How do routine laboratory tests change in coronavirus disease 2019?

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction

BACKGROUND

There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking.

METHODS

A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed.

RESULTS

A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis.

CONCLUSION

Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

Development of Biomarkers and Prognosis Model of Mortality Risk in Patients with COVID-19

Background

As of 30 April 2023, the COVID-19 pandemic has resulted in over 6.9 million deaths worldwide. The virus continues to spread and mutate, leading to continuously evolving pathological and physiological processes. It is imperative to reevaluate predictive factors for identifying the risk of early disease progression.

Methods

A retrospective study was conducted on a cohort of 1379 COVID-19 patients who were discharged from Xin Hua Hospital affiliated with Shanghai Jiao Tong University School of Medicine between 15 December 2022 and 15 February 2023. Patient symptoms, comorbidities, demographics, vital signs, and laboratory test results were systematically documented. The dataset was split into testing and training sets, and 15 different machine learning algorithms were employed to construct prediction models. These models were assessed for accuracy and area under the receiver operating characteristic curve (AUROC), and the best-performing model was selected for further analysis.

Results

AUROC for models generated by 15 machine learning algorithms all exceeded 90%, and the accuracy of 10 of them also surpassed 90%. Light Gradient Boosting model emerged as the optimal choice, with accuracy of 0.928 ± 0.0006 and an AUROC of 0.976 ± 0.0028. Notably, the factors with the greatest impact on in-hospital mortality were growth stimulation expressed gene 2 (ST2,19.3%), interleukin-8 (IL-8,17.2%), interleukin-6 (IL-6,6.4%), age (6.1%), NT-proBNP (5.1%), interleukin-2 receptor (IL-2R, 5%), troponin I (TNI,4.6%), congestive heart failure (3.3%) in Light Gradient Boosting model.

Conclusion

ST-2, IL-8, IL-6, NT-proBNP, IL-2R, TNI, age and congestive heart failure were significant predictors of in-hospital mortality among COVID-19 patients.

Association Between Hemoglobin-Albumin-Lymphocyte-Platelet Index and Mortality in Hospitalized COVID-19 Omicron BA.2 Infected Patients

Background

The hemoglobin-albumin-lymphocyte-platelet (HALP) index is a novel biomarker reflecting systemic inflammation and nutritional status which are important for coronavirus disease 2019 (COVID-19) mortality. However, the association between HALP and mortality in patients with COVID-19 has yet to be investigated.

Methods

A cohort of COVID-19 Omicron BA.2 infected patients admitted to the Shanghai Fourth People's Hospital, School of Medicine, Tongji University from April 12, 2022 to June 17, 2022 was retrospectively analyzed. Laboratory examinations on hospital admission, including hemoglobin, albumin, and lymphocyte and platelet, were collected. The association between baseline HALP and in-hospital poor overall survival (OS) was assessed using Kaplan-Meier curves, Cox regression models, interaction, and stratified analyses.

Results

A total of 2147 patients with COVID-19 Omicron BA.2 infection were included in the final analyses, and mortality in the hospital was 2.65%. Multivariate analysis indicated that low HALP index was independently associated with in-hospital mortality of COVID-19 patients [hazard ratio (HR) = 2.08; 95% confidence interval (CI) = 1.17-3.73]. Subgroup analysis demonstrated that low HALP index was an independent risk factor for in-hospital mortality in COVID-19 patients with age ≥70 (HR = 2.22, CI = 1.18-4.15) and severe cases (HR = 2.09, CI = 1.13-3.86).

Conclusion

HALP index is independently related to in-hospital poor OS for COVID-19 Omicron BA.2 infected patients, especially for age ≥70 and severe cases. HALP index on hospital admission is a useful candidate biomarker for identifying high risk of mortality in COVID-19 Omicron BA.2 infected patients.

Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia

BACKGROUND

Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers.

METHODS

A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken. COVID-19 exposure was assessed by performing real-time PCR on nasopharyngeal (NP) swabs. Descriptive and inferential statistics were applied for both continuous and categorical variables using Rstudio-version-4.0.2. Pearson correlation and regression were executed with a cut-off of p < 0.05 for evaluating significance. Data representation by R-packages and ggplot2.

