Katherine M Breen1, Lorna Finnegan2, Karen M Vuckovic3, Anne M Fink3, Wayne Rosamond4, Holli A DeVon5. 1. Nell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, GA 30322, United States. Electronic address: Kbreen2@emory.edu. 2. Loyola University Chicago, Marcella Niehoff School of Nursing, Chicago, IL, United States. 3. University of Illinois at Chicago, College of Nursing, Department of Biobehavioral Health Science, Chicago, IL, United States. 4. University of North Carolina, Gillings School of Global Public Health, Chapel Hill, NC, United States. 5. University of California Los Angeles School of Nursing, Los Angeles, CA, United States.
Abstract
BACKGROUND: Multimorbidity (> 2 conditions) increases the risk of adverse outcomes and challenges health care systems for patients with acute coronary syndrome (ACS). These complications may be partially attributed to ACS clinical care which is driven by single-disease-based practice guidelines; current guidelines do not consider multimorbidity. OBJECTIVES: To identify multimorbidity phenotypes (combinations of conditions) with suspected ACS. We hypothesized that: 1) subgroups of patients with similar multimorbidity phenotypes could be identified, 2) classes would differ according to diagnosis, and 3) class membership would differ by sex, age, functional status, family history, and discharge diagnosis. METHODS: This was a secondary analysis of data from a large multi-site clinical study of patients with suspected ACS. Conditions were determined by items on the Charlson Comorbidity Index and the ACS Patient Information Questionnaire. Latent class analysis was used to identify phenotypes. RESULTS: The sample (n = 935) was predominantly male (68%) and middle-aged (mean= 59 years). Four multimorbidity phenotypes were identified: 1) high multimorbidity (Class 1) included hyperlipidemia, hypertension (HTN), obesity, diabetes, and respiratory disorders (COPD or asthma); 2) low multimorbidity (Class 2) included only obesity; 3) cardiovascular multimorbidity (Class 3) included HTN, hyperlipidemia, and coronary heart disease; and 4) cardio-oncology multimorbidity (Class 4) included HTN, hyperlipidemia, and cancer. Patients ruled-in for ACS primarily clustered in Classes 3 and 4 (OR 2.82, 95% CI 1.95-4.05, p = 0.001 and OR 1.76, 95% CI 1.13-2.74, p = 0.01). CONCLUSION: Identifying and understanding multimorbidity phenotypes may assist with risk-stratification and better triage of high-risk patients in the emergency department.
BACKGROUND: Multimorbidity (> 2 conditions) increases the risk of adverse outcomes and challenges health care systems for patients with acute coronary syndrome (ACS). These complications may be partially attributed to ACS clinical care which is driven by single-disease-based practice guidelines; current guidelines do not consider multimorbidity. OBJECTIVES: To identify multimorbidity phenotypes (combinations of conditions) with suspected ACS. We hypothesized that: 1) subgroups of patients with similar multimorbidity phenotypes could be identified, 2) classes would differ according to diagnosis, and 3) class membership would differ by sex, age, functional status, family history, and discharge diagnosis. METHODS: This was a secondary analysis of data from a large multi-site clinical study of patients with suspected ACS. Conditions were determined by items on the Charlson Comorbidity Index and the ACS Patient Information Questionnaire. Latent class analysis was used to identify phenotypes. RESULTS: The sample (n = 935) was predominantly male (68%) and middle-aged (mean= 59 years). Four multimorbidity phenotypes were identified: 1) high multimorbidity (Class 1) included hyperlipidemia, hypertension (HTN), obesity, diabetes, and respiratory disorders (COPD or asthma); 2) low multimorbidity (Class 2) included only obesity; 3) cardiovascular multimorbidity (Class 3) included HTN, hyperlipidemia, and coronary heart disease; and 4) cardio-oncology multimorbidity (Class 4) included HTN, hyperlipidemia, and cancer. Patients ruled-in for ACS primarily clustered in Classes 3 and 4 (OR 2.82, 95% CI 1.95-4.05, p = 0.001 and OR 1.76, 95% CI 1.13-2.74, p = 0.01). CONCLUSION: Identifying and understanding multimorbidity phenotypes may assist with risk-stratification and better triage of high-risk patients in the emergency department.
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