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

Vargas, Maria; Palma, D. De; Savoia, Marcella; Miscioscia, Anna; Pinchera, Biagio; Vano, Martina; Servillo, Giuseppe; Gentile, Ivan; Fortunato, Giuliana

doi:10.3390/jcm10122726

Cited by 10 publications

(9 citation statements)

References 28 publications

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“…In line with other reported data, BNP did not show a predictive value for the outcomes in noncritically ill survivors versus non-survivors [ 39 ]. This may be explained by the fact that, compared to BNP, NT-pro BNP and hs-TnI appeared to be better performers in predicting mortality for inpatients [ 9 , 39 ]. NT-pro BNP acts as a more sensitive prognostic biomarker after admission [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…In critically ill COVID-19 patients, defined as individuals with respiratory failure, septic shock, and/or multiple organ dysfunction [ 7 ], the use of cardiac biomarkers for risk stratification proved to be useful [ 8 ], since elevated troponin is associated with greater mortality in COVID-19, and N-terminal pro-B-type natriuretic peptide (NT-pro BNP) is strongly and independently associated with in-hospital mortality and other complications in patients with and without heart failure [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Inflammatory and Cardiac Biomarkers in Relation with Post-Acute COVID-19 and Mortality: What We Know after Successive Pandemic Waves

et al. 2022

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Background: Biomarkers were correlated with mortality in critically ill COVID-19 patients. No prediction tools exist for noncritically ill COVID-19 patients. We aimed to compare the independent prognostic value of inflammation and cardiac biomarkers for post-acute COVID-19 patients and the 30-day mortality rate in noncritically ill COVID-19 patients, as well as the relation with the virus variant involved. Methods: This observational cohort study was conducted at an emergency clinical hospital between 1 October 2020 and 31 December 2021. We included consecutive patients with biomarkers determined within 24 h of presentation, followed up at least 30 days postdischarge. Results: Post-acute COVID-19 was diagnosed in 20.3% of the cases and the all-cause 30-day mortality rate was 35.1% among 978 patients infected with variants of concern. Neutrophil-to-lymphocyte ratio (1.06 [95%CI, 1.01–1.11], p = 0.015) and NT-pro BNP were correlated with 30-daymortality, while the monocyte-to-lymphocyte ratio (2.77 [95%CI, 1.10–6.94], p = 0.03) and NT-pro BNP (1.68 [95%CI, 1.00–2.84], p = 0.05) were correlated with post-acute COVID-19. High-sensitivity to troponin was associated with 30-day mortality (1.55 [95%CI, 1.00–2.42], p = 0.05). A Cox proportional-hazards model confirmed that NT-pro BNP was independently associated with mortality. NT-pro BNP remained independently associated with 30-day mortality during follow-up (1.29 [95%CI, 1.07–1.56], p = 0.007) after adjustment for confounders. Conclusion: Inflammation and cardiac biomarkers, determined upon admission and predischarge, in a cohort of hospitalized noncritically ill COVID-19 patients throughout successive pandemic waves, showed a predictive value for post-acute COVID-19 and 30-day mortality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inflammatory and Cardiac Biomarkers in Relation with Post-Acute COVID-19 and Mortality: What We Know after Successive Pandemic Waves

et al. 2022

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“…As a sign of myocardial stress, NT-proBNP increase could be due to a cytokine storm in response to the infection trigger and to the direct action of the virus on the heart walls [ 35 , 36 ]. More accurately, NT-proBNP appears as the best representative prognosis biomarker in COVID-19 disease [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

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

Imbalzano

Orlando

Sciacqua

et al. 2021

JCM

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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.

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“…In SARS-CoV-2 infection, the dynamic changes of NT-proBNP, CRP, and cTn concentrations are basically related to the extent of the inflammatory response, tissue injury, and functional impairment. Guo et al reported higher levels of natriuretic peptides in severe forms of COVID-19 pneumonia, especially in deceased patients [ 26 ], while De Falco et al showed that the prognosis value of NT-proBNP is similar to cTn, specifically for mortality risk stratification [ 136 ].…”

Section: Biomarkers In the Diagnosis And Prognosis Of Acute Myocardia...mentioning

confidence: 99%

A Real Pandora’s Box in Pandemic Times: A Narrative Review on the Acute Cardiac Injury Due to COVID-19

Timpau

Miftode

Leca

et al. 2022

Life

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The intricate relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the cardiovascular system is an extensively studied pandemic topic, as there is an ever-increasing amount of evidence that reports a high prevalence of acute cardiac injury in the context of viral infection. In patients with Coronavirus disease 2019, COVID-19, a significant increase in serum levels of cardiac troponin or other various biomarkers was observed, suggesting acute cardiac injury, thus predicting both a severe course of the disease and a poor outcome. Pathogenesis of acute cardiac injury is not yet completely elucidated, though several mechanisms are allegedly involved, such as a direct cardiomyocyte injury, oxygen supply-demand inequity caused by hypoxia, several active myocardial depressant factors during sepsis, and endothelial dysfunction due to the hyperinflammatory status. Moreover, the increased levels of plasma cytokines and catecholamines and a significantly enhanced prothrombotic environment may lead to the destabilization and rupture of atheroma plaques, subsequently triggering an acute coronary syndrome. In the present review, we focus on describing the epidemiology, pathogenesis, and role of biomarkers in the diagnosis and prognosis of patients with acute cardiac injury in the setting of the COVID-19 pandemic. We also explore some novel therapeutic strategies involving immunomodulatory therapy, as well as their role in preventing a severe form of the disease, with both the short-term outcome and the long-term cardiovascular sequelae being equally important in patients with SARS-CoV-2 induced acute cardiac injury.

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

Cited by 10 publications

References 28 publications

Inflammatory and Cardiac Biomarkers in Relation with Post-Acute COVID-19 and Mortality: What We Know after Successive Pandemic Waves

Inflammatory and Cardiac Biomarkers in Relation with Post-Acute COVID-19 and Mortality: What We Know after Successive Pandemic Waves

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

A Real Pandora’s Box in Pandemic Times: A Narrative Review on the Acute Cardiac Injury Due to COVID-19

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