{"title":"Sauna Therapy in HFpEF: More than Just Heat?","authors":"Philip Haaf,Christian Ukena,Felix Mahfoud","doi":"10.1093/ejhf/xuag093","DOIUrl":"https://doi.org/10.1093/ejhf/xuag093","url":null,"abstract":"","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"22 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alasdair D Henderson,Samira Soltani,Johann Bauersachs,Pardeep S Jhund
{"title":"Can we differentiate between finerenone, eplerenone and spironolactone in HFmrEF/HFpEF through a Bayesian lens?","authors":"Alasdair D Henderson,Samira Soltani,Johann Bauersachs,Pardeep S Jhund","doi":"10.1093/ejhf/xuag095","DOIUrl":"https://doi.org/10.1093/ejhf/xuag095","url":null,"abstract":"","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"191 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nina Stødkilde-Jørgensen,Roni R Nielsen,Kevin Kw Olesen,Christine Gyldenkerne,Pernille G Thrane,Malene K Hansen,Michael Maeng
BACKGROUNDHeart failure with reduced ejection fraction (HFrEF) is associated with increased mortality. However, the impact of age on excess mortality among patients with HFrEF remains unclear.OBJECTIVESTo assess the effect of age on excess mortality in patients with HFrEF.METHODSWe included patients aged 40 to 85 years with a left ventricular ejection fraction of ≤40% who underwent coronary angiography for suspected cardiomyopathy in Western Denmark between 2010 and 2021. Each patient was matched by sex and age with up to five individuals from the Danish general population. Excess mortality was examined by 5-year mortality risk differences and hazard ratios (HRs) across different age groups.RESULTSAmong 5,425 patients with HFrEF and 27,087 matched controls, 5-year excess mortality was lowest in patients aged 40 to 49 years (6.8%, 95% CI: 4.0-9.7) and highest in those aged 65 to 69 years (17.0%, 95% CI: 13.8-20.2). In contrast, the highest relative risk was observed in those aged 40 to 49 years (HR: 6.41, 95% CI: 3.73-11.05), with a decline as age increased. A 10% 5-year mortality was reached at age 50 in patients with HFrEF, and at age 70 in controls.CONCLUSIONSHFrEF was associated with excess mortality across all age groups. Younger HFrEF patients had similar 5-year mortality as general population controls who were more than 20 years older.
{"title":"Effect of age on 5-year excess mortality in heart failure with reduced ejection fraction.","authors":"Nina Stødkilde-Jørgensen,Roni R Nielsen,Kevin Kw Olesen,Christine Gyldenkerne,Pernille G Thrane,Malene K Hansen,Michael Maeng","doi":"10.1093/ejhf/xuag098","DOIUrl":"https://doi.org/10.1093/ejhf/xuag098","url":null,"abstract":"BACKGROUNDHeart failure with reduced ejection fraction (HFrEF) is associated with increased mortality. However, the impact of age on excess mortality among patients with HFrEF remains unclear.OBJECTIVESTo assess the effect of age on excess mortality in patients with HFrEF.METHODSWe included patients aged 40 to 85 years with a left ventricular ejection fraction of ≤40% who underwent coronary angiography for suspected cardiomyopathy in Western Denmark between 2010 and 2021. Each patient was matched by sex and age with up to five individuals from the Danish general population. Excess mortality was examined by 5-year mortality risk differences and hazard ratios (HRs) across different age groups.RESULTSAmong 5,425 patients with HFrEF and 27,087 matched controls, 5-year excess mortality was lowest in patients aged 40 to 49 years (6.8%, 95% CI: 4.0-9.7) and highest in those aged 65 to 69 years (17.0%, 95% CI: 13.8-20.2). In contrast, the highest relative risk was observed in those aged 40 