Research Area, Consorci Sanitari Alt Penedès i Garraf, Sant Pere de Ribes-Barcelona, Barcelona, Spain.
Department of Pneumology, IIS Fundación Jiménez Díaz, CIBERES, Madrid, Spain.
Sci Rep. 2023 Aug 5;13(1):12709. doi: 10.1038/s41598-023-39329-6.
Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.
心力衰竭 (HF) 和慢性阻塞性肺疾病 (COPD) 是对普通人群影响最大的两种慢性疾病，早期发现其失代偿是一个重要目标。然而，很少有诊断模型能够达到足够的诊断性能。本试验旨在利用基于生理参数的机器学习技术开发心力衰竭失代偿或 COPD 加重的诊断模型。共招募了 135 名因心力衰竭失代偿和/或 COPD 加重住院的患者。每位患者接受了三次评估：一次在失代偿期（住院期间），两次在代偿期（出院后 30 天在家中）。在每次评估中，心率 (HR) 和氧饱和度 (Ox) 连续（使用脉搏血氧仪）记录在行走 6 分钟期间，然后进行 4 分钟的恢复期。为了开发诊断模型，首先通过分类算法选择与 HR 和 Ox 相关的预测特征。潜在的预测因素包括年龄、性别和基线疾病（心力衰竭或 COPD）。接下来，通过不同的机器学习技术开发诊断分类模型（代偿期与失代偿期）。根据灵敏度 (S)、特异性 (E) 和准确性 (A) 评估开发模型的诊断性能。分析中包括 22 例心力衰竭失代偿患者、25 例 COPD 加重患者和 13 例同时存在两种失代偿病变的患者。在最初评估的 HR 和 Ox 的 96 个特征中，选择了 19 个。年龄、性别和基线疾病对模型没有提供更大的区分能力。灵敏度和特异性值均超过 80%的技术是逻辑回归（S：80.83%；E：86.25%；A：83.61%）和支持向量机（S：81.67%；E：85%；A：82.78%）技术。开发的诊断模型对心力衰竭失代偿或 COPD 加重具有良好的诊断性能。据我们所知，这是第一项报告潜在适用于 COPD 和 HF 患者的失代偿诊断模型的研究。然而，这些结果是初步的，需要进一步研究证实。