RESULTS

A significant variation in the mean ± SD (highly-sever (HS)/moderately severe (MS)) of CRP (HS/MS: 102.4 ± 22.9/21.3 ± 6.9, p-value < 0.001), D-dimer (HS/MS: 661.1 ± 80.6/348.7 ± 42.9, p-value < 0.001), and ferritin (HS/MS: 875.8 ± 126.8/593.4 ± 67.3, p-value < 0.001) were observed. Thrombocytopenia, high PT, and PTT exhibited an association with the HS individuals (p < 0.001). CRP was correlated with neutrophil (r = 0.77), ferritin (r = 0.74), and WBC (r = 0.8). D-dimer correlated with platelets (r = -0.82), PT (r = 0.22), and PTT (r = 0.37). The adjusted odds ratios (Ad-OR) of CRP, ferritin, D-dimer, platelet, PT, and PTT for HS compared to MS were 1.30 (95% CI -1.137, 1.50; p < 0.001), 1.048 (95% CI -1.03, 1.066; p < 0.001), 1.3 (95% CI -1.24, 1.49, p > 0.05), -0.813 (95% CI -0.734, 0.899, p < 0.001), 1.347 (95% CI -1.15, 1.57, p < 0.001), and 1.234 (95% CI -1.16, 1.314, p < 0.001), respectively.

CONCLUSION

SARS-CoV-2 caused alterations in vital laboratory parameters and raised ferritin, CRP, and D-dimer presented an association with disease severity at a significant level.

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.

Hematological and Biochemical Laboratory Parameters in COVID-19 Patients: A Retrospective Modeling Study of Severity and Mortality Predictors

Background

It is well known that laboratory markers could help in identifying risk factors of severe illness and predicting outcomes of diseases. Here, we performed a retrospective modeling study of severity and mortality predictors of hematological and biochemical laboratory parameters in Iranian COVID-19 patients.

Methods

Data were obtained retrospectively from medical records of 564 confirmed Iranian COVID-19 cases. According to the disease severity, the patients were categorized into two groups (severe or nonsevere), and based on the outcome of the disease, patients were divided into two groups (recovered or deceased). Demographic and laboratory data were compared between groups, and statistical analyses were performed to define predictors of disease severity and mortality in the patients.

Results

The study identified a panel of hematological and biochemical markers associated with the severe outcome of COVID-19 and constructed different predictive models for severity and mortality. The disease severity and mortality rate were significantly higher in elderly inpatients, whereas gender was not a determining factor of the clinical outcome. Age-adjusted white blood cells (WBC), platelet cells (PLT), neutrophil-to-lymphocyte ratio (NLR), red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), erythrocyte sedimentation rate (ESR), mean corpuscular hemoglobin (MCHC), blood urea nitrogen (BUN), and creatinine (Cr) also showed high accuracy in predicting severe cases at the time of hospitalization, and logistic regression analysis suggested grouped hematological parameters (age, WBC, NLR, PLT, HGB, and international normalized ratio (INR)) and biochemical markers (age, BUN, and lactate dehydrogenase (LDH)) as the best models of combined laboratory predictors for severity and mortality.

Conclusion

The findings suggest that a panel of several routine laboratory parameters recorded on admission could be helpful for clinicians to predict and evaluate the risk of disease severity and mortality in COVID-19 patients.

Long non-coding RNAs ANRIL, THRIL, and NEAT1 as potential circulating biomarkers of SARS-CoV-2 infection and disease severity

The current outbreak of coronavirus disease 2019 (COVID-19) is a global emergency, as its rapid spread and high mortality rate, which poses a significant threat to public health. Innate immunity plays a crucial role in the primary defense against infections, and recent studies have highlighted the pivotal regulatory function of long non-coding RNAs (lncRNAs) in innate immune responses. This study aims to assess the circulating levels of lncRNAs namely ANRIL, THRIL, NEAT1, and MALAT1 in the blood of moderate and severe SARS-CoV-2 infected patients, in comparison to healthy individuals. Additionally, it aims to explore the potential of these lncRNAs as biomarkers for determining the severity of the disease. The blood samples were collected from a total of 38 moderate and 25 severe COVID-19 patients, along with 30 healthy controls. The total RNA was extracted and qPCR was performed to evaluate the blood levels of the lncRNAs. The results indicate significantly higher expression levels of lncRNAs ANRIL and THRIL in severe patients when compared to moderate patients (P value = 0.0307, P value = 0.0059, respectively). Moreover, the expression levels of lncRNAs ANRIL and THRIL were significantly up-regulated in both moderate and severe patients in comparison to the control group (P value < 0.001, P value < 0.001, P value = 0.001, P value < 0.001, respectively). The expression levels of lncRNA NEAT1 were found to be significantly higher in both moderate and severe COVID-19 patients compared to the healthy group (P value < 0.001, P value < 0.001, respectively), and there was no significant difference in the expression levels of NEAT1 between moderate and severe patients (P value = 0.6979). The expression levels of MALAT1 in moderate and severe patients did not exhibit a significant difference compared to the control group (P value = 0.677, P value = 0.764, respectively). Furthermore, the discriminative power of ANRIL and THRIL was significantly higher in the severe patient group than the moderate group (Area under curve (AUC) = 0.6879; P-value = 0.0122, AUC = 0.6947; P-value = 0.0093, respectively). In conclusion, the expression levels of the lncRNAs ANRIL and THRIL are correlated with the severity of COVID-19 and can be regarded as circulating biomarkers for disease progression.

