一种用于早期识别有发展为常见新冠长期症状风险的新冠患者的预后模型的开发与验证。
Development and validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common long COVID symptoms.
作者信息
Deforth Manja, Gebhard Caroline E, Bengs Susan, Buehler Philipp K, Schuepbach Reto A, Zinkernagel Annelies S, Brugger Silvio D, Acevedo Claudio T, Patriki Dimitri, Wiggli Benedikt, Twerenbold Raphael, Kuster Gabriela M, Pargger Hans, Schefold Joerg C, Spinetti Thibaud, Wendel-Garcia Pedro D, Hofmaenner Daniel A, Gysi Bianca, Siegemund Martin, Heinze Georg, Regitz-Zagrosek Vera, Gebhard Catherine, Held Ulrike
机构信息
Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland.
Intensive Care Unit, Department of Acute Medicine, University Hospital Basel, Basel, Switzerland.
出版信息
Diagn Progn Res. 2022 Nov 17;6(1):22. doi: 10.1186/s41512-022-00135-9.
BACKGROUND
The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19.
METHODS
The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance.
RESULTS
In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was -0.06 (95% CI: -0.22 to 0.09).
CONCLUSION
The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.
背景
2019年冠状病毒病（COVID-19）大流行需要可靠的预后模型来估计长期感染新冠的风险。我们开发并验证了一个预测模型，以估计急性COVID-19后至少60天出现已知常见长期新冠症状的概率。
方法
该预后模型基于一项多中心前瞻性瑞士队列研究的数据构建。纳入的是2020年2月至12月期间被诊断为COVID-19并作为门诊患者、在病房或重症/中级护理单元接受治疗的成年患者。在60至425天的随访期后，评估了感知到的长期健康损害，包括运动耐量降低/恢复力下降、呼吸急促和/或疲劳（REST）。数据集被分为一个推导队列和一个地理验证队列。基于三种方法从十二个候选预测因子中选择预测因子，即增强向后消除（ABE）方法、自适应最佳子集选择（ABESS）方法和基于模型的递归划分（MBRP）方法。使用缩放后的Brier评分、一致性c统计量和校准图评估模型性能。根据最佳模型性能确定最终的预后模型。
结果
总共2799名患者纳入分析，其中1588名患者在推导队列，1211名患者在验证队列。各队列中REST的患病率相似，推导队列中为21.6%（n = 343），验证队列中为22.1%（n = 268）。使用ABE和ABESS方法选择了相同的预测因子。最终的预后模型基于ABE和ABESS选择的预测因子。验证队列中相应的缩放Brier评分为18.74%，模型辨别力为0.78（95%CI：0.75至0.81），校准斜率为0.92（95%CI：0.78至1.06），校准截距为 -0.06（95%CI：-0.22至0.09）。
结论
所提出的模型经验证可识别有REST症状高风险的COVID-19感染患者。在日常临床实践中实施该预后模型之前，建议进行一项影响研究。
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