Sudden cardiac arrest in patients with schizophrenia: A population-based study of resuscitation outcomes and pre-existing cardiovascular disease

Edwards, Gabriel G; Uy‐Evanado, Audrey; Stecker, Eric C.; Salvucci, Angelo; Jui, Jonathan; Chugh, Sumeet S.; Reinier, Kyndaron

doi:10.1016/j.ijcha.2022.101027

Search citation statements

Order By: Relevance

Paper Sections

Select...

Molecular Basis Of Expected and Unexpected Cardiotoxic Adver...1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Book1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients with schizophrenia are at heightened risk of sudden cardiac arrest and higher mortality after such an event. This does not appear to be related to an elevated cardiovascular burden associated with the psychiatric disorder [29] . Whether psychotropic medication contributes to the risk of cardiac arrest and mortality remains uncertain.…”

Section: Molecular Basis Of Expected and Unexpected Cardiotoxic Adver...mentioning

confidence: 76%

Recent highlights from the International Journal of Cardiology Heart & Vasculature: Basic and translational research

Fender,

Dobrev

2024

IJC Heart & Vasculature

View full text Add to dashboard Cite

Section: Molecular Basis Of Expected and Unexpected Cardiotoxic Adver...mentioning

confidence: 76%

Recent highlights from the International Journal of Cardiology Heart & Vasculature: Basic and translational research

Fender,

Dobrev

2024

IJC Heart & Vasculature

View full text Add to dashboard Cite

The Impact of Schizophrenia Misdiagnosis Rates on Machine Learning Models Performance

Martins

Egas

Arrais

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction

Martins,

Abbasi,

Egas

et al. 2024

Journal of Integrative Bioinformatics

View full text Add to dashboard Cite

This study delves into the intricate genetic and clinical aspects of Schizophrenia, a complex mental disorder with uncertain etiology. Deep Learning (DL) holds promise for analyzing large genomic datasets to uncover new risk factors. However, based on reports of non-negligible misdiagnosis rates for SCZ, case-control cohorts may contain outlying genetic profiles, hindering compelling performances of classification models. The research employed a case-control dataset sourced from the Swedish populace. A gene-annotation-based DL architecture was developed and employed in two stages. First, the model was trained on the entire dataset to highlight differences between cases and controls. Then, samples likely to be misclassified were excluded, and the model was retrained on the refined dataset for performance evaluation. The results indicate that SCZ prevalence and misdiagnosis rates can affect case-control cohorts, potentially compromising future studies reliant on such datasets. However, by detecting and filtering outliers, the study demonstrates the feasibility of adapting DL methodologies to large-scale biological problems, producing results more aligned with existing heritability estimates for SCZ. This approach not only advances the comprehension of the genetic background of SCZ but also opens doors for adapting DL techniques in complex research for precision medicine in mental health.

show abstract

Sudden cardiac arrest in patients with schizophrenia: A population-based study of resuscitation outcomes and pre-existing cardiovascular disease

Cited by 3 publications

References 40 publications

Recent highlights from the International Journal of Cardiology Heart & Vasculature: Basic and translational research

Recent highlights from the International Journal of Cardiology Heart & Vasculature: Basic and translational research

The Impact of Schizophrenia Misdiagnosis Rates on Machine Learning Models Performance

Detecting outliers in case-control cohorts for improving deep learning networks on Schizophrenia prediction

Contact Info

Product

Resources

About