A new logistic regression derived combined index for early prediction of in-hospital mortality in COVID-19 patients

BACKGROUND: While the type and the number of treatments for Coronavirus Disease 2019 (COVID-19) have substantially evolved since the start of the pandemic a significant number of hospitalized patients continue to succumb. This requires ongoing research in the development and improvement of early risk stratification tools. METHOD(S): We developed a prognostic score using epidemiological, clinical, laboratory, and treatment variables collected on admission in 130 adult COVID-19 patients followed until in-hospital death (N.=38) or discharge (N.=92). Potential variables were selected via multivariable logistic regression modelling conducted using a logistic regression univariate analysis to create a combined index. RESULT(S): Age, Charlson Comorbidity Index, P/F ratio, prothrombin time, C-reactive protein and troponin were the selected variables. AUROC indicated that the model had an excellent AUC value (0.971, 95% CI 0.926 to 0.993) with 100% sensitivity and 83% specificity for in-hospital mortality. The Hosmer-Lemeshow calibration test yielded non-significant P values (chi2=1.79, P=0.99) indicates good calibration. CONCLUSION(S): This newly developed combined index could be useful to predict mortality of hospitalized COVID-19 patients on admission.Copyright © 2022 EDIZIONI MINERVA MEDICA.

Prothrombin time, international normalized rate and in-hospital mortality in COVID-19

BACKGROUND: Blood coagulation alterations are frequent in patients with Coronavirus disease 2019 (COVID-19), particularly in those with severe forms. We investigated the association between standard parameters of coagulation and in-hospital mortality in COVID-19. METHODS: Demographic, clinical and laboratory data at hospital admission, including prothrombin time (PT), international normalized rate (INR), activated thromboplastin time (aPTT), and D-dimer were retrospectively collected in a consecutive series of 309 COVID-19 hospitalized patients. The associations between parameters of coagulation and in-hospital mortality were investigated with receiver operating characteristics (ROC), multiple regression and Kaplan- Meyer analyses. RESULTS: In the overall population, 220 (71.2%) patients were discharged alive, whereas the remaining 89 (28.8%) died. Non-survivors had significantly higher INR (median: 1.20;IQR: 1.03-1.32 vs. 1.06;IQR: 1.02-1.11, P<0.001), PT (median: 12.0 sec;IQR: 11.1-14.0 vs. 11.4 sec;IQR: 11.0-11.9, P<0.001), aPTT (median: 25.1 sec;IQR: 22.7-29.6 vs. 23.4 sec;IQR: 21.4-25.1, P<0.001) and D-dimer (median: 1.36 μg/mL;IQR: 0.87-4.11 vs. 0.77 μg/mL;IQR: 0.43-1.58, P<0.001). In multivariate Cox regression analysis, both the INR (HR=1.8459;95% CI: 1.0713-3.1806, P=0.027) and PT (HR=1.071;95% CI: 1.0132-1.1303, P=0.015), but not the aPTT and D-dimer, remained independently associated with survival. CONCLUSIONS: Both the PTand INRare independently associated with in-hospital mortality in COVID-19. The clinical utility of these parameters for risk stratification warrants further investigations.