Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study

Yang Li; Yanlei Kong; Mark H Ebell; Leonardo Martinez; Xinyan Cai; Robert P Lennon; Derjung M Tarn; Arch G Mainous; Aleksandra E Zgierska; Bruce Barrett; Wen-Jan Tuan; Kevin Maloy; Munish Goyal; Alex H Krist; Tamas S Gal; Meng-Hsuan Sung; Changwei Li; Yier Jin; Ye Shen

doi:10.3389/fmed.2022.827261

Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study

Front Med (Lausanne). 2022 Apr 7:9:827261. doi: 10.3389/fmed.2022.827261. eCollection 2022.

Authors

Yang Li^{1

2}, Yanlei Kong¹, Mark H Ebell³, Leonardo Martinez⁴, Xinyan Cai³, Robert P Lennon⁵, Derjung M Tarn⁶, Arch G Mainous⁷, Aleksandra E Zgierska⁸, Bruce Barrett⁹, Wen-Jan Tuan⁵, Kevin Maloy¹⁰, Munish Goyal¹⁰, Alex H Krist¹¹, Tamas S Gal¹², Meng-Hsuan Sung³, Changwei Li¹³, Yier Jin¹⁴, Ye Shen³

Affiliations

¹ Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.
² RSS and China-Re Life Joint Lab on Public Health and Risk Management, Renmin University of China, Beijing, China.
³ Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, United States.
⁴ Department of Epidemiology, School of Public Health, Boston University, Boston, MA, United States.
⁵ Department of Family and Community Medicine, Penn State College of Medicine, Hershey, PA, United States.
⁶ Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States.
⁷ Department of Health Services Research, Management and Policy, University of Florida, Gainesville, FL, United States.
⁸ Departments of Family and Community Medicine, Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey, PA, United States.
⁹ Department of Family Medicine and Community Health, University of Wisconsin, Madison, WI, United States.
¹⁰ Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC, United States.
¹¹ Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond, VA, United States.
¹² Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States.
¹³ Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States.
¹⁴ Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States.

Abstract

Objectives: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information.

Design: Multicenter retrospective observational cohort study.

Setting: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles.

Patients: Coronavirus Disease 2019-confirmed and hospitalized adult patients.

Measurements and main results: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO₂ <93% into the predictive model. Besides age and SpO₂, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score.

Conclusions: The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.

Keywords: COVID-19; multicenter cohort study; prognostic score; time-and cost-saving tool; two-step.

Grants and funding

UL1 TR002649/TR/NCATS NIH HHS/United States