The association between mean platelet volume and poor outcome in patients with COVID-19: Systematic review, meta-analysis, and meta-regression

Introduction

This study aims to assess the association between mean platelet volume (MPV) and poor outcome in patients with COVID-19.

Methods

We performed a comprehensive literature search using the PubMed, Embase and Scopus databases with keywords "2019-nCoV" OR "SARS-CoV-2" OR "COVID-19" AND "mean platelet volume" OR "MPV" on 8 July 2021. The primary outcome was composite poor outcome, defined as severe COVID-19 or mortality. The pooled effect estimate was reported as mean differences in terms of MPV between the group with and without outcome.

Results

There were 17 studies which consist of 4549 patients with COVID-19 were included in this study. The incidence of poor outcome was 25% (20%-30%). Mean MPV was found to be higher in the poor outcome group in compare to no poor outcome group (10.3 ± 1.9 fL vs 9.9 ± 1.7 fL). The mean MPV difference between both group was 0.47 fL [95% CI 0.27, 0.67], p < 0.001; I2: 62.91%, p < 0.001). In the sub-group analysis, patients with severe COVID-19 had higher MPV (mean difference 0.54 fL [95% CI 0.28, 0.80], p < 0.001; I2: 54.84%, p = 0.014). Furthermore, MPV was also higher in the mortality group (mean difference 0.54 fL [95% CI 0.29, 0.80], p = 0.020; I2: 71.11%, p = 0.004). Meta-regression analysis showed that the association between MPV and poor outcome was not affected by age (p = 0.789), gender (p = 0.167), platelets (p = 0.056), white blood cells (p = 0.639), and lymphocytes (p = 0.733).

Conclusion

This meta-analysis indicated that increased MPV was associated with severity and mortality in patients with COVID-19. Further research is needed to determine the optimum cut-off point.

The Diagnostic Value of Inflammatory Markers (CRP, IL6, CRP/IL6, CRP/L, LCR) for Assessing the Severity of COVID-19 Symptoms Based on the MEWS and Predicting the Risk of Mortality

Introduction

Various diagnostic tools are used to assess the severity of COVID-19 symptoms and the risk of mortality, including laboratory tests and scoring indices such as the Modified Early Warning Score (MEWS). The diagnostic value of inflammatory markers for assessing patients with different severity of COVID-19 symptoms according to the MEWS was evaluated in this study.

Materials and Methods

The concentrations of CRP (C-reactive protein) (immunoassay) and IL6 (interleukin 6) (electrochemiluminescence assay) were determined, and CRP/IL6, CRP/L, and LCR ratios were calculated in blood serum samples collected from 374 COVID-19 patients.

Results

We demonstrated that CRP, IL6, CRP/IL6, CRP/L, LCR inflammatory markers increase significantly with disease progression assessed based on the MEWS in COVID-19 patients and may be used to differentiating patients with severe and non-severe COVID-19 and to assess the mortality.

Conclusion

The diagnostic value of inflammatory markers for assessing the risk of mortality and differentiating between patients with mild and severe COVID-19 was confirmed.

Prognostic Value of C-Reactive Protein in SARS-CoV-2 Infection: A Simplified Biomarker of COVID-19 Severity in Northern Ethiopia

Purpose

To evaluate the role of C-reactive protein (CRP) in predicting severe COVID-19 patients.

Methods

A prospective observational cohort study was conducted from July 15 to October 28, 2020, at Kuyha COVID-19 isolation and treatment center hospital, Mekelle City, Northern Ethiopia. A total of 670 blood samples were collected serially. SARS-CoV-2 infection was confirmed by RT-PCR from nasopharyngeal swabs and CRP concentration was determined using Cobas Integra 400 Plus (Roche). Data were analyzed using STATA version 14. P-value <0.05 was considered statistically significant.

