Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19
Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19
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DOI：
10.1272/jnms.jnms.2022_89-210
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发表时间：
2022-04-01
影响因子：
1
通讯作者：
Yokobori, Shoji
中科院分区：
文献类型：
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作者：
Igarashi, Yutaka;Nishimura, Kan;Yokobori, Shoji
背景资料：冠状病毒病（COVID-19）对全球公共卫生构成紧迫威胁，其特征是即使在轻微病例中也会迅速进展。在这项研究中，我们调查了机器学习是否可以用于预测哪些患者将在无症状或轻度COVID-19病例中病情恶化并需要氧合。这项单中心、回顾性、观察性研究纳入了2020年2月1日至2020年5月31日期间入院的COVID-19患者，这些病人没有症状或症状轻微，入院时不需要氧气支持。入院时收集患者特征和生命体征数据。我们使用了七种机器学习算法，评估了它们预测病情加重的能力，并使用最佳算法分析了重要的影响特征。其中43例（19%）需要氧疗。在所有模型中，logistic回归模型具有最高的准确度和精密度。Logistic回归分析表明，该模型的准确率为0.900，精确率为0.893，召回率为0.605。预测能力的最重要的参数是SpO 2，其次是年龄，呼吸频率和收缩压。结论：在这项研究中，我们开发了一种机器学习模型，可以作为临床医生的分流工具，以检测高危患者和疾病进展早。需要前瞻性验证研究来验证该工具在临床实践中的应用。（日本医学杂志2022; 89：161？（168）
Background: The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19.Methods: This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm.Results: In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for predictive capability was SpO2, followed by age, respiratory rate, and systolic blood pressure.Conclusion: In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier. Prospective validation studies are needed to verify the application of the tool in clinical practice. (J Nippon Med Sch 2022; 89: 161?168)