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


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
15 10 2022
Historique:
received: 27 10 2021
accepted: 09 09 2022
entrez: 15 10 2022
pubmed: 16 10 2022
medline: 19 10 2022
Statut: epublish

Résumé

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 < 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 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.

Identifiants

pubmed: 36243878
doi: 10.1038/s41598-022-20176-w
pii: 10.1038/s41598-022-20176-w
pmc: PMC9568958
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

17313

Informations de copyright

© 2022. The Author(s).

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Auteurs

Magda Bucholc (M)

Intelligent Systems Research Centre, School of Computing, Engineering & Intelligent Systems, Ulster University, Londonderry, BT48 7JL, UK. m.bucholc@ulster.ac.uk.
Public Health Agency, Belfast, BT2 8BS, UK. m.bucholc@ulster.ac.uk.

Declan Bradley (D)

Public Health Agency, Belfast, BT2 8BS, UK.
Centre for Public Health, Queen's University Belfast, Belfast, BT12 6BA, UK.

Damien Bennett (D)

Public Health Agency, Belfast, BT2 8BS, UK.

Lynsey Patterson (L)

Public Health Agency, Belfast, BT2 8BS, UK.

Rachel Spiers (R)

Public Health Agency, Belfast, BT2 8BS, UK.

David Gibson (D)

Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry, BT47 6SB, UK.

Hugo Van Woerden (H)

Public Health Agency, Belfast, BT2 8BS, UK.
Institute of Nursing and Health Research, Ulster University, Coleraine, BT52 1SA 3, UK.
Division of Rural Health and Wellbeing, University of the Highlands and Islands, Inverness, IV2 3JH, UK.

Anthony J Bjourson (AJ)

Personalised Medicine Centre, School of Medicine, Ulster University, Londonderry, BT47 6SB, UK.

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