TY - JOUR
AU - Bucholc, Magda
AU - Bradley, Declan T.
AU - Bennett, Damien
AU - Patterson, Lynsey
AU - Spiers, Rachel
AU - Gibson, David S.
AU - Van Woerden, Hugo
AU - Bjourson, Anthony J.
PY - 2022
DA - October
TI - Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19
JO - Scientific Reports
VL - 12
DO - https://doi.org/10.1038/s41598-022-20176-w
AB - We investigated the association between a wide range of comorbidities and COVID-19 in-hospital mortality and assessed the influence 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 Classification 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 &lt; 0.001), and three or more comorbidities (OR, 1.80; 95% CI, 1.43–2.27; P &lt; 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 COVID-19 patients using k-mode clustering, an unsupervised machine learning technique. Six patient clusters were identified, with recognisable co-occurrences of COVID-19 with different 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.
PB - Springer Nature
UR - https://pure.uhi.ac.uk/en/publications/8d484f72-96dd-4b73-91c7-6c2ee8bbf002
KW - Coronavirus (COVID-19)
KW - Hospital care
ER