PMID- 33534722 OWN - NLM STAT- MEDLINE DCOM- 20210302 LR - 20210304 IS - 1438-8871 (Electronic) IS - 1439-4456 (Print) IS - 1438-8871 (Linking) VI - 23 IP - 2 DP - 2021 Feb 22 TI - Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study. PG - e23390 LID - 10.2196/23390 [doi] LID - e23390 AB - 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. CI - (c)Wanfa Dai, Pei-Feng Ke, Zhen-Zhen Li, Qi-Zhen Zhuang, Wei Huang, Yi Wang, Yujuan Xiong, Xian-Zhang Huang. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.02.2021. FAU - Dai, Wanfa AU - Dai W AUID- ORCID: 0000-0002-6074-2650 AD - Department of Respiration, Gong An County People's Hospital, Jingzhou, China. FAU - Ke, Pei-Feng AU - Ke PF AUID- ORCID: 0000-0001-9414-0458 AD - Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China. AD - Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, China. FAU - Li, Zhen-Zhen AU - Li ZZ AUID- ORCID: 0000-0002-9547-1969 AD - Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Zhuang, Qi-Zhen AU - Zhuang QZ AUID- ORCID: 0000-0002-5195-0503 AD - Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Huang, Wei AU - Huang W AUID- ORCID: 0000-0001-7421-5002 AD - Department of Respiration, Gong An County People's Hospital, Jingzhou, China. FAU - Wang, Yi AU - Wang Y AUID- ORCID: 0000-0002-0811-3742 AD - Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China. AD - Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Xiong, Yujuan AU - Xiong Y AUID- ORCID: 0000-0001-6668-700X AD - Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China. AD - Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, China. FAU - Huang, Xian-Zhang AU - Huang XZ AUID- ORCID: 0000-0003-4320-9181 AD - Department of Laboratory Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China. AD - Guangdong Provincial Key Laboratory of Research on Emergency in Traditional Chinese Medicine, Guangzhou, China. LA - eng PT - Journal Article DEP - 20210222 PL - Canada TA - J Med Internet Res JT - Journal of medical Internet research JID - 100959882 RN - 0 (Procalcitonin) SB - IM MH - Area Under Curve MH - COVID-19/blood/*diagnosis/virology MH - Community-Acquired Infections/blood/*diagnosis MH - Female MH - Humans MH - Laboratories MH - Leukocyte Count MH - Logistic Models MH - *Machine Learning MH - Male MH - Middle Aged MH - Pneumonia/blood/*diagnosis MH - Procalcitonin/blood MH - ROC Curve MH - Retrospective Studies MH - SARS-CoV-2/*pathogenicity PMC - PMC7901596 OTO - NOTNLM OT - COVID-19 OT - classification algorithm OT - classifier OT - clinical laboratory indicators OT - community-acquired pneumonia COIS- Conflicts of Interest: None declared. EDAT- 2021/02/04 06:00 MHDA- 2021/03/03 06:00 PMCR- 2021/02/22 CRDT- 2021/02/03 17:10 PHST- 2020/08/11 00:00 [received] PHST- 2021/02/01 00:00 [accepted] PHST- 2020/12/29 00:00 [revised] PHST- 2021/02/04 06:00 [pubmed] PHST- 2021/03/03 06:00 [medline] PHST- 2021/02/03 17:10 [entrez] PHST- 2021/02/22 00:00 [pmc-release] AID - v23i2e23390 [pii] AID - 10.2196/23390 [doi] PST - epublish SO - J Med Internet Res. 2021 Feb 22;23(2):e23390. doi: 10.2196/23390.