Results

Overall, COVID-19 patients had significantly elevated CRP at baseline when compared to PCR-negative controls [median 11.1 (IQR: 2.0-127.8) mg/L vs 0.9 (IQR: 0.5-1.9) mg/L; p=0.0004)]. Those with severe COVID-19 clinical presentation had significantly higher median CRP levels compared to those with non-severe cases [166.1 (IQR: 48.6-332.5) mg/L vs 2.4 (IQR: 1.2-7.6) mg/L; p<0.00001)]. Moreover, COVID-19 patients exhibited higher median CRP levels at baseline [58 (IQR: 2.0-127.8) mg/L] that decreased significantly to 2.4 (IQR: 1.4-3.9) mg/L after 40 days after symptom onset (p<0.0001). Performance of CRP levels determined using ROC analysis distinguished severe from non-severe COVID-19 patients, with an AUC value of 0.83 (95% CI: 0.73-0.91; p=0.001; 77.4% sensitivity and 89.4% specificity). In multivariable analysis, CRP levels above 30 mg/L were significantly associated with an increased risk of developing severe COVID-19 for those who have higher ages and comorbidities (ARR 3.99, 95% CI: 1.35-11.82; p=0.013).

Conclusion

CRP was found to be an independent determinant factor for severe COVID-19 patients. Therefore, CRP levels in COVID-19 patients in African settings may provide a simple, prompt, and inexpensive assessment of the severity status at baseline and monitoring of treatment outcomes.

Hemogram-based decision tree models for discriminating COVID-19 from RSV in infants

OBJECTIVE

Decision trees are efficient and reliable decision-making algorithms, and medicine has reached its peak of interest in these methods during the current pandemic. Herein, we reported several decision tree algorithms for a rapid discrimination between coronavirus disease (COVID-19) and respiratory syncytial virus (RSV) infection in infants.

METHODS

A cross-sectional study was conducted on 77 infants: 33 infants with novel betacoronavirus (SARS-CoV-2) infection and 44 infants with RSV infection. In total, 23 hemogram-based instances were used to construct the decision tree models via 10-fold cross-validation method.

RESULTS

The Random forest model showed the highest accuracy (81.8%), while in terms of sensitivity (72.7%), specificity (88.6%), positive predictive value (82.8%), and negative predictive value (81.3%), the optimized forest model was the most superior one.

CONCLUSION

Random forest and optimized forest models might have significant clinical applications, helping to speed up decision-making when SARS-CoV-2 and RSV are suspected, prior to molecular genome sequencing and/or antigen testing.

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Background and objective

A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis.

Material and method

This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease.

Results

Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients.

Conclusions

This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Hematological, Inflammatory, Coagulation, and Oxidative/Antioxidant Biomarkers as Predictors for Severity and Mortality in COVID-19: A Prospective Cohort-Study

Purpose

Oxidative stress (OS) and inflammation are pivotal points in the pathophysiology of coronavirus disease-2019 (COVID-19). This study aims to use routine laboratory and oxidative stress/antioxidative biomarkers as predictors for the mortality of the disease.

Patients and Methods

This prospective cohort study, made up of 120 COVID-19 patients from emergency units in Erbil, Duhok, Kirkuk, and Sulaymaniyah cities in Iraq, from May the 1st to May the 30th, 2021, and 60 healthy controls (HCs) (n = 60). The patients were re-categorized into mild (n = 54), severe (n = 40), and critical (n = 26) groups based on the clinical criteria. Following admission to the hospital, blood was directly collected for measuring routine laboratory biomarkers.

Results

Neutrophils and neutrophil/lymphocyte ratio (NLR) were higher in the critical group, while lymphocytes were lower in the severe and critical groups compared to the mild group. The CRP, ferritin, and D-dimer values were more elevated in severe and critical cases than in mild COVID-19 cases. The levels of malondialdehyde (MDA), nitric oxide (NO), and copper were elevated, while the superoxide dismutase (SOD) activity level and total antioxidant capacity (TAC) level were lower. However, vitamin C, glutathione peroxidase (GPx), and catalase activity levels were not changed in the COVID-19 groups compared to the HCs. NO and ferritin were predictors of ICU hospitalization; D-dimer, MDA, and NLR were predictors of mortality. NO, and NLR were predictors of SpO2 depression. Moreover, NO, and copper have both good diagnostic values, their cutoffs were 39.01 and 11.93, respectively.

Conclusion

There is an association between immune dysregulation and oxidative imbalance. The biomarkers, that could be considered as predictors for the severity and mortality of COVID-19, are the NLR, NO, ferritin, and D-dimer. The age equal to and older than 50 has a poor prognosis in the Kurdish population.

Ct, IL-18 polymorphism, and laboratory biomarkers for predicting chemosensory dysfunctions and mortality in COVID-19

Aim

Patients with COVID-19 often experience chemosensory dysfunction. This research intends to uncover the association of RT-PCR Ct value with chemosensory dysfunctions and SpO2. This study also aims to investigate Ct, SpO2, CRP, D-dimer, and -607 IL-18 T/G polymorphism in order to find out predictors of chemosensory dysfunctions and mortality.

