NATAP：用于肝纤维化筛查的 FIB-4 和 NAFLD 纤维化评分的 NAFLD 低

StephenW

NATAP：用于肝纤维化筛查的 FIB-4 和 NAFLD 纤维化评分的 NAFLD 低准确性
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附件 11 月 12 日星期六，凌晨 4:34（2 天前）


在本研究中，我们测试了 2 种最常用的无创评分 FIB-4 和 NFS 筛查肝硬度升高的能力，将其作为肝纤维化进展为失代偿和肝脏相关死亡率的主要预测因素。

我们的大型队列研究报告了很大比例的 FIB-4 或 NFS 假阴性受试者，以及大量假阳性受试者。基于这两个分数中的任何一个的筛查策略都会错过需要转诊的患者，并有过度诊断和无效转诊的风险。腰围显示为一种潜在的有用工具，可以从普通人群中识别出肝纤维化高风险患者。

超过三分之一的患者被 FIB-4 和 NFS 错误分类，在高危人群中比一般人群更严重。具体而言，使用 TE ≥ 8 kPa，所有受试者中共有 2% 至 4% 的 FIB-4/NFS 假阴性值可能会被遗漏。这一比例增加到高危人群的 8% 到 9%。在患有糖尿病、危险饮酒或使用 FIB-4 的年龄 <35 岁的患者中，假阴性的比例甚至更高。这很重要，因为筛查工具应该特别适用于高危患者的纤维化检测。尽管有大量假阴性，但 FIB-4 和 NFS 的 NPV 在一般人群队列中仍高于 90%。这可能会给人留下良好的测试表现以排除疾病的印象。然而，NPV 在低流行率人群中是一个误导性指标，因为即使抛硬币也会有很高的 NPV。如果流行率为 5.6%，就像这里介绍的一般人群一样，抛硬币将导致 NPV 为 94.4%。因此，如补充表 2 所示，95% 的 FIB-4 NPV 或 96% 的 NFS NPV 仅比抛硬币略好。因此，我们对建议将 FIB-4 或 NFS 作为筛选工具持谨慎态度。相反，我们建议继续寻找更敏感的测试。最佳筛查计划可能包括 3 个阶段：根据慢性肝病的危险因素进行预选，然后使用高度敏感的测试进行筛查以排除疾病，但具有足够的特异性以避免过度诊断。最后，随后将对那些筛查呈阳性的患者进行具有高度特异性的确认测试。
在我们的研究中，假阳性的比例也很高，为 28% 至 29%。对于 65 岁以上的参与者尤其如此，其中 FIB-4 经常高估纤维化的风险。过度诊断可能对健康受试者有害，并导致医疗资源的无用。

我们研究的一个有趣发现是腰围与肝脏硬度增加密切相关，并且在检测普通人群中 TE ≥ 8 kPa 的患者方面优于 FIB-4 和 NFS，但在高危人群中则不然。腰围易于测量，价格便宜，最近被证明是 ALD 和 NAFLD 患者全因死亡率的独立预测指标。

然而，其作为诊断和预后标志物的能力需要验证。
我们知道，我们的参考标准 LSM with TE 是肝纤维化和肝相关事件风险的替代指标。然而，TE ≥ 8 kPa 患者的结果评估需要很长的随访期。此外，肝活检在筛查环境中不可行，是不完善的金标准。

几项研究支持肝脏硬度作为低患病率人群肝纤维化的有用替代指标的适用性和有效性。

关于 TE 假阳性，值得注意的是炎症引起的 LSM 增加也会增加 FIB-4/NFS。此外，排除 ALT 高于 2 × ULN 或 BMI >35 的患者并没有改变我们的结果。本研究的另一个局限性是我们无法评估增强型肝纤维化测试在两步法中的作用，正如最近提出的那样。

目前欧美有多个项目（LiverScreen、Renown、Seal、Scarred liver project）正在评估中，旨在建立纤维化筛查的最佳策略。

来自这些联盟的数据将有助于确定肝纤维化检测和个性化转诊途径的最佳方法。
结论
总之，虽然一些研究报告了引入 NIT 后转诊减少，但我们的数据显示，在一般人群环境中，FIB-4 和 NFS 的假阴性和假阳性比例很高，在高危人群中甚至更高人口。因此，尽管 FIB-4 和 NFS 代表了朝着正确方向迈出的一步，但我们应该谨慎实施它们，而不是首先寻找更好的一线筛查工具和转诊途径。

StephenW

NATAP: NAFLD Low Accuracy of FIB-4 & NAFLD Fibrosis Score for Liver Fibrosis Screenng
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AttachmentsSat, Nov 12, 4:34 AM (2 days ago)


In the present study, we tested the ability of the 2 most used noninvasive scores, FIB-4 and NFS, for screening for elevated liver stiffness as the main predictive factor of liver fibrosis progression to decompensation and liver-related mortality.

