Clustering-aided prediction of outcomes in patients with idiopathic pulmonary fibrosis.

IF 5.8 2区医学 Q1 Medicine

Respiratory Research Pub Date : 2024-10-23 DOI:10.1186/s12931-024-03015-6

Lijun Wang, Peitao Wu, Yi Liu, Divya C Patel, Thomas B Leonard, Hongyu Zhao

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Abstract

Background: Blood biomarkers predictive of the progression of idiopathic pulmonary fibrosis (IPF) would be of value for research and clinical practice. We used data from the IPF-PRO Registry to investigate whether the addition of "omics" data to risk prediction models based on demographic and clinical characteristics improved prediction of the progression of IPF.

Methods: The IPF-PRO Registry enrolled patients with IPF at 46 sites across the US. Patients were followed prospectively. Median follow-up was 27.2 months. Prediction models for disease progression included omics data (proteins and microRNAs [miRNAs]), demographic factors and clinical factors, all assessed at enrollment. Data on proteins and miRNAs were included in the models either as raw values or based on clusters in various combinations. Least absolute shrinkage and selection operator (Lasso) Cox regression was applied for time-to-event composite outcomes and logistic regression with L1 penalty was applied for binary outcomes assessed at 1 year. Model performance was assessed using Harrell's C-index (for time-to-event outcomes) or area under the curve (for binary outcomes).

Results: Data were analyzed from 231 patients. The models based on demographic and clinical factors, with or without omics data, were the top-performing models for prediction of all the time-to-event outcomes. Relative changes in average C-index after incorporating omics data into models based on demographic and clinical factors ranged from 1.7 to 3.2%. Of the blood biomarkers, surfactant protein-D, serine protease inhibitor A7 and matrix metalloproteinase-9 (MMP-9) were among the top predictors of the outcomes. For the binary outcomes, models based on demographics alone and models based on demographics plus omics data had similar performances. Of the blood biomarkers, CC motif chemokine 11, vascular cell adhesion protein-1, adiponectin, carcinoembryonic antigen and MMP-9 were the most important predictors of the binary outcomes.

Conclusions: We identified circulating protein and miRNA biomarkers associated with the progression of IPF. However, the integration of omics data into prediction models that included demographic and clinical factors did not materially improve the performance of the models.

Trial registration: ClinicalTrials.gov; No: NCT01915511; registered August 5, 2013; URL: www.

Clinicaltrials: gov .

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特发性肺纤维化患者预后的聚类辅助预测。

背景：预测特发性肺纤维化（IPF）进展的血液生物标志物将对研究和临床实践具有重要价值。我们利用 IPF-PRO 登记处的数据，研究在基于人口统计学和临床特征的风险预测模型中添加 "omics "数据是否能改善对 IPF 进展的预测：IPF-PRO登记处在全美46个地点登记了IPF患者。对患者进行了前瞻性随访。中位随访时间为 27.2 个月。疾病进展预测模型包括omics数据（蛋白质和microRNAs [miRNAs]）、人口统计学因素和临床因素，所有这些都在入组时进行了评估。蛋白质和 miRNAs 数据以原始值或基于不同组合的聚类被纳入模型。时间到事件复合结果采用最小绝对收缩和选择算子（Lasso）Cox回归，1年评估的二元结果采用带L1惩罚的Logistic回归。使用哈雷尔 C 指数（针对时间到事件的结果）或曲线下面积（针对二元结果）评估模型性能：结果：分析了 231 名患者的数据。基于人口统计学和临床因素的模型，无论是否包含 omics 数据，都是预测所有时间到事件结果的最佳模型。在基于人口统计学和临床因素的模型中加入全息数据后，平均 C 指数的相对变化范围为 1.7% 至 3.2%。在血液生物标志物中，表面活性蛋白-D、丝氨酸蛋白酶抑制剂 A7 和基质金属蛋白酶-9（MMP-9）是预测结果的首要指标。对于二元结果，仅基于人口统计学数据的模型和基于人口统计学数据加 Omics 数据的模型表现相似。在血液生物标志物中，CC motif趋化因子11、血管细胞粘附蛋白-1、脂肪连素、癌胚抗原和MMP-9是预测二元结局的最重要指标：我们发现了与 IPF 进展相关的循环蛋白和 miRNA 生物标志物。结论：我们发现了与 IPF 进展相关的循环蛋白和 miRNA 生物标志物，但将 omics 数据整合到包含人口统计学和临床因素的预测模型中并不能显著提高模型的性能：试验注册：ClinicalTrials.gov；编号：NCT01915511；注册日期：2013 年 8 月 5 日；URL：www.Clinicaltrials: gov .

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

Respiratory Research RESPIRATORY SYSTEM-

CiteScore

9.70

自引率

1.70%

发文量

314

审稿时长

4-8 weeks

期刊介绍： Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.