Materials & methods

This study included 120 COVID-19 patients, of which 54 were mild, 40 were severe and 26 were critical. CRP, D-dimer, RT-PCR, and IL-18 polymorphism were evaluated. Results & conclusion: Low Ct was associated with SpO2 dropping and chemosensory dysfunctions. IL-18 T/G polymorphism did not show an association with COVID-19 mortality; conversely, age, BMI, D-dimer and Ct values did.

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

Objectives

When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.

Material and methods

The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.

Results

In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.

Conclusion

The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.

The prognostic value of procalcitonin in critically ill cases of systematic inflammatory response syndrome in dogs

Using markers for early diagnosis can help to reduce mortality and morbidity in systemic inflammatory response syndrome (SIRS). This study investigates the role of procalcitonin (PCT) as a prognostic value in dogs with SIRS in the intensive care unit. Fifty-five dogs were selected and studied. Blood samples were collected and investigated for PCT, white and red blood cells, iron, creatinine, platelet, glucose, albumin, urea, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), bandcell, body temperature, and hospitalized days and costs. The animals were grouped into survivors and deceased groups, and their results were compared. The results showed negative significant relations between PCT with hematocrit (r ² = 0.294, P < 0.05) and the serum concentration of iron (r ² = 0.280, P < 0.05) and also positive relation with IL-6 (r ² = 0.456, P < 0.01) and TNF-α (r ² = 0.391, P < 0.01). Significant relations were not seen between PCT with other parameters (P > 0.05). The results also showed a significant relation between glucose and albumin with body temperature (P < 0.05). The results showed that the serum concentrations of PCT, IL-6, and TNF-α were significantly higher in deceased dogs compared with survivors, while white blood cells, glucose, albumin, urea, lymphocyte, neutrophil, and body temperature were higher in survivors compared with others. PCT can be utilized as a prognostic value and helps early diagnosis in dogs with SIRS.

COVID-19 and monkeypox co-infection: A rapid systematic review

In this paper we aimed to study the characteristics, laboratory data and outcomes of monkeypox virus (MPV) and COVID-19 co-infection. On 2nd October 2022, we used the search term "("monkeypox virus" OR "MPV" OR "monkey pox" OR "monkeypox") AND ("COVID-19" OR "COVID 19" OR "novel coronavirus" OR "SARS-CoV-2")" in five databases to collect the relevant articles. We found three male patients, who had sex with men prior to the infection, had multiple comorbid conditions, were diagnosed with PCR, and were admitted to the hospital. The length of hospital stay was 4, 6, and 9 days. On admission, two cases had multiple vesicular lesions on various sites of the body associated with tonsillar inflammation, while the third case had genital ulcers and inguinal lymph node enlargement. All cases were managed in the hospital and recovered well. It might still be too early to establish solid evidence about the exact cause-effect association between SARS-CoV-2 and MPV co-infection and patient's outcomes because of the current low sample size. Accordingly, future relevant investigations, estimating the risk ratio of this association are needed to formulate definite evidence.

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

Mean Platelet Volume as a Predictor of COVID-19 Severity: A Prospective Cohort Study in the Highlands of Peru

Introduction: Although 80% of symptomatic individuals with COVID-19 develop mild forms, it is the severe (15%) and critical (5%) forms that have the greatest impact in the hospital setting. Recognizing markers that can predict severe forms is essential, especially in high-altitude populations. Methods: We conducted a prospective cohort study at 3200 masl (meters above sea level) in a city in Peru to determine if MPV (mean platelet volume) level is a predictor of COVID-19 severity. Patients with mild/moderate disease were enrolled and followed for 21 days or until the development of severe disease (primary outcome). A bivariate analysis was used to identify variables associated with severe disease. A ROC analysis determined the best MPV (mean platelet count) cut-off to predict COVID-19 severity, and then, a multiple regression analysis was performed. Results: 64 patients were enrolled. The median age was 48.5 years (IQT 39–64.5) and the proportion of women was 51.6%, the most frequent symptoms were chest pain (73%), fever (71%), and dyspnea (67%). The median time to develop a severe form from the onset of symptoms was 11 days (IQT 10.5–13). The most common radiographic phase on CT scan (computed tomography) was progressive (60.38%). We observed that an MPV of more than 10.15 fL in the first week of disease predicted severity regardless of age and sex at high altitudes. Conclusions: MPV in the first week of the disease may predict severity in patients diagnosed with COVID-19 at high altitudes; however, we need prospective studies with a larger population and at a different altitude, levels to confirm these findings.

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.

What is the impact and efficacy of routine immunological, biochemical and hematological biomarkers as predictors of COVID-19 mortality?

