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Minerva Respiratory Medicine 2023 March;62(1):25-32

DOI: 10.23736/S2784-8477.22.02007-1


lingua: Inglese

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

Stefania BASSU 1 , Elena MASOTTO 2, Chiara SANNA 2, Verdiana MUSCAS 2, Dario ARGIOLAS 2, Ciriaco CARRU 1, Pietro PIRINA 2, Arduino A. MANGONI 3, 4, Panagiotis PALIOGIANNIS 2, Alessandro G. FOIS 2, Angelo ZINELLU 1

1 Department of Biomedical Sciences, University of Sassari, Sassari, Italy; 2 Department of Medical, Surgical and Experimental Medicine, University of Sassari, Sassari, Italy; 3 Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, Australia; 4 Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, Australia

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.
METHODS: 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.
RESULTS: 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 (χ2=1.79, P=0.99) indicates good calibration.
CONCLUSIONS: This newly developed combined index could be useful to predict mortality of hospitalized COVID-19 patients on admission.

KEY WORDS: COVID-19; SARS-CoV-2; Respiratory insufficiency; Hospital mortality

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