Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19

Magda Bucholc, Declan Bradley, Damien Bennett, Lynsey Patterson, Rachel Spiers, David Gibson, Hugo Van Woerden, Anthony J. Bjourson

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Abstract

We investigated the association between a wide range of comorbidities and COVID-19 in-hospital
mortality and assessed the infuence of multi morbidity on the risk of COVID-19-related death using
a large, regional cohort of 6036 hospitalized patients. This retrospective cohort study was conducted
using Patient Administration System Admissions and Discharges data. The International Classifcation
of Diseases 10th edition (ICD-10) diagnosis codes were used to identify common comorbidities and the
outcome measure. Individuals with lymphoma (odds ratio [OR], 2.78;95% CI,1.64–4.74), metastatic
cancer (OR, 2.17; 95% CI,1.25–3.77), solid tumour without metastasis (OR, 1.67; 95% CI,1.16–2.41),
liver disease (OR: 2.50, 95% CI,1.53–4.07), congestive heart failure (OR, 1.69; 95% CI,1.32–2.15),
chronic obstructive pulmonary disease (OR, 1.43; 95% CI,1.18–1.72), obesity (OR, 5.28; 95% CI,2.92–
9.52), renal disease (OR, 1.81; 95% CI,1.51–2.19), and dementia (OR, 1.44; 95% CI,1.17–1.76) were
at increased risk of COVID-19 mortality. Asthma was associated with a lower risk of death compared
to non-asthma controls (OR, 0.60; 95% CI,0.42–0.86). Individuals with two (OR, 1.79; 95% CI, 1.47–
2.20; P< 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43–2.27; P< 0.001) were at
increasingly higher risk of death when compared to those with no underlying conditions. Furthermore,
multi morbidity patterns were analysed by identifying clusters of conditions in hospitalised COVID19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identifed, with recognisable co-occurrences of COVID-19 with diferent combinations
of diseases, namely, cardiovascular (100%) and renal (15.6%) diseases in patient Cluster 1; mental
and neurological disorders (100%) with metabolic and endocrine diseases (19.3%) in patient Cluster
2; respiratory (100%) and cardiovascular (15.0%) diseases in patient Cluster 3, cancer (5.9%) with
genitourinary (9.0%) as well as metabolic and endocrine diseases (9.6%) in patient Cluster 4; metabolic
and endocrine diseases (100%) and cardiovascular diseases (69.1%) in patient Cluster 5; mental and
neurological disorders (100%) with cardiovascular diseases (100%) in patient Cluster 6. The highest
mortality of 29.4% was reported in Cluster 6.
Original languageEnglish
Article number17313
Number of pages14
JournalScientific Reports
Volume12
DOIs
Publication statusPublished - 15 Oct 2022

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