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Development and validation of the COVID-19 Hospitalized Patient Deterioration Index
Claudia Nau1, Rebecca K Butler, Cheng-Wei Huang
1Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, 98 S Los Robles Ave, Pasadena, CA 91101.
Insights
A new COVID-19 Deterioration Index (COVID-HDI) accurately identifies hospitalized patients at low risk of respiratory deterioration or death. This tool aids clinical decisions regarding patient discharge and care escalation.
Area of Science:
- Medical Informatics
- Clinical Decision Support
- Epidemiology
Background:
- Hospitalized patients with COVID-19 face risks of respiratory deterioration and death.
- Existing deterioration indices may not be optimized for COVID-19's specific clinical trajectory.
- Accurate risk stratification is crucial for timely intervention and resource allocation.
Purpose of the Study:
- To develop and validate a COVID-19-specific deterioration index (COVID-HDI) for hospitalized patients.
- To improve prediction of respiratory deterioration events or death in COVID-19 patients.
- To support clinical decision-making for discharge and care escalation.
Main Methods:
- Retrospective observational cohort study.
- Development and validation of the COVID-HDI model using machine learning and logistic regression.
- Split-sample cross-validation for training and testing the predictive model.
Main Results:
- The COVID-HDI demonstrated strong predictive performance (Area Under the Curve = 0.83).
- The index accurately identified low-risk patients (NPV > 98.5%), with 74% falling into low or borderline low-risk categories.
- A high-risk group (12% of patients) was identified with a positive predictive value of 51%.
Conclusions:
- The COVID-HDI is a parsimonious, well-calibrated, and accurate tool for predicting deterioration in COVID-19 patients.
- This index can assist clinicians in making informed decisions about patient discharge and escalation of care.
- The model's performance remained robust in recent patient cohorts, suggesting sustained clinical utility.
Objectives:
To develop a COVID-19-specific deterioration index for hospitalized patients: the COVID Hospitalized Patient Deterioration Index (COVID-HDI). This index builds on the proprietary Epic Deterioration Index, which was not developed for predicting respiratory deterioration events among patients with COVID-19.
Study Design:
A retrospective observational cohort was used to develop and validate the COVID-HDI model to predict respiratory deterioration or death among hospitalized patients with COVID-19. Deterioration events were defined as death or requiring high-flow oxygen, bilevel positive airway pressure, mechanical ventilation, or intensive-level care within 72 hours of run time. The sample included hospitalized patients with COVID-19 diagnoses or positive tests at Kaiser Permanente Southern California between May 3, 2020, and October 17, 2020.
Methods:
Machine learning models and 118 candidate predictors were used to generate benchmark performance. Logit regression with least absolute shrinkage and selection operator and physician input were used to finalize the model. Split-sample cross-validation was used to train and test the model.
Results:
The area under the receiver operating curve was 0.83. COVID-HDI identifies patients at low risk (negative predictive value [NPV] > 98.5%) and borderline low risk (NPV > 95%) of an event. Of all patients, 74% were identified as being at low or borderline low risk at some point during their hospitalization and could be considered for discharge with or without home monitoring. A high-risk group with a positive predictive value of 51% included 12% of patients. Model performance remained high in a recent cohort of patients.
Conclusions:
COVID-HDI is a parsimonious, well-calibrated, and accurate model that may support clinical decision-making around discharge and escalation of care.
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