目的:作为中重度急性胰腺炎(MSAP)和重症急性胰腺炎(SAP)的严重并发症,感染性胰腺坏死(IPN)可导致介入治疗过程延长。设计用于识别此类患者的大多数预测模型是复杂的或缺乏验证。本研究的目的是建立MSAP和SAP中IPN早期检测的预测模型。
方法:本研究共纳入594例MSAP或SAP患者。为了减少维度,最小绝对收缩和选择算子回归分析用于筛选潜在的预测变量,然后使用逻辑回归分析构建列线图.接收机工作特性(ROC)曲线,校正曲线,和决策曲线分析(DCA)用于评价歧视,准确度,模型的临床疗效。还获得了外部数据以进一步验证所构建的模型。
结果:训练中有476、118和82名患者,内部验证,和外部验证队列,分别。血小板计数,血细胞比容,白蛋白/球蛋白,急性胰腺炎的严重程度,和改良CT严重度指数评分是预测MSAP和SAPIPN的独立因素。ROC曲线下面积分别为0.923、0.940和0.817,在三组中。实际概率和预测概率之间有很好的一致性。DCA显示出优异的临床效用。
结论:所构建的列线图是一个简单可行的模型,在MSAP和SAP中IPN的临床决策中具有良好的临床预测价值和有效性。
OBJECTIVE: As a serious complication of moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP), infected pancreatic necrosis (IPN) can lead to a prolonged course of interventional therapy. Most predictive models designed to identify such patients are complex or lack validation. The aim of this study was to develop a predictive model for the early detection of IPN in MSAP and SAP.
METHODS: A total of 594 patients with MSAP or SAP were included in the study. To reduce dimensionality, least absolute shrinkage and selection operator regression analysis was used to screen potential predictive variables, a nomogram was then constructed using logistic regression analysis. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, accuracy, and clinical efficacy of the model. External data were also obtained to further validate the constructed model.
RESULTS: There were 476, 118, and 82 patients in the training, internal validation, and external validation cohorts, respectively. Platelet count, hematocrit, albumin/globulin, severity of acute pancreatitis, and modified computed tomography severity index score were independent factors for predicting IPN in MSAP and SAP. The area under the ROC curves were 0.923, 0.940, and 0.817, respectively, in the three groups. There was a good consistency between the actual probabilities and the predicted probabilities. DCA revealed excellent clinical utility.
CONCLUSIONS: The constructed nomogram is a simple and feasible model that has good clinical predictive value and efficacy in clinical decision-making for IPN in MSAP and SAP.