Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Sleep medicine centre, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Virol J. 2024 May 31;21(1):123. doi: 10.1186/s12985-024-02400-3.
Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model.
A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort.
We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.
新冠病毒感染后长期的新冠疾病（Long COVID）持续威胁着全世界人民的健康。早期预测住院患者发生 Long COVID 的风险将有助于新冠管理，但目前仍缺乏可靠和有效的预测模型。
本研究纳入了 1905 例新冠感染住院患者，并在出院后 4-8 周对其 Long COVID 状况进行了随访。采用单变量和多变量逻辑回归分析确定了 Long COVID 的危险因素。患者被随机分为训练队列（70%）和验证队列（30%），并在训练队列中使用 Lasso 回归筛选构建模型的因素。使用列线图可视化 Long COVID 风险预测模型。使用曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型在训练和验证队列中的性能。
共有 657 例（34.5%）患者报告出现 Long COVID 症状。最常见的症状是疲劳或肌肉无力（16.8%），其次是睡眠困难（11.1%）和咳嗽（9.5%）。年龄、糖尿病、慢性肾脏病、疫苗接种状态、降钙素原、白细胞、淋巴细胞、白细胞介素-6 和 D-二聚体的风险预测列线图被纳入该模型，用于早期识别 Long COVID 的高危患者。模型在训练队列和验证队列中的 AUC 分别为 0.762 和 0.713，表明模型具有较高的区分度。校准曲线进一步证实了列线图预测结果与理想曲线的接近程度、预测结果与实际结果的一致性，以及 DCA 所示的所有患者的潜在获益。这一观察结果在验证队列中得到了进一步验证。
我们建立了一个预测 COVID-19 住院患者 Long COVID 风险的列线图模型，并证明了其具有较好的预测性能。该模型有助于新冠患者的 Long COVID 管理。
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