Charlson comorbidity index in predicting deaths in COVID-19 patients

Aim. To assess the clinical performance and factors associated with inhospital mortality in patients with coronavirus disease 2019 (COVID-19).

Material and methods. Our results are based on data from hospital charts of inpatients hospitalized in the Asinovskaya District Hospital in the period from March 11, 2020 to December 31, 2020, with a verified COVID-19 by polymerase chain reaction. The study included 151 patients, the median age of which was 66,2 (50- 92) years (women, 91; 60,3%). The study endpoints were following hospitalization outcomes: discharge or death. Depending on the outcomes, the patients were divided into 2 groups: the 1st group included 138 patients (survivors), while the 2nd one included 13 patients (death). To objectify the severity of multimorbidity status, the Charlson comorbidity index was used. The final value was estimated taking into account the patient age by summing the points assigned to a certain nosological entity using a calculator table.

Results. Hypertension was recorded in the majority of patients — 79,5%, chronic kidney disease — in 61,1%. The prevalence of type 2 diabetes and coronary artery disease was high — 31,8% each. Prior myocardial infarction was diagnosed in 11,3% of cases. The prevalence of percutaneous coronary intervention and coronary bypass surgery was 5,3% and 3,3%, respectively. Stroke was detected in 9,3% of participants. Prior chronic pulmonary pathologies in COVID-19 patients were rare (asthma — 3,3%, chronic obstructive pulmonary disease — 2,0%). In order to predict the death risk in COVID-19 patients, a logistic regression analysis was performed, which showed that age and Charlson comorbidity index were the most significant predictors.

Conclusion. Independent factors of inhospital mortality were age and Charlson’s comorbidity index. The risk assessment model will allow clinicians to identify patients with a poor prognosis at an earlier disease stage, thereby reducing mortality by implementing more effective COVID-19 treatment strategies in conditions with limited medical resources.

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