Troy High School, Troy, Michigan, United States of America.
Department of Family Medicine, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, United States of America.
PLoS One. 2022 Mar 8;17(3):e0264957. doi: 10.1371/journal.pone.0264957. eCollection 2022.
Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biomarkers and near misses. Of the various models tested, support vector machine with radial bias function kernels and support vector machine with linear kernels performed the best, with training accuracies of 85% and 79% respectively. When evaluated on a test dataset, both models had prediction accuracies of around 80%. The pre-shift cortisol to DHEA-S ratio was shown to be the most important predictor in interpretable models tested. Results suggest that interventions that help emergency room physicians relax before they begin their shift could reduce risk of errors and improve patient and physician outcomes. This pilot demonstrates promising results regarding using machine learning to better understand the stress biology of near misses. Future studies should use larger groups and relate these variables to information in electronic medical records, such as objective and patient-reported quality measures.
医生压力与医疗差错和不良医疗事件有关。然而，人们对将压力与这些事件联系起来的生理机制知之甚少。我们探讨了机器学习在确定代谢应激激素皮质醇和合成代谢、抗应激激素硫酸脱氢表雄酮 (DHEA-S) ，以及皮质醇与 DHEA-S 比值是否与创伤 1 急诊部急诊住院医师在现役期间的医疗差错之间的关系中的应用。与更适合推理的统计模型相比，机器学习模型允许在尚未发生的情况下进行预测，因此更适合临床应用。这项探索性研究使用了多种机器学习模型来确定生物标志物与医疗差错之间的可能关系。在测试的各种模型中，具有径向偏差函数核的支持向量机和具有线性核的支持向量机表现最好，训练准确率分别为 85%和 79%。在测试数据集上进行评估时，这两个模型的预测准确率都约为 80%。在可解释模型中，测试前的皮质醇与 DHEA-S 比值被证明是最重要的预测因子。结果表明，在开始轮班前帮助急诊室医生放松的干预措施可以降低出错风险，改善患者和医生的预后。这项试点研究在使用机器学习更好地理解医疗差错的应激生物学方面取得了有希望的结果。未来的研究应使用更大的样本量，并将这些变量与电子病历中的信息（如客观和患者报告的质量指标）相关联。
