A hepatocellular carcinoma (HCC) prediction model (ASAP), including age, sex, and the biomarkers alpha-fetoprotein and prothrombin induced by vitamin K absence-II, showed potential clinical value in the early detection of HCC. We validated and updated the model in a real-world cohort and promoted its transferability to daily clinical practice. This retrospective cohort analysis included 1012 of the 2479 eligible patients aged 35 years or older undergoing surveillance for HCC. The data were extracted from the electronic medical records. Biomarker values within the test-to-diagnosis interval were used to validate the ASAP model. Due to its unsatisfactory calibration, three logistic regression models were constructed to recalibrate and update the model. Their discrimination, calibration, and clinical utility were compared. The performance statistics of the final updated model at several risk thresholds are presented. The outcomes of 855 non-HCC patients were further assessed during a median of 10.2 months of follow-up. Statistical analyses were performed using packages in R software. The ASAP model had superior discriminative performance in the validation cohort [C-statistic = 0.982, (95% confidence interval 0.972–0.992)] but significantly overestimated the risk of HCC (intercept − 3.243 and slope 1.192 in the calibration plot), reducing its clinical usefulness. Recalibration-in-the-large, which exhibited performance comparable to that of the refitted model revision, led to the retention of the excellent discrimination and substantial improvements in the calibration and clinical utility, achieving a sensitivity of 100% at the median prediction probability of the absence of HCC (1.3%). The probability threshold of 1.3% and the incidence of HCC in the cohort (15.5%) were used to stratify the patients into low-, medium-, and high-risk groups. The cumulative HCC incidences in the non-HCC patients significantly differed among the risk groups (log-rank test, p-value < 0.001). The 3-month, 6-month and 18-month cumulative incidences in the low-risk group were 0.6%, 0.9% and 0.9%, respectively. The ASAP model is an accurate tool for HCC risk estimation that requires recalibration before use in a new region because calibration varies with clinical environments. Additionally, rational risk stratification and risk-based management decision-making, e.g., 3-month follow-up recommendations for targeted individuals, helped improve HCC surveillance, which warrants assessment in larger cohorts.

中文翻译：

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用于识别和管理有肝细胞癌风险的患者的多变量预测模型的验证和更新

肝细胞癌 (HCC) 预测模型 (ASAP)，包括年龄、性别以及由维生素 K 缺失-II 诱导的生物标志物甲胎蛋白和凝血酶原，在 HCC 的早期检测中显示出潜在的临床价值。我们在真实世界的队列中验证并更新了该模型，并将其推广到日常临床实践中。这项回顾性队列分析包括 2479 名接受 HCC 监测的 35 岁或以上符合条件的患者中的 1012 名。数据是从电子病历中提取的。测试到诊断间隔内的生物标志物值用于验证 ASAP 模型。由于其校准不理想，构建了三个逻辑回归模型来重新校准和更新模型。比较了它们的辨别力、校准和临床效用。给出了最终更新模型在几个风险阈值下的性能统计数据。在中位 10.2 个月的随访期间，进一步评估了 855 名非 HCC 患者的结果。使用 R 软件中的软件包进行统计分析。ASAP 模型在验证队列中具有出色的判别性能 [C 统计量 = 0.982，（95% 置信区间 0.972–0.992）] 但显着高估了 HCC 的风险（校准图中的截距 - 3.243 和斜率 1.192），降低了其临床实用性。大规模重新校准，表现出与改装模型修订版相当的性能，导致保留了出色的辨别力，并在校准和临床效用方面取得了实质性改进，在不存在 HCC 的中值预测概率 (1.3%) 下实现 100% 的灵敏度。使用 1.3% 的概率阈值和队列中 HCC 的发生率 (15.5%) 将患者分为低、中和高风险组。非 HCC 患者的累积 HCC 发病率在风险组之间显着不同（对数秩检验，p 值 < 0.001）。低风险组 3 个月、6 个月和 18 个月的累积发生率分别为 0.6%、0.9% 和 0.9%。ASAP 模型是一种准确的 HCC 风险评估工具，在用于新区域之前需要重新校准，因为校准因临床环境而异。此外，合理的风险分层和基于风险的管理决策，例如针对目标个体的 3 个月随访建议，