Background: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. Objective: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. Methods: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. Results: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. Conclusions: The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.

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中文翻译：

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建立具有临床实验室指标的分类器，以区分社区获得性肺炎中的COVID-19：回顾性队列研究

背景：COVID-19患者的初始症状与社区获得性肺炎（CAP）患者的症状非常相似。通过临床症状和影像学检查很难区分COVID-19和CAP。目的：我们的研究目的是建立一个早期识别COVID-19的有效模型，该模型也将其与CAP区别开来。方法：回顾性分析61例COVID-19患者和60例CAP患者的临床实验室指标。利用各种CLI的随机组合（即CLI组合）通过机器学习算法来建立COVID-19与CAP分类器，其中包括随机森林分类器（RFC），逻辑回归分类器和梯度提升分类器（GBC）。通过使用测试数据集计算接收器工作特性曲线（AUROC）下的面积和COVID-19预测中的召回率来评估分类器的性能。结果：使用43种CLI组合的三种算法构建的分类器在测试数据集的COVID-19预测中显示出高性能（召回率> 0.9和AUROC> 0.85）。在高性能分类器中，几个CLI的使用率很高。其中包括降钙素（PCT），平均血红蛋白浓度（MCHC），尿酸，白蛋白，白蛋白与球蛋白之比（AGR），中性粒细胞计数，红细胞（RBC）计数，单核细胞计数，嗜碱性粒细胞计数和白细胞（ WBC）计数。除嗜碱性粒细胞计数外，它们还具有很高的功能重要性。PCT，AGR，尿酸，WBC计数的功能组合（FC），当使用RFC或GBC算法时，中性粒细胞计数，嗜碱性粒细胞计数，RBC计数和MCHC是九个FC的代表之一，这些FC用于构建AUROC等于1.0的分类器。在这些FC中替换任何CLI将导致使用它们构建的分类器的性能大大降低。结论：仅使用几个特定的​​CLI构建的分类器可以有效区分COVID-19和CAP，这可以帮助临床医生对COVID-19患者进行早期隔离和集中管理。在这些FC中替换任何CLI将导致使用它们构建的分类器的性能大大降低。结论：仅使用几个特定CLI构建的分类器可以有效地将COVID-19与CAP区分，这可以帮助临床医生对COVID-19患者进行早期隔离和集中管理。在这些FC中替换任何CLI将导致使用它们构建的分类器的性能大大降低。结论：仅使用几个特定的​​CLI构建的分类器可以有效区分COVID-19和CAP，这可以帮助临床医生对COVID-19患者进行早期隔离和集中管理。

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