Base case model parameterisation.

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.南非新型冠状病毒肺炎（COVID-19）感染率仍居高不下。针对疑似COVID-19感染患者，临床预测模型可助力快速分诊与临床决策支持。南非西开普省（Western Cape）已整合电子医疗数据，可支撑大规模关联常规数据集的构建。本研究旨在开发一款适用于中等收入地区场景、可预测疑似COVID-19感染患者不良结局的机器学习模型。本研究为回顾性队列研究，纳入2020年8月27日至2021年10月31日期间，南非西开普省公立医疗机构急诊科（emergency departments, ED）收治的疑似COVID-19感染患者的关联常规医疗数据。本研究的主要结局为患者入院后30天内死亡或需转入重症监护。本研究采用拆分样本验证法对极限梯度提升（XGBoost）机器学习模型进行训练与内部验证；并通过3个外部队列开展外部验证：西开普省奥密克戎（Omicron）流行期间收治的患者队列、英国原型株（ancestral COVID-19）流行期间的患者队列，以及苏丹地区原型株与埃塔（Eta）变异株流行期间的患者队列。完整病例训练数据集共纳入282051例患者，其中30天不良结局发生率为4.0%。预测不良结局的最重要特征依次为：需氧疗、外周血氧饱和度、意识状态与年龄。基于拆分样本测试数据的内部验证结果显示，模型具有优异的区分度（C统计量0.91，95%置信区间0.90~0.91）与校准度（临床改进阈值CITL为1.05）。在奥密克戎、英国及苏丹测试队列中，该模型的C统计量分别为0.84（95%置信区间0.84~0.85）、0.72（95%置信区间0.71~0.73）与0.62（95%置信区间0.59~0.65）。针对缺失数据的敏感性分析显示，研究结果未发生实质性改变。本研究开发的XGBoost机器学习模型可较好地预测西开普省急诊科疑似COVID-19患者的不良结局，但在跨时间与跨地域的外部验证中，模型性能有所下降。