Gu Kaier, Wang Qianchun
Medical Intensive Care Unit, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang Province, People's Republic of China.
Maternity and Child Health Care Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang Province, People's Republic of China.
J Inflamm Res. 2024 Nov 8;17:8513-8530. doi: 10.2147/JIR.S489044. eCollection 2024.
The objective of this study was to create a predictive model for the onset of persistent organ failure (POF) in individuals suffering from acute biliary pancreatitis (ABP) by utilizing indicators observed within 24 hours of hospital admission. Early detection of high-risk POF patients is crucial for clinical decision-making.
Clinical data and laboratory indicators within 24 hours of admission from ABP patients diagnosed at The First Affiliated Hospital of Wenzhou Medical University between January 1, 2016, and January 1, 2024 were collected and retrospectively analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression (stepwise regression) methods were employed to identify variables for constructing the prediction model. The prediction model's performance was evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). It was compared with other scoring systems such as SIRS, BISAP, APACHE II, CTSI, and MCTSI. Additionally, a web-based calculator was created to simplify the calculation process.
Out of 324 ABP patients, 25 developed POF. Initial screening identified 18 variables; through LASSO regression and multivariable logistic regression analysis, five variables including BMI, Hb, ALB, Ca, and LIP were determined as independent predictors of POF. According to these factors to build prediction model, draw the nomogram. The AUC's receiver operating characteristic curve analysis demonstrated a significantly higher value in comparison to other scoring systems. Calibration curve and DCA show that the established model to predict the accuracy of POF is higher, clinical decision of net benefit is also higher. A network calculator utilizing this predictive model was developed.
A predictive model incorporating five risk indicators has been established exhibiting high discriminatory power and accuracy which aids in early identification of ABP patients at risk for developing POF. This holds significant value in guiding clinical decision-making.
本研究的目的是通过利用入院24小时内观察到的指标,为急性胆源性胰腺炎(ABP)患者持续性器官衰竭(POF)的发生创建一个预测模型。早期发现高危POF患者对于临床决策至关重要。
收集温州医科大学附属第一医院2016年1月1日至2024年1月1日诊断的ABP患者入院24小时内的临床资料和实验室指标,并进行回顾性分析。采用最小绝对收缩和选择算子(LASSO)回归和多变量逻辑回归(逐步回归)方法来识别构建预测模型的变量。使用曲线下面积(AUC)、校准曲线和决策曲线分析(DCA)评估预测模型的性能。将其与其他评分系统如全身炎症反应综合征(SIRS)、床边急性胰腺炎严重程度评分(BISAP)、急性生理与慢性健康状况评分系统II(APACHE II)、计算机断层扫描严重指数(CTSI)和改良CTSI(MCTSI)进行比较。此外,创建了一个基于网络的计算器以简化计算过程。
在324例ABP患者中,25例发生POF。初步筛选确定了18个变量;通过LASSO回归和多变量逻辑回归分析,确定体重指数(BMI)、血红蛋白(Hb)、白蛋白(ALB)、钙(Ca)和脂肪酶(LIP)这五个变量为POF的独立预测因素。根据这些因素构建预测模型,绘制列线图。AUC的受试者工作特征曲线分析显示,与其他评分系统相比,其值显著更高。校准曲线和DCA表明,所建立的预测POF的模型准确性更高,临床决策的净效益也更高。开发了一个利用该预测模型的网络计算器。
已建立一个包含五个风险指标的预测模型,该模型具有较高的辨别力和准确性,有助于早期识别有发生POF风险的ABP患者。这在指导临床决策方面具有重要价值。