Pub Date : 2025-02-13DOI: 10.1016/j.cpcardiol.2025.103019
Filippos Triposkiadis, Alexandros Briasoulis, Randall C Starling, Dimitrios E Magouliotis, Christos Kourek, George E Zakynthinos, Efstathios K Iliodromitis, Ioannis Paraskevaidis, Andrew Xanthopoulos
Hereditary transthyretin (TTR) amyloidosis (ATTRv amyloidosis) is a devastating disease characterized by broad range of clinical manifestations, including predominantly neurological, predominantly cardiac, and mixed phenotypes. This wide phenotypic variability hindered timely disease diagnosis and risk stratification in the past, especially in individuals with absent or uncharted family history. However, recent advances in noninvasive testing have led to greater awareness and earlier diagnosis. Further, medications have been discovered which proved effective in controlling the disease and improving outcomes including stabilizing TTR, silencing TTR variants, and removing TTR amyloid from affected tissues. Importantly, CRISPR gene editing, a groundbreaking technology, offers the unique potential to cure ATTRv amyloidosis, transforming lives and opening new doors in medical science. This review provides an update on ATTRv amyloidosis mechanisms, diagnosis, and management emphasizing the importance of early diagnosis as the steadfast underpinning for the capitalization of the advances in medical treatment to the benefit of the patients.
{"title":"Hereditary Transthyretin Amyloidosis (ATTRv).","authors":"Filippos Triposkiadis, Alexandros Briasoulis, Randall C Starling, Dimitrios E Magouliotis, Christos Kourek, George E Zakynthinos, Efstathios K Iliodromitis, Ioannis Paraskevaidis, Andrew Xanthopoulos","doi":"10.1016/j.cpcardiol.2025.103019","DOIUrl":"https://doi.org/10.1016/j.cpcardiol.2025.103019","url":null,"abstract":"<p><p>Hereditary transthyretin (TTR) amyloidosis (ATTRv amyloidosis) is a devastating disease characterized by broad range of clinical manifestations, including predominantly neurological, predominantly cardiac, and mixed phenotypes. This wide phenotypic variability hindered timely disease diagnosis and risk stratification in the past, especially in individuals with absent or uncharted family history. However, recent advances in noninvasive testing have led to greater awareness and earlier diagnosis. Further, medications have been discovered which proved effective in controlling the disease and improving outcomes including stabilizing TTR, silencing TTR variants, and removing TTR amyloid from affected tissues. Importantly, CRISPR gene editing, a groundbreaking technology, offers the unique potential to cure ATTRv amyloidosis, transforming lives and opening new doors in medical science. This review provides an update on ATTRv amyloidosis mechanisms, diagnosis, and management emphasizing the importance of early diagnosis as the steadfast underpinning for the capitalization of the advances in medical treatment to the benefit of the patients.</p>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":" ","pages":"103019"},"PeriodicalIF":3.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-08DOI: 10.1016/S0146-2806(25)00033-7
{"title":"Information for Readers","authors":"","doi":"10.1016/S0146-2806(25)00033-7","DOIUrl":"10.1016/S0146-2806(25)00033-7","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103010"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-08DOI: 10.1016/S0146-2806(25)00038-6
{"title":"Guidelines for Authors","authors":"","doi":"10.1016/S0146-2806(25)00038-6","DOIUrl":"10.1016/S0146-2806(25)00038-6","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 3","pages":"Article 103015"},"PeriodicalIF":3.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1016/j.cpcardiol.2025.103004
André Luiz Carvalho Ferreira MD , Luanna Paula Garcez de Carvalho Feitoza , Maria E. Benitez MD , Buena Aziri MD , Edin Begic MD , Luciana Vergara Ferraz de Souza MD , Elísio Bulhões , Sarah O.N. Monteiro , Maria L.R. Defante , Roberto Augusto Mazetto Silva Vieira , Camila Guida MD
Introduction
AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF <40 %) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40 %. Aims: We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40 %.
Methods
We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model.
Results
We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40 % according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years.
Conclusions
In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40 %.
{"title":"Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis","authors":"André Luiz Carvalho Ferreira MD , Luanna Paula Garcez de Carvalho Feitoza , Maria E. Benitez MD , Buena Aziri MD , Edin Begic MD , Luciana Vergara Ferraz de Souza MD , Elísio Bulhões , Sarah O.N. Monteiro , Maria L.R. Defante , Roberto Augusto Mazetto Silva Vieira , Camila Guida MD","doi":"10.1016/j.cpcardiol.2025.103004","DOIUrl":"10.1016/j.cpcardiol.2025.103004","url":null,"abstract":"<div><h3>Introduction</h3><div>AI-based ECG has shown good accuracy in diagnosing heart failure. However, due to the heterogeneity of studies regarding cutoff points, its precision for specifically detecting heart failure with left ventricle reduced ejection fraction (LVEF <40 %) is not yet well established. What is the sensitivity and specificity of artificial-based electrocardiogram to diagnose heart failure with low ejection fraction (cut-off of 40 %. Aims: We conducted a meta-analysis and systematic review to evaluate the accuracy of artificial intelligence electrocardiograms in estimating an ejection fraction below 40 %.</div></div><div><h3>Methods</h3><div>We searched PubMed, Embase, and Cochrane Library for studies evaluating the performance of AI ECGs in diagnosing heart failure with reduced ejection fraction. We computed true positives, true negatives, false positives, and false negatives events to estimate pooled sensitivity, specificity, and area under the curve, using R software version 4.3.1, under a random-effects model.</div></div><div><h3>Results</h3><div>We identified 9 studies, including patients with a paired artificial intelligence-enabled electrocardiogram with an echocardiography. patients had an ejection fraction below 40 % according to the echocardiogram. The AI-ECG data yielded areas under the receiver operator of, the sensitivity of), specificity of, and area under the curve of. The mean/median age ranged from 60±9 to 68.05± 11.9 years.</div></div><div><h3>Conclusions</h3><div>In this systematic review and meta-analysis, the use of electrocardiogram-based artificial intelligence models demonstrated high sensitivity and specificity to estimate a left ventricular ejection fraction below 40 %.</div></div>","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 4","pages":"Article 103004"},"PeriodicalIF":3.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/S0146-2806(24)00597-8
{"title":"Guidelines for Authors","authors":"","doi":"10.1016/S0146-2806(24)00597-8","DOIUrl":"10.1016/S0146-2806(24)00597-8","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102962"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/S0146-2806(24)00592-9
{"title":"Information for Readers","authors":"","doi":"10.1016/S0146-2806(24)00592-9","DOIUrl":"10.1016/S0146-2806(24)00592-9","url":null,"abstract":"","PeriodicalId":51006,"journal":{"name":"Current Problems in Cardiology","volume":"50 2","pages":"Article 102957"},"PeriodicalIF":3.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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