As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (IBM Explorys), including demographics, comorbidities, symptoms, and hospitalization. Out of 15753 COVID-19 patients, 2050 went into critical state or deceased. Non-random train-test splits by time were repeated 100 times and led to a ROC AUC of 0.861 [0.838, 0.883] and a precision-recall AUC of 0.434 [0.414, 0.485] (median and interquartile range). The interpretability analysis confirmed evidence on major risk factors (e.g., older age, higher BMI, male gender, diabetes, and cardiovascular disease) in an efficient way compared to clinical studies, demonstrating the model validity. Such personalized predictions could enable fine-graded risk stratification for optimized care management.

由于 COVID-19 大流行正在挑战全球医疗保健系统，因此早期识别具有高并发症风险的患者至关重要。我们提出了一个预后模型，预测 COVID-19 诊断后 28 天内的临界状态，该模型根据来自美国电子健康记录 (IBM Explorys) 的数据进行训练，包括人口统计学、合并症、症状和住院治疗。在 15753 名 COVID-19 患者中，2050 名进入危急状态或死亡。按时间进行的非随机训练测试拆分重复 100 次，导致 ROC AUC 为 0.861 [0.838, 0.883]，精确召回 AUC 为 0.434 [0.414, 0.485]（中位数和四分位距）。与临床研究相比，可解释性分析以有效的方式证实了主要风险因素（例如，年龄较大、BMI 较高、男性、糖尿病和心血管疾病）的证据，证明模型的有效性。这种个性化的预测可以实现精细分级的风险分层，以优化护理管理。