Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction

Background Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year. Methods and Results W...

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Autores principales:	Rachel P. Dreyer, Valeria Raparelli, Sui W. Tsang, Gail D’Onofrio, Nancy Lorenze, Catherine F. Xie, Mary Geda, Louise Pilote, Terrence E. Murphy
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	acute myocardial infarction Bayesian model averaging psychosocial factors risk prediction model young adults Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/67a09aec069144c9a4172359e3a17366
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spelling	oai:doaj.org-article:67a09aec069144c9a4172359e3a173662021-11-23T11:36:35ZDevelopment and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction10.1161/JAHA.121.0210472047-9980https://doaj.org/article/67a09aec069144c9a4172359e3a173662021-09-01T00:00:00Zhttps://www.ahajournals.org/doi/10.1161/JAHA.121.021047https://doaj.org/toc/2047-9980Background Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year. Methods and Results We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all‐cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01–1.05), better physical health (OR, 0.98; 95% CI, 0.97–0.99), in‐hospital complication of heart failure (OR, 1.44; 95% CI, 0.99–2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96–1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00–1.52), female sex (OR, 1.31; 95% CI, 1.05–1.65), low income (OR, 1.13; 95% CI, 0.89–1.42), prior AMI (OR, 1.47; 95% CI, 1.15–1.87), in‐hospital length of stay (OR, 1.13; 95% CI, 1.04–1.23), and being employed (OR, 0.88; 95% CI, 0.69–1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts). Conclusions Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.Rachel P. DreyerValeria RaparelliSui W. TsangGail D’OnofrioNancy LorenzeCatherine F. XieMary GedaLouise PiloteTerrence E. MurphyWileyarticleacute myocardial infarctionBayesian model averagingpsychosocial factorsrisk prediction modelyoung adultsDiseases of the circulatory (Cardiovascular) systemRC666-701ENJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 10, Iss 18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	acute myocardial infarction Bayesian model averaging psychosocial factors risk prediction model young adults Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	acute myocardial infarction Bayesian model averaging psychosocial factors risk prediction model young adults Diseases of the circulatory (Cardiovascular) system RC666-701 Rachel P. Dreyer Valeria Raparelli Sui W. Tsang Gail D’Onofrio Nancy Lorenze Catherine F. Xie Mary Geda Louise Pilote Terrence E. Murphy Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction
description	Background Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year. Methods and Results We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all‐cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01–1.05), better physical health (OR, 0.98; 95% CI, 0.97–0.99), in‐hospital complication of heart failure (OR, 1.44; 95% CI, 0.99–2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96–1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00–1.52), female sex (OR, 1.31; 95% CI, 1.05–1.65), low income (OR, 1.13; 95% CI, 0.89–1.42), prior AMI (OR, 1.47; 95% CI, 1.15–1.87), in‐hospital length of stay (OR, 1.13; 95% CI, 1.04–1.23), and being employed (OR, 0.88; 95% CI, 0.69–1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts). Conclusions Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.
format	article
author	Rachel P. Dreyer Valeria Raparelli Sui W. Tsang Gail D’Onofrio Nancy Lorenze Catherine F. Xie Mary Geda Louise Pilote Terrence E. Murphy
author_facet	Rachel P. Dreyer Valeria Raparelli Sui W. Tsang Gail D’Onofrio Nancy Lorenze Catherine F. Xie Mary Geda Louise Pilote Terrence E. Murphy
author_sort	Rachel P. Dreyer
title	Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction
title_short	Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction
title_full	Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction
title_fullStr	Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction
title_full_unstemmed	Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction
title_sort	development and validation of a risk prediction model for 1‐year readmission among young adults hospitalized for acute myocardial infarction
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/67a09aec069144c9a4172359e3a17366
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Development and Validation of a Risk Prediction Model for 1‐Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction

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