of further risk scores was outside the scope of this work, we welcome collaborators who might want to contribute in this respect.

Every Best,

Dylan Collins

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Competing Interests

No competing interests were disclosed.

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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[1] 1. Mathers CD, Loncar D: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 2006; 3(11): e442. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. WHO: Noncommunicable Diseases, Fact Sheet. 2015. Reference Source

[3] 3. WHO: Package of essential noncommunicable (PEN) disease interventions for primary health care in low-resource settings. Geneva, Switzerland; 2010. Reference Source

[4] 4. Mendis S, Lindholm LH, Mancia G, et al.: World Health Organization (WHO) and International Society of Hypertension (ISH) risk prediction charts: assessment of cardiovascular risk for prevention and control of cardiovascular disease in low and middle-income countries. J Hypertens. 2007; 25(8): 1578–82. PubMed Abstract | Publisher Full Text

[5] 5. Cooney MT, Dudina A, D'Agostino R, et al.: Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation. 2010; 122(3): 300–10. PubMed Abstract | Publisher Full Text

[6] 6. R Core Team: R: A language and environment for statistical computing. 2016. Reference Source

[7] 7. Gamer M, Lemon J, Fellows I, et al.: irr: Various Coefficients of Interrater Reliability and Agreement. 2012. Reference Source

[8] 8. Raghu A, Praveen D, Peiris D, et al.: Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India. PLoS One. 2015; 10(8): e0133618. PubMed Abstract | Publisher Full Text | Free Full Text

[9] 9. Wickham H, Chang W: devtools: Tools to Make Developing R Packages Easier. 2016. Reference Source

[10] 10. WHO/ISH: World Health organization/International Society of Hypertension (WHO/ISH) risk prediction charts. 2007. Reference Source

[11] 11. Collins D, Lee J, Bobrovitz N, et al.: Dataset 1 in: whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world. F1000Research. 2017. Data Source

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whoishRisk – an R package to calculate WHO/ISH cardiovascular risk scores for all epidemiological subregions of the world

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Methods

Extraction of WHO/ISH cardiovascular risk charts

Development of the WHO/ISH risk function

Validation

Results

whoishRisk Package

Extraction of WHO/ISH cardiovascular risk charts

Development of the WHO_ISH_Risk() Function

Table 1. Description of the WHO_ISH_Risk() function parameters.

Table 2. WHO_ISH_Risk() warning messages, their conditions, and intended interpretation.

Worked example

Validation

Discussion

Conclusion

Data and software availability

Author contributions

Competing interests

Grant information

References

Comments on this article Comments (2)

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