使用常规测定的生物标志物（如糖类抗原19-9（CA19-9）和胆红素血清水平）来进行准确的风险预测模型对于胰腺癌的早期检测和预防低风险患者进行潜在的不必要的诊断测试具有重要意义。需要建立一个外部验证的模型，利用更大样本量的胰腺癌或良性周围乳头状疾病患者的CA19-9和胆红素血清水平来进行评估，以评估简便易得的血液生物标志物（糖类抗原19-9 [CA19-9]和胆红素）预测模型的区分度、校准度和临床效用，用于区分早期胰腺癌和良性周围乳头状疾病。 本诊断研究使用来自意大利、荷兰和英国4所学术医院2014年至2022年接受治疗的胰腺癌或良性周围乳头状疾病成年患者的数据。分析时间为2022年9月至2023年2月。 测定诊断和任何治疗干预开始前收集的血清CA19-9和胆红素水平。 分别评估预测模型和生物标志物的区分度（由曲线下面积[AUC]测量）、校准度和临床效用。 研究样本分为开发队列（249名患者，平均（SD）诊断年龄为67 [11]岁，112名[45%]女性）和验证队列（296名患者，平均（SD）诊断年龄为68 [12]岁，157名[53%]女性）。在外部验证中，预测模型对早期胰腺癌与良性周围乳头状疾病的AUC为0.89（95% CI，0.84-0.93），优于CA19-9（AUC差异[ΔAUC]，0.10；95% CI，0.06-0.14；P < .001）和胆红素（∆AUC，0.07；95% CI，0.02-0.12；P = .004）。在没有升高的肿瘤标志物水平（CA19-9 ＜ 37 U/mL）的患者亚组中，该模型的AUC为0.84（95% CI，0.77-0.92）。决策曲线分析表明，在风险阈值为30%时，基于预测模型进行活检等价于将活检程序率降低6%（95% CI，1%-11%），而不会错过患者的早期胰腺癌。 在这项诊断研究中，对于胰腺癌或良性周围乳头状疾病患者，一个易于应用的风险评分显示出了高准确性，可以区分早期胰腺癌和良性周围乳头状疾病。该模型可以用于评估新生物标志物的附加诊断和临床价值，并预防低风险患者进行潜在的不必要的侵入性诊断程序。

Accurate risk prediction models using routinely measured biomarkers-eg, carbohydrate antigen 19-9 (CA19-9) and bilirubin serum levels-for pancreatic cancer could facilitate early detection of pancreatic cancer and prevent potentially unnecessary diagnostic tests for patients at low risk. An externally validated model using CA19-9 and bilirubin serum levels in a larger cohort of patients with pancreatic cancer or benign periampullary diseases is needed.To assess the discrimination, calibration, and clinical utility of a prediction model using readily available blood biomarkers (carbohydrate antigen 19-9 [CA19-9] and bilirubin) to distinguish early-stage pancreatic cancer from benign periampullary diseases.This diagnostic study used data from 4 academic hospitals in Italy, the Netherlands, and the UK on adult patients with pancreatic cancer or benign periampullary disease treated from 2014 to 2022. Analyses were conducted from September 2022 to February 2023.Serum levels of CA19-9 and bilirubin from samples collected at diagnosis and before start of any medical intervention.Discrimination (measured by the area under the curve [AUC]), calibration, and clinical utility of the prediction model and the biomarkers, separately.The study sample comprised 249 patients in the development cohort (mean [SD] age at diagnosis, 67 [11] years; 112 [45%] female individuals), and 296 patients in the validation cohort (mean [SD] age at diagnosis, 68 [12] years; 157 [53%] female individuals). At external validation, the prediction model showed an AUC of 0.89 (95% CI, 0.84-0.93) for early-stage pancreatic cancer vs benign periampullary diseases, and outperformed CA19-9 (difference in AUC [ΔAUC], 0.10; 95% CI, 0.06-0.14; P < .001) and bilirubin (∆AUC, 0.07; 95% CI, 0.02-0.12; P = .004). In the subset of patients without elevated tumor marker levels (CA19-9 <37 U/mL), the model showed an AUC of 0.84 (95% CI, 0.77-0.92). At a risk threshold of 30%, decision curve analysis indicated that performing biopsies based on the prediction model was equivalent to reducing the biopsy procedure rate by 6% (95% CI, 1%-11%), without missing early-stage pancreatic cancer in patients.In this diagnostic study of patients with pancreatic cancer or benign periampullary diseases, an easily applicable risk score showed high accuracy for distinguishing early-stage pancreatic cancer from benign periampullary diseases. This model could be used to assess the added diagnostic and clinical value of novel biomarkers and prevent potentially unnecessary invasive diagnostic procedures for patients at low risk.