Meta-analysis of cardiac markers for predictive factors on severity and mortality of COVID-19

The Value of Ischemic Cardiac Biomarkers to Predict Spontaneous Breathing Trial or Extubation Failure: A Systematic Review

Background: It is unclear whether other cardiac biomarkers than NT-proBNP can be useful in the risk stratification of patients weaning from mechanical ventilation. The aim of this study is to summarize the role of ischemic cardiac biomarkers in predicting spontaneous breathing trial (SBT) or extubation failure. Methods: We systematically searched Embase, MEDLINE, Web of Science, and Cochrane Central for studies published before January 2024 that reported the association between ischemic cardiac biomarkers and SBT or extubation failure. Data were extracted using a standardized form and methodological assessment was performed using the QUIPS tool. Results: Seven observational studies investigating four ischemic cardiac biomarkers (Troponin-T, Troponin-I, CK-MB, Myoglobin) were included. One study reported a higher peak Troponin-I in patients with extubation failure compared to extubation success (50 ng/L [IQR, 20-215] versus 30 ng/L [IQR, 10-86], p = 0.01). A second study found that Troponin-I measured before the SBT was higher in patients with SBT failure in comparison to patients with SBT success (100 ± 80 ng/L versus 70 ± 130 ng/L, p = 0.03). A third study reported a higher CK-MB measured at the end of the SBT in patients with weaning failure (SBT or extubation failure) in comparison to weaning success (8.77 ± 20.5 ng/mL versus 1.52 ± 1.42 ng/mL, p = 0.047). Troponin-T and Myoglobin as well as Troponin-I and CK-MB measured at other time points were not found to be related to SBT or extubation failure. However, most studies were underpowered and with high risk of bias. Conclusions: The association with SBT or extubation failure is limited for Troponin-I and CK-MB and appears absent for Troponin-T and Myoglobin, but available studies are hampered by significant methodological drawbacks. To more definitively determine the role of ischemic cardiac biomarkers, future studies should prioritize larger sample sizes, including patients at risk of cardiac disease, using stringent SBTs and structured timing of laboratory measurements before and after SBT.

Changes in the investigation and management of suspected myocardial infarction and injury during COVID-19: a multi-centre study using routinely collected healthcare data

Objective

The COVID-19 pandemic was associated with a reduction in the incidence of myocardial infarction (MI) diagnosis, in part because patients were less likely to present to hospital. Whether changes in clinical decision making with respect to the investigation and management of patients with suspected MI also contributed to this phenomenon is unknown.

Methods

Multicentre retrospective cohort study in three UK centres contributing data to the National Institute for Health Research Health Informatics Collaborative. Patients presenting to the Emergency Department (ED) of these centres between 1st January 2020 and 1st September 2020 were included. Three time epochs within this period were defined based on the course of the first wave of the COVID-19 pandemic: pre-pandemic (epoch 1), lockdown (epoch 2), post-lockdown (epoch 3).

Results

During the study period, 10,670 unique patients attended the ED with chest pain or dyspnoea, of whom 6,928 were admitted. Despite fewer total ED attendances in epoch 2, patient presentations with dyspnoea were increased (p < 0.001), with greater likelihood of troponin testing in both chest pain (p = 0.001) and dyspnoea (p < 0.001). There was a dramatic reduction in elective and emergency cardiac procedures (both p < 0.001), and greater overall mortality of patients (p < 0.001), compared to the pre-pandemic period. Positive COVID-19 and/or troponin test results were associated with increased mortality (p < 0.001), though the temporal risk profile differed.

Conclusions

The first wave of the COVID-19 pandemic was associated with significant changes not just in presentation, but also the investigation, management, and outcomes of patients presenting with suspected myocardial injury or MI.

Assessment of the predictive value of plasma calprotectin in the evolution of SARS-Cov-2 primo-infection

BACKGROUND

The COVID-19 epidemic still calls for anticipation aimed at preventing the overloading of critical care services. With this in mind, the predictive value of easily accessible biomarkers is to be assessed.

OBJECTIVE

Secretion of calprotectin is stimulated during an inflammatory process, especially in the cytokine storm. We tried to determine whether early plasma concentration of calprotectin in patients with primary SARS-CoV-2 infection could predict an adverse outcome in cases of COVID-19.

METHODS

We included 308 patients with a primary diagnosis of SARS-CoV-2 confirmed by PCR. Heparinized tube samples, collected within the first 24 h of hospitalization, were used for biomarker assays, in which plasma calprotectin was included. Data from the patients' medical records and severity groups established subsequent to diagnosis at the end of hospitalization were collected.

