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

Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population

Background

Since the outbreak of COVID-19 pandemic the interindividual variability in the course of the disease has been reported, indicating a wide range of factors influencing it. Factors which were the most often associated with increased COVID-19 severity include higher age, obesity and diabetes. The influence of cytokine storm is complex, reflecting the complexity of the immunological processes triggered by SARS-CoV-2 infection. A modern challenge such as a worldwide pandemic requires modern solutions, which in this case is harnessing the machine learning for the purpose of analysing the differences in the clinical properties of the populations affected by the disease, followed by grading its significance, consequently leading to creation of tool applicable for assessing the individual risk of SARS-CoV-2 infection.

Methods

Biochemical and morphological parameters values of 5,000 patients (Curisin Healthcare (India) were gathered and used for calculation of eGFR, SII index and N/L ratio. Spearman's rank correlation coefficient formula was used for assessment of correlations between each of the features in the population and the presence of the SARS-CoV-2 infection. Feature importance was evaluated by fitting a Random Forest machine learning model to the data and examining their predictive value. Its accuracy was measured as the F1 Score.

Results

The parameters which showed the highest correlation coefficient were age, random serum glucose, serum urea, gender and serum cholesterol, whereas the highest inverse correlation coefficient was assessed for alanine transaminase, red blood cells count and serum creatinine. The accuracy of created model for differentiating positive from negative SARS-CoV-2 cases was 97%. Features of highest importance were age, alanine transaminase, random serum glucose and red blood cells count.

Conclusion

The current analysis indicates a number of parameters available for a routine screening in clinical setting. It also presents a tool created on the basis of these parameters, useful for assessing the individual risk of developing COVID-19 in patients. The limitation of the study is the demographic specificity of the studied population, which might restrict its general applicability.

Sex-Dependent Performance of the Neutrophil-to-Lymphocyte, Monocyte-to-Lymphocyte, Platelet-to-Lymphocyte and Mean Platelet Volume-to-Platelet Ratios in Discriminating COVID-19 Severity

Background

The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and mean platelet volume-to-platelet ratio (MPR) are combined hematology tests that predict COVID-19 severity, although with different cut-off values. Because sex significantly impacts immune responses and the course of COVID-19, the ratios could be biased by sex.

Purpose

This study aims to evaluate sex-dependent differences in the contribution of NLR, PLR, MLR, and MPR to COVID-19 severity and mortality upon hospital admission using a sample of pneumonia patients with SARS-CoV-2 infection.

Methods

This single-center observational cross-sectional study included 3,280 confirmed COVID-19 cases (CDC 2019-Novel Coronavirus real-time RT-PCR Diagnostic) from Quito (Ecuador). The receiver operating characteristic (ROC) curve analysis was conducted to identify optimal cut-offs of the above parameters when discriminating severe COVID-19 pneumonia and mortality risks after segregation by sex. Severe COVID-19 pneumonia was defined as having PaO₂ < 60 mmHg and SpO₂ < 94%, whereas non-severe COVID-19 pneumonia was defined as having PaO₂ ≥ 60 mmHg and SpO₂ ≥ 94%.

Results

The mortality rate of COVID-19 among men was double that in women. Severe COVID-19 pneumonia and non-surviving patients had a higher level of NLR, MLR, PLR, and MPR. The medians of NLR, MLR, and MPR in men were significantly higher, but PLR was not different between men and women. In men, these ratios had lower cut-offs than in women (NLR: 2.42 vs. 3.31, MLR: 0.24 vs. 0.35, and PLR: 83.9 vs. 151.9). The sensitivity of NLR, MLR, and PLR to predict pneumonia severity was better in men (69–77%), whereas their specificity was enhanced in women compared to men (70–76% vs. 23–48%).

Conclusion

These ratios may represent widely available biomarkers in COVID-19 since they were significant predictors for disease severity and mortality although with different performances in men and women.

CAPRL Scoring System for Prediction of 30-day Mortality in 949 Patients with Coronavirus Disease 2019 in Wuhan, China: A Retrospective, Observational Study

Background

Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces.

Methods

In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality.

Results

The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively.

Conclusions

We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions.

Comparison of hematological parameters and perinatal outcomes between COVID-19 pregnancies and healthy pregnancy cohort

OBJECTIVES

To evaluate the relationship between Coronavirus Disease 2019 (COVID-19) in pregnancy and adverse perinatal outcomes. The secondary aim is to analyze the diagnostic value of hematologic parameters in COVID-19 complicated pregnancies.

