Acute Myocardial Infarction Due to Eltrombopag Therapy in a Patient with Immune Thrombocytopenic Purpura

Sert, Sena; Özdil, Hasan; Sünbül, Murat

doi:10.4274/tjh.2016.0169

Cited by 8 publications

(2 citation statements)

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“…High levels of Urea in blood have been connected to increased risk of cardiovascular events that include cardiac murmur [48], irregular heart rate [49] and acute cardiac failure [50]. Serious cardiovascular events that include block heart, cardiac murmur, irregular heart rate, cardiac failure, arrhythmia and cardiac failure acute were predicted for the drug, Eltrombopag [51–53]. According to a FDA report, Tretinoin was found to be associated with various cardiac side effects that include arrhythmias, hypotension, hypertension, cardiac failure, and cardiac murmur.…”

Section: Resultsmentioning

confidence: 99%

Computational models for the prediction of adverse cardiovascular drug reactions

et al. 2019

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Background Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. Methods In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. Results This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. Conclusions The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development. Electronic supplementary material The online version of this article (10.1186/s12967-019-1918-z) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Computational models for the prediction of adverse cardiovascular drug reactions

et al. 2019

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“…Patients with concurrent ITP and HF face an increased risk of complications, particularly in cases like acute myocardial infarction, where the propensity for bleeding due to thrombocytopenia complicates clinical decision-making. 3 Additionally, the abnormally enlarged immature platelets and increased antibody-mediated damage to the endothelium contributes to a heightened risk of thrombotic complications. This combination of factors highlights the need for a more nuanced understanding of the interplay between ITP and heart failure, and the consequential impacts on patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

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2024

JCPCR

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Background: Patients admitted with autoimmune thrombocytopenic purpura (ITP) tend to be high-risk individuals, primarily because of their susceptibility to bleeding and the constraints on potential medical interventions. Heart failure (HF) is a complex and widespread condition that significantly impairs quality of life. These patients face a multitude of respiratory and cardiovascular complications, compounded by the fact that they are often prescribed blood thinner medications which can exacerbate these risks. We analyzed the data to comprehensively understand the far-reaching impact and varied outcomes of underlying heart failure on patients diagnosed with autoimmune thrombocytopenic purpura. Materials and Methods:The National Inpatient Sample database for 2019-2020 was utilized to identify individuals admitted with ITP. The cohort was further divided into patients with and without a secondary diagnosis of concomitant HF. Multivariate regression analysis was performed to evaluate the impact of HF on ITP patients and the outcomes. Results:The study cohort consisted of 27885 patients hospitalized with ITP; among these patients, 1950 (7%) had the concurrent diagnosis of HF. Primary and secondary outcomes were analyzed after adjustment of confounding variables through multivariate regression analysis. There was no difference in Mortality (OR 1.1, 95% CI 0.52-2.29, P=0.795) and length of stay between the two patient groups (+1.14 days, 95% CI 0.32-1.95, P=0.006). However, the total cost of hospitalization increased significantly in patients with concurrent HF (+17762 USD, 95% CI 3439-35439, P=0.04). Patients with HF also had increased odds of acute respiratory failure (

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Impact of immune thrombocytopenic purpura on clinical outcomes in patients with acute myocardial infarction

Chehab

Abdallah

Kanj

et al. 2019

Clinical Cardiology

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BackgroundPatients with immune thrombocytopenic purpura (ITP) admitted with acute myocardial infarction (AMI) may be challenging to manage given their increased risk of bleeding complications. There is limited evidence in the literature guiding appropriate interventions in this population. The objective of this study is to determine the difference in clinical outcomes in AMI patients with and without ITP.MethodsUsing the United States national inpatient sample database, adults aged ≥18 years, who were hospitalized between 2007 and 2014 for AMI, were identified. Among those, patients with ITP were selected. A propensity‐matched cohort analysis was performed. The primary outcome was in‐hospital mortality. Secondary outcomes were coronary revascularization procedures, bleeding and cardiovascular complications, and length of stay (LOS).ResultsThe propensity‐matched cohort included 851 ITP and 851 non‐ITP hospitalizations for AMI. There was no difference in mortality between ITP and non‐ITP patients with AMI (6% vs7.3%, OR:0.81; 95% CI:0.55‐1.19; P = .3). When compared to non‐ITP patients, ITP patients with AMI underwent fewer revascularization procedures (40.9% vs 45.9%, OR:0.81; 95% CI:0.67‐0.98; P = .03), but had a higher use of bare metal stents (15.4% vs 11.3%, OR:1.43; 95% CI:1.08‐1.90; P = .01), increased risk of bleeding complications (OR:1.80; CI:1.36‐2.38; P < .0001) and increased length of hospital stay (6.14 vs 5.4 days; mean ratio: 1.14; CI:1.05‐1.23; P = .002). More cardiovascular complications were observed in patients requiring transfusions.ConclusionsPatients with ITP admitted for AMI had a similar in‐hospital mortality risk, but a significantly higher risk of bleeding complications and a longer LOS compared to those without ITP. Further studies are needed to assess optimal management strategies of AMI that minimize complications while improving outcomes in this population.

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Acute Myocardial Infarction Due to Eltrombopag Therapy in a Patient with Immune Thrombocytopenic Purpura

Cited by 8 publications

References 10 publications

Computational models for the prediction of adverse cardiovascular drug reactions

Computational models for the prediction of adverse cardiovascular drug reactions

Untitled

Impact of immune thrombocytopenic purpura on clinical outcomes in patients with acute myocardial infarction

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