Ceylan Bahadır, Olmuşçelik Oktay, Karaalioğlu Banu, Ceylan Şule, Şahin Meyha, Aydın Selda, Yılmaz Ezgi, Dumlu Rıdvan, Kapmaz Mahir, Çiçek Yeliz, Kansu Abdullah, Duger Mustafa, Mert Ali
Department of Infectious Diseases and Clinical Microbiology, Medical Faculty, Istanbul Medipol University, Istanbul 34214, Türkyie.
Department of Internal Medicine, Medical Faculty, Istanbul Medipol University, Istanbul 34214, Türkyie.
J Clin Med. 2024 Dec 4;13(23):7386. doi: 10.3390/jcm13237386.
Studies attempting to predict the development of severe respiratory failure in patients with a COVID-19 infection using machine learning algorithms have yielded different results due to differences in variable selection. We aimed to predict the development of severe respiratory failure, defined as the need for high-flow oxygen support, continuous positive airway pressure, or mechanical ventilation, in patients with COVID-19, using machine learning algorithms to identify the most important variables in achieving this prediction. This retrospective, cross-sectional study included COVID-19 patients with mild respiratory failure (mostly receiving oxygen through a mask or nasal cannula). We used XGBoost, support vector machines, multi-layer perceptron, k-nearest neighbor, random forests, decision trees, logistic regression, and naïve Bayes methods to accurately predict severe respiratory failure in these patients. A total of 320 patients (62.1% male; average age, 54.67 ± 15.82 years) were included in this study. During the follow-ups of these cases, 114 patients (35.6%) required high-level oxygen support, 67 (20.9%) required intensive care unit admission, and 43 (13.4%) died. The machine learning algorithms with the highest accuracy values were XGBoost, support vector machines, k-nearest neighbor, logistic regression, and multi-layer perceptron (0.7395, 0.7395, 0.7291, 0.7187, and 0.75, respectively). The method that obtained the highest ROC-AUC value was logistic regression (ROC-AUC = 0.7274). The best predictors of severe respiratory failure were a low lymphocyte count, a high computed tomography score in the right and left upper lung zones, an elevated neutrophil count, a small decrease in CRP levels on the third day of admission, a high Charlson comorbidity index score, and a high serum procalcitonin level. The development of severe respiratory failure in patients with COVID-19 could be successfully predicted using machine learning methods, especially logistic regression, and the best predictors of severe respiratory failure were the lymphocyte count and the degree of upper lung zone involvement.
由于变量选择的差异，试图使用机器学习算法预测新型冠状病毒肺炎（COVID-19）感染患者严重呼吸衰竭发展情况的研究得出了不同的结果。我们旨在预测COVID-19患者严重呼吸衰竭的发展情况，严重呼吸衰竭定义为需要高流量氧疗、持续气道正压通气或机械通气，使用机器学习算法来确定实现这一预测的最重要变量。这项回顾性横断面研究纳入了患有轻度呼吸衰竭的COVID-19患者（大多通过面罩或鼻导管吸氧）。我们使用极端梯度提升（XGBoost）、支持向量机、多层感知器、k近邻、随机森林、决策树、逻辑回归和朴素贝叶斯方法来准确预测这些患者的严重呼吸衰竭。本研究共纳入320例患者（男性占62.1%；平均年龄54.67±15.82岁）。在这些病例的随访期间，114例患者（35.6%）需要高水平氧疗，67例（20.9%）需要入住重症监护病房，43例（13.4%）死亡。准确率最高的机器学习算法是XGBoost、支持向量机、k近邻、逻辑回归和多层感知器（分别为0.7395、0.7395、0.7291、0.7187和0.75）。获得最高受试者工作特征曲线下面积（ROC-AUC）值的方法是逻辑回归（ROC-AUC = 0.7274）。严重呼吸衰竭的最佳预测指标是淋巴细胞计数低、左右上肺区计算机断层扫描评分高、中性粒细胞计数升高、入院第三天C反应蛋白（CRP）水平略有下降、查尔森合并症指数评分高和血清降钙素原水平高。使用机器学习方法，尤其是逻辑回归，可以成功预测COVID-19患者严重呼吸衰竭的发展情况，严重呼吸衰竭的最佳预测指标是淋巴细胞计数和上肺区受累程度。
