Development and validation of a risk prediction model for hospital admission in COVID-19 patients presenting to primary care

There is a paucity of prognostic models for COVID-19 that are usable for in-office patient assessment in general practice (GP). To develop and validate a risk prediction model for hospital admission with readily available predictors. A retrospective cohort study linking GP records from 8 COVID-19 centres and 55 general practices in the Netherlands to hospital admission records. The development cohort spanned March to June 2020, the validation cohort March to June 2021. The primary outcome was hospital admission within 14 days. We used geographic leave-region-out cross-validation in the development cohort and temporal validation in the validation cohort. In the development cohort, 4,806 adult patients with COVID-19 consulted their GP (median age 56, 56% female); in the validation cohort 830 patients did (median age 56, 52% female). In the development and validation cohort respectively, 292 (6.1%) and 126 (15.2%) were admitted to the hospital within 14 days, respectively. A logistic regression model based on sex, smoking, symptoms, vital signs and comorbidities predicted hospital admission with a c-index of 0.84 (95% CI 0.83 to 0.86) at geographic cross-validation and 0.79 (95% CI 0.74 to 0.83) at temporal validation, and was reasonably well calibrated (intercept −0.08, 95% CI −0.98 to 0.52, slope 0.89, 95% CI 0.71 to 1.07 at geographic cross-validation and intercept 0.02, 95% CI −0.21 to 0.24, slope 0.82, 95% CI 0.64 to 1.00 at temporal validation). We derived a risk model using readily available variables at GP assessment to predict hospital admission for COVID-19. It performed accurately across regions and waves. Further validation on cohorts with acquired immunity and newer SARS-CoV-2 variants is recommended. A general practice prediction model based on signs and symptoms of COVID-19 patients reliably predicted hospitalisation. The model performed well in second-wave data with other dominant variants and changed testing and vaccination policies. In an emerging pandemic, GP data can be leveraged to develop prognostic models for decision support and to predict hospitalisation rates.

目前适用于全科医疗（General Practice，GP）诊室患者评估的新型冠状病毒病（COVID-19）预后模型十分匮乏。本研究旨在开发并验证一款可利用便捷获取预测因子的新冠住院风险预测模型。本研究为回顾性队列研究，将荷兰8家新冠诊疗中心与55家全科诊所的门诊病历与住院登记记录进行关联。开发队列的纳入时间为2020年3月至6月，验证队列则为2021年3月至6月。本研究的主要结局为患者确诊新冠后14天内的住院事件。研究在开发队列中采用区域留一交叉验证（geographic leave-region-out cross-validation），在验证队列中采用时间验证。开发队列中共纳入4806名成年新冠患者，他们均曾就诊于全科诊所，患者中位年龄为56岁，女性占比56%；验证队列则纳入830名同类患者，中位年龄同样为56岁，女性占比52%。开发队列与验证队列中，分别有292例（6.1%）与126例（15.2%）患者在14天内住院。本研究构建的基于性别、吸烟史、症状、生命体征与合并症的逻辑回归模型，在区域留一交叉验证中取得了0.84的C指数（95%置信区间CI：0.83~0.86），在时间验证中则为0.79（95%CI：0.74~0.83）；模型校准度良好（区域留一交叉验证下截距为-0.08，95%CI：-0.98~0.52，斜率为0.89，95%CI：0.71~1.07；时间验证下截距为0.02，95%CI：-0.21~0.24，斜率为0.82，95%CI：0.64~1.00）。本研究利用全科门诊评估时即可便捷获取的变量构建了新冠住院风险预测模型，该模型在不同地域与疫情波次中均表现出良好的预测性能。未来建议在伴有获得性免疫群体与新型SARS-CoV-2变异株队列中开展进一步验证。这款基于新冠患者体征与症状的全科诊疗预测模型，可可靠预测患者住院风险。该模型在以其他变异株为主、且检测与疫苗政策发生变化的第二波疫情数据中同样表现优异。在新兴大流行背景下，全科医疗数据可用于开发预后模型以辅助临床决策，并预测住院率。