RT Journal Article
SR Electronic
T1 Comorbidity phenotypes and risk of mortality in patients with ischaemic heart disease in the UK
JF Heart
JO Heart
FD BMJ Publishing Group Ltd and British Cardiovascular Society
SP 810
OP 816
DO 10.1136/heartjnl-2019-316091
VO 106
IS 11
A1 Francesca Crowe
A1 Dawit T Zemedikun
A1 Kelvin Okoth
A1 Nicola Jaime Adderley
A1 Gavin Rudge
A1 Mark Sheldon
A1 Krishnarajah Nirantharakumar
A1 Tom Marshall
YR 2020
UL http://heart.bmj.com/content/106/11/810.abstract
AB Objectives The objective of this study is to use latent class analysis of up to 20 comorbidities in patients with a diagnosis of ischaemic heart disease (IHD) to identify clusters of comorbidities and to examine the associations between these clusters and mortality.Methods Longitudinal analysis of electronic health records in the health improvement network (THIN), a UK primary care database including 92 186 men and women aged ≥18 years with IHD and a median of 2 (IQR 1–3) comorbidities.Results Latent class analysis revealed five clusters with half categorised as a low-burden comorbidity group. After a median follow-up of 3.2 (IQR 1.4–5.8) years, 17 645 patients died. Compared with the low-burden comorbidity group, two groups of patients with a high-burden of comorbidities had the highest adjusted HR for mortality: those with vascular and musculoskeletal conditions, HR 2.38 (95% CI 2.28 to 2.49) and those with respiratory and musculoskeletal conditions, HR 2.62 (95% CI 2.45 to 2.79). Hazards of mortality in two other groups of patients characterised by cardiometabolic and mental health comorbidities were also higher than the low-burden comorbidity group; HR 1.46 (95% CI 1.39 to 1.52) and 1.55 (95% CI 1.46 to 1.64), respectively.Conclusions This analysis has identified five distinct comorbidity clusters in patients with IHD that were differentially associated with risk of mortality. These analyses should be replicated in other large datasets, and this may help shape the development of future interventions or health services that take into account the impact of these comorbidity clusters.