| Autor: |
Griffin M. Weber, Chuan Hong, Zongqi Xia, Nathan P. Palmer, Paul Avillach, Sehi L’Yi, Mark S. Keller, Shawn N. Murphy, Alba Gutiérrez-Sacristán, Clara-Lea Bonzel, Arnaud Serret-Larmande, Antoine Neuraz, Gilbert S. Omenn, Shyam Visweswaran, Jeffrey G. Klann, Andrew M. South, Ne Hooi Will Loh, Mario Cannataro, Brett K. Beaulieu-Jones, Riccardo Bellazzi, Giuseppe Agapito, Mario Alessiani, Bruce J. Aronow, Douglas S. Bell, Vincent Benoit, Florence T. Bourgeois, Luca Chiovato, Kelly Cho, Arianna Dagliati, Scott L. DuVall, Noelia García Barrio, David A. Hanauer, Yuk-Lam Ho, John H. Holmes, Richard W. Issitt, Molei Liu, Yuan Luo, Kristine E. Lynch, Sarah E. Maidlow, Alberto Malovini, Kenneth D. Mandl, Chengsheng Mao, Michael E. Matheny, Jason H. Moore, Jeffrey S. Morris, Michele Morris, Danielle L. Mowery, Kee Yuan Ngiam, Lav P. Patel, Miguel Pedrera-Jimenez, Rachel B. Ramoni, Emily R. Schriver, Petra Schubert, Pablo Serrano Balazote, Anastasia Spiridou, Amelia L. M. Tan, Byorn W. L. Tan, Valentina Tibollo, Carlo Torti, Enrico M. Trecarichi, Xuan Wang, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Isaac S. Kohane, Tianxi Cai, Gabriel A. Brat |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
npj Digital Medicine, Vol 5, Iss 1, Pp 1-8 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2398-6352 |
| DOI: |
10.1038/s41746-022-00601-0 |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
Full text from SpringerLink