Zou Yue, Li Kunpeng, Geng Ping
Department of Emergency, Northern Jiangsu People's Hospital, No. 98 Nantong West Road, Yangzhou 225001, Jiangsu Province, China.
Pancreas. 2025 Jun 25. doi: 10.1097/MPA.0000000000002526.
To investigate the contributing factors for the development of systemic inflammatory response syndrome (SIRS) in acute pancreatitis (AP) patients and subsequently develop a novel nomogram prediction model.
A multivariate logistic regression analysis was conducted to determine independent predictors of SIRS, where the variables were chosen based on statistical significance from univariate analysis. Based on their presence, 238 AP patients were grouped into non-sIRS (n=170) and sIRS (n=68). Logistic regression analysis identified independent predictors of sIRS complications. We then developed a visual nomogram prediction model alongside a logistic regression model. The model's predictive power cut-off was determined by receiver operating characteristic (ROC) curve analysis, providing sensitivity, specificity, and predictive accuracy.
The study found that in the cohort of acute pancreatitis (AP) patients, systemic inflammatory response syndrome (SIRS) incidence was 28.6%. From our analysis, we determined that red blood cell distribution width (RDW), fibrinogen (FIB), amylase (AMY), blood glucose (Glu), and lactate dehydrogenase (LDH) were independent risk factors for SIRS. Additionally, we calculated the area under the ROC curve (AUC) for our prediction model of SIRS reached 0.816, which exceeded the AUCs of the individual risk indicators (RDW, FIB, AMY, Glu, LDH) and the bedside index of severity in acute pancreatitis (BISAP) score. In addition, we conducted a correlation analysis to validate the relationships among the predictive factors and to eliminate possible multicollinearity. The calibration curve plot showed that the nomogram agreed well between the predicted SIRS and actual risks. Finally, the clinical decision curve for our model also indicated its clinical utility by guiding decision-making for timely interventions at a threshold probability range of 0.4 to 1.
The model predicted non-SIRS with a critical value ≥0.332, a sensitivity of 71.3% and specificity of 87.1%, and a Kappa value of 0.56. These results indicate that this prediction model is based on admission data, with recommended additional validation assessments at multiple time points (e.g., 24, 48, and 72 h) to characterize the progression of SIR's risk fully. Overall, this nomogram prediction model provides an efficient and simple means to predict SIRS for patients with AP.
探讨急性胰腺炎（AP）患者发生全身炎症反应综合征（SIRS）的相关因素，并建立一种新的列线图预测模型。
进行多因素逻辑回归分析以确定SIRS的独立预测因素，这些变量是根据单因素分析的统计学意义选择的。根据是否存在SIRS，将238例AP患者分为非SIRS组（n = 170）和SIRS组（n = 68）。逻辑回归分析确定了SIRS并发症的独立预测因素。然后，我们开发了一个可视化列线图预测模型以及一个逻辑回归模型。通过受试者工作特征（ROC）曲线分析确定模型的预测能力截断值，提供敏感性、特异性和预测准确性。
研究发现，在急性胰腺炎（AP）患者队列中，全身炎症反应综合征（SIRS）的发生率为28.6%。通过分析，我们确定红细胞分布宽度（RDW）、纤维蛋白原（FIB）、淀粉酶（AMY）、血糖（Glu）和乳酸脱氢酶（LDH）是SIRS的独立危险因素。此外，我们计算出SIRS预测模型的ROC曲线下面积（AUC）达到0.816，超过了各单个风险指标（RDW、FIB、AMY、Glu、LDH）和急性胰腺炎严重程度床边指数（BISAP）评分的AUC。此外，我们进行了相关性分析，以验证预测因素之间的关系并消除可能的多重共线性。校准曲线显示，列线图预测的SIRS与实际风险之间吻合良好。最后，我们模型的临床决策曲线也表明了其临床实用性，即在阈值概率范围为0.4至1时指导及时干预的决策。
该模型预测非SIRS的临界值≥0.33²，敏感性为71.3%，特异性为87.1%，Kappa值为0.56。这些结果表明，该预测模型基于入院数据，建议在多个时间点（如24、48和72小时）进行额外的验证评估，以全面描述SIR风险的进展。总体而言，该列线图预测模型为预测AP患者的SIRS提供了一种高效且简单的方法。
