Related Experiment Video

Updated: Nov 11, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.5K

A model to predict the risk of mortality in severely ill COVID-19 patients

Bo Chen1, Hong-Qiu Gu2, Yi Liu 刘艺1

  • 1Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Computational and Structural Biotechnology Journal
|March 29, 2021
PubMed

Insights

A new mortality risk prediction model was developed and validated for severely and critically ill patients with coronavirus disease 2019 (COVID-19). This tool aids clinicians in identifying high-risk individuals for better patient management.

Area of Science:

  • Critical Care Medicine
  • Infectious Diseases
  • Epidemiology

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant mortality risk for severely and critically ill patients.
  • Accurate prediction of mortality risk is crucial for effective clinical management and resource allocation.

Purpose of the Study:

  • To identify and select key prognostic parameters for predicting mortality in severe and critical COVID-19 cases.
  • To develop and externally validate a robust mortality risk prediction model for these patients.

Main Methods:

  • A retrospective cohort study was conducted with laboratory-confirmed COVID-19 patients (≥18 years) from two tertiary hospitals.
  • The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable logistic regression were employed to select predictors and build the model.
  • A total of 566 patients were included in the training cohort and 436 in the validation cohort.

Main Results:

  • The developed prediction model, presented as a nomograph, identified age, chronic lung disease, C-reactive protein (CRP), D-dimer, neutrophil-to-lymphocyte ratio (NLR), creatinine, and total bilirubin as key predictors.
  • The model demonstrated strong predictive performance in both training (AUC: 0.912) and validation (AUC: 0.922) cohorts, with good calibration.
  • Decision curve analysis confirmed the clinical utility of the nomogram for predicting mortality risk.

Conclusions:

  • An externally validated risk score for predicting mortality in severe and critically ill COVID-19 patients has been established.
  • This model serves as a valuable tool for clinicians to identify patients at high risk of mortality.
  • The findings support the use of this model in clinical practice for improved patient stratification and care.
Abstract

Keywords:
COVID-19CriticalMortalityPredictive modelRiskSevere

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.3K
An R-Based Landscape Validation of a Competing Risk Model05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Related Concept Videos

Acute Respiratory Failure-II01:21

Acute Respiratory Failure-II

Type I Respiratory Failure, or hypoxemic respiratory failure, occurs when the partial pressure of oxygen (PaO2) in arterial blood falls below 60 mmHg while breathing room air without a corresponding increase in arterial carbon dioxide levels (PaCO2). This condition highlights a significant impairment in the lungs' capacity to oxygenate the blood.
The underlying physiological abnormalities that contribute to hypoxemic respiratory failure include:
631
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
508
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
237
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
362
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
184
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
161