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Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with
Lara Jehi1, Xinge Ji2, Alex Milinovich2
1Neurological Institute, Chief Research Information Officer, Cleveland Clinic, Cleveland, Ohio, United States of America.
Insights
This study developed a COVID-19 hospitalization risk calculator. It identifies key factors like age, race, and comorbidities to predict severe outcomes, aiding clinical decisions.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Coronavirus Disease 2019 (COVID-19) pandemic strains healthcare systems, necessitating better prediction of hospitalization.
- Identifying risk factors for severe COVID-19 is crucial for resource allocation and patient management.
Purpose of the Study:
- To characterize a large cohort of hospitalized COVID-19 patients and their outcomes.
- To develop and validate a statistical model for predicting individual hospitalization risk in newly diagnosed COVID-19 patients.
Main Methods:
- Retrospective cohort study utilizing LASSO logistic regression for feature selection.
- Model development and validation in distinct patient cohorts, with results presented as a nomogram and online risk calculator.
Main Results:
- Identified increased hospitalization risk associated with older age, Black race, male sex, smoking history, diabetes, hypertension, chronic lung disease, and socioeconomic factors.
- Reduced risk observed with prior influenza vaccination. Model demonstrated excellent discrimination (AUC 0.900 development, 0.813 validation).
- An online risk calculator was developed and validated for predicting COVID-19 hospitalization risk.
Conclusions:
- The study refines understanding of COVID-19 risk factors, including social determinants of health, race, and influenza vaccination.
- Individualized risk prediction tools, like the developed nomogram and calculator, can significantly aid complex medical decision-making during the pandemic.
Background:
Coronavirus Disease 2019 is a pandemic that is straining healthcare resources, mainly hospital beds. Multiple risk factors of disease progression requiring hospitalization have been identified, but medical decision-making remains complex.
Objective:
To characterize a large cohort of patients hospitalized with COVID-19, their outcomes, develop and validate a statistical model that allows individualized prediction of future hospitalization risk for a patient newly diagnosed with COVID-19.
Design:
Retrospective cohort study of patients with COVID-19 applying a least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to retain the most predictive features for hospitalization risk, followed by validation in a temporally distinct patient cohort. The final model was displayed as a nomogram and programmed into an online risk calculator.
Setting:
One healthcare system in Ohio and Florida.
Participants:
All patients infected with SARS-CoV-2 between March 8, 2020 and June 5, 2020. Those tested before May 1 were included in the development cohort, while those tested May 1 and later comprised the validation cohort.
Measurements:
Demographic, clinical, social influencers of health, exposure risk, medical co-morbidities, vaccination history, presenting symptoms, medications, and laboratory values were collected on all patients, and considered in our model development.
Results:
4,536 patients tested positive for SARS-CoV-2 during the study period. Of those, 958 (21.1%) required hospitalization. By day 3 of hospitalization, 24% of patients were transferred to the intensive care unit, and around half of the remaining patients were discharged home. Ten patients died. Hospitalization risk was increased with older age, black race, male sex, former smoking history, diabetes, hypertension, chronic lung disease, poor socioeconomic status, shortness of breath, diarrhea, and certain medications (NSAIDs, immunosuppressive treatment). Hospitalization risk was reduced with prior flu vaccination. Model discrimination was excellent with an area under the curve of 0.900 (95% confidence interval of 0.886-0.914) in the development cohort, and 0.813 (0.786, 0.839) in the validation cohort. The scaled Brier score was 42.6% (95% CI 37.8%, 47.4%) in the development cohort and 25.6% (19.9%, 31.3%) in the validation cohort. Calibration was very good. The online risk calculator is freely available and found at https://riskcalc.org/COVID19Hospitalization/.
Limitation:
Retrospective cohort design.
Conclusion:
Our study crystallizes published risk factors of COVID-19 progression, but also provides new data on the role of social influencers of health, race, and influenza vaccination. In a context of a pandemic and limited healthcare resources, individualized outcome prediction through this nomogram or online risk calculator can facilitate complex medical decision-making.
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