https://orcid.org/0000-0001-5362-0455
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Abstract
Purpose
To assess the contribution of age and comorbidity to the risk of critical illness in hospitalized COVID-19 patients using increasingly exhaustive tools for measuring comorbidity burden.
Patients and Methods
We assessed the effect of age and comorbidity burden in a retrospective, multicenter cohort of patients hospitalized due to COVID-19 in Catalonia (North-East Spain) between March 1, 2020, and January 31, 2022. Vaccinated individuals and those admitted within the first of the six COVID-19 epidemic waves were excluded from the primary analysis but were included in secondary analyses. The primary outcome was critical illness, defined as the need for invasive mechanical ventilation, transfer to the intensive care unit (ICU), or in-hospital death. Explanatory variables included age, sex, and four summary measures of comorbidity burden on admission extracted from three indices: the Charlson index (17 diagnostic group codes), the Elixhauser index and count (31 diagnostic group codes), and the Queralt DxS index (3145 diagnostic group codes). All models were adjusted by wave and center. The proportion of the effect of age attributable to comorbidity burden was assessed using a causal mediation analysis.
Results
The primary analysis included 10,551 hospitalizations due to COVID-19; of them, 3632 (34.4%) experienced critical illness. The frequency of critical illness increased with age and comorbidity burden on admission, irrespective of the measure used. In multivariate analyses, the effect size of age decreased with the number of diagnoses considered to estimate comorbidity burden. When adjusting for the Queralt DxS index, age showed a minimal contribution to critical illness; according to the causal mediation analysis, comorbidity burden on admission explained the 98.2% (95% CI 84.1–117.1%) of the observed effect of age on critical illness.
Conclusion
Comorbidity burden (when measured exhaustively) explains better than chronological age the increased risk of critical illness observed in patients hospitalized with COVID-19.
Acknowledgments
The authors would like to thank Toni Fuentes for his support in building the dataset used in this analysis. The study did not receive specific funding.
Disclosure
DM declares that he is the developer of the Queralt System, which is freely available. The other authors report no conflicts of interest in this work.