It remains important to investigate the changing and impact of routine blood values (RBVs) in order to predict mortality and follow an appropriate treatment in COVID-19 patients. In the study, the importance of RBVs in the mortality of patients with COVID-19 was investigated. The changes in the biochemical, hematological, and immunological parameters of patients who recovered (n = 4364) and died (n = 233) from COVID-19 over time and their relationship with the mortality of the disease were evaluated retrospectively. Odds ratios of the parameters affecting one-month mortality were calculated by running multiple-logistic-regression analysis. The cut off values and diagnostic efficiencies of the parameters that posed a risk for mortality were obtained via receiver operating curve analysis. It was determined that the C-reactive protein (CRP), D-dimer, procalcitonin, erythrocyte-sedimentation-rate (ESR), troponin values were at abnormal levels until death occurred in the patients who died. In addition, the procalcitonin levels were consistently high in patients who died. The patients who died generally had a sustained increase in their leukocyte and neutrophil levels and biochemical variables, and an ongoing decrease in lymphopenia and eosinopenia levels. Although significant changes were observed in liver function tests, cardiac troponin, hemogram values, kidney function tests and parameters related to inflammation in deceased patients, high ESR, international-normalized-ratio (INR), prothrombin-time (PT), CRP, D-dimer, ferritin and red-cell-distribution width (RDW) values, respectively, were the most effective predictive mortality risk biomarkers of COVID-19. In addition, neutrophilia, leukocytosis, thrombocytopenia, erythrocytopenia were other risk predictors of mortality. Indicators was found in this study can be successfully used to predict mortality from COVID-19.

COVID-19 is more dangerous for older people and its severity is increasing: a case-control study

Coronavirus disease 2019 (COVID-19) triggers important changes in routine blood tests. In this retrospective case-control study, biochemical, hematological and inflammatory biomarkers between March 10, 2020, and November 30, 2020 from 3969 COVID-19 patients (3746 in the non-intensive care unit (non-ICU) group and 223 in the ICU group) were analyzed by dividing into three groups as spring, summer and autumn. In the non-ICU group, lymphocyte to monocyte ratio was lower in autumn than the other two seasons and neutrophil to lymphocyte ratio was higher in autumn than the other two seasons. Also, monocyte and platelet were higher in spring than autumn; and eosinophil, hematocrit, hemoglobin, lymphocyte, and red blood cells decreased from spring to autumn. In the non-ICU group, alanine aminotransferase and gamma-glutamyltransferase gradually increased from spring to autumn, while albumin, alkaline phosphatase, calcium, total bilirubin and total protein gradually decreased. Additionally, C-reactive protein was higher in autumn than the other seasons, erythrocyte sedimentation rate was higher in autumn than summer. The changes in routine blood biomarkers in COVID-19 varied from the emergence of the disease until now. Also, the timely changes of blood biomarkers were mostly more negative, indicating that the disease progresses severely. The study was approved by the Erzincan Binali Yildirim University Non-interventional Clinical Trials Ethic Committee (approval No. 86041) on June 21, 2021.

Dynamic profiles and predictive values of some biochemical and haematological quantities in COVID-19 inpatients

Introduction

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in some hospitalized patients has shown some important alterations in laboratory tests. The aim of this study was to establish the most relevant quantities associated with the worst prognosis related to COVID-19.

Materials and methods

This was a descriptive, longitudinal, observational and retrospective study, in a cohort of 845 adult inpatients from Bellvitge University Hospital (L’Hospitalet de Llobregat, Barcelona, Spain). A multivariate regression analysis was carried out in demographic, clinical and laboratory data, comparing survivors (SURV) and non-survivors (no-SURV). A receiver operating characteristic analysis was also carried out to establish the cut-off point for poor prognostic with better specificity and sensibility. Dynamic changes in clinical laboratory measurements were tracked from day 1 to day 28 after the onset of symptoms.

Results

During their hospital stay, 18% of the patients died. Age, kidney disease, creatinine (CREA), lactate-dehydrogenase (LD), C-reactive-protein (CRP) and lymphocyte (LYM) concentration showed the strongest independent associations with the risk of death in the multivariate regression analysis. Established cut-off values for poor prognosis for CREA, LD, CRP and LYM concentrations were 75.0 μmol /L, 320 U/L, 80.9 mg/L and 0.69 x10⁹/L. Dynamic profile of laboratory findings, were in agreement with the consequences of organ damage and tissue destruction.

Conclusions

Age, kidney disease, CREA, LD, CRP and LYM concentrations in COVID-19 patients from the southern region of Catalonia provide important information for their prognosis. Measurement of LD has demonstrated to be very good indicator of poor prognosis at initial evaluation because of its stability over time.

