TY  - JOUR
N1  - This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
TI  - International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality
Y1  - 2022/06/13/
AV  - public
VL  - 5
JF  - npj Digital Medicine
KW  - Data mining; Viral infection
A1  - Weber, GM
A1  - Hong, C
A1  - Xia, Z
A1  - Palmer, NP
A1  - Avillach, P
A1  - L?Yi, S
A1  - Keller, MS
A1  - Murphy, SN
A1  - Gutiérrez-Sacristán, A
A1  - Bonzel, CL
A1  - Serret-Larmande, A
A1  - Neuraz, A
A1  - Omenn, GS
A1  - Visweswaran, S
A1  - Klann, JG
A1  - South, AM
A1  - Loh, NHW
A1  - Cannataro, M
A1  - Beaulieu-Jones, BK
A1  - Bellazzi, R
A1  - Agapito, G
A1  - Alessiani, M
A1  - Aronow, BJ
A1  - Bell, DS
A1  - Benoit, V
A1  - Bourgeois, FT
A1  - Chiovato, L
A1  - Cho, K
A1  - Dagliati, A
A1  - DuVall, SL
A1  - Barrio, NG
A1  - Hanauer, DA
A1  - Ho, YL
A1  - Holmes, JH
A1  - Issitt, RW
A1  - Liu, M
A1  - Luo, Y
A1  - Lynch, KE
A1  - Maidlow, SE
A1  - Malovini, A
A1  - Mandl, KD
A1  - Mao, C
A1  - Matheny, ME
A1  - Moore, JH
A1  - Morris, JS
A1  - Morris, M
A1  - Mowery, DL
A1  - Ngiam, KY
A1  - Patel, LP
A1  - Pedrera-Jimenez, M
A1  - Ramoni, RB
A1  - Schriver, ER
A1  - Schubert, P
A1  - Balazote, PS
A1  - Spiridou, A
A1  - Tan, ALM
A1  - Tan, BWL
A1  - Tibollo, V
A1  - Torti, C
A1  - Trecarichi, EM
A1  - Wang, X
A1  - Aaron, JR
A1  - Albayrak, A
A1  - Albi, G
A1  - Alloni, A
A1  - Amendola, DF
A1  - Angoulvant, F
A1  - Anthony, LLLJ
A1  - Ashraf, F
A1  - Atz, A
A1  - Avillach, P
A1  - Azevedo, PS
A1  - Balshi, J
A1  - Beaulieu-Jones, BK
A1  - Bellasi, A
A1  - Benoit, V
A1  - Beraghi, M
A1  - Bernal-Sobrino, JL
A1  - Bernaux, M
A1  - Bey, R
A1  - Bhatnagar, S
A1  - Blanco-Martínez, A
A1  - Boeker, M
A1  - Booth, J
A1  - Bosari, S
A1  - Bradford, RL
A1  - Brat, GA
A1  - Bréant, S
A1  - Brown, NW
A1  - Bruno, R
A1  - Bryant, WA
A1  - Bucalo, M
A1  - Bucholz, E
A1  - Burgun, A
A1  - Cai, T
A1  - Carmona, A
A1  - Caucheteux, C
A1  - Champ, J
A1  - Chen, KY
A1  - Chen, J
ID  - discovery10152493
N2  - Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
SN  - 2398-6352
UR  - https://doi.org/10.1038/s41746-022-00601-0
PB  - Springer Science and Business Media LLC
ER  -