Razavian Narges, Major Vincent J, Sudarshan Mukund, Burk-Rafel Jesse, Stella Peter, Randhawa Hardev, Bilaloglu Seda, Chen Ji, Nguy Vuthy, Wang Walter, Zhang Hao, Reinstein Ilan, Kudlowitz David, Zenger Cameron, Cao Meng, Zhang Ruina, Dogra Siddhant, Harish Keerthi B, Bosworth Brian, Francois Fritz, Horwitz Leora I, Ranganath Rajesh, Austrian Jonathan, Aphinyanaphongs Yindalon
Department of Population Health, NYU Grossman School of Medicine, New York, NY USA.
Center for Healthcare Innovation and Delivery Science, NYU Langone Health, New York, NY USA.
NPJ Digit Med. 2020 Oct 6;3:130. doi: 10.1038/s41746-020-00343-x. eCollection 2020.
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.
新冠疫情给一线临床决策带来了挑战，催生了众多已发表的预后工具。然而，很少有模型经过前瞻性验证，也没有一个报告在实际中得到应用。在此，我们利用3345例回顾性和474例前瞻性住院病例，开发并验证了一个简约模型，该模型基于实时实验室值、生命体征和氧疗支持变量，在预测后96小时内识别出预后良好的患者。在回顾性和前瞻性验证中，该模型分别实现了较高的平均精度（88.6%，95%置信区间：[88.4 - 88.7]和90.8% [90.8 - 90.8]）和区分度（95.1% [95.1 - 95.2]和86.8% [86.8 - 86.9]）。我们将该模型实施并整合到电子健康记录中，阳性预测值达到93.3%，灵敏度为41%。初步结果表明临床医生正在将这些评分纳入他们的临床工作流程。
