Differential Cell Count and CRP Level in Blood as Predictors for Middle East Respiratory Syndrome Coronavirus Infection in Acute Febrile Patients during Nosocomial Outbreak

Park, Ga Eun; Kang, Cheol-In; Ko, Jae-Hoon; Cho, Sun Young; Ha, Young Eun; Kim, Yae Jean; Peck, Kyong Ran; Song, Jae Hoon; Chung, Doo Ryeon

doi:10.3346/jkms.2017.32.1.151

Cited by 17 publications

(17 citation statements)

References 14 publications

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“…As seen in Table 2 , more than 80% of the patients exhibited lymphopenia, especially for CD4+ and CD8+ T lymphocytes (91.3%), which confirmed the previous study that SARS-CoV-2 infection damaged the immune system ( Huang et al, 2020a ). Approximately half of the patients exhibited decreased Th/Ts ratios, which differed from Middle East respiratory syndrome( Park et al, 2017 ). C-reactive protein (CRP) was elevated in 85.6% of the patients, and procalcitonin (PCT) was slightly increased.…”

Section: Resultsmentioning

confidence: 99%

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Wang

Yu²,

Hua

et al. 2020

PeerJ

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Background The outbreak of coronavirus disease 2019 (COVID-19) that occurred in Wuhan, China, has become a global public health threat. It is necessary to identify indicators that can be used as optimal predictors for clinical outcomes of COVID-19 patients. Methods The clinical information from 126 patients diagnosed with COVID-19 were collected from Wuhan Fourth Hospital. Specific clinical characteristics, laboratory findings, treatments and clinical outcomes were analyzed from patients hospitalized for treatment from 1 February to 15 March 2020, and subsequently died or were discharged. A random forest (RF) algorithm was used to predict the prognoses of COVID-19 patients and identify the optimal diagnostic predictors for patients’ clinical prognoses. Results Seven of the 126 patients were excluded for losing endpoints, 103 of the remaining 119 patients were discharged (alive) and 16 died in the hospital. A synthetic minority over-sampling technique (SMOTE) was used to correct the imbalanced distribution of clinical patients. Recursive feature elimination (RFE) was used to select the optimal subset for analysis. Eleven clinical parameters, Myo, CD8, age, LDH, LMR, CD45, Th/Ts, dyspnea, NLR, D-Dimer and CK were chosen with AUC approximately 0.9905. The RF algorithm was built to predict the prognoses of COVID-19 patients based on the best subset, and the area under the ROC curve (AUC) of the test data was 100%. Moreover, two optimal clinical risk predictors, lactate dehydrogenase (LDH) and Myoglobin (Myo), were selected based on the Gini index. The univariable logistic analysis revealed a substantial increase in the risk for in-hospital mortality when Myo was higher than 80 ng/ml (OR = 7.54, 95% CI [3.42–16.63]) and LDH was higher than 500 U/L (OR = 4.90, 95% CI [2.13–11.25]). Conclusion We applied an RF algorithm to predict the mortality of COVID-19 patients with high accuracy and identified LDH higher than 500 U/L and Myo higher than 80 ng/ml to be potential risk factors for the prognoses of COVID-19 patients in the early stage of the disease.

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Section: Resultsmentioning

confidence: 99%

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Wang

Yu²,

Hua

et al. 2020

PeerJ

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“…Several studies have suggested that a lymphocyte‐to‐monocyte ratio value <2 is a surrogate indicator for influenza A 12,13 . In addition, increased monocyte and decreased lymphocyte counts were found in patients with Middle East respiratory syndrome coronavirus (MERS‐CoV), indicating the potential predictive value of MLR for MERS 22 . It has been reported that some serum cytokines (IL‐6 and IFN‐α) and chemokines (IL‐8, CXCL‐10, and CCL‐5) are correlated with increased peripheral blood monocytes in patients with MERS, suggesting a possible effect on increasing the MLR 23 .…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID‐19): A multicenter, cross‐sectional study

Peng

Yuan

et al. 2020

Clinical Laboratory Analysis

104

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Background To determine the diagnostic value of hematologic markers for coronavirus disease 2019 (COVID‐19) and explore their relationship with disease severity. Methods Subjects included 190 COVID‐19 patients, 190 healthy subjects, and 105 influenza pneumonia (IP) patients. COVID‐19 patients were divided into the ARDS and non‐ARDS groups. Routine blood examination, biochemistry indicator, days in hospital, body temperature, pneumonia severity index (PSI), CURB‐65, and MuLBSTA were recorded. Correlations between variables were assessed using Spearman's correlation analysis. Receiver operating characteristic (ROC) curves were used to study the accuracy of the various diagnostic tests. Results Compared with healthy subjects, COVID‐19 patients had lower white blood cell (WBC), lymphocyte, platelet, and hemoglobin levels; higher percentages of neutrophils and monocytes; lower percentages of lymphocytes and higher neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) values (P < .05). COVID‐19 patients had higher WBC and neutrophil levels and lower percentages of lymphocytes compared to IP (P < .05). ROC curve analysis revealed that MLR had a high diagnostic value in differentiating COVID‐19 patients from healthy subjects, but not from IP patients. NLR showed significant positive correlations with PSI, CURB‐65, and MuLBSTA. Lymphocyte count was lower in the ARDS group and yielded a higher diagnostic value than the other variables. Conclusions Monocyte‐to‐lymphocyte ratio showed an acceptable efficiency to separate COVID‐19 patients from healthy subjects, but failed to rule out IP patients. NLR may be a reliable marker to evaluate the disease severity of COVID‐19. Lymphocyte count may be useful to establish the early diagnosis of ARDS in the COVID‐19 patients.

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“…It has been demonstrated that pneumonia caused by 2009 H1N1 influenza alone had significantly lower C‐reactive protein level than mixed bacterial and viral infection pneumonia 19 . Additionally, patients with Middle East respiratory syndrome coronavirus (MERS‐CoV) also often have lower C‐reactive protein level among patients with acute febrile illness 20 …”

Section: Discussionmentioning

confidence: 99%

A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID‐19) in suspected patients: A retrospective, multicenter, and observational study

Huang

Wang

Chen

et al. 2020

Journal of Medical Virology

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The aim of the study was to explore a novel risk score to predict diagnosis with COVID-19 among all suspected patients at admission. This was a retrospective, multicenter, and observational study. The clinical data of all suspected patients were analyzed. Independent risk factors were identified via multivariate logistic regression analysis. Finally, 336 confirmed COVID-19 patients and 139 control patients were included. We found nine independent risk factors for diagnosis with COVID-19

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Differential Cell Count and CRP Level in Blood as Predictors for Middle East Respiratory Syndrome Coronavirus Infection in Acute Febrile Patients during Nosocomial Outbreak

Cited by 17 publications

References 14 publications

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

A descriptive study of random forest algorithm for predicting COVID-19 patients outcome

Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID‐19): A multicenter, cross‐sectional study

A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID‐19) in suspected patients: A retrospective, multicenter, and observational study

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