Purpose: Building and validating a clinical prediction model for novel coronavirus (COVID-19) re-positive cases in malnourished older adults.
Patients and Methods: Malnourished older adults from January to May 2023 were retrospectively collected from the Department of Geriatrics of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine. They were divided into a “non-re-positive” group and a “re-positive” group based on the number of COVID-19 infections, and into a training set and a validation set at a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to identify predictive factors for COVID-19 re-positivity in malnourished older adults, and a nomogram was constructed. Independent influencing factors were screened by multivariate logistic regression. The model’s goodness-of-fit, discrimination, calibration, and clinical impact were assessed by Hosmer-Lemeshow test, area under the curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CIC), respectively.
Results: We included 347 cases, 243 in the training set, and 104 in the validation set. We screened 10 variables as factors influencing the outcome. By multivariate logistic regression analysis, preliminary identified protective factors, risk factors, and independent influencing factors that affect the re-positive outcome. We constructed a clinical prediction model for COVID-19 re-positivity in malnourished older adults. The Hosmer-Lemeshow test yielded χ² =5.916, P =0.657; the AUC was 0.881; when the threshold probability was > 8%, using this model to predict whether malnourished older adults were re-positive for COVID-19 was more beneficial than implementing intervention programs for all patients; when the threshold was > 80%, the positive estimated value was closer to the actual number of cases.
Conclusion: This model can help identify the risk of COVID-19 re-positivity in malnourished older adults early, facilitate early clinical decision-making and intervention, and have important implications for improving patient outcomes. We also expect more large-scale, multicenter studies to further validate, refine, and update this model.

Keywords: malnutrition, COVID-19, re-positive, clinical prediction model


中文翻译：

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预测营养不良老年人中的 COVID-19 重新阳性病例：临床模型开发和验证

目的：建立并验证营养不良老年人中新型冠状病毒（COVID-19）复发病例的临床预测模型。
患者和方法：回顾性收集成都中医药大学附属医院老年科2023年1月至5月营养不良的老年人。根据COVID-19感染人数将他们分为“非复阳”组和“复阳”组，并按7:3的比例分为训练集和验证集。使用最小绝对收缩和选择算子 (LASSO) 回归分析来确定营养不良老年人中 COVID-19 复发的预测因素，并构建列线图。采用多因素logistic回归筛选独立影响因素。通过 Hosmer-Lemeshow 检验、曲线下面积 (AUC)、校准曲线、决策曲线分析 (DCA) 和临床影响曲线分析 (CIC) 评估模型的拟合优度、区分度、校准和临床影响，分别。
结果：我们纳入了 347 个案例，其中 243 个属于训练集，104 个属于验证集。我们筛选了 10 个变量作为影响结果的因素。通过多因素Logistic回归分析，初步确定了影响复阳结果的保护因素、危险因素和独立影响因素。我们构建了营养不良老年人中 COVID-19 复发的临床预测模型。Hosmer-Lemeshow检验得出χ ² =5.916，P =0.657；AUC为0.881；当阈值概率> 8%时，使用该模型来预测营养不良的老年人是否对COVID-19重新呈阳性比对所有患者实施干预计划更有益；当阈值>80%时，阳性估计值更接近实际病例数。
结论：该模型有助于早期识别营养不良的老年人中 COVID-19 复发的风险，促进早期临床决策和干预，并对改善患者预后具有重要意义。我们还期望进行更大规模、多中心的研究来进一步验证、完善和更新该模型。

关键词：营养不良，COVID-19，复阳，临床预测模型