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

Stefania Bassu; Elena Masotto; Chiara Sanna; Verdiana Muscas; Dario Argiolas; Ciriaco Carru; Pietro Pirina; Arduino A. Mangoni; Panagiotis Paliogiannis; Alessandro G. Fois; Angelo Zinellu

doi:10.23736/S2784-8477.22.02007-1

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

Stefania Bassu, Elena Masotto, Chiara Sanna, Verdiana Muscas, Dario Argiolas, Ciriaco Carru, Pietro Pirina, Arduino A. Mangoni, Panagiotis Paliogiannis, Alessandro G. Fois, Angelo Zinellu

College of Medicine and Public Health

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	25-32
Number of pages	8
Journal	Minerva Respiratory Medicine
Volume	62
Issue number	1
DOIs	https://doi.org/10.23736/S2784-8477.22.02007-1
Publication status	Published - Mar 2023

Keywords

COVID-19
Hospital mortality
Respiratory insufficiency
SARS-CoV-2

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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

abstract = "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.",

keywords = "COVID-19, Hospital mortality, Respiratory insufficiency, SARS-CoV-2",

author = "Stefania Bassu and Elena Masotto and Chiara Sanna and Verdiana Muscas and Dario Argiolas and Ciriaco Carru and Pietro Pirina and Mangoni, {Arduino A.} and Panagiotis Paliogiannis and Fois, {Alessandro G.} and Angelo Zinellu",

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doi = "10.23736/S2784-8477.22.02007-1",

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T1 - A new logistic regression derived combined index for early prediction of in-hospital mortality in COVID-19 patients

AU - Bassu, Stefania

AU - Masotto, Elena

AU - Sanna, Chiara

AU - Muscas, Verdiana

AU - Argiolas, Dario

AU - Carru, Ciriaco

AU - Pirina, Pietro

AU - Mangoni, Arduino A.

AU - Paliogiannis, Panagiotis

AU - Fois, Alessandro G.

AU - Zinellu, Angelo

PY - 2023/3

Y1 - 2023/3

N2 - 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.

AB - 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.

KW - COVID-19

KW - Hospital mortality

KW - Respiratory insufficiency

KW - SARS-CoV-2

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

Abstract

Keywords

UN SDGs

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