to 49 years (HR: 6.41, 95% CI: 3.73-11.05), with a decline as age increased. A 10% 5-year mortality was reached at age 50 in patients with HFrEF, and at age 70 in controls.CONCLUSIONSHFrEF was associated with excess mortality across all age groups. Younger HFrEF patients had similar 5-year mortality as general population controls who were more than 20 years older.","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"15 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147518460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesco Fioretti,Faiez Zannad,Irina Barash,Stefano Corda,Justin A Ezekowitz,Carolyn S P Lam,Robert J Mentz,Christopher M O'Connor,Inga Bayh,Aiwen Xing,Javed Butler,
{"title":"The effect of vericiguat on sudden cardiac death: insights from the VICTOR trial.","authors":"Francesco Fioretti,Faiez Zannad,Irina Barash,Stefano Corda,Justin A Ezekowitz,Carolyn S P Lam,Robert J Mentz,Christopher M O'Connor,Inga Bayh,Aiwen Xing,Javed Butler, ","doi":"10.1093/ejhf/xuag049","DOIUrl":"https://doi.org/10.1093/ejhf/xuag049","url":null,"abstract":"","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"105 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIMSThe natural history of hepatic involvement in tricuspid regurgitation (TR) remains undercharacterized. We aimed to assess the incidence and risk of liver-related outcomes associated with TR severity and to identify the predictors and survival impact of cirrhosis in patients with ≥moderate TR.METHODSFrom 2004 to 2022, adults with documented TR severity were retrospectively included. Liver-related outcomes including cirrhosis, hepatocellular carcinoma (HCC), and liver-related death were followed longitudinally.RESULTSAmong 41 950 patients without prevalent liver disease, 7226 (17.3%) had ≥moderate TR. The median follow-up was 6.4 years. Incidence rates of cirrhosis, HCC and liver-related death in patients with ≥moderate TR were 4.54 [95% confidence interval (CI), 3.89-5.29], 1.25 (95% CI, 0.92-1.68), and 1.33 (95% CI, 0.99-1.77) per 1000 person-years, respectively. Compared with no/trace TR, both moderate and severe TR were associated with an increased risk of cirrhosis, while risks of HCC and liver-related death did not differ statistically across TR severity groups. Among patients with ≥moderate TR, independent predictors of cirrhosis were heart failure with preserved ejection fraction, right ventricular dysfunction, and cholestatic liver injury. Ventricular TR [hazard ratio (HR): 2.33, 95% CI, 1.22-4.43, P = .009] and lead-associated TR (HR: 2.66, 95% CI, 1.16-6.09, P = .02) were associated with a higher risk of cirrhosis than atrial TR. Incident cirrhosis increased all-cause and cardiovascular death risks in patients with ≥moderate TR.CONCLUSIONSSignificant TR is associated with incident cirrhosis, with risk varying by aetiology. The development of cirrhosis portends a worse survival, supporting more comprehensive hepatic evaluation during TR management.
目的：三尖瓣反流（TR）中肝脏受累的自然历史仍然不清楚。我们的目的是评估与TR严重程度相关的肝脏相关结局的发生率和风险，并确定≥中度TR患者肝硬化的预测因素和生存影响。方法从2004年到2022年，回顾性纳入有记录的TR严重程度的成年人。肝脏相关结果包括肝硬化、肝细胞癌（HCC）和肝脏相关死亡。结果41,950例无流行肝病患者中，7226例（17.3%）有中度以上TR，中位随访时间为6.4年。≥中度TR患者的肝硬化、HCC和肝脏相关死亡发生率分别为每1000人年4.54（95%可信区间3.89-5.29）、1.25 （95% CI 0.92-1.68）和1.33 （95% CI 0.99-1.77）。与无/微量TR相比，中度和重度TR均与肝硬化风险增加相关，而HCC和肝脏相关死亡的风险在TR严重程度组间无统计学差异。在中度TR≥的患者中，肝硬化的独立预测因子是保留射血分数的心力衰竭、右心室功能障碍和胆汁淤积性肝损伤。心室TR[危险比（HR）： 2.33, 95% CI, 1.22-4.43, P = 0.009]和铅相关TR （HR: 2.66, 95% CI, 1.16-6.09, P = 0.02）与心房TR相比，肝硬化风险更高。中度TR≥的患者发生肝硬化的全因和心血管死亡风险增加。结论显著TR与肝硬化相关，其风险因病因而异。肝硬化的发展预示着更差的生存，支持在TR治疗期间更全面的肝脏评估。
{"title":"Liver-related outcomes in patients with tