Evidence map›Paper›PMID 42866737›Full record

ArticleAnnals of medicine and surgery (2012)2026

Prevalence, causes, and predictors of 30-day readmissions after acute myocardial infarction with ventricular arrhythmias: a cohort study using the 2016-2017 national readmissions database.

Adarsh Raja, Muhammad Umer Sohail, Muhammad Ans, Saad Ahmed, Ahsan Feroze, Manaal Ashar, Kehan Ali Rizvi, Muhammad Huzaifa Ahmed Khan, Sara Rahman, Muhammad Umer Hayat and 7 more

Abstract read
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Article in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Adarsh RajaDepartment of Internal Medicine, Shaheed Mohtarma Benazir Bhutto Medical College Lyari, Karachi, Pakistan.
Muhammad Umer SohailDepartment of Medicine, Dow University of Health Sciences, Karachi, Pakistan.
Muhammad AnsDepartment of Medicine, Fatima Memorial Hospital, College of Medicine and Dentistry, Lahore, Pakistan.ORCID https://orcid.org/0009-0002-6475-3813
Saad AhmedKarachi Institute of Medical Sciences, Karachi, Pakistan.
Ahsan FerozeJinnah Medical and Dental College, Karachi, Pakistan.
Manaal AsharUnited Medical and Dental College, Karachi, Pakistan.
Kehan Ali RizviKarachi Medical and Dental College, Karachi, Pakistan.ORCID https://orcid.org/0009-0004-9900-5721
Muhammad Huzaifa Ahmed KhanKing Edward Medical University, Lahore, Pakistan.
Sara RahmanServices Hospital, Lahore, Pakistan.
Muhammad Umer HayatGujranwala Medical College, Gujranwala, Pakistan.
Noor Ul AinRahbar Medical and Dental College, Lahore, Pakistan.
Laraib KamranGujranwala Medical College, Gujranwala, Pakistan.
Fnu PirihPeople's University of Medical and Health Sciences, Shaheed Benazirabad, Pakistan.ORCID https://orcid.org/0000-0001-5228-8286
Muhammad Bilal LiaqatCentre for Non-Communicable Diseases (CNCD), Karachi, Pakistan.
Laksh KumarDepartment of Internal Medicine, Shaheed Mohtarma Benazir Bhutto Medical College Lyari, Karachi, Pakistan.
Sumet KumarDepartment of Internal Medicine, Shaheed Mohtarma Benazir Bhutto Medical College Lyari, Karachi, Pakistan.
Aayush ChaulagainPatan Academy of Health Sciences, Lalitpur, Nepal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients surviving acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VAs) are at high risk of early post-discharge readmissions due to multiple complications. These readmissions worsen clinical outcomes and add substantial financial strain to the U.S. healthcare system. This study investigates the prevalence, causes, and predictors of 30-day readmissions following hospitalization for AMI complicated by VAs. Methods and results: Hospitalizations for AMI complicated by VAs were identified from the 2016-2017 Nationwide Readmissions Database. Independent predictors of 30-day readmission were analyzed using multivariable survey-weighted logistic regression in the R statistical software (version 4.5.0). Among 93 588 index hospitalizations, 8154 (8.7%) resulted in 30-day readmissions. In-hospital mortality occurred in 16.43% of index admissions and 5.92% of readmissions. Significant predictors of readmission included older age (OR: 1.01; Conclusions: AMI complicated by VAs is associated with high 30-day readmission rates, driven mainly by noncardiac and select cardiac causes such as heart failure and atrial fibrillation. Enhanced monitoring of high-risk groups may improve outcomes and reduce healthcare costs.

Indexed as

30-day readmissionacute myocardial infarctionhealthcare costsmultivariable logistic regressionNationwide Readmissions Databasepredictors of readmissionventricular arrhythmias

Identifiers

PMID42866737
PMCPMC13657531

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.