关键词: Connective tissue disease Idiopathic pulmonary fibrosis Interstitial lung disease Machine learning Pulmonary function testing

Mesh : Humans Comprehensive Metabolic Panel Idiopathic Pulmonary Fibrosis / complications diagnosis Lung Diseases, Interstitial / etiology complications Leukocyte Count Patient Acuity

来  源:   DOI:10.1007/s00408-024-00673-7

Abstract:
BACKGROUND: Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT.
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.
摘要:
背景:特发性肺纤维化(IPF)的诊断通常依赖于高分辨率计算机断层扫描成像(HRCT)或组织病理学,而监测疾病严重程度是通过频繁的肺功能测试(PFT)进行的。诊断纤维化间质性肺病(ILD)类型和监测严重程度的更可靠和方便的方法将允许早期识别并增强当前的治疗干预。这项研究测试了以下假设:综合代谢面板(CMP)和全血细胞计数(CBC)数据的机器学习(ML)集成分析可以准确区分IPF与结缔组织疾病ILD(CTD-ILD)并预测疾病严重程度,如PFT所见。
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。
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