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

Abstract

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.

Risk of COVID‐19 death for people with a pre‐existing cancer diagnosis prior to COVID‐19‐vaccination: A systematic review and meta‐analysis

“…Based on three of these studies, risk estimates were higher (non-overlapping 95% CIs) for hematological cancers (aHR = 2.80 [2.08-3.77], 1 study 19 ; aOR = 2.13 [1.68-2.68], 2 studies). 20,21 Risk estimate results were similar based on studies of hospital inpatients with COVID-19, with a slightly lower aHR estimate for any/solid cancers (any/solid cancer: aHR = 1.34 [1.19-1.50], 5 studies [23][24][25][26][27] ; aOR = 1.66 [1.34-2.06], 8 studies [28][29][30][31][32][33][34][35] ; hematological cancer: aOR = 2.20 [1.97-2.46], 1 study 30 ). For studies of hospital inpatients with COVID-19, there was moderate to high overall risk of bias in the hazard ratio meta-analysis (moderate risk for one study contributing 52% weight, and high risk of bias for other studies due to exposure measurement or potential over-adjustment), and moderate to high risk of bias in the odds ratio meta-analyses (four studies and 50% weight with moderate and high overall risk of bias each, due to exposure measurement, adjustment for confounders or potential overadjustment).…”

The Covid Response Study (COVRES) Protocol: a comprehensive genomic, immunological, proteomic and phenotypic comparison of hospitalised versus non-hospitalised SARS-CoV-2 patients in Northern Ireland

“…The COVRES provides a novel opportunity to identify multi-omics biomarkers from blood and saliva indicative of COVID-19 severity in a relatively homogenous population using detailed medical data (31), recruited during a short period (1 ST January 2021-31 st March 2021) in a pandemic peak from one viral variant (B.1.1.7) (Figure 2). Compared to other work, the COVRES study has a high participant number, a signi cantly wider biomarker identi cation approach, and a 12-month longitudinal immunological and in ammatory pro ling component.…”

A C57BL/6 Mouse Model of SARS-CoV-2 Infection Recapitulates Age- and Sex-Based Differences in Human COVID-19 Disease and Recovery

“…As with many viral infections in humans, older individuals and older males in particular are more sensitive to infection with SARS-CoV-2 and are more prone to experience and succumb to severe disease [ 22 , 23 , 24 , 44 ]. This is likely due to a combination of factors that include comorbidities such as heart, lung, renal, and liver disease, obesity, dementia, and cancer which are more prevalent in older individuals [ 45 ] as well as X-lined expression of many immune genes and the contrary responsiveness of immune cells to estrogen/progesterone and testosterone [ 22 ]. We show here, though a comprehensive analysis of the effects of both age and sex on SARS-CoV-2 infection in mice, that older mice are much more sensitive and susceptible to infection than younger mice (10-wk), consistent with what is seen in humans.…”