Veterans Affairs Portland Health Care System, Portland, Oregon, United States of America.
Center of Innovation to Improve Veteran Involvement in Care (CIVIC), Veterans Affairs Portland Healthcare System, Portland, Oregon, United States of America.
PLoS One. 2024 Oct 4;19(10):e0307235. doi: 10.1371/journal.pone.0307235. eCollection 2024.
The epidemiology of COVID-19 has substantially changed since its emergence given the availability of effective vaccines, circulation of different viral variants, and re-infections. We aimed to develop models to predict 30-day COVID-19 hospitalization and death in the Omicron era for contemporary clinical and research applications.
We used comprehensive electronic health records from a national cohort of patients in the Veterans Health Administration (VHA) who tested positive for SARS-CoV-2 between March 1, 2022, and March 31, 2023. Full models incorporated 84 predictors, including demographics, comorbidities, and receipt of COVID-19 vaccinations and anti-SARS-CoV-2 treatments. Parsimonious models included 19 predictors. We created models for 30-day hospitalization or death, 30-day hospitalization, and 30-day all-cause mortality. We used the Super Learner ensemble machine learning algorithm to fit prediction models. Model performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration intercepts and slopes in a 20% holdout dataset.
Models were trained and tested on 198,174 patients, of whom 8% were hospitalized or died within 30 days of testing positive. AUCs for the full models ranged from 0.80 (hospitalization) to 0.91 (death). Brier scores were close to 0, with the lowest error in the mortality model (Brier score: 0.01). All three models were well calibrated with calibration intercepts <0.23 and slopes <1.05. Parsimonious models performed comparably to full models.
We developed prediction models that accurately estimate COVID-19 hospitalization and mortality risk following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments. These models may be used for risk stratification to inform COVID-19 treatment and to identify high-risk patients for inclusion in clinical trials.
自 COVID-19 出现以来，由于有效疫苗的可用性、不同病毒变体的传播以及再感染，其流行病学情况发生了重大变化。我们旨在开发模型，以预测奥密克戎时代 COVID-19 的 30 天住院和死亡情况，用于当代临床和研究应用。
我们使用来自退伍军人健康管理局（VHA）全国队列中在 2022 年 3 月 1 日至 2023 年 3 月 31 日期间检测出 SARS-CoV-2 呈阳性的患者的综合电子健康记录。完整模型纳入了 84 个预测因子，包括人口统计学特征、合并症以及 COVID-19 疫苗接种和抗 SARS-CoV-2 治疗的情况。简约模型纳入了 19 个预测因子。我们为 30 天住院或死亡、30 天住院和 30 天全因死亡率创建了模型。我们使用 Super Learner 集成机器学习算法来拟合预测模型。在 20%的保留数据集上，使用接受者操作特征曲线下面积（AUC）、Brier 评分以及校准截距和斜率来评估模型性能。
在 198174 名患者中进行了模型训练和测试，其中 8%的患者在检测呈阳性后的 30 天内住院或死亡。全模型的 AUC 范围为 0.80（住院）至 0.91（死亡）。Brier 评分接近 0，死亡率模型的误差最低（Brier 评分：0.01）。所有三个模型的校准都很好，校准截距<0.23，斜率<1.05。简约模型的性能与全模型相当。
我们开发了预测模型，可以准确估计奥密克戎变异出现后 COVID-19 的住院和死亡风险，以及 COVID-19 疫苗接种和抗病毒治疗的情况下的风险。这些模型可用于风险分层，以指导 COVID-19 的治疗，并确定纳入临床试验的高危患者。
