METHODS: Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe.
RESULTS: ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils.
CONCLUSIONS: Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.
方法:通过ML方法学分析诊断为IPF或CTD-ILD的门诊患者数据(53例患者的103次就诊),以评估(1)IPF与CTD-ILD诊断;(2)一氧化碳(DLCO)中度或轻度与重度的预测肺弥漫性百分比;(3)预测强迫生命能力(FVC)中度或轻度与重度百分比;
结果:ML方法从CTD-ILD中识别出IPF,AUCTEST=0.893,而PFT分类为DLCO中度或轻度与重度,AUCTEST=0.749,FVC中度或轻度与重度,AUCTEST=0.741,FVC轻度与中度或重度,AUCTEST=0.739。主要特征包括白蛋白,丙氨酸转氨酶,%淋巴细胞,血红蛋白,嗜酸性粒细胞,白细胞计数,%单核细胞,和中性粒细胞百分比。
结论:通过提出的ML方法学分析CMP和CBC数据提供了区分IPF和CTD-ILD的潜力,并预测相关PFT的严重程度,准确性达到或超过当前临床实践。