Ali Hassam, Moond Vishali, Vikash Fnu, Dahiya Dushyant Singh, Gangwani Manesh Kumar, Sohail Amir Humza, Chang Amy, Liu Jinye, Hayat Umar, Patel Pratik, Khalaf Mohamed, Adler Douglas G
Department of Gastroenterology, Hepatology & Nutrition, ECU Health Medical Center/Brody School of Medicine, Greenville, NC, 27834, USA.
Department of Internal Medicine, Saint Peter's University Hospital/Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA.
Pancreatology. 2024 Dec;24(8):1213-1218. doi: 10.1016/j.pan.2024.10.010. Epub 2024 Oct 22.
BACKGROUND/OBJECTIVES: Predicting inpatient mortality for acute pancreatitis (AP) patients in the ICU is crucial for optimal treatment planning. This study aims to develop a concise risk score model for this purpose, enhancing the predictability and management of AP in ICU settings.
We included 380 patients in our training set. Twenty-seven variables were retrospectively collected, and predictive variables were selected using LASSO penalized regression and refined through backward elimination multivariate models. Effect sizes were used to create the final model to predict 7 and 30-day mortality among AP patients admitted to the ICU.
Of 380 patients, the mortality rate was 23.2 %. The final model included five predictors: INR, Albumin, Lactic Acid, BUN, and Bilirubin. The 5-fold cross-validated mean AUC was 0.93 (SD: 0.048) for 7-day mortality and 0.84 (SD: 0.033) for 30-day mortality, with a sensitivity of 77 % and specificity of 74 %. The risk score outperformed BISAP (AUC: 0.60) and APACHE-II (AUC: 0.76) in predicting mortality.
Our model offers a convenient tool using commonly available laboratory results to predict mortality among AP patients, with potential applicability in both ICU settings.
背景/目的：预测重症监护病房（ICU）中急性胰腺炎（AP）患者的住院死亡率对于优化治疗方案至关重要。本研究旨在为此开发一个简洁的风险评分模型，以提高ICU环境中AP的预测能力和管理水平。
我们的训练集纳入了380例患者。回顾性收集了27个变量，使用套索惩罚回归选择预测变量，并通过向后消除多元模型进行优化。使用效应量创建最终模型，以预测入住ICU的AP患者的7天和30天死亡率。
380例患者中，死亡率为23.2%。最终模型包括五个预测因子：国际标准化比值（INR）、白蛋白、乳酸、血尿素氮（BUN）和胆红素。7天死亡率的5倍交叉验证平均曲线下面积（AUC）为0.93（标准差：0.048），30天死亡率为0.84（标准差：0.033），敏感性为77%，特异性为74%。在预测死亡率方面，该风险评分优于床边指数（BISAP）（AUC：0.60）和急性生理与慢性健康状况评分系统II（APACHE-II）（AUC：0.76）。
我们的模型提供了一种便捷工具，利用常见的实验室检查结果预测AP患者的死亡率，在ICU环境中具有潜在的适用性。