Our large cohort study reports a significant proportion of false-negative subjects with FIB-4 or NFS, and a larger number of false-positives. A screening strategy based on either of these 2 scores would both miss patients that need referral and risk overdiagnosis and futile referrals. Waist circumference showed up as a potential useful tool to identify patients from the general population with high risk of liver fibrosis.

More than one-third of patients were misclassified by FIB-4 and NFS, worse in at-risk cohorts than the general population. Specifically, using TE ≥8 kPa, a total of 2% to 4% of all subjects had false-negative values of FIB-4/NFS and would have been missed. This proportion increased to 8% to 9% of the at-risk cohorts. In patients with diabetes, hazardous alcohol consumption, or age <35 years using FIB-4, the proportion of false-negatives was even higher. This is important, because screening tools should be especially suited for fibrosis detection in at-risk patients. Despite the high number of false-negatives, NPVs for FIB-4 and NFS were above 90% in the general population cohorts. This may give the impression of a good test performance to rule out disease. However, NPV is a misleading metric in low prevalence cohorts, because even a coin toss would have a high NPV. If the prevalence is 5.6%, as in the general population cohort presented here, flipping a coin would result in a NPV of 94.4%. Accordingly, the FIB-4 NPV of 95% or NFS NPV of 96% as seen in Supplementary Table 2 is only marginally better than a coin toss. We are therefore cautious of suggesting FIB-4 or NFS as screening tools. We instead suggest to continue searching for more sensitive tests. An optimal screening program will likely consist of 3 phases: preselection based on risk factors for chronic liver disease, followed by screening with a highly sensitive test to rule in disease, but with adequate specificity to avoid over-diagnosis. Finally, this would be followed by a confirmatory test with high specificity in those patients who screened positive.
The proportion of false-positives was also high in our study, at 28% to 29%. This was especially true for participants >65 years, where FIB-4 frequently overestimated the risk of fibrosis. Overdiagnosis is potentially harmful to healthy subjects and leads to futile use of health care resources.

An interesting finding of our study is that waist circumference was strongly associated with increased liver stiffness, and outperformed FIB-4 and NFS for detection of patients with TE ≥8 kPa in the general population, but not in at-risk cohorts. Waist circumference is easy to measure, cheap, and was recently shown to be an independent predictor of all-cause mortality in patients with ALD and NAFLD.

However, its ability as a diagnostic and prognostic marker needs validation.
We are aware that our reference standard, LSM with TE, is a surrogate for liver fibrosis and risk of liver-related events. However, outcome assessments in patients with TE ≥8 kPa require a very long follow-up period. Additionally, liver biopsy is not feasible in the screening setting and is an imperfect gold standard.

Several studies support the applicability and validity of liver stiffness as a useful surrogate for liver fibrosis in low prevalence population.

With respect to TE false-positives, it is worth noting that increased LSM due to inflammation would also raise FIB-4/NFS. Moreover, exclusion of patients with ALT above 2 × ULN or BMI >35 did not change our results. Another limitation of the present study is that we could not assess the Enhanced Liver Fibrosis Test’s role in a 2-step approach, as recently proposed.

Currently there are several projects under evaluation in Europe and the United States (LiverScreen, Renown, Seal, Scarred liver project) to establish the best strategy for fibrosis screening.

Data coming from these consortia will help define the best approach for liver fibrosis detection and personalized referral pathways.
Conclusion
In conclusion, although several studies have reported a reduction in referrals after introduction of NITs, our data show a significant percentage of false-negatives and false-positives with FIB-4 and NFS in the general population setting, even higher in the at-risk population. Therefore, although FIB-4 and NFS represent a step in the right direction, we should be cautious implementing them without first searching for better first-line screening tools and referral pathways.