Comorbidity clusters and in-hospital outcomes in patients admitted with acute myocardial infarction in the USA: A national population-based study.

Autor: Zghebi SS; Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Manchester, United Kingdom.; Department of Pharmaceutics, Faculty of Pharmacy, University of Tripoli, Tripoli, Libya., Rutter MK; Diabetes, Endocrinology & Metabolism Centre, Manchester University NHS Foundation Trust, NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester, United Kingdom.; Division of Diabetes, Endocrinology & Gastroenterology, School of Medical Sciences, The University of Manchester, Manchester, United Kingdom., Sun LY; Division of Cardiothoracic Anesthesiology, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, California, United States of America., Ullah W; Department of Cardiology, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, United States of America., Rashid M; Keele Cardiovascular Research Group, Centre for Prognosis Research, School of Medicine, Keele University, Stoke-on-Trent, United Kingdom.; Department of Academic Cardiology, Royal Stoke University Hospital, Stoke-on-Trent, United Kingdom., Ashcroft DM; Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, The University of Manchester, Manchester, United Kingdom.; NIHR Greater Manchester Patient Safety Research Collaboration (PSRC), The University of Manchester, Manchester, United Kingdom., Steinke DT; Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, The University of Manchester, Manchester, United Kingdom., Weng S; Development Biostatistics, GSK, Stevenage, United Kingdom., Kontopantelis E; Centre for Primary Care and Health Services Research, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Manchester, United Kingdom.; Division of Informatics, Imaging and Data Sciences, School of Health Sciences, The University of Manchester, Manchester, United Kingdom., Mamas MA; Keele Cardiovascular Research Group, Centre for Prognosis Research, School of Medicine, Keele University, Stoke-on-Trent, United Kingdom.; Department of Academic Cardiology, Royal Stoke University Hospital, Stoke-on-Trent, United Kingdom.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Oct 26; Vol. 18 (10), pp. e0293314. Date of Electronic Publication: 2023 Oct 26 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0293314
Abstrakt: Background: The prevalence of multimorbidity in patients with acute myocardial infarction (AMI) is increasing. It is unclear whether comorbidities cluster into distinct phenogroups and whether are associated with clinical trajectories.
Methods: Survey-weighted analysis of the United States Nationwide Inpatient Sample (NIS) for patients admitted with a primary diagnosis of AMI in 2018. In-hospital outcomes included mortality, stroke, bleeding, and coronary revascularisation. Latent class analysis of 21 chronic conditions was used to identify comorbidity classes. Multivariable logistic and linear regressions were fitted for associations between comorbidity classes and outcomes.
Results: Among 416,655 AMI admissions included in the analysis, mean (±SD) age was 67 (±13) years, 38% were females, and 76% White ethnicity. Overall, hypertension, coronary heart disease (CHD), dyslipidaemia, and diabetes were common comorbidities, but each of the identified five classes (C) included ≥1 predominant comorbidities defining distinct phenogroups: cancer/coagulopathy/liver disease class (C1); least burdened (C2); CHD/dyslipidaemia (largest/referent group, (C3)); pulmonary/valvular/peripheral vascular disease (C4); diabetes/kidney disease/heart failure class (C5). Odds ratio (95% confidence interval [CI]) for mortality ranged between 2.11 (1.89-2.37) in C2 to 5.57 (4.99-6.21) in C1. For major bleeding, OR for C1 was 4.48 (3.78; 5.31); for acute stroke, ORs ranged between 0.75 (0.60; 0.94) in C2 to 2.76 (2.27; 3.35) in C1; for coronary revascularization, ORs ranged between 0.34 (0.32; 0.36) in C1 to 1.41 (1.30; 1.53) in C4.
Conclusions: We identified distinct comorbidity phenogroups that predicted in-hospital outcomes in patients admitted with AMI. Some conditions overlapped across classes, driven by the high comorbidity burden. Our findings demonstrate the predictive value and potential clinical utility of identifying patients with AMI with specific comorbidity clustering.
Competing Interests: SSZ, LYS, EK, MKR, DS, DMA MAM, MR declare no competing interests. SW is a currently an employee of GSK. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
(Copyright: © 2023 Zghebi 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 author and source are credited.)
Databáze: MEDLINE
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