Multimorbidity and survival for patients with acute myocardial infarction in England and Wales: Latent class analysis of a nationwide population-based cohort.

Marlous Hall,Tatendashe B Dondo,John E Deanfield,Harry Hemingway,Andrew T Yan,Chris P Gale,Keith A A Fox,Mamas A Mamas,Adam D Timmis,Tomas Jernberg

doi:10.1371/journal.pmed.1002501

Abstract

BackgroundThere is limited knowledge of the scale and impact of multimorbidity for patients who have had an acute myocardial infarction (AMI). Therefore, this study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI.Methods and findingsThis national observational study included 693,388 patients (median age 70.7 years, 452,896 [65.5%] male) from the Myocardial Ischaemia National Audit Project (England and Wales) who were admitted with AMI between 1 January 2003 and 30 June 2013. There were 412,809 (59.5%) patients with multimorbidity at the time of admission with AMI, i.e., having at least 1 of the following long-term health conditions: diabetes, chronic obstructive pulmonary disease or asthma, heart failure, renal failure, cerebrovascular disease, peripheral vascular disease, or hypertension. Those with heart failure, renal failure, or cerebrovascular disease had the worst outcomes (39.5 [95% CI 39.0–40.0], 38.2 [27.7–26.8], and 26.6 [25.2–26.4] deaths per 100 person-years, respectively). Latent class analysis revealed 3 multimorbidity phenotype clusters: (1) a high multimorbidity class, with concomitant heart failure, peripheral vascular disease, and hypertension, (2) a medium multimorbidity class, with peripheral vascular disease and hypertension, and (3) a low multimorbidity class. Patients in class 1 were less likely to receive pharmacological therapies compared with class 2 and 3 patients (including aspirin, 83.8% versus 87.3% and 87.2%, respectively; β-blockers, 74.0% versus 80.9% and 81.4%; and statins, 80.6% versus 85.9% and 85.2%). Flexible parametric survival modelling indicated that patients in class 1 and class 2 had a 2.4-fold (95% CI 2.3–2.5) and 1.5-fold (95% CI 1.4–1.5) increased risk of death and a loss in life expectancy of 2.89 and 1.52 years, respectively, compared with those in class 3 over the 8.4-year follow-up period. The study was limited to all-cause mortality due to the lack of available cause-specific mortality data. However, we isolated the disease-specific association with mortality by providing the loss in life expectancy following AMI according to multimorbidity phenotype cluster compared with the general age-, sex-, and year-matched population.ConclusionsMultimorbidity among patients with AMI was common, and conferred an accumulative increased risk of death. Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes.Trial registrationClinicalTrials.gov NCT03037255.

Highlights

The increasing prevalence of long-term health conditions, and consequent growing prevalence of multimorbidity, is a major global challenge facing healthcare systems [1,2]
Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes
Many prior studies have assessed the association between individual co-morbidities—including diabetes [9,10], chronic obstructive pulmonary disease (COPD) [11,12,13,14,15], and heart failure [16]—and survival in patients with acute myocardial infarction (AMI), few have quantified the burden of multimorbidity—in particular how the complex patterns of multiple conditions simultaneously associate with mortality

Summary

Introduction

The increasing prevalence of long-term health conditions, and consequent growing prevalence of multimorbidity (the presence of multiple co-morbidities), is a major global challenge facing healthcare systems [1,2]. Previous data are mostly limited to short(30 days) and medium-term (1 year) outcomes, with the exception of the study by Di Angelantonio et al [24] (12.8 years of follow-up), even though such conditions are lifelong diseases and, warrant investigation of outcomes over the longer term. More sophisticated methods, such as latent class analysis, enable insights into multidimensional disease patterns based on probabilistic modelling of specific conditions without the aforementioned limitations [25]. This study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI.

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Conclusion