Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study.

Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study.

Publication date: Oct 08, 2024

With the emergence of new COVID-19 variants (Omicron BA. 5.2. 48 and B. 7.14), predicting the mortality of infected patients has become increasingly challenging due to the continuous mutation of the virus. Existing models have shown poor performance and limited clinical utility. This study aims to identify the independent risk factors and develop practical predictive models for mortality among patients infected with new COVID-19 variants. A retrospective study. We extracted data from 1029 COVID-19 patients in the respiratory disease wards of a general hospital in China between 22 December 2022 and 15 February 2023. Mortality within 15 days after hospital discharge. A total of 987 cases with new COVID-19 variants (Omicron BA. 5.2. 48 and B. 7.14) were eventually included, among them, 153 (15. 5%) died. Non-invasive ventilation, intubation, myoglobin, international normalised ratio, age, number of diagnoses, respiratory rate, pulse, neutrophil count and albumin were the most important predictors of mortality among new COVID-19 variants. The area under the curve of logistic regression (LR), decision tree (DT) and Extreme Gradient Boosting (XGBoost) models were 0. 959, 0. 883 and 0. 993, respectively. The diagnostic accuracy was 0. 926 for LR, 0. 918 for DT and 0. 977 for XGBoost. XGBoost model had the highest sensitivity (0. 908) and specificity (0. 989). Our study developed and validated three practical models for predicting mortality in patients with new COVID-19 variants. All models performed well, and XGBoost was the best-performing model.

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Concepts Keywords
China Adult
February Aged
Hospital China
Invasive COVID-19
COVID-19
Female
Humans
Male
Middle Aged
mortality
prognosis
Retrospective Studies
Risk Factors
SARS-CoV-2

Semantics

Type Source Name
disease MESH COVID-19
drug DRUGBANK Flunarizine
disease MESH emergency
disease MESH sore throat
disease MESH pneumonia
disease MESH complications
disease MESH acute respiratory distress syndrome
disease MESH death
drug DRUGBANK Coenzyme M
disease MESH recurrent infection
disease MESH infection
disease IDO nucleic acid
drug DRUGBANK Trestolone
disease MESH critically ill
disease IDO history
disease IDO blood
disease MESH hypertension
disease MESH COPD
disease MESH phlebothrombosis
disease MESH malnutrition
disease MESH hypoproteinemia
disease IDO protein
drug DRUGBANK Dextrose unspecified form
drug DRUGBANK Creatinine
drug DRUGBANK Urea
drug DRUGBANK Prothrombin
drug DRUGBANK Thrombin
drug DRUGBANK Fibrinogen Human
drug DRUGBANK Carbon dioxide
drug DRUGBANK Oxygen
disease MESH confusion
drug DRUGBANK Saquinavir
drug DRUGBANK Aspartame

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