Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data.

IF 4.6 2区 医学 Q1 RESPIRATORY SYSTEM
Lung Pub Date : 2024-04-01 Epub Date: 2024-02-20 DOI:10.1007/s00408-024-00673-7
Alex N Mueller, Hunter A Miller, Matthew J Taylor, Sally A Suliman, Hermann B Frieboes
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引用次数: 0

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.

Conclusion: 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.

Abstract Image

通过对综合代谢面板和全血细胞计数数据进行机器学习分析,识别特发性肺纤维化并预测疾病严重程度。
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,而疾病严重程度的监测则通过频繁的肺功能测试(PFT)来完成。如果能有更可靠、更方便的方法来诊断纤维化间质性肺病(ILD)的类型并监测其严重程度,就能及早发现并加强目前的治疗干预措施。本研究对以下假设进行了测试:对综合代谢面板(CMP)和全血细胞计数(CBC)数据进行机器学习(ML)组合分析,可以准确区分 IPF 和结缔组织病 ILD(CTD-ILD),并预测 PFT 观察到的疾病严重程度:通过ML方法对诊断为IPF或CTD-ILD的门诊数据(53名患者103次就诊)进行分析,以评估(1)IPF与CTD-ILD的诊断;(2)一氧化碳肺弥散容量(DLCO)中度或轻度与重度的预测百分比;(3)用力肺活量(FVC)中度或轻度与重度的预测百分比;以及(4)FVC轻度与中度或重度的预测百分比:ML方法从CTD-ILD中识别出IPF,AUCTEST=0.893,而PFT分为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、淋巴细胞百分比、血红蛋白、嗜酸性粒细胞百分比、白细胞计数、单核细胞百分比和中性粒细胞百分比:通过所提出的 ML 方法对 CMP 和 CBC 数据进行分析,有可能将 IPF 与 CTD-ILD 区分开来,并预测相关 PFT 的严重程度,其准确性达到或超过了目前的临床实践水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Lung
Lung 医学-呼吸系统
CiteScore
9.10
自引率
10.00%
发文量
95
审稿时长
6-12 weeks
期刊介绍: Lung publishes original articles, reviews and editorials on all aspects of the healthy and diseased lungs, of the airways, and of breathing. Epidemiological, clinical, pathophysiological, biochemical, and pharmacological studies fall within the scope of the journal. Case reports, short communications and technical notes can be accepted if they are of particular interest.
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