Are platelet volume indices of clinical use in COVID-19? A systematic review

Background

The incidence of thrombotic complications is high in COVID-19 patients with severe disease. As key regulators of thrombus formation, platelets likely play a crucial role as mediators of severe acute respiratory syndrome coronavirus 2 associated pathogenesis. Studies have reported that parameters reflecting platelet size, known as platelet volume indices (PVI), are raised in patients with thrombosis and can predict poor outcomes. This systematic review evaluates the potential for PVI to be used as a predictor of COVID-19 morbidity and mortality.

Methods

English and Chinese databases were searched electronically to identify studies reporting data on mean platelet volume, platelet distribution width or platelet-large cell ratio in COVID-19 patients. Included articles underwent a quality rating and descriptive narrative analysis.

Results

Thirty-two studies were included in the systematic review. The results show a general trend for PVI to be raised in severe COVID-19 patients and non-survivors, with 14 studies reporting significant differences of baseline PVI between severe and mild disease. Nonetheless, longitudinal studies showed varying PVI trends over the course of the disease and evidence for PVI to be associated with disease progression was limited. The quality rating of 12 studies was poor, 16 were rated fair and four were good. Most studies were retrospective in design, used small study populations and did not consider confounding factors that influence platelet volume. Studies also contained technical flaws in PVI measurement, limiting the reliability of the results.

Conclusion

The evidence on the clinical usefulness of PVI is greatly limited by the lack of prospective evaluation, together with technical problems in measuring PVI. Carefully designed prospective studies are warranted.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=304305, identifier CRD42022304305.

Red blood cell distribution width as a prognostic biomarker for viral infections: prospects and challenges

Viral diseases remain a significant global health threat, and therefore prioritization of limited healthcare resources is required to effectively manage dangerous viral disease outbreaks. In a pandemic of a newly emerged virus that is yet to be well understood, a noninvasive host-derived prognostic biomarker is invaluable for risk prediction. Red blood cell distribution width (RDW), an index of red blood cell size disorder (anisocytosis), is a potential predictive biomarker for severity of many diseases. In view of the need to prioritize resources during response to outbreaks, this review highlights the prospects and challenges of RDW as a prognostic biomarker for viral infections, with a focus on hepatitis and COVID-19, and provides an outlook to improve the prognostic performance of RDW for risk prediction in viral diseases.

Prediction of diagnosis and prognosis of COVID-19 disease by blood gas parameters using decision trees machine learning model: a retrospective observational study

The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.

Forecasting of Oxidant/Antioxidant levels of COVID-19 patients by using Expert models with biomarkers used in the Diagnosis/Prognosis of COVID-19

Background

Early detection of oxidant-antioxidant levels and special care in severe patients are important in combating the COVID-19 epidemic. However, this process is costly and time consuming. Therefore, there is a need for faster, reliable and economical methods.

Methods

In this study, antioxidant/oxidant levels of patients were estimated by Expert-models using biomarkers, which are effective in the diagnosis/prognosis of COVID-19 disease. For this purpose, Expert-models were trained and created between the white-blood-cell-count (WBC), lymphocyte-count (LYM), C-reactive-protein (CRP), D-dimer, ferritin values of 35 patients with COVID-19 and antioxidant/oxidant parameter values of the same patients. Error criteria and R² ratio were taken into account for the performance of the models. The validity of the all models was checked by the Box-Jenkis-method.

Results

Antioxidant/Oxidant levels were estimated with 95% confidence-coefficient using the values of WBC, LYM, CRP, D-dimer, ferritin of different 500 patients diagnosed with COVID-19 with the trained models. The error rate of all models was low and the coefficients of determination were sufficient. In the first data set, there was no significant difference between measured antioxidant/oxidant levels and predicted antioxidant/oxidant levels. This result showed that the models are accurate and reliable. In determining antioxidant/oxidant levels, LYM and ferritin biomarkers had the most effect on models, while WBC and CRP biomarkers had the least effect. The antioxidant/oxidant parameter estimated with the highest accuracy was Native-Thiol divided by Total-Thiol.

Conclusions

The results showed that the antioxidant/oxidant levels of infected patients can be estimated accurately and reliably with LYM, ferritin, D-dimer, WBC, CRP biomarkers in the COVID-19 outbreak.

INR and COVID-19 severity and mortality: A systematic review with meta-analysis and meta-regression

Objectives

D-dimer elevations, suggesting a pro-thrombotic state and coagulopathy, predict adverse outcomes in coronavirus disease 2019 (COVID-19). However, the clinical significance of other coagulation markers, particularly the international normalized ratio (INR), is not well established. We conducted a systematic review and meta-analysis of the INR in COVID-19.

