Medical School, University of Western Australia, Nedlands, Western Australia, Australia.
Department of Hepatology, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia.
Hepatology. 2023 Oct 1;78(4):1240-1251. doi: 10.1097/HEP.0000000000000356. Epub 2023 Mar 31.
Management of NAFLD involves noninvasive prediction of fibrosis, which is a surrogate for patient outcomes. We aimed to develop and validate a model predictive of liver-related events (LREs) of decompensation and/or HCC and compare its accuracy with fibrosis models.
Patients with NAFLD from Australia and Spain who were followed for up to 28 years formed derivation (n = 584) and validation (n = 477) cohorts. Competing risk regression and information criteria were used for model development. Accuracy was compared with fibrosis models using time-dependent AUC analysis. During follow-up, LREs occurred in 52 (9%) and 11 (2.3%) patients in derivation and validation cohorts, respectively. Age, type 2 diabetes, albumin, bilirubin, platelet count, and international normalized ratio were independent predictors of LRE and were combined into a model [NAFLD outcomes score (NOS)]. The NOS model calibrated well [calibration slope, 0.99 (derivation), 0.98 (validation)] with excellent overall performance [integrated Brier score, 0.07 (derivation) and 0.01 (validation)]. A cutoff ≥1.3 identified subjects at a higher risk of LRE, (sub-HR 24.6, p < 0.001, 5-year cumulative incidence 38% vs 1.0%, respectively). The predictive accuracy at 5 and 10 years was excellent in both derivation (time-dependent AUC,0.92 and 0.90, respectively) and validation cohorts (time-dependent AUC,0.80 and 0.82, respectively). The NOS was more accurate than the fibrosis-4 or NAFLD fibrosis score for predicting LREs at 5 and 10 years ( p < 0.001).
The NOS model consists of readily available measures and has greater accuracy in predicting outcomes in patients with NAFLD than existing fibrosis models.
非酒精性脂肪性肝病（NAFLD）的管理涉及纤维化的无创预测，纤维化是患者预后的替代指标。我们旨在开发和验证一种可预测肝功能失代偿和/或 HCC 相关不良事件（LREs）的模型，并比较其与纤维化模型的准确性。
来自澳大利亚和西班牙的 NAFLD 患者在 28 年的随访中形成了推导（n=584）和验证（n=477）队列。使用竞争风险回归和信息标准进行模型开发。使用时间依赖性 AUC 分析比较与纤维化模型的准确性。在随访期间，推导和验证队列中分别有 52（9%）和 11（2.3%）例患者发生 LREs。年龄、2 型糖尿病、白蛋白、胆红素、血小板计数和国际标准化比值是 LRE 的独立预测因素，并被组合成一个模型[非酒精性脂肪性肝病结局评分（NOS）]。NOS 模型校准良好[推导的校准斜率为 0.99，验证的校准斜率为 0.98]，整体性能优异[推导的综合 Brier 评分为 0.07，验证的综合 Brier 评分为 0.01]。临界值≥1.3 可识别出 LRE 风险较高的患者（亚组 HR 24.6，p<0.001，5 年累积发生率分别为 38%和 1.0%）。在推导和验证队列中，5 年和 10 年的预测准确性均很高（时间依赖性 AUC 分别为 0.92 和 0.90，0.80 和 0.82）。NOS 在预测 5 年和 10 年的 LRE 方面比纤维化-4 或 NAFLD 纤维化评分更准确（p<0.001）。
NOS 模型由易于获得的指标组成，在预测 NAFLD 患者的结局方面比现有的纤维化模型更准确。
