eprintid: 10152493 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/15/24/93 datestamp: 2022-07-22 09:43:49 lastmod: 2022-07-22 09:43:49 status_changed: 2022-07-22 09:43:49 type: article metadata_visibility: show sword_depositor: 699 creators_name: Weber, GM creators_name: Hong, C creators_name: Xia, Z creators_name: Palmer, NP creators_name: Avillach, P creators_name: L’Yi, S creators_name: Keller, MS creators_name: Murphy, SN creators_name: Gutiérrez-Sacristán, A creators_name: Bonzel, CL creators_name: Serret-Larmande, A creators_name: Neuraz, A creators_name: Omenn, GS creators_name: Visweswaran, S creators_name: Klann, JG creators_name: South, AM creators_name: Loh, NHW creators_name: Cannataro, M creators_name: Beaulieu-Jones, BK creators_name: Bellazzi, R creators_name: Agapito, G creators_name: Alessiani, M creators_name: Aronow, BJ creators_name: Bell, DS creators_name: Benoit, V creators_name: Bourgeois, FT creators_name: Chiovato, L creators_name: Cho, K creators_name: Dagliati, A creators_name: DuVall, SL creators_name: Barrio, NG creators_name: Hanauer, DA creators_name: Ho, YL creators_name: Holmes, JH creators_name: Issitt, RW creators_name: Liu, M creators_name: Luo, Y creators_name: Lynch, KE creators_name: Maidlow, SE creators_name: Malovini, A creators_name: Mandl, KD creators_name: Mao, C creators_name: Matheny, ME creators_name: Moore, JH creators_name: Morris, JS creators_name: Morris, M creators_name: Mowery, DL creators_name: Ngiam, KY creators_name: Patel, LP creators_name: Pedrera-Jimenez, M creators_name: Ramoni, RB creators_name: Schriver, ER creators_name: Schubert, P creators_name: Balazote, PS creators_name: Spiridou, A creators_name: Tan, ALM creators_name: Tan, BWL creators_name: Tibollo, V creators_name: Torti, C creators_name: Trecarichi, EM creators_name: Wang, X creators_name: Aaron, JR creators_name: Albayrak, A creators_name: Albi, G creators_name: Alloni, A creators_name: Amendola, DF creators_name: Angoulvant, F creators_name: Anthony, LLLJ creators_name: Ashraf, F creators_name: Atz, A creators_name: Avillach, P creators_name: Azevedo, PS creators_name: Balshi, J creators_name: Beaulieu-Jones, BK creators_name: Bellasi, A creators_name: Benoit, V creators_name: Beraghi, M creators_name: Bernal-Sobrino, JL creators_name: Bernaux, M creators_name: Bey, R creators_name: Bhatnagar, S creators_name: Blanco-Martínez, A creators_name: Boeker, M creators_name: Booth, J creators_name: Bosari, S creators_name: Bradford, RL creators_name: Brat, GA creators_name: Bréant, S creators_name: Brown, NW creators_name: Bruno, R creators_name: Bryant, WA creators_name: Bucalo, M creators_name: Bucholz, E creators_name: Burgun, A creators_name: Cai, T creators_name: Carmona, A creators_name: Caucheteux, C creators_name: Champ, J creators_name: Chen, KY creators_name: Chen, J title: International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality ispublished: pub divisions: UCL divisions: G25 divisions: D13 divisions: B02 keywords: Data mining; Viral infection note: 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. 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. date: 2022-06-13 date_type: published publisher: Springer Science and Business Media LLC official_url: https://doi.org/10.1038/s41746-022-00601-0 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1966181 doi: 10.1038/s41746-022-00601-0 lyricists_name: Sebire, Neil lyricists_id: NJSEB45 actors_name: Barczynska, Patrycja actors_id: PBARC91 actors_role: owner full_text_status: public publication: npj Digital Medicine volume: 5 article_number: 74 issn: 2398-6352 citation: Weber, GM; Hong, C; Xia, Z; Palmer, NP; Avillach, P; L’Yi, S; Keller, MS; ... Chen, J; + view all <#> Weber, GM; Hong, C; Xia, Z; Palmer, NP; Avillach, P; L’Yi, S; Keller, MS; Murphy, SN; Gutiérrez-Sacristán, A; Bonzel, CL; Serret-Larmande, A; Neuraz, A; Omenn, GS; Visweswaran, S; Klann, JG; South, AM; Loh, NHW; Cannataro, M; Beaulieu-Jones, BK; Bellazzi, R; Agapito, G; Alessiani, M; Aronow, BJ; Bell, DS; Benoit, V; Bourgeois, FT; Chiovato, L; Cho, K; Dagliati, A; DuVall, SL; Barrio, NG; Hanauer, DA; Ho, YL; Holmes, JH; Issitt, RW; Liu, M; Luo, Y; Lynch, KE; Maidlow, SE; Malovini, A; Mandl, KD; Mao, C; Matheny, ME; Moore, JH; Morris, JS; Morris, M; Mowery, DL; Ngiam, KY; Patel, LP; Pedrera-Jimenez, M; Ramoni, RB; Schriver, ER; Schubert, P; Balazote, PS; Spiridou, A; Tan, ALM; Tan, BWL; Tibollo, V; Torti, C; Trecarichi, EM; Wang, X; Aaron, JR; Albayrak, A; Albi, G; Alloni, A; Amendola, DF; Angoulvant, F; Anthony, LLLJ; Ashraf, F; Atz, A; Avillach, P; Azevedo, PS; Balshi, J; Beaulieu-Jones, BK; Bellasi, A; Benoit, V; Beraghi, M; Bernal-Sobrino, JL; Bernaux, M; Bey, R; Bhatnagar, S; Blanco-Martínez, A; Boeker, M; Booth, J; Bosari, S; Bradford, RL; Brat, GA; Bréant, S; Brown, NW; Bruno, R; Bryant, WA; Bucalo, M; Bucholz, E; Burgun, A; Cai, T; Carmona, A; Caucheteux, C; Champ, J; Chen, KY; Chen, J; - view fewer <#> (2022) International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality. npj Digital Medicine , 5 , Article 74. 10.1038/s41746-022-00601-0 <https://doi.org/10.1038/s41746-022-00601-0>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10152493/1/Sebire_s41746-022-00601-0.pdf