Goals and Background: 

Using natural language processing to create a nonalcoholic fatty liver disease (NAFLD) cohort in primary care, we assessed advanced fibrosis risk with the Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS) and evaluated risk score agreement.

Materials and Methods: 

In this retrospective study of adults with radiographic evidence of hepatic steatosis, we calculated patient-level FIB-4 and NFS scores and categorized them by fibrosis risk. Risk category and risk score agreement was analyzed using weighted κ, Pearson correlation, and Bland-Altman analysis. A multinomial logistic regression model evaluated associations between clinical variables and discrepant FIB-4 and NFS results.

Results: 

Of the 767 patient cohorts, 71% had a FIB-4 or NFS score in the indeterminate-risk or high-risk category for fibrosis. Risk categories disagreed in 43%, and scores would have resulted in different clinical decisions in 30% of the sample. The weighted κ statistic for risk category agreement was 0.41 [95% confidence interval (CI): 0.36-0.46] and the Pearson correlation coefficient for log FIB-4 and NFS was 0.66 (95% CI: 0.62-0.70). The multinomial logistic regression analysis identified black race (odds ratio=2.64, 95% CI: 1.84-3.78) and hemoglobin A1c (odds ratio=1.37, 95% CI: 1.23-1.52) with higher odds of having an NFS risk category exceeding FIB-4.

Conclusions: 

In a primary care NAFLD cohort, many patients had elevated FIB-4 and NFS risk scores and these risk categories were often in disagreement. The choice between FIB-4 and NFS for fibrosis risk assessment can impact clinical decision-making and may contribute to disparities of care.

中文翻译：

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在初级保健 NAFLD 队列中识别有纤维化风险的患者

目标和背景： 

我们使用自然语言处理在初级保健中创建非酒精性脂肪性肝病 (NAFLD) 队列，使用 Fibrosis-4 指数 (FIB-4) 和 NAFLD 纤维化评分 (NFS) 评估晚期纤维化风险，并评估风险评分一致性。

材料和方法： 

在这项针对有肝脂肪变性影像学证据的成人的回顾性研究中，我们计算了患者水平的 FIB-4 和NFS评分，并根据纤维化风险对其进行分类。使用加权 κ、Pearson 相关性和 Bland-Altman 分析分析风险类别和风险评分一致性。多项逻辑回归模型评估了临床变量与不一致的 FIB-4 和NFS结果之间的关联。

结果： 

在 767 名患者队列中，71% 的 FIB-4 或NFS评分属于纤维化的不确定风险或高风险类别。43% 的风险类别不一致，而分数会导致 30% 的样本做出不同的临床决策。风险类别一致性的加权 κ 统计量为 0.41 [95% 置信区间 (CI)：0.36-0.46]，log FIB-4 和NFS的 Pearson 相关系数为 0.66（95% CI：0.62-0.70）。多项逻辑回归分析确定黑人种族（比值比=2.64，95% CI：1.84-3.78）和血红蛋白 A1c（比值比=1.37，95% CI：1.23-1.52）具有更高的 NFS 风险类别超过 FIB的比值-4。

结论： 

在初级保健 NAFLD 队列中，许多患者的 FIB-4 和NFS风险评分升高，并且这些风险类别通常存在分歧。在 FIB-4 和NFS之间进行纤维化风险评估的选择会影响临床决策，并可能导致护理差异。