单中心造血干细胞移植后代谢综合征临床资料分析和诊断预测模型构建

Analysis of Clinical Data and Construction of A Diagnostic Prediction Model for Metabolic Syndrome after Single-Center Hematopoietic Stem Cell Transplantation

目的:分析单中心血液病行造血干细胞移植(HSCT)患者的临床资料并构建代谢综合征(MS)诊断预测模型。方法:选择2015年7月至2022年9月在兰州大学第一医院进行HSCT的93例血液病患者,收集基本资料、移植情况和术后指标,比较分析移植后发生和未发生MS患者的临床资料。采用logistic回归模型分析移植后发生MS的影响因素,并构建独立影响因素影响下的HSCT-MS诊断预测模型,采用受试者工作特征曲线(ROC曲线)对模型进行评估。结果:93例HSCT患者中36例发生MS,57例未发生MS,HSCT-MS组移植前空腹血糖(FBG)水平、移植前病程和移植后胆红素水平与无HSCT-MS组比较,差异具有统计学意义(P<0.05)。将差异有统计学意义的临床指标进行多因素logistic回归分析,结果显示,移植前高FBG、移植前短病程和移植后高胆红素是HSCT-MS的独立影响因素,以此构建临床预测模型列线图。根据临床模型预测HSCT-MS发生的标准误0.048,ROC曲线下面积AUC=0.776,95%CI:0.683-0.869,根据约登指数最大时选出最佳临界点为0.58,灵敏度为0.694,特异度为0.772,具有一定准确性。结论:本研究通过临床资料分析构建了基于Logistic回归的HSCT-MS临床预测模型,且具有一定临床价值。

Objective: To analysis the clinical data of patients after single-center hematopoietic stem cell transplantation(HSCT) and construct a predictive model for metabolic syndrome(MS) diagnosis. Methods: Ninety-three hematology patients who underwent HSCT at the First Hospital of Lanzhou University from July 2015 to September 2022 were selected to collect basic data, transplantation status and postoperative data, the clinical characteristics of patients with and without MS after transplantation were compared and analyzed. Logistic regression model was used to analyze the influence fators of MS after transplantation, and a predictive model of HSCT-MS diagnosis was constructed under the influence of independent influence factors. The model was evaluated using the ceceiver operating characteristic curve(ROC curve). Results: Metabolic syndrome occurred in 36 of 93 HSCT patients and did not occur in 57. Compared with non-HSCT-MS group, HSCT-MS had significantly higher fasting blood glucose(FBG) levels before transplantation, shorter course before transplantation, and higher bilirubin levels after transplantation(P<0.05). The statistically significant clinical indicators were subjected to multi-factor logistic regression analysis, and the results showed that pre-transplant high FBG, pre-transplant short disease course and post-transplant high bilirubin were independent influence factors for HSCT-MS. The standard error of predicting the occurrence of HSCT-MS based on the clinical model was 0.048, the area under the curve AUC=0.776, 95% CI :0.683-0.869, the optimal threshold was 0.58 based on the Jorden index at maximum, the sensitivity was 0.694, and the specificity was 0.772, which has certain accuracy. Conclusion: A clinical prediction model for HSCT-MS based on logistic regression analysis is constructed through the analysis of clinical data, which has certain clinical value.