RESULTS

Early plasma calprotectin concentration is significantly associated with progression to a severe form of COVID-19 in patients with primary infection (Relative Risk: 2.2 [1.6-2.7]). In multivariate analysis, however, it does not appear to provide additional information compared to other parameters (age, GFR, CRP…).

CONCLUSION

Our study shows that while an early single blood test for calprotectin could help to predict the progression of a primary SARS-CoV-2 infection, it is not superior to the other parameters currently used in emergency medicine. However, it paves the way for future considerations, such as the interest of this biomarker for high-risk infected patients (immunocompromised individuals…). Finally, the usefulness of early serial measurements of plasma calprotectin to assess progression towards severity of COVID-19 requires further assessment.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Temporal patterns of cytokine and injury biomarkers in hospitalized COVID-19 patients treated with methylprednisolone

Background

The novel coronavirus disease 2019 (COVID-19) presents with complex pathophysiological effects in various organ systems. Following the COVID-19, there are shifts in biomarker and cytokine equilibrium associated with altered physiological processes arising from viral damage or aggressive immunological response. We hypothesized that high daily dose methylprednisolone improved the injury biomarkers and serum cytokine profiles in COVID-19 patients.

Methods

Injury biomarker and cytokine analysis was performed on 50 SARS-Cov-2 negative controls and 101 hospitalized severe COVID-19 patients: 49 methylprednisolone-treated (MP group) and 52 placebo-treated serum samples. Samples from the treated groups collected on days D1 (pre-treatment) all the groups, D7 (2 days after ending therapy) and D14 were analyzed. Luminex assay quantified the biomarkers HMGB1, FABP3, myoglobin, troponin I and NTproBNP. Immune mediators (CXCL8, CCL2, CXCL9, CXCL10, TNF, IFN-γ, IL-17A, IL-12p70, IL-10, IL-6, IL-4, IL-2, and IL-1β) were quantified using cytometric bead array.

Results

At pretreatment, the two treatment groups were comparable demographically. At pre-treatment (D1), injury biomarkers (HMGB1, TnI, myoglobin and FABP3) were distinctly elevated. At D7, HMGB1 was significantly higher in the MP group (p=0.0448) compared to the placebo group, while HMGB1 in the placebo group diminished significantly by D14 (p=0.0115). Compared to healthy control samples, several immune mediators (IL-17A, IL-6, IL-10, MIG, MCP-1, and IP-10) were considerably elevated at baseline (all p≤0.05). At D7, MIG and IP-10 of the MP-group were significantly lower than in the placebo-group (p=0.0431, p=0.0069, respectively). Longitudinally, IL-2 (MP-group) and IL-17A (placebo-group) had increased significantly by D14. In placebo group, IL-2 and IL-17A continuously increased, as IL-12p70, IL-10 and IP-10 steadily decreased during follow-up. The MP treated group had IL-2, IFN-γ, IL-17A and IL-12p70 progressively increase while IL-1β and IL-10 gradually decreased towards D14. Moderate to strong positive correlations between chemokines and cytokines were observed on D7 and D14.

Conclusion

These findings suggest MP treatment could ameliorate levels of myoglobin and FABP3, but appeared to have no impact on HMGB1, TnI and NTproBNP. In addition, methylprednisolone relieves the COVID-19 induced inflammatory response by diminishing MIG and IP-10 levels. Overall, corticosteroid (methylprednisolone) use in COVID-19 management influences the immunological molecule and injury biomarker profile in COVID-19 patients.

Association Between COVID-19 Infection and Cardiac Biomarkers in Hospitalized Patients at a Tertiary Care Center

Background The effects of coronavirus disease 2019 (COVID-19) on the cardiovascular system are well established. However, knowledge gaps in the clinical implications of cardiac involvement in COVID-19 patients are yet to be addressed. This study aimed to investigate acute cardiac injury (ACI) risk factors and outcomes associated with COVID-19 infection with cardiac involvement. Methodology In this retrospective study, we included hospitalized patients between March 2020 and May 2022 with confirmed COVID-19 infection and evidence of cardiac involvement. Results In total, 501 patients were included, of whom 396 (79%) had evidence of ACI. The median troponin level was 25.8 (interquartile range (IQR) = 10.8-71). Patients with evidence of ACI were significantly more likely to have diabetes mellitus (75% vs. 60%), cardiovascular disease (48% vs. 37%), chronic lung disease (22.2% vs. 12.4%), and chronic kidney disease (32.3% vs. 16.2%). Additionally, patients with ACI were significantly more likely to have cardiomegaly (60.6% vs. 44.8%) and bilateral lobe infiltrates (77.8% vs. 60%) on X-ray. Patients with ACI were significantly more likely to suffer from complications such as cardiogenic shock (5.3% vs. 0%), pneumonia (80.1% vs. 65.7%), sepsis (24.2% vs. 9.5%), and acute respiratory distress syndrome (33.1% vs. 8.6%). Patients with ACI were also significantly more likely to be admitted to the intensive care unit (ICU) (57% vs. 26.7%) and significantly more likely to die (38.1% vs. 11.4%). The results of the multivariate regression analysis indicated that mortality was significantly higher in patients with elevated troponin levels (adjusted odds ratio = 4.73; 95% confidence interval = 2.49-8.98). Conclusions In COVID-19-infected patients, old age, diabetes mellitus, cardiovascular disease, chronic lung disease, and chronic kidney disease were associated with an increased risk of ACI. The presence of ACI in the context of COVID-19 infection was noted to increase the risk for severe complications, such as cardiogenic shock, ICU admission, sepsis, and death.

