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.

COVID-19 大流行对一线临床决策提出了挑战，导致出现了大量已发表的预后工具。然而，很少有模型经过前瞻性验证，也没有报告在实践中的实施情况。在这里，我们使用 3345 例回顾性住院病例和 474 例前瞻性住院病例来开发和验证一个简约模型，根据实时实验室值、生命体征和氧气支持变量，识别预测后 96 小时内具有良好结果的患者。在回顾性和前瞻性验证中，该模型实现了较高的平均精度（88.6% 95% CI：[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] ]） 分别。我们将该模型实施并集成到 EHR 中，实现了 93.3% 的阳性预测值和 41% 的敏感性。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。