Analysis of Clinical Data and Construction of A Diagnostic Prediction Model for Metabolic Syndrome after Single-Center Hematopoietic Stem Cell Transplantation / 中国实验血液学杂志

ABSTRACT

UNLABELLED@#AbstractObjective: 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.