CVD22: Explainable artificial intelligence determination of the relationship of troponin to D-Dimer, mortality, and CK-MB in COVID-19 patients

BACKGROUND AND PURPOSE

COVID-19, which emerged in Wuhan (China), is one of the deadliest and fastest-spreading pandemics as of the end of 2019. According to the World Health Organization (WHO), there are more than 100 million infectious cases worldwide. Therefore, research models are crucial for managing the pandemic scenario. However, because the behavior of this epidemic is so complex and difficult to understand, an effective model must not only produce accurate predictive results but must also have a clear explanation that enables human experts to act proactively. For this reason, an innovative study has been planned to diagnose Troponin levels in the COVID-19 process with explainable white box algorithms to reach a clear explanation.

METHODS

Using the pandemic data provided by Erzurum Training and Research Hospital (decision number: 2022/13-145), an interpretable explanation of Troponin data was provided in the COVID-19 process with SHApley Additive exPlanations (SHAP) algorithms. Five machine learning (ML) algorithms were developed. Model performances were determined based on training, test accuracies, precision, F1-score, recall, and AUC (Area Under the Curve) values. Feature importance was estimated according to Shapley values by applying the SHApley Additive exPlanations (SHAP) method to the model with high accuracy. The model created with Streamlit v.3.9 was integrated into the interface with the name CVD22.

RESULTS

Among the five-machine learning (ML) models created with pandemic data, the best model was selected with the values of 1.0, 0.83, 0.86, 0.83, 0.80, and 0.91 in train and test accuracy, precision, F1-score, recall, and AUC values, respectively. As a result of feature selection and SHApley Additive exPlanations (SHAP) algorithms applied to the XGBoost model, it was determined that DDimer mean, mortality, CKMB (creatine kinase myocardial band), and Glucose were the features with the highest importance over the model estimation.

CONCLUSIONS

Recent advances in new explainable artificial intelligence (XAI) models have successfully made it possible to predict the future using large historical datasets. Therefore, throughout the ongoing pandemic, CVD22 (https://cvd22covid.streamlitapp.com/) can be used as a guide to help authorities or medical professionals make the best decisions quickly.

Natural Course of COVID-19 and Independent Predictors of Mortality

BACKGROUND

During the SARS-CoV-2 pandemic, several biomarkers were shown to be helpful in determining the prognosis of COVID-19 patients. The aim of our study was to evaluate the prognostic value of N-terminal pro-Brain Natriuretic Peptide (NT-pro-BNP) in a cohort of patients with COVID-19.

METHODS

One-hundred and seven patients admitted to the Covid Hospital of Messina University between June 2022 and January 2023 were enrolled in our study. The demographic, clinical, biochemical, instrumental, and therapeutic parameters were recorded. The primary outcome was in-hospital mortality. A comparison between patients who recovered and were discharged and those who died during the hospitalization was performed. The independent parameters associated with in-hospital death were assessed by multivariable analysis and a stepwise regression logistic model.

RESULTS

A total of 27 events with an in-hospital mortality rate of 25.2% occurred during our study. Those who died during hospitalization were older, with lower GCS and PaO2/FiO2 ratio, elevated D-dimer values, INR, creatinine values and shorter PT (prothrombin time). They had an increased frequency of diagnosis of heart failure (p < 0.0001) and higher NT-pro-BNP values. A multivariate logistic regression analysis showed that higher NT-pro-BNP values and lower PT and PaO2/FiO2 at admission were independent predictors of mortality during hospitalization.

CONCLUSIONS

This study shows that NT-pro-BNP levels, PT, and PaO2/FiO2 ratio are independently associated with in-hospital mortality in subjects with COVID-19 pneumonia. Further longitudinal studies are warranted to confirm the results of this study.

