Multimorbidity impacts cardiovascular disease risk following percutaneous coronary intervention: latent class analysis of the Melbourne Interventional Group (MIG) registry

• Published in: BMC cardiovascular disorders Vol. 24; no. 1; pp. 66 - 6
• Main Authors: Ho, Chau Le Bao, Si, Si, Brennan, Angela, Briffa, Tom, Stub, Dion, Ajani, Andrew, Reid, Christopher M
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23.01.2024; BioMed Central; BMC
• Subjects: Acute coronary syndromes; Angioplasty; Cardiovascular Disease; Cardiovascular Diseases; Care and treatment; Chronic illnesses; Comorbidity; Diabetes; Diabetes mellitus; Diagnosis; Disease; Dyslipidemia; Dyslipidemias; Glomerular filtration rate; Heart attacks; Heart failure; Hospitals; Humans; Hypertension; Latent Class Analysis; Length of stay; Metabolic disorders; Multimorbidity; Patient outcomes; Patients; Percutaneous Coronary Intervention; Prevention; Quality of Life; Registries; Regression analysis; Retrospective Studies; Risk factors; Transluminal angioplasty; Victoria; Victoria; Australia; United Kingdom > UK; Cardiovascular Disease; Latent class analysis; Multimorbidity
• ISSN: 1471-2261; 1471-2261
• DOI: 10.1186/s12872-023-03636-7

Multimorbidity is strongly associated with disability or functional decline, poor quality of life and high consumption of health care services. This study aimed (1) To identify patterns of multimorbidity among patients undergoing first recorded percutaneous coronary intervention (PCI); (2) To explore the association between the identified patterns of multimorbidity on length of hospital stay, 30-day and 12- month risk of major adverse cardiac and cerebrovascular events (MACCE) after PCI. A retrospective cohort study of the Melbourne Interventional Group (MIG) registry. This study included 14,025 participants who underwent their first PCI from 2005 to 2015 in Victoria, Australia. Based on a probabilistic modelling approach, Latent class analysis was adopted to classify clusters of people who shared similar combinations and magnitude of the comorbidity of interest. Logistic regression models were used to estimate odd ratios and 95% confidence interval (CI) for the 30-day and 12-month MACCE. More than two-thirds of patients had multimorbidity, with the most prevalent conditions being hypertension (59%) and dyslipidaemia (60%). Four distinctive multimorbidity clusters were identified each with significant associations for higher risk of 30-day and 12-month MACCE. The cluster B had the highest risk of 30-day MACCE event that was characterised by a high prevalence of reduced estimated glomerular filtration rate (92%), hypertension (73%) and reduced ejection fraction (EF) (57%). The cluster C, characterised by a high prevalence of hypertension (94%), dyslipidaemia (88%), reduced eGFR (87%), diabetes (73%) and reduced EF (65%) had the highest risk of 12-month MACCE and highest length of hospital stay. Hypertension and dyslipidaemia are prevalent in at least four in ten patients undergoing coronary angioplasty. This study showed that clusters of patients with multimorbidity had significantly different risk of 30-day and 12-month MACCE after PCI. This suggests the necessity for treatment approaches that are more personalised and customised to enhance patient outcomes and the quality of care delivered to patients in various comorbidity clusters. These results should be validated in a prospective cohort and to evaluate the potential impacts of these clusters on the prevention of MACCE after PCI.