Related Experiment Video
Updated: Aug 28, 2025
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Early COVID-19 respiratory risk stratification using machine learning
Molly J Douglas1,2, Brian W Bell2, Adrienne Kinney2
1Department of Surgery, University of Arizona, Tucson, Arizona, USA.
Insights
A new machine learning model, the Early COVID-19 Respiratory Risk Stratification (ECoRRS) score, can predict the need for endotracheal intubation in COVID-19 patients within 48 hours. This tool aids in early triage and resource allocation during pandemics.
Area of Science:
- Medical informatics
- Machine learning in healthcare
- Critical care medicine
Background:
- COVID-19 pandemic has significantly strained global healthcare systems.
- Accurate patient triage is crucial for effective resource allocation, especially in critical care.
- Distinguishing patients needing aggressive care, like endotracheal intubation, is a key challenge for providers with limited critical care experience.
Purpose of the Study:
- To develop a machine learning-informed score for early risk stratification of COVID-19 patients.
- To predict the likelihood of endotracheal intubation within 48 hours using objective clinical parameters.
- To assist healthcare providers in triage decisions and resource forecasting.
Main Methods:
- Utilized electronic health record data from 3447 COVID-19 hospitalizations.
- A LASSO regression model was developed and tuned for sensitivity and sparsity.
- Data were split into derivation (80%) and validation (20%) cohorts with multiple randomizations.
Main Results:
- Identified six highly predictive parameters, with fraction of inspired oxygen being the most significant.
- The model achieved an area under the receiver operating characteristic curve of 0.789 (95% CI 0.785 to 0.812).
- At 90% sensitivity, the negative predictive value was 0.997, indicating minimal undertriage.
Conclusions:
- The Early COVID-19 Respiratory Risk Stratification (ECoRRS) score aids non-specialists in identifying COVID-19 patients at high risk of intubation.
- This score facilitates accurate triage and prediction of ventilator needs up to 48 hours in advance.
- The ECoRRS score can improve healthcare system preparedness for future pandemics.
Background:
COVID-19 has strained healthcare systems globally. In this and future pandemics, providers with limited critical care experience must distinguish between moderately ill patients and those who will require aggressive care, particularly endotracheal intubation. We sought to develop a machine learning-informed Early COVID-19 Respiratory Risk Stratification (ECoRRS) score to assist in triage, by providing a prediction of intubation within the next 48 hours based on objective clinical parameters.
Methods:
Electronic health record data from 3447 COVID-19 hospitalizations, 20.7% including intubation, were extracted. 80% of these records were used as the derivation cohort. The validation cohort consisted of 20% of the total 3447 records. Multiple randomizations of the training and testing split were used to calculate confidence intervals. Data were binned into 4-hour blocks and labeled as cases of intubation or no intubation within the specified time frame. A LASSO (least absolute shrinkage and selection operator) regression model was tuned for sensitivity and sparsity.
Results:
Six highly predictive parameters were identified, the most significant being fraction of inspired oxygen. The model achieved an area under the receiver operating characteristic curve of 0.789 (95% CI 0.785 to 0.812). At 90% sensitivity, the negative predictive value was 0.997.
Discussion:
The ECoRRS score enables non-specialists to identify patients with COVID-19 at risk of intubation within 48 hours with minimal undertriage and enables health systems to forecast new COVID-19 ventilator needs up to 48 hours in advance.
Level Of Evidence:
IV.
More Related Videos
08:05Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
03:39Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
Published on: June 20, 2025
Related Concept Videos
01:31Common Respiratory Disorders
Upper respiratory disorders impact the airways above the vocal cords, encompassing areas like the nose, sinuses, and throat. Various conditions fall under this category, including the common cold and allergic rhinitis. These disorders can stem from several causes,...
01:21Acute Respiratory Failure-II
The underlying physiological abnormalities that contribute to hypoxemic respiratory failure include:
01:21Acute Respiratory Failure-I
Definition: It is defined by specific criteria based on blood gas measurements. Hypoxemia happens when the partial pressure of oxygen (PaO2) falls below 60 mmHg. At the same time,...
01:29Acute Respiratory Failure-V
Ensure that patients are monitored continuously for their response to therapy, including changes in...
01:30Acute Respiratory Failure-III
01:26Asthma-II: Pathophysiology and Classification
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include: