Du Shenghua, Su Ning, Yu Zhaoxian, Li Junhong, Jiang Yingyi, Zeng Limeng, Hu Jinxing
Department of Nephrology, Guangzhou Chest Hospital, Guangzhou Medical University, Guangdong, China.
Department of Oncology, Guangzhou Chest Hospital, Guangzhou Medical University, Guangdong, China.
Front Med (Lausanne). 2024 May 30;11:1413541. doi: 10.3389/fmed.2024.1413541. eCollection 2024.
Currently, a scarcity of prognostic research exists that concentrates on patients with nephrotic syndrome (NS) who also have tuberculosis. The purpose of this study was to assess the in-hospital mortality status of NS patients with tuberculosis, identify crucial risk factors, and create a sturdy prognostic prediction model that can improve disease evaluation and guide clinical decision-making.
We utilized the Medical Information Mart for Intensive Care IV version 2.2 (MIMIC-IV v2.2) database to include 1,063 patients with NS complicated by TB infection. Confounding factors included demographics, vital signs, laboratory indicators, and comorbidities. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and the diagnostic experiment the receiver operating characteristic (ROC) curve analyses were used to select determinant variables. A nomogram was established by using a logistic regression model. The performance of the nomogram was tested and validated using the concordance index (C-index) of the ROC curve, calibration curves, internal cross-validation, and clinical decision curve analysis.
The cumulative in-hospital mortality rate for patients with NS and TB was 18.7%. A nomogram was created to predict in-hospital mortality, utilizing Alb, Bun, INR, HR, Abp, Resp., Glu, CVD, Sepsis-3, and AKI stage 7 days. The area under the curve of the receiver operating characteristic evaluation was 0.847 (0.812-0.881), with a calibration curve slope of 1.00 (0.83-1.17) and a mean absolute error of 0.013. The cross-validated C-index was 0.860. The decision curves indicated that the patients benefited from this model when the risk threshold was 0.1 and 0.81.
Our clinical prediction model nomogram demonstrated a good predictive ability for in-hospital mortality among patients with NS combined with TB. Therefore, it can aid clinicians in assessing the condition, judging prognosis, and making clinical decisions for such patients.
目前，针对同时患有肾病综合征（NS）和结核病的患者的预后研究较为匮乏。本研究的目的是评估NS合并结核病患者的院内死亡状况，识别关键危险因素，并创建一个强大的预后预测模型，以改善疾病评估并指导临床决策。
我们利用重症监护医学信息集市第四版2.2（MIMIC-IV v2.2）数据库纳入了1063例NS合并结核感染的患者。混杂因素包括人口统计学特征、生命体征、实验室指标和合并症。使用最小绝对收缩和选择算子（LASSO）回归以及诊断试验中的受试者工作特征（ROC）曲线分析来选择决定变量。通过逻辑回归模型建立列线图。使用ROC曲线的一致性指数（C指数）、校准曲线、内部交叉验证和临床决策曲线分析对列线图的性能进行测试和验证。
NS合并结核病患者的累积院内死亡率为18.7%。利用白蛋白（Alb）、血尿素氮（Bun）、国际标准化比值（INR）、心率（HR）、血压（Abp）、呼吸频率（Resp.）、血糖（Glu）、心血管疾病（CVD）、脓毒症-3以及急性肾损伤（AKI）7天分期创建了一个预测院内死亡的列线图。受试者工作特征评估曲线下面积为0.847（0.812 - 0.881），校准曲线斜率为1.00（0.83 - 1.17），平均绝对误差为0.013。交叉验证的C指数为0.860。决策曲线表明，当风险阈值为0.1和0.81时，患者可从该模型中获益。
我们的临床预测模型列线图对NS合并结核病患者的院内死亡率具有良好的预测能力。因此，它可以帮助临床医生评估此类患者的病情、判断预后并做出临床决策。