Cardiac Injury in COVID-19: A Systematic Review of Relevant Meta-Analyses

Background

Cardiac injury (CI) is not a rare condition among hospitalized patients with coronavirus disease 2019 (COVID-19). Its prognostic value has been extensively reported through the literature, mainly in the context of observational studies. An impressive number of relevant meta-analyses has been conducted. These meta-analyses present similar and consistent results; yet interesting methodological issues emerge.

Methods

A systematic literature search was conducted aiming to identify all relevant meta-analyses on (i) the incidence, and (ii) the prognostic value of CI among hospitalized patients with COVID-19.

Results

Among 118 articles initially retrieved, 73 fulfilled the inclusion criteria and were included in the systematic review. Various criteria were used for CI definition mainly based on elevated cardiac biomarkers levels. The most frequently used biomarker was troponin. 30 meta-analyses reported the pooled incidence of CI in hospitalized patients with COVID-19 that varies from 5% to 37%. 32 meta-analyses reported on the association of CI with COVID-19 infection severity, with only 6 of them failing to show a statistically significant association. Finally, 46 meta-analyses investigated the association of CI with mortality and showed that patients with COVID-19 with CI had increased risk for worse prognosis. Four meta-analyses reported pooled adjusted hazard ratios for death in patients with COVID-19 and CI vs those without CI ranging from 1.5 to 3.

Conclusions

The impact of CI on the prognosis of hospitalized patients with COVID-19 has gained great interest during the pandemic. Methodological issues such as the inclusion of not peer-reviewed studies, the inclusion of potentially overlapping populations or the inclusion of studies with unadjusted analyses for confounders should be taken into consideration. Despite these limitations, the adverse prognosis of patients with COVID-19 and CI has been consistently demonstrated.

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

OBJECTIVE

To develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features.

METHODS

Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%) followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation, followed by testing using the test set.

RESULTS

Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC.

CONCLUSION

The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

COVID-19 Severity and Mortality in Two Pandemic Waves in Poland and Predictors of Poor Outcomes of SARS-CoV-2 Infection in Hospitalized Young Adults

SARS-CoV-2 variants pose a significant threat to global public health. However, their influence on disease severity, especially among young adults who may exhibit different clinical characteristics, is debatable. In this retrospective study of 229 young adults hospitalized with COVID-19, we investigated the differences between Poland's second and third waves of the pandemic. To identify potential predictors of severe COVID-19 in young adults, we analyzed patient characteristics and laboratory findings between survivors and non-survivors and we performed logistic regression to assess the risk of death, mechanical ventilation, and intensive care unit treatment. We found no increase in COVID-19 severity comparing the third and second waves of the pandemic, indicating that the alpha variant had no influence on disease severity. In addition, we found that factors, such as obesity, comorbidities, lung involvement, leukocytosis, neutrophilia, lymphopenia, higher IG count, the neutrophil-to-lymphocyte ratio, C-reactive protein, procalcitonin, interleukin-6, D-Dimer, lactate dehydrogenase, high-sensitive troponin I, creatine kinase-myocardial band, myoglobin, N-terminal-pro-B-type natriuretic peptide, creatinine, urea and gamma-glutamyl transferase, lower estimated glomerular filtration rate, albumin, calcium and vitamin D3, possibly a decrease in red blood cell counts, hemoglobin and hematocrit, and an increase in creatine kinase during hospitalization may be associated with poor outcomes of COVID-19.

Biomarkers Associated with Cardiovascular Disease in COVID-19

Coronavirus disease-19 (COVID-19) emerged late December 2019 in the city of Wuhan, China and has since spread rapidly all over the world causing a global pandemic. While the respiratory system is the primary target of disease manifestation, COVID-19 has been shown to also affect several other organs, making it a rather complex, multi-system disease. As such, cardiovascular involvement has been a topic of discussion since the beginning of the COVID-19 pandemic, primarily due to early reports of excessive myocardial injury in these patients. Treating physicians are faced with multiple challenges in the management and early triage of patients with COVID-19, as disease severity is highly variable ranging from an asymptomatic infection to critical cases rapidly deteriorating to intensive care treatment or even fatality. Laboratory biomarkers provide important prognostic information which can guide decision making in the emergency department, especially in patients with atypical presentations. Several cardiac biomarkers, most notably high-sensitive cardiac troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), have emerged as valuable predictors of prognosis in patients with COVID-19. The purpose of this review was to offer a concise summary on prognostic cardiac biomarkers in COVID-19 and discuss whether routine measurements of these biomarkers are warranted upon hospital admission.

Machine learning approaches in Covid-19 severity risk prediction in Morocco

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer

To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.

Biomarkers in the management of acute heart failure: state of the art and role in COVID-19 era

Acute heart failure (AHF) affects millions of people worldwide, and it is a potentially life‐threatening condition for which the cardiologist is more often brought into play. It is crucial to rapidly identify, among patients presenting with dyspnoea, those with AHF and to accurately stratify their risk, in order to define the appropriate setting of care, especially nowadays due to the coronavirus disease 2019 (COVID‐19) outbreak. Furthermore, with physical examination being limited by personal protective equipment, the use of new alternative diagnostic and prognostic tools could be of extreme importance. In this regard, usage of biomarkers, especially when combined (a multimarker approach) is beneficial for establishment of an accurate diagnosis, risk stratification and post‐discharge monitoring. This review highlights the use of both traditional biomarkers such as natriuretic peptides (NP) and troponin, and emerging biomarkers such as soluble suppression of tumourigenicity (sST2) and galectin‐3 (Gal‐3), from patients' emergency admission to discharge and follow‐up, to improve risk stratification and outcomes in terms of mortality and rehospitalization.

Natriuretic Peptide Levels and Clinical Outcomes Among Patients Hospitalized With Coronavirus Disease 2019 Infection

OBJECTIVES

There is increasing evidence of cardiovascular morbidity associated with severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019). Pro-B-type natriuretic peptide is a biomarker of myocardial stress, associated with various respiratory and cardiac outcomes. We hypothesized that pro-B-type natriuretic peptide level would be associated with mortality and clinical outcomes in hospitalized coronavirus disease 2019 patients.

DESIGN

We performed a retrospective analysis using adjusted logistic and linear regression to assess the association of admission pro-B-type natriuretic peptide (analyzed by both cutoff > 125 pg/mL and log transformed pro-B-type natriuretic peptide) with clinical outcomes. We additionally treated body mass index, a confounder of both pro-B-type natriuretic peptide levels and coronavirus disease 2019 outcomes, as an ordinal variable.

SETTING

We reviewed hospitalized patients with coronavirus disease 2019 who had a pro-B-type natriuretic peptide level measured within 48 hours of admission between March 1, and August 31, 2020, from a multihospital U.S. health system.

PATIENTS

Adult patients (≥ 18 yr old; n = 1232) with confirmed coronavirus disease 2019 admitted to the health system.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

After adjustment for demographics, comorbidities, and troponin I level, higher pro-B-type natriuretic peptide level was significantly associated with death and secondary outcomes of new heart failure, length of stay, ICU duration, and need for ventilation among hospitalized coronavirus disease 2019 patients. This significance persisted after adjustment for body mass index as an ordinal variable. The adjusted hazard ratio of death for log transformed pro-B-type natriuretic peptide was 1.56 (95% CI, 1.23-1.97; p < 0.0001).

CONCLUSIONS

Further investigation is warranted on the utility of pro-B-type natriuretic peptide for clinical prognostication in coronavirus disease 2019 as well as implications of abnormal pro-B-type natriuretic peptide in the underlying pathophysiology of coronavirus disease 2019-related myocardial injury.

B-Type Natriuretic Peptides and High-Sensitive Troponin I as COVID-19 Survival Factors: Which One Is the Best Performer?

Increased concentrations of B-type natriuretic peptide (BNP), N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity troponin I (HsTnI) in COVID-19 patients have already been reported. The aim of this study is to evaluate which of these common markers of cardiac disease is the most useful predictor of fatal outcome in COVID-19 patients. One hundred and seventy-four patients affected with COVID-19 were recruited, and markers of cardiac disease and the clinical history of the patients were collected at admission in the infectious disease unit or intensive care unit. NT-proBNP, BNP and HsTnI values were higher in in-hospital non-surviving patients. Receiver operating characteristic (ROC) curve analysis of NT-proBNP, BNP and HsTnI was performed, with NT-proBNP (AUC = 0.951) and HsTnI (AUC = 0.947) being better performers (p = 0.01) than BNP (AUC = 0.777). Logistic regression was performed assessing the relation of HsTnI and NT-proBNP to fatal outcome adjusting for age and gender, with only NT-proBNP being significant. The population was then divided into two groups, one with higher NT-proBNP values at admission than the cut-off resulted from the ROC curve (511 ng/L) and a second one with lower values. The Kaplan-Meier analysis showed an absence of fatal outcome in the group of patients with NT-proBNP values lower than the cut-off (p < 0.001). NT-proBNP proved to be the best prognostic tool for fatal outcome among markers of cardiac disease in COVID-19 patients.