Combination of serum lactate dehydrogenase and sex is predictive of severe disease in patients with COVID-19

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Lactate Dehydrogenase Levels as A Marker of COVID-19 Severity

Coronavirus Disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Knowing the severity of COVID-19 is important during a pandemic. Measurement of Lactate Dehydrogenase (LDH) levels is a simple, quick, and widely available laboratory test in most health facilities. Lactate dehydrogenase levels change significantly in patients with tissue damage including COVID-19 disease. The purpose of this study was to analyze the LDH levels as a marker of the severity of COVID-19. The research method used was a cross-sectional approach using primary data from 70 suspected COVID-19 patients from June to July 2021 at Labuang Baji Hospital, Hasanuddin University Hospital, and Makassar City Hospital. Samples were grouped into mild, moderate, and severe COVID-19. The LDH levels at the time of hospital admission were measured using an Architect device. Chi-Square, Kruskal-Wallis, and ROC curve statistical tests were used to obtain the LDH value with a significant value of p<0.05. The sample consisted of 24 mild COVID-19, 23 moderate COVID-19, and 23 severe COVID-19. The LDH levels in mild COVID-19 were 101.00 U/L (74.00-156.00 U/L) significantly different from moderate COVID-19 was 143.00 U/L (126.00-253.00 U/L) and COVID-19 were 291.00 U/L (177.00-655.00 U/L) (p<0.001) and had a very strong positive correlation (r=0.914). The ROC curve showed that LDH had a sensitivity of 91.3%, specificity of 94.7% with the cut-off >250.5 U/L, NPV of 96.4%, PPV of 87.5%, and accuracy of 91.3%. LDH levels increase along with the increasing severity of COVID-19 caused by tissue damage due to increased inflammatory response. LDH can be used as a marker of COVID-19 severity.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study

OBJECTIVE

The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (M_RM), clinical (M_CM), and combined clinical-radiomics (M_RCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans.

METHODS

We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D₁ = 787, and University Hospitals, US D₂ = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T .

RESULTS

The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (M_RM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The M_RM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, M_RCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The M_RCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over M_CM.

CONCLUSION

The novel integrated imaging and clinical model (M_RCM) outperformed both models (M_RM) and (M_CM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.

Derivation and external validation of a simple prediction rule for the development of respiratory failure in hospitalized patients with influenza

BACKGROUND

Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.

METHODS

A prospective cohort study was conducted during two consecutive Influenza seasons (December 2016-March 2017 and December 2017-April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 and May 2019.

RESULTS

Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p < 0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher's test p > 0.43).

CONCLUSION

we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient's stratification during seasonal Influenza epidemics.

Profiling of Lactate Dehydrogenase Isoenzymes in COVID-19 Disease

INTRODUCTION

Serum total lactate dehydrogenase (LDH) activity was elevated and showed a positive correlation with disease severity and outcome in severe COVID-19 disease. However, it is still unknown whether the relative abundance or calculated activity of any LDH isoenzyme is predominately increased in COVID-19 subjects.

METHODS

Twenty-two consecutive patients suffered from moderate or severe COVID-19 pneumonia were recruited into this study who showed enhanced total LDH activity. The ratio of LDH isoenzyme activities was further investigated using gel electrophoresis (Hydragel^®, Sebia) with densitometric evaluation. Calculated activity values of these isoenzymes were correlated with routine laboratory parameters, the degree of lung parenchymal affection based on chest CT and clinical outcome.

RESULTS

Total LDH activity was raised in the range of 272-2141 U/L and significantly correlated with calculated LDH-3 and LDH-4 activities (r=0.765, P=0.0001; and r=0.783, P=0.0001, respectively). In contrast, the relative abundance of neither LDH isoenzyme was exclusively abnormal in COVID-19 patients. Calculated activity of LDH-3 and LDH-4 demonstrated a modest but statistically significant association with serum ferritin (r=0.437, P=0.042; r=0.505, P=0.016, respectively). When the relationship between the severity of pulmonary affection by SARS-CoV-2 infection and relative abundance of LDH isoenzymes was studied, a larger ratio of mid-zone fractions was observed in the presence of ≥ 50% lung parenchymal involvement. Finally, regardless of LDH isoenzyme pattern, abnormal relative ratio of LDH-4 and higher calculated LDH-3 and LDH-4 activity values were detected in subjects with unfavorable outcome.

CONCLUSION

No characteristic profile of LDH isoenzymes can be detected in COVID-19 pneumonia, however, elevated activities of LDH-3 and LDH-4 are associated with worse 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.

Serum albumin concentrations are associated with disease severity and outcomes in coronavirus 19 disease (COVID-19): a systematic review and meta-analysis

Coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the most threatening pandemic in modern history. The aim of this systematic review and meta-analysis was to investigate the associations between serum albumin concentrations and COVID-19 disease severity and adverse outcomes. A systematic literature search was conducted in PubMed, from inception to October 30, 2020. Sixty-seven studies in 19,760 COVID-19 patients (6141 with severe disease or poor outcome) were selected for analysis. Pooled results showed that serum albumin concentrations were significantly lower in patients with severe disease or poor outcome (standard mean difference, SMD: - 0.99 g/L; 95% CI, - 1.11 to - 0.88, p < 0.001). In multivariate meta-regression analysis, age (t =  - 2.13, p = 0.043), publication geographic area (t = 2.16, p = 0.040), white blood cell count (t =  - 2.77, p = 0.008) and C-reactive protein (t =  - 2.43, p = 0.019) were significant contributors of between-study variance. Therefore, lower serum albumin concentrations are significantly associated with disease severity and adverse outcomes in COVID-19 patients. The assessment of serum albumin concentrations might assist with early risk stratification and selection of appropriate care pathways in this group.

Lactate-dehydrogenase associated with mortality in hospitalized patients with COVID-19 in Mexico: a multi-centre retrospective cohort study

INTRODUCTION AND OBJECTIVES

As of January 2021, over 88 million people have been infected with COVID-19. Almost two million people have died of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A high SOFA score and a D-Dimer >1 µg/mL identifies patients with high risk of mortality. High lactate dehydrogenase (LDH) levels on admission are associated with severity and mortality. Different degrees of alanine aminotransferase (ALT) and/or aspartate aminotransferase (AST) abnormalities have been reported in these patients, its association with a mortality risk remains controversial. The aim of this study was to explore the correlation between LDH and in-hospital mortality in Mexican patients admitted with COVID-19.

MATERIALS & METHODS

We performed a retrospective multi-centre cohort study with 377 hospitalized patients with confirmed SARS-CoV-2 in three centres in Mexico City, Mexico, who were ≥18 years old and died or were discharged between April 1 and May 31, 2020.

RESULTS

A total of 377 patients were evaluated, 298 (79.1%) patients were discharged, and 79 (20.9%) patients died during hospitalization. Non-survivors were older, with a median age of 46.7 ± 25.7 years old, most patients were male. An ALT > 61 U/l (OR 3.45, 95% CI 1.27-9.37; p = 0.015), C-reactive protein (CRP) > 231 mg/l (OR 4.71, 95% CI 2.35-9.46; p = 0.000), LDH > 561 U/l (OR 3.03, 95% CI 1.40-6.55; p = 0.005) were associated with higher odds for in-hospital death.

CONCLUSIONS

Our results indicate that higher levels of LDH, CRP, and ALT are associated with higher in-hospital mortality risk in Mexican patients admitted with COVID-19.