TY - JOUR
T1 - Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients
T2 - A proposal for the COVID-AID risk tool
AU - Hajifathalian, Kaveh
AU - Sharaiha, Reem Z.
AU - Kumar, Sonal
AU - Krisko, Tibor
AU - Skaf, Daniel
AU - Ang, Bryan
AU - Redd, Walker D.
AU - Zhou, Joyce C.
AU - Hathorn, Kelly E.
AU - McCarty, Thomas R.
AU - Bazarbashi, Ahmad Najdat
AU - Njie, Cheikh
AU - Wong, Danny
AU - Shen, Lin
AU - Sholle, Evan
AU - Cohen, David E.
AU - Brown, Robert S.
AU - Chan, Walter W.
AU - Fortune, Brett E.
N1 - Publisher Copyright:
Copyright: © 2020 Hajifathalian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2020/9
Y1 - 2020/9
N2 - Background The 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19. Methods We performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020. Results We developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74–0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69–0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78–0.92; GOF p = 0.340) and 0.83 (95%CI 0.76–0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability. Conclusions The COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk stratification instrument for future research trials.
AB - Background The 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19. Methods We performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020. Results We developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74–0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69–0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78–0.92; GOF p = 0.340) and 0.83 (95%CI 0.76–0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability. Conclusions The COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk stratification instrument for future research trials.
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U2 - 10.1371/journal.pone.0239536
DO - 10.1371/journal.pone.0239536
M3 - Article
C2 - 32997700
AN - SCOPUS:85092227320
SN - 1932-6203
VL - 15
JO - PLoS ONE
JF - PLoS ONE
IS - 9 September
M1 - e0239536
ER -