METHODS

The current study is conducted in a high volume tertiary obstetrics center burdened by COVID-19 pandemics, in Turkey. In this cohort study, perinatal outcomes and complete blood count indices performed at the time of admission of 39 pregnancies (Study group) complicated by COVID-19 were compared with 69 uncomplicated pregnancies (Control group).

RESULTS

There was no significant difference between the obstetric and neonatal outcomes of pregnancies with COVID-19 compared to data of healthy pregnancies, except the increased C-section rate (p=0.026). Monocyte count, red cell distribution width (RDW), neutrophil/lymphocyte ratio (NLR), and monocyte/lymphocyte ratio (MLR) were significantly increased (p<0.0001, p=0.009, p=0.043, p<0.0001, respectively) whereas the MPV and plateletcrit were significantly decreased (p=0.001, p=0.008) in pregnants with COVID-19. ROC analysis revealed that the optimal cut-off value for MLR was 0.354 which indicated 96.7% specificity and 59.5% sensitivity in diagnosis of pregnant women with COVID-19. A strong positive correlation was found between the MLR and the presence of cough symptom (r=41.4, p=<0.0001).

CONCLUSIONS

The study revealed that, pregnancies complicated by COVID-19 is not related with adverse perinatal outcomes. MLR may serve as a supportive diagnostic parameter together with the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) in assessment of COVID-19 in pregnant cohort.

The hematology laboratory's response to the COVID-19 pandemic: A scoping review

The ongoing COVID-19 pandemic has had a profound worldwide impact on the laboratory hematology community. Nevertheless, the pace of COVID-19 hematology-related research has continued to accelerate and has established the role of laboratory hematology data for many purposes including disease prognosis and outcome. The purpose of this scoping review was to assess the current state of COVID-19 laboratory hematology research. A comprehensive search of the literature published between December 1, 2019, and July 3, 2020, was performed, and we analyzed the sources, publication dates, study types, and topics of the retrieved studies. Overall, 402 studies were included in this scoping review. Approximately half of these studies (n = 202, 50.37%) originated in China. Retrospective cohort studies comprised the largest study type (n = 176, 43.89%). Prognosis/ risk factors, epidemiology, and coagulation were the most common topics. The number of studies published per day has increased through the end of May. The studies were heavily biased in favor of papers originating in China and on retrospective clinical studies with limited use of and reporting of laboratory data. Despite the major improvements in our understanding of the role of coagulation, automated hematology, and cell morphology in COVID-19, there are gaps in the literature, including biosafety and the laboratory role in screening and prevention of COVID-19. There is a gap in the publication of papers focused on guidelines for the laboratory. Our findings suggest that, despite the large number of publications related to laboratory data and their use in COVID-19 disease, many areas remain unexplored or under-reported.

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

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.

Comparative Review of SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza A Respiratory Viruses

The 2019 novel coronavirus (SARS-CoV-2) pandemic has caused a global health emergency. The outbreak of this virus has raised a number of questions: What is SARS-CoV-2? How transmissible is SARS-CoV-2? How severely affected are patients infected with SARS-CoV-2? What are the risk factors for viral infection? What are the differences between this novel coronavirus and other coronaviruses? To answer these questions, we performed a comparative study of four pathogenic viruses that primarily attack the respiratory system and may cause death, namely, SARS-CoV-2, severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and influenza A viruses (H1N1 and H3N2 strains). This comparative study provides a critical evaluation of the origin, genomic features, transmission, and pathogenicity of these viruses. Because the coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 is ongoing, this evaluation may inform public health administrators and medical experts to aid in curbing the pandemic's progression.

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

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.

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

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 at admission to hospital: epidemiological exposure histories (OR:13.32; 95%CI, 6.39‐27.75), weakness/fatigue (OR:4.51, 95%CI, 1.70‐11.96), heart rate less than  100 beat/minutes (OR:3.80, 95%CI, 2.00‐7.22), bilateral pneumonia (OR:3.60, 95%CI, 1.83‐7.10), neutrophil count less than equal to  6.3 × 10⁹/L (OR: 6.77, 95%CI, 2.52‐18.19), eosinophil count less than equal to 0.02  ×  10⁹/L (OR:3.14, 95%CI, 1.58‐6.22), glucose more than equal to 6  mmol/L (OR:2.43, 95%CI, 1.04‐5.66), D‐dimer ≥ 0.5 mg/L (OR:3.49, 95%CI, 1.22‐9.96), and C‐reactive protein less than 5  mg/L (OR:3.83, 95%CI, 1.86‐7.92). As for the performance of this risk score, a cut‐off value of 20 (specificity: 0.866; sensitivity: 0.813) was identified to predict COVID‐19 according to reciever operator characteristic curve and the area under the curve was 0.921 (95%CI: 0.896‐0.945; P  <  .01). We designed a novel risk score which might have a promising predictive capacity for diagnosis with COVID‐19 among suspected patients.

