■肝细胞癌(HCC)预测模型可以告知HCC筛查的临床决策,前提是其预测是稳健的。我们对英国肝硬化患者和HCV病毒学治愈的6个HCC预测模型进行了外部验证。
■从苏格兰HCV临床数据库(N=2,139)和已批准的优化丙型肝炎病毒治疗药物(STOP-HCV)研究(N=606)中确定了肝硬化和治愈的HCV患者。我们计算了4个竞争性非遗传HCC预测模型的患者值,加2个遗传模型(仅用于STOP-HCV队列)。随访在实现持续病毒学应答(SVR)之日开始。HCC诊断是通过与全国癌症的联系来确定的,住院治疗,和死亡率登记册。我们比较了预测模型之间的区分和校准措施。
■平均随访时间为3.4-3.9年,观察到118例(苏格兰)和40例(STOP-HCV)事件的HCC。年龄-男性-ALBI-血小板计数评分(aMAP)模型显示出最佳的区分;例如,苏格兰队列的一致性指数(C指数)为0.77(95%CI0.73-0.81).然而,对于所有型号,歧视因队列(苏格兰队列更好)和年龄(年轻患者更好)而异.此外,基因模型在HCV基因型3的患者中表现更好.在苏格兰和STOP-HCV队列中,观察到的3年HCC风险为3.3%(95%CI2.6-4.2)和5.1%(3.5-7.0%),分别。这些与aMAP最接近,在苏格兰和STOP-HCV队列中,平均预测的3年风险分别为3.6%和5.0%,分别。
■aMAP在辨别和校准方面都是表现最好的模型,因此,应作为竞争对手车型超越的基准。这项研究强调了肝硬化和治愈的HCV患者的“现实世界”风险分层的机会。然而,需要辅助研究来帮助将HCC风险预测转化为HCC筛查决策.
■患有肝硬化和治愈的HCV的患者患肝癌的风险很高,尽管不同患者的风险差异很大。风险计算器工具可以提醒临床医生高危患者,从而影响决策。在这项研究中,我们在2,500多名肝硬化和治愈的HCV患者中测试了6种风险计算器的性能。我们表明,一些风险计算器比其他风险计算器好得多。总的来说,我们发现\'aMAP\'计算器工作得最好,但是需要更多的工作来将预测转化为临床决策。
UNASSIGNED: Hepatocellular carcinoma (HCC) prediction models can inform clinical decisions about HCC screening provided their predictions are robust. We conducted an external validation of 6 HCC prediction models for UK patients with cirrhosis and a HCV virological cure.
UNASSIGNED: Patients with cirrhosis and cured HCV were identified from the Scotland HCV clinical database (N = 2,139) and the STratified medicine to Optimise Treatment of Hepatitis C Virus (STOP-HCV) study (N = 606). We calculated patient values for 4 competing non-genetic HCC prediction models, plus 2 genetic models (for the STOP-HCV cohort only). Follow-up began at the date of sustained virological response (SVR) achievement. HCC diagnoses were identified through linkage to nation-wide cancer, hospitalisation, and mortality registries. We compared discrimination and calibration measures between prediction models.
UNASSIGNED: Mean follow-up was 3.4-3.9 years, with 118 (Scotland) and 40 (STOP-HCV) incident HCCs observed. The age-male sex-ALBI-platelet count score (aMAP) model showed the best discrimination; for example, the Concordance index (C-index) in the Scottish cohort was 0.77 (95% CI 0.73-0.81). However, for all models, discrimination varied by cohort (being better for the Scottish cohort) and by age (being better for younger patients). In addition, genetic models performed better in patients with HCV genotype 3. The observed 3-year HCC risk was 3.3% (95% CI 2.6-4.2) and 5.1% (3.5-7.0%) in the Scottish and STOP-HCV cohorts, respectively. These were most closely matched by aMAP, in which the mean predicted 3-year risk was 3.6% and 5.0% in the Scottish and STOP-HCV cohorts, respectively.
UNASSIGNED: aMAP was the best-performing model in terms of both discrimination and calibration and, therefore, should be used as a benchmark for rival models to surpass. This study underlines the opportunity for \'real-world\' risk stratification in patients with cirrhosis and cured HCV. However, auxiliary research is needed to help translate an HCC risk prediction into an HCC-screening decision.
UNASSIGNED: Patients with cirrhosis and cured HCV are at high risk of developing liver cancer, although the risk varies substantially from one patient to the next. Risk calculator tools can alert clinicians to patients at high risk and thereby influence decision-making. In this study, we tested the performance of 6 risk calculators in more than 2,500 patients with cirrhosis and cured HCV. We show that some risk calculators are considerably better than others. Overall, we found that the \'aMAP\' calculator worked the best, but more work is needed to convert predictions into clinical decisions.