Methods

A literature search was conducted in PubMed, Web of Science and Scopus, between January 2020 and February 2021, for studies reporting INR values, measures of COVID-19 severity, and mortality (PROSPERO registration number: CRD42021241468).

Results

Thirty-eight studies in 7440 COVID-19 patients with low disease severity or survivor status during follow up (50 ​% males, mean age 57 years) and 2331 with high severity or non-survivor status (60 ​% males, mean age 69 years) were identified. The INR was significantly prolonged in patients with severe disease or non-survivor status than in patients with mild disease or survivor status (standard mean difference, SMD, 0.60; 95 ​% confidence interval, CI 0.42 to 0.77; p ​< ​0.001). There was extreme between-study heterogeneity (I² ​= ​90.2 ​%; p ​< ​0.001). Sensitivity analysis, performed by sequentially removing each study and re-assessing the pooled estimates, showed that the magnitude and direction of the effect size was not modified. The Begg's and Egger's t-tests did not show publication bias. In meta-regression, the SMD of the INR was significantly associated with C-reactive protein (p ​= ​0.048) and D-dimer (p ​= ​0.001).

Conclusions

Prolonged INR values were significantly associated with COVID-19 severity and mortality. Both INR prolongation and D-dimer elevations can be useful in diagnosing COVID-19-associated coagulopathy and predicting clinical outcomes.

Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression

OBJECTIVES

We conducted a systematic review and meta-analysis with meta-regression of creatine kinase-MB (CK-MB), a biomarker of myocardial injury, in COVID-19 patients.

METHODS

We searched PubMed, Web of Science and Scopus, for studies published between January 2020 and January 2021 that reported CK-MB, COVID-19 severity and mortality (PROSPERO registration number: CRD42021239657).

RESULTS

Fifty-five studies in 11,791 COVID-19 patients were included in the meta-analysis. The pooled results showed that CK-MB concentrations were significantly higher in patients with high disease severity or non-survivor status than patients with low severity or survivor status (standardized mean difference, SMD, 0.81, 95% CI 0.61 to 1.01, p<0.001). The rate of patients with CK-MB values above the normal range was also significantly higher in the former than the latter (60/350 vs 98/1,780; RR ​= ​2.84, 95%CI 1.89 to 4.27, p<0.001; I² ​= ​19.9, p ​= ​0.254). Extreme between-study heterogeneity was observed (I² ​= ​93.4%, p<0.001). Sensitivity analysis, performed by sequentially removing each study and re-assessing the pooled estimates, showed that the magnitude and direction of the effect size was not modified (effect size range, 0.77 to 0.84). Begg's (p ​= ​0.50) and Egger's (p ​= ​0.86) t-tests did not show publication bias. In meta-regression analysis, the SMD was significantly and positively associated with the white blood count, aspartate aminotransferase, myoglobin, troponin, brain natriuretic peptide, lactate dehydrogenase, and D-dimer.

CONCLUSIONS

Higher CK-MB concentrations were significantly associated with severe disease and mortality in COVID-19 patients. This biomarker of myocardial injury might be useful for risk stratification in this group.

The effectiveness of blood routine parameters and some biomarkers as a potential diagnostic tool in the diagnosis and prognosis of Covid-19 disease

Since February-2020, the world has been battling a tragic public-health crisis with the emergence and spread of 2019-nCoV. Due to the lack of information about the pathogenesis-specific treatment of Covid-19, early diagnosis and timely treatment are important. However, there is still a lack of information about routine-blood-parameteres (RBP) findings and effects in the disease process. Although the literature includes various interventions, existing studies need to be generalized and their reliability improved. In this study, the efficacy of routine blood values used in the diagnosis and prognosis of Covid-19 and independent biomarkers obtained from them were evaluated retrospectively in a large patient group. Low lymphocyte (LYM) and white-blood-cell (WBC), high CRP and Ferritin were effective in the diagnosis of the disease. The (d-CWL) = CRPWBC∗LYM and (d-CFL) = CRP∗FerritinLYM biomarkers derived from them were the most important risk factors in diagnosing the disease and were more successful than direct RBP values. High d-CWL and d-CFL values largely confirmed the Covid-19 diagnosis. The most effective RBP in the prognosis of the disease was CRP. (d-CIT) = CRP*INR*Troponin; (d-CT) = CRP*Troponin; (d-PPT) = PT*Troponin*Procalcitonin biomarkers were found to be more successful than direct RBP values and biomarkers used in previous studies in the prognosis of the disease. In this study, biomarkers derived from RBP were found to be more successful in both diagnosis and prognosis of Covid-19 than previously used direct RBP and biomarkers.