Identified nine independent risk factors for COVID‐19 among all suspected patients. A novel, convenient risk score with good performance in clinical practice. First predictive tool for diagnosis with COVID‐19 among all suspected patients.

Combined use of the neutrophil-to-lymphocyte ratio and CRP to predict 7-day disease severity in 84 hospitalized patients with COVID-19 pneumonia: a retrospective cohort study

Background

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide from Wuhan. An easy-to-use index capable of the early identification of inpatients who are at risk of becoming critically ill is urgently needed in clinical practice. Hence, the aim of this study was to explore an easy-to-use nomogram and a model to triage patients into risk categories to determine the likelihood of developing a critical illness.

Methods

A retrospective cohort study was conducted. We extracted data from 84 patients with laboratory-confirmed COVID-19 from one designated hospital. The primary endpoint was the development of severe/critical illness within 7 days after admission. Predictive factors of this endpoint were selected by LASSO Cox regression model. A nomogram was developed based on selected variables. The predictive performance of the derived nomogram was evaluated by calibration curves and decision curves. Additionally, the predictive performances of individual and combined variables under study were evaluated by receiver operating characteristic curves. The developed model was also tested in a separate validation set with 71 laboratory-confirmed COVID-19 patients.

Results

None of the 84 inpatients were lost to follow-up in this retrospective study. The primary endpoint occurred in 23 inpatients (27.4%). The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were selected as the final prognostic factors. A nomogram was developed based on the NLR and CRP. The calibration curve and decision curve indicated that the constructed nomogram model was clinically useful. The AUCs for the NLR, CRP and Combined Index in both training set and validation sets were 0.685 (95% CI: 0.574-0.783), 0.764 (95% CI: 0.659-0.850), 0.804 (95% CI: 0.702-0.883), and 0.881 (95% CI: 0.782-0.946), respectively.

Conclusions

Our results demonstrated that the nomogram and Combined Index calculated from the NLR and CRP are potential and reliable predictors of COVID-19 prognosis and can triage patients at the time of admission.

Clinical predictors of mortality of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection: A cohort study

Background

Since the emergence of the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012, the virus had caused a high case fatality rate. The clinical presentation of MERS varied from asymptomatic to severe bilateral pneumonia, depending on the case definition and surveillance strategies. There are few studies examining the mortality predictors in this disease. In this study, we examined clinical predictors of mortality of Middle East Respiratory Syndrome (MERS) infection.

Methods

This is a retrospective analysis of symptomatic admitted patients to a large tertiary MERS-CoV center in Saudi Arabia over the period from April 2014 to March 2018. Clinical and laboratory data were collected and analysis was done using a binary regression model.

Results

A total of 314 symptomatic MERS-CoV patients were included in the analysis, with a mean age of 48 (±17.3) years. Of these cases, 78 (24.8%) died. The following parameters were associated with increased mortality, age, WBC, neutrophil count, serum albumin level, use of a continuous renal replacement therapy (CRRT) and corticosteroid use. The odd ratio for mortality was highest for CRRT and corticosteroid use (4.95 and 3.85, respectively). The use of interferon-ribavirin was not associated with mortality in this cohort.

Conclusion

Several factors contributed to increased mortality in this cohort of MERS-CoV patients. Of these factors, the use of corticosteroid and CRRT were the most significant. Further studies are needed to evaluate whether these factors were a mark of severe disease or actual contributors to higher mortality.

Middle East respiratory syndrome: what we learned from the 2015 outbreak in the Republic of Korea

Middle East Respiratory Syndrome coronavirus (MERS-CoV) was first isolated from a patient with severe pneumonia in 2012. The 2015 Korea outbreak of MERSCoV involved 186 cases, including 38 fatalities. A total of 83% of transmission events were due to five superspreaders, and 44% of the 186 MERS cases were the patients who had been exposed in nosocomial transmission at 16 hospitals. The epidemic lasted for 2 months and the government quarantined 16,993 individuals for 14 days to control the outbreak. This outbreak provides a unique opportunity to fill the gap in our knowledge of MERS-CoV infection. Therefore, in this paper, we review the literature on epidemiology, virology, clinical features, and prevention of MERS-CoV, which were acquired from the 2015 Korea outbreak of MERSCoV.