降钙素原诊断感染性胰腺坏死：系统评价与荟萃分析。
完整摘要 · 中文译文
背景：感染性胰腺坏死（IPN）是急性胰腺炎（AP）最严重的局部并发症之一，并在驱动AP相关死亡中起核心作用。降钙素原（PCT）是一种优先对细菌感染产生反应的血清生物标志物，在区分感染性坏死与无菌性坏死方面具有理论上的前景。然而，此前的系统评价受限于样本量小、亚组分析浅显以及缺乏正式的证据分级。目前尚缺少以IPN为临床终点进行专门更新的综合评估。 目的：本荟萃分析主要旨在评估PCT检测AP患者IPN的诊断效能。对重症急性胰腺炎（SAP）的预测作为次要分析进行评估，对PCT指导抗生素治疗的证据作为探索性分析进行总结。 方法：检索PubMed、Embase和Cochrane Library中评估PCT用于诊断IPN（主要目标）、预测重症急性胰腺炎（SAP；次要分析）或指导AP患者抗生素使用的原始研究。通过双变量随机效应模型计算汇总灵敏度、特异度、似然比和诊断比值比。绘制层次综合受试者工作特征（HSROC）曲线。预先指定的亚组分析涵盖PCT截断值、采样时间、研究设计和参考标准。使用QUADAS-2进行偏倚评估；证据确定性采用GRADE-DTA框架进行评级。 结果：39项研究符合纳入标准：16项关于PCT用于 IPN诊断（2156例患者；512例IPN病例），20项关于PCT预测SAP的研究（3386例患者；934例SAP病例），以及3项关于PCT指导抗生素治疗的随机对照试验。对于IPN诊断，合并敏感性为0.82（95% CI：0.75-0.87），合并特异性为0.84（95% CI：0.78-0.89），曲线下面积（AUC）为0.90（95% CI：0.87-0.92）；GRADE证据确定性为低。对于SAP预测，合并敏感性为0.83，特异性为0.76，AUC为0.87；证据确定性为低。1.0-3.5 ng/mL的截断值在敏感性和特异性之间取得了最佳平衡（AUC 0.92）。连续动态监测（AUC 0.93）大幅优于单次入院测量（AUC 0.85）。 结论：PCT在识别IPN方面总体表现出良好能力，可作为IPN风险分层的辅助生物标志物纳入应用。1.0-3.5 ng/mL的截断值范围及连续监测可能有助于临床风险分层。这些与截断值相关的发现需要在具有预设截断值和标准化判定参考标准的前瞻性多中心研究中进行外部验证，该参考标准应整合临床、影像学、微生物学及操作证据，并在临床实施引流或坏死组织清除术时纳入培养确认。
完整摘要 · 英文原文
BACKGROUND: Infected pancreatic necrosis (IPN) ranks among the most severe local complications of acute pancreatitis (AP) and plays a central role in driving AP-related death. Procalcitonin (PCT), a serum biomarker that responds preferentially to bacterial infection, has theoretical promise for differentiating infected from sterile necrosis. Earlier systematic reviews, though, are limited by small sample sizes, shallow subgroup analysis, and a lack of formal evidence grading. An updated synthesis that focuses specifically on IPN as the clinical endpoint is missing. AIM: This meta-analysis primarily aimed to evaluate the diagnostic performance of PCT for detecting IPN in patients with AP. Prediction of severe acute pancreatitis (SAP) was assessed as a secondary analysis, and evidence on PCT-guided antibiotic therapy was summarized as an exploratory analysis. METHODS: PubMed, Embase, and the Cochrane Library were searched for original studies that assessed PCT for diagnosing IPN (primary objective), predicting severe AP (SAP; secondary analysis), or guiding antibiotic use in AP patients. Pooled sensitivity, specificity, likelihood ratios, and diagnostic odds ratios were calculated through a bivariate random-effects model. Hierarchical summary receiver operating characteristic (HSROC) curves were plotted. Pre-specified subgroup analyses covered PCT cutoff value, sampling time, study design, and reference standard. QUADAS-2 was used for bias assessment; evidence certainty was rated with the GRADE-DTA framework. RESULTS: Thirty-nine studies met inclusion criteria: 16 on PCT for IPN diagnosis (2,156 patients; 512 IPN cases), 20 on PCT for SAP prediction (3,386 patients; 934 SAP cases), and 3 randomized controlled trials on PCT-guided antibiotic therapy. For IPN diagnosis, pooled sensitivity was 0.82 (95% CI: 0.75-0.87), pooled specificity 0.84 (95% CI: 0.78-0.89), and area under the curve (AUC) 0.90 (95% CI: 0.87-0.92); GRADE certainty was low. For SAP prediction, pooled sensitivity was 0.83, specificity 0.76, AUC 0.87; evidence certainty was low. A cutoff of 1.0-3.5 ng/mL gave the best trade-off between sensitivity and specificity (AUC 0.92). Serial dynamic monitoring (AUC 0.93) outperformed single admission measurement (AUC 0.85) by a wide margin. CONCLUSIONS: PCT shows good overall ability to identify IPN and may be incorporated as an adjunctive biomarker for IPN risk stratification. A cutoff range of 1.0-3.5 ng/mL and serial monitoring may help clinical risk stratification. These cutoff-related findings require external validation in prospective multicenter studies with pre-specified cutoffs and standardized adjudicated reference criteria integrating clinical, radiologic, microbiologic, and procedural evidence, with culture confirmation incorporated when drainage or necrosectomy is clinically performed.