INTRODUCTION Well-trained staff is needed to interpret cardiopulmonary exercise tests (CPET). We aimed to examine the accuracy of machine learning-based algorithms to classify exercise limitations and their severity in clinical practice compared with expert consensus using patients presenting at a pulmonary clinic. METHODS This study included 200 historical CPET data sets (48.5% female) of patients older than 40 yr referred for CPET because of unexplained dyspnea, preoperative examination, and evaluation of therapy progress. Data sets were independently rated by experts according to the severity of pulmonary-vascular, mechanical-ventilatory, cardiocirculatory, and muscular limitations using a visual analog scale. Decision trees and random forests analyses were calculated. RESULTS Mean deviations between experts in the respective limitation categories ranged from 1.0 to 1.1 points (SD, 1.2) before consensus. Random forests identified parameters of particular importance for detecting specific constraints. Central parameters were nadir ventilatory efficiency for CO 2 , ventilatory efficiency slope for CO 2 (pulmonary-vascular limitations); breathing reserve, forced expiratory volume in 1 s, and forced vital capacity (mechanical-ventilatory limitations); and peak oxygen uptake, O 2 uptake/work rate slope, and % change of the latter (cardiocirculatory limitations). Thresholds differentiating between different limitation severities were reported. The accuracy of the most accurate decision tree of each category was comparable to expert ratings. Finally, a combined decision tree was created quantifying combined system limitations within one patient. CONCLUSIONS Machine learning-based algorithms may be a viable option to facilitate the interpretation of CPET and identify exercise limitations. Our findings may further support clinical decision making and aid the development of standardized rating instruments.

中文翻译：

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使用基于机器学习的算法来识别和量化临床实践中的运动限制：我们做到了吗？


简介 需要训练有素的工作人员来解释心肺运动测试 (CPET)。我们的目的是检查基于机器学习的算法在临床实践中对运动限制及其严重程度进行分类的准确性，与使用肺部诊所就诊患者的专家共识进行比较。方法 本研究纳入了 200 个历史 CPET 数据集（48.5% 女性），患者年龄超过 40 岁，因不明原因呼吸困难、术前检查和治疗进展评估而转诊接受 CPET。专家根据肺血管、机械通气、心循环和肌肉限制的严重程度，使用视觉模拟量表对数据集进行独立评级。计算决策树和随机森林分析。结果 在达成共识之前，各个限制类别的专家之间的平均偏差范围为 1.0 至 1.1 分（SD，1.2）。随机森林确定了对于检测特定约束特别重要的参数。中心参数是CO 2 的最低通气效率、CO 2 的通气效率斜率（肺血管限制）；呼吸储备、1 秒用力呼气量和用力肺活量（机械通气限制）；和峰值摄氧量、O 2 摄取/做功率斜率以及后者的变化百分比（心脏循环限制）。报告了区分不同限制严重程度的阈值。每个类别最准确的决策树的准确性可与专家评级相媲美。最后，创建了一个组合决策树，量化一名患者的组合系统限制。 结论 基于机器学习的算法可能是促进 CPET 解释和识别运动限制的可行选择。我们的研究结果可能会进一步支持临床决策并有助于标准化评级工具的开发。