A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients
Abstract The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to...
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Nature Portfolio
2020
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oai:doaj.org-article:24a1e6844b2b4deb962b0c15b73995db2021-12-02T17:13:14ZA validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients10.1038/s41746-020-00343-x2398-6352https://doaj.org/article/24a1e6844b2b4deb962b0c15b73995db2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00343-xhttps://doaj.org/toc/2398-6352Abstract The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.Narges RazavianVincent J. MajorMukund SudarshanJesse Burk-RafelPeter StellaHardev RandhawaSeda BilalogluJi ChenVuthy NguyWalter WangHao ZhangIlan ReinsteinDavid KudlowitzCameron ZengerMeng CaoRuina ZhangSiddhant DograKeerthi B. HarishBrian BosworthFritz FrancoisLeora I. HorwitzRajesh RanganathJonathan AustrianYindalon AphinyanaphongsNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-13 (2020) |
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Computer applications to medicine. Medical informatics R858-859.7 |
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Computer applications to medicine. Medical informatics R858-859.7 Narges Razavian Vincent J. Major Mukund Sudarshan Jesse Burk-Rafel Peter Stella Hardev Randhawa Seda Bilaloglu Ji Chen Vuthy Nguy Walter Wang Hao Zhang Ilan Reinstein David Kudlowitz Cameron Zenger Meng Cao Ruina Zhang Siddhant Dogra Keerthi B. Harish Brian Bosworth Fritz Francois Leora I. Horwitz Rajesh Ranganath Jonathan Austrian Yindalon Aphinyanaphongs A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients |
| description |
Abstract The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows. |
| format |
article |
| author |
Narges Razavian Vincent J. Major Mukund Sudarshan Jesse Burk-Rafel Peter Stella Hardev Randhawa Seda Bilaloglu Ji Chen Vuthy Nguy Walter Wang Hao Zhang Ilan Reinstein David Kudlowitz Cameron Zenger Meng Cao Ruina Zhang Siddhant Dogra Keerthi B. Harish Brian Bosworth Fritz Francois Leora I. Horwitz Rajesh Ranganath Jonathan Austrian Yindalon Aphinyanaphongs |
| author_facet |
Narges Razavian Vincent J. Major Mukund Sudarshan Jesse Burk-Rafel Peter Stella Hardev Randhawa Seda Bilaloglu Ji Chen Vuthy Nguy Walter Wang Hao Zhang Ilan Reinstein David Kudlowitz Cameron Zenger Meng Cao Ruina Zhang Siddhant Dogra Keerthi B. Harish Brian Bosworth Fritz Francois Leora I. Horwitz Rajesh Ranganath Jonathan Austrian Yindalon Aphinyanaphongs |
| author_sort |
Narges Razavian |
| title |
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients |
| title_short |
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients |
| title_full |
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients |
| title_fullStr |
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients |
| title_full_unstemmed |
A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients |
| title_sort |
validated, real-time prediction model for favorable outcomes in hospitalized covid-19 patients |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/24a1e6844b2b4deb962b0c15b73995db |
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