Abstract
In this study, we investigated whether the CHA2DS2-VASc score could be used to estimate the need for hospitalization in the intensive care unit (ICU), the length of stay in the ICU, and mortality in patients with COVID-19. Patients admitted to Merkezefendi State Hospital because of COVID-19 diagnosis confirmed by RNA detection of virus by using polymerase chain reaction between March 24, 2020 and July 6, 2020, were screened retrospectively. The CHA2DS2-VASc and modified CHA2DS2-VASc score of all patients was calculated. Also, we received all patients’ complete biochemical markers including D-dimer, Troponin I, and c-reactive protein on admission. We enrolled 1000 patients; 791 were admitted to the general medical service and 209 to the ICU; 82 of these 209 patients died. The ROC curves of the CHA2DS2-VASc and M-CHA2DS2-VASc scores were analyzed. The cut-off values of these scores for predicting mortality were ≥ 3 (2 or under and 3). The CHA2DS2-VASc and M-CHA2DS2-VASc scores had an area under the curve value of 0.89 on the ROC. The sensitivity and specificity of the CHA2DS2-VASc scores were 81.7% and 83.8%, respectively; the sensitivity and specificity of the M-CHA2DS2-VASc scores were 85.3% and 84.1%, respectively. Multivariate logistic regression analysis showed that CHA2DS2-VASc, Troponin I, D-Dimer, and CRP were independent predictors of mortality in COVID-19 patients. Using a simple and easily available scoring system, CHA2DS2-VASc and M-CHA2DS2-VASc scores can be assessed in patients diagnosed with COVID-19. These scores can predict mortality and the need for ICU hospitalization in these patients.
Keywords: CHA2DS2-VASc score; COVID-19; Intensive care unit hospitalization; Modified CHA2DS2-VASc score; Mortality.
【저자키워드】 COVID-19, Mortality, CHA2DS2-VASc score, Intensive care unit hospitalization, Modified CHA2DS2-VASc score, 【초록키워드】 Mortality, intensive care, Hospitalization, intensive care unit, C-reactive protein, CRP, D-dimer, troponin, virus, ICU, RNA, Protein, polymerase chain reaction, ROC, Sensitivity and specificity, Patient, COVID-19 diagnosis, troponin I, Admission, Care, patients, predict, COVID-19 patients, independent predictors, ROC Curve, on admission, an area, Chain Reaction, medical service, scoring system, Logistic regression analysis, dimer, multivariate, cut-off value, multivariate logistic regression analysis, ROC curves, Complete, state, polymerase chain, biochemical marker, enrolled, analyzed, investigated, screened, calculated, diagnosed with COVID-19, independent predictor, patients died, patients with COVID-19, 【제목키워드】 Mortality, intensive care, Hospitalization, predict, COVID-19 patient,