Factors Predicting Hospital Readmission among Thais with Post Myocardial Infarction

Introduction

Acute myocardial infarction (AMI) has become a significant health problem with high morbidity and mortality rates. Despite dramatic improvement in outcomes with medical therapy, admission rates following AMI hospitalization remain high. Previous studies have found that early readmission rates within seven-day of post‐AMI discharge ranged from 4.8%¹ to 11%², while late readmissions rate of 30-day, six-month, and one-year readmissions ranged from 11% to 14%³, 20.4%¹ to 33.8%², and 21.3⁴ to 49.9%⁵, respectively. The highest incidences of readmissions not only increase healthcare costs by 60%⁶, but also expose patients to long-term hospitalisation-associated complications. Readmissions for AMI are typically preceded by a recurrent AMI and related cardiovascular conditions¹ in which are often assumed to indicate incomplete treatment in hospital, poor coordination of services or communication of discharge plans, or lack of healthcare access in early follow-up care⁷. As a consequence, readmission is of high interest, and considered as a quality indicator for hospital care⁸.

In order to reduce readmission rates in patients with AMI, the predictions on patients who are likely to be readmitted and the intervention should be taken into account. Nevertheless, due to inconsistency of risk-predictive models, and the performance of these models, the problem of readmission rate continues. Most existing models were developed in different settings and periods, thus may not be appropriate to be applied in other contexts. Previous studies^1–3,9–11 have identified that clinical and laboratory parameters, including atrial fibrillation, severity of AMI, and hypertension, confer a higher risk for an early period of cardiovascular admission, whereas smoking and the burden of comorbid non-cardiac illness, including chronic kidney disease, diabetes mellitus, hypertension, anaemia, and pulmonary disease, raises the risk for AMI–related complications in late readmission. These factors may potentially modify the target for future interventions.

In Thailand, it is suggested that readmissions have negative impacts on both hospitals and patients. Also, it is a huge economic burden to the nation. A prior study in a Thai hospital¹² revealed that the unplanned readmission rate at one year after hospital discharge was 13.5% and 7.8% in the group of patients with unstable angina and non-ST elevation MI (NSTEMI), respectively. Most patients had angina at presentation^12,13. A recent study¹⁴ has also revealed that one-third of coronary artery disease patients had been readmitted at one year after hospital discharge once (40.2%) whereas twice and three-time readmissions were found in 35.5% and 11.2% of patients, respectively. The most significant predictive factor for readmission was social support. Nonetheless, there were few studies investigating the factors predicting rates of rehospitalization, especially in AMI patients after hospital discharge over variable time periods. According to previous studies^15,16, it was indicated that different variables might have an influence on readmission period over different timescales. It is suggested that early rehospitalization is associated with clinical and laboratory parameters. On the contrary, late rehospitalization is associated with patients’ behaviours and the burden of chronic diseases. To address this gap of knowledge, this study was designed to determine predictors of hospital readmissions among Thai AMI patients in early and late periods, in which early readmission were defined as readmissions within seven-day post discharge from index hospitalisation while late readmissions were defined as readmissions within 30-day, six-month, and one-year following hospital discharge.

Methods

Settings and study population

This study was a retrospective study using an electronic medical database (EMD) review at Roi-Et hospital, a tertiary care hospital in a large metropolitan area of Roi-Et province, Thailand. The 820-bed hospital serves as a referral centre and an excellent centre of heart diseases in the middle north-eastern part of Thailand. Data entry into the EMD concerned post myocardial infarction patients admitted between October 1, 2014, to September 30, 2018. Patients were identified based on qualifying diagnostic-related grouping codes (ICD I210-I213, ICD I214) indicating a primary diagnosis of myocardial infarction (MI) at discharge. Hospitalizations for patients aged ≥18 years were included. In alignment with the qualifying diagnostic-related grouping codes, all MI patients at discharge were eligible (N=484). We excluded hospitalizations during which a patient died (N=27), was transferred to another hospital (N=17), or was discharged against medical advice (N=8). Incomplete clinical data of the patients were excluded (N=56). Thus, a total of 376 patients met the selection criteria. Of those discharged with a primary diagnosis of MI during the designated time period, 161 were readmitted for MI and 215 were not readmitted (Figure 1).

