多因素评分联合生物标志物对急性重症胰腺炎的早期预测价值

Multifactorial scoring combined with biomarkers for early prediction of severe acute pancreatitis

摘要

目的：探讨多因素临床评分系统联合生物标志物在急性重症胰腺炎（SAP）早期预测中的价值。方法：收集2019年1月—2025年8月新疆医科大学第一附属医院收治的293例急性胰腺炎（AP）患者，按病情严重程度分为急性轻症胰腺炎（MAP，n=194）和急性重症胰腺炎（SAP，n=99）。收集患者发病48 h内的相关实验室指标，计算生物标志物水平[白蛋白-胆红素评分（ALBI）、血浆致动脉粥样硬化指数（AIP）、中性粒细胞/淋巴细胞比率（NLR）、血小板/淋巴细胞比率（PLR）、C反应蛋白/白蛋白比值（CAR）等]及临床评分[BISAP评分、Ranson评分、急性生理学与慢性健康状况（APACHE）Ⅱ评分和MCTSI评分等]。采用单因素与多因素Logistic回归分析筛选SAP的独立危险因素，绘制ROC曲线评估各指标及联合模型的预测效能，并利用限制性立方样条（RCS）模型分析指标与SAP发生风险的非线性关系。结果：Logistic回归分析表明，白细胞计数（WBC）、中性粒细胞计数（NEUT）、血红蛋白（HGB）、红细胞压积（HCT）、总胆红素（TBIL）、尿素氮（BUN）、血清肌酐（CRE）、C-反应蛋白（CRP）、NLR、ALBI及CAR均为SAP发生的危险因素（P均<0.05），其中ALBI（OR=2.98，95% CI：1.70~2.54，P<0.001）、NEUT（OR=1.39，95% CI：1.28~1.52，P<0.001）、CAR（OR=1.32，95% CI：1.12~1.56，P<0.001）和NLR（OR=1.11，95% CI：1.07~1.16，P<0.001）与SAP发生关联的强度大。ROC曲线分析显示，BISAP评分的预测效能最佳（AUC=0.972），其次为Ranson评分（AUC=0.955）。在生物标志物中，NLR（AUC=0.757）表现突出。联合模型分析表明，生物标志物与评分系统联合可显著提升预测效能，其中NLR联合BISAP评分的AUC达到0.980。RCS分析显示，NLR、红细胞压积（HCT）、CRP、CAR、AIP等指标与SAP风险存在非线性关系（P均<0.05），其中NLR和CRP在高值区间SAP发生风险急剧上升，提示炎症反应水平与疾病严重程度密切相关。结论：BISAP评分在早期识别SAP方面表现出最优的预测性能。联合关键生物标志物（如NLR）可进一步提升预测能力，有助于实现更精准的早期风险分层和临床干预。

Abstract

Objective: To explore the value of a multi-factor clinical scoring system combined with biomarkers in the early prediction of acute severe pancreatitis (SAP). Methods: A retrospective study was conducted. Data of 293 patients with acute pancreatitis (AP) admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2019 to August 2025 were collected. The patients were divided into acute mild pancreatitis (n=194, MAP group) and acute severe pancreatitis (n=99, SAP group) based on the severity of the disease. Relevant laboratory indicators within 48 hours of onset were collected, and biomarker levels [albumin-bilirubin score (ALBI), plasma atherogenic index (AIP), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), C-reactive protein/albumin ratio (CAR), etc.] and clinical scores [BISAP score, Ranson score, acute physiology and chronic health status (APACHE) Ⅱ score, and MCTSI score, etc.] were calculated. Univariate and multivariate Logistic regression analyses were used to screen independent risk factors for SAP, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of each indicator and the combined model. The non-linear relationship between the indicators and SAP risk was analyzed using the restricted cubic spline (RCS) model. Results: Logistic regression analysis showed that white blood cell count (WBC), neutrophil count (NEUT), hemoglobin (HGB), hematocrit (HCT), total bilirubin (TBIL), urea nitrogen (BUN), serum creatinine (CRE), C-reactive protein (CRP), NLR, ALBI, and CAR were all risk factors for SAP (P<0.05), among which ALBI (OR=2.98, 95% CI: 1.70~2.54, P<0.001), NEUT (OR=1.39, 95% CI: 1.28~1.52, P<0.001), CAR (OR=1.32, 95% CI: 1.12~1.56, P<0.001), and NLR (OR=1.11, 95% CI: 1.07~1.16, P<0.001) had the strongest association with SAP risk. ROC curve analysis showed that the BISAP score had the best predictive efficacy (AUC=0.972), followed by the Ranson score (AUC=0.955). Among the biomarkers, NLR (AUC=0.757) performed particularly well. The combined model analysis indicated that the combination of biomarkers and the scoring system could significantly improve the predictive efficacy, with the AUC of NLR combined with BISAP score reaching 0.980. RCS analysis showed that NLR, hematocrit (HCT), CRP, CAR, AIP, etc. had non-linear relationships with SAP risk (P<0.05), among which NLR and CRP showed a sharp increase in SAP risk in the high-value range, suggesting that the level of inflammatory response is closely related to the severity of the disease. Conclusion: The BISAP score shows the optimal predictive performance in the early identification of SAP. The combination of key biomarkers (such as NLR) can further improve the predictive ability and help achieve more accurate early risk stratification and clinical intervention.

关键词

重症急性胰腺炎 / 多因素评分 / 生物标志物 / 预测

Key words

Severe acute pancreatitis / Multi-factor scoring / Biomarkers / Prediction

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