Klein Dorthe Odyl, Wilmes Nick, Waardenburg Sophie F, Bonsel Gouke J, Birnie Erwin, Wintjens Marieke Sjn, Heemskerk Stella Cm, Janssen Emma Bnj, Ghossein-Doha Chahinda, Warlé Michiel C, Jacobs Lotte Mc, Hemmen Bea, Verbunt Jeanine A, van Bussel Bas Ct, van Santen Susanne, Kietselaer Bas Ljh, Jansen Gwyneth, Asselbergs Folkert W, Linschoten Marijke, Haagsma Juanita A, van Kuijk S M J
Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center+ (MUMC+), P.O. Box 5800, 6202, AZ, Maastricht, The Netherlands.
Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands.
Diagn Progn Res. 2025 Sep 1;9(1):18. doi: 10.1186/s41512-025-00203-w.
A subset of COVID-19 patients develops post-COVID-19 condition (PCC). This condition results in disability in numerous areas of patients' lives and a reduced health-related quality of life, with societal impact including work absences and increased healthcare utilization. There is a scarcity of models predicting PCC, especially those considering the severity of the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and incorporating long-term follow-up data. Therefore, we developed and internally validated a prediction model for PCC 2 years after SARS-CoV-2 infection in a cohort of COVID-19 patients.
Data from the CORona Follow-Up (CORFU) study were used. This research initiative integrated data from multiple Dutch COVID-19 cohort studies. We utilized 2-year follow-up data collected via the questionnaires between October 1st of 2021 and December 31st of 2022. Participants were former COVID-19 patients, approximately 2-year post-SARS-CoV-2 infection. Candidate predictors were selected based on literature and availability across cohorts. The outcome of interest was the prevalence of PCC at 2 years after the initial infection. Logistic regression with backward stepwise elimination identified significant predictors such as sex, BMI and initial disease severity. The model was internally validated using bootstrapping. Model performance was quantified as model fit, discrimination and calibration.
In total 904 former COVID-19 patients were included in the analysis. The cohort included 146 (16.2%) non-hospitalized patients, 511 (56.5%) ward admitted patients, and 247 (27.3%) intensive care unit (ICU) admitted patients. Of all participants, 551 (61.0%) participants suffered from PCC. We included 20 candidate predictors in the multivariable analysis. The final model, after backward elimination, identified sex, body mass index (BMI), ward admission, ICU admission, and comorbidities such as arrhythmia, asthma, angina pectoris, previous stroke, hernia, osteoarthritis, and rheumatoid arthritis as predictors of post-COVID-19 condition. Nagelkerke's R-squared value for the model was 0.19. The optimism-adjusted AUC was 71.2%, and calibration was good across predicted probabilities.
This internally validated prediction model demonstrated moderate discriminative ability to predict PCC 2 years after COVID-19 based on sex, BMI, initial disease severity, and a collection of comorbidities.
一部分新冠病毒感染患者会出现新冠后状况（PCC）。这种状况导致患者生活的多个方面出现功能障碍，并降低了与健康相关的生活质量，对社会产生的影响包括旷工和医疗资源利用增加。预测PCC的模型稀缺，尤其是那些考虑到初始严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染的严重程度并纳入长期随访数据的模型。因此，我们开发并在内部验证了一个针对新冠病毒感染患者群体在SARS-CoV-2感染2年后发生PCC的预测模型。
使用了新冠随访（CORFU）研究的数据。该研究整合了来自多个荷兰新冠队列研究的数据。我们利用了2021年10月1日至2022年12月31日期间通过问卷收集的2年随访数据。参与者为曾感染新冠病毒的患者，感染SARS-CoV-2约2年后。根据文献和各队列的可得性选择候选预测因素。感兴趣的结局是初始感染2年后PCC的患病率。采用向后逐步淘汰法的逻辑回归确定了性别、体重指数（BMI）和初始疾病严重程度等显著预测因素。该模型通过自抽样法进行内部验证。模型性能通过模型拟合、区分度和校准进行量化。
共有904名曾感染新冠病毒的患者纳入分析。该队列包括146名（16.2%）非住院患者、511名（56.5%）病房收治患者和247名（27.3%）重症监护病房（ICU）收治患者。所有参与者中，551名（61.0%）患有PCC。我们在多变量分析中纳入了20个候选预测因素。经过向后淘汰后的最终模型确定了性别、体重指数（BMI）、病房收治、ICU收治以及心律失常、哮喘、心绞痛、既往中风、疝气、骨关节炎和类风湿关节炎等合并症为新冠后状况的预测因素。该模型的Nagelkerke's R平方值为0.19。乐观调整后的AUC为71.2%，预测概率的校准良好。
这个经过内部验证的预测模型显示，基于性别、BMI、初始疾病严重程度和一系列合并症，对新冠后2年发生PCC具有中等的区分预测能力。