Figure 1. Patients’ flow diagram.

For this study, a readmission was defined as the first admission to Roi-Et hospital within seven-day, 30-day, six-month, and one-year of being discharge. A readmission was only counted once as a readmission, relative to the prior index admission. All subsequent admissions then re-entered the cohort as a new index admission. All elective readmissions were excluded from the data set.

Ethics approval and reporting

The study was approved by the Human Research Ethics Committee of the Roi-Et Hospital and Mahasarakham University Institutional Review Board for use of deidentified data from existing hospital database. The need for consent from the participants was waived by the ethics committee due to the retrospective nature of the analysis. This study is reported following Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 Checklist)

Results

Discussion

The results of this study highlight the predictors of readmissions in early (seven-day) and late (30-day, six-month, and one-year) periods following hospital discharge in Thai healthcare settings. As with previous studies, we found that comorbidities, health, and illness were associated with readmission. For 30-day readmission, a significant finding is that HT comorbidity was identified as the significant predictor. This finding is congruent with previous study, revealing that HT is highly prevalent in Thailand. One out of four of Thai people had HT but less than one out of three had their blood pressure under control¹⁷, even with the expanded use of antihypertensive medications. The HT is a well-known cardiovascular risk factor associated with increased cardiovascular events¹⁸. An empirical study supports that 84.4% of readmitted MI patients had additional hypertension comorbidity¹⁹. Furthermore, we also found that another significant predictor for 30-day readmission was anaemia with hematocrit < 33 vol%. Patients who were malnourished with anaemia during the index of hospitalisation had a high risk of being readmitted. Several studies revealed that malnutrition is associated with adverse health outcomes for patients and leads to increased healthcare costs^20,21. A recent study² also supported the hypothesis that malnutrition status is a strong predictor of rehospitalisation.

For six-month readmission, we also found that atrial fibrillation (AF) was a predictor that is widely known as a common complication of AMI and contributes to high rates of in-hospital adverse events²². The overall incidence of AF complicating AMI was 10.8%. Patients developing new-onset AF following AMI were at higher risk for in-hospital stroke²². In this study, there was a new-onset AF following AMI in up to 5 cases. In addition, we also found that patients who had chronic kidney disease with creatinine serum level ≥2 mg/dL admission were likely to have late readmissions at six-month after discharge. The relevant finding is that the mildly elevated admission serum creatinine markedly increased one year mortality in patients with AMI²³.

A significant and interesting finding of this study is that smoking predicts six-month readmission after hospital discharge. This study validates the findings of a previous study which found that smoking increases the risk of readmissions among CAD patients across all specialties. The relevant finding showed that only 33.2% of the patients underwent smoking cessation counseling during hospital admission, which highlights that a significant proportion of patients missed smoking cessation counseling. Studies indicated that smoking cessation intervention has a beneficial effect in improving clinical outcomes and preventing complications and readmission. Tan and et al.²⁴ conducted a meta-analysis involving 1,607 patients and found that readmission rate was significantly reduced in patients who received smoking cessation counseling, and that the prolonged abstinence rate of the gradual cessation was significantly lower than that of the abrupt cessation (relative risk, RR=0.77). However, intervention effects of smoking cessation were not significant at long term follow-up²⁵ and need to further examination, especially in primary care setting²⁶. Therefore, this is a window of opportunity to target smoking cessation among hospitalised patients and continue the intervention in patients after discharge to help reduce readmissions.

In conclusion, multiple factors including HT comorbidity, anemia, atrial fibrillation, chronic kidney disease, and smoking predict readmission among Thais with post myocardial infarction. Moreover, this study demonstrates that rates and predictors of readmissions in short-term and long-term periods are different. Therefore, various screening tools and interventions are required.

Conclusions

This study developed potential factors to identify seven-day, 30-day, six-month, and one-year readmissions in Roi-Et hospital. Among patients discharged, multiple factors predicting readmission in short-term and long-term periods are different. Therefore, various screening tools and appropriate preventive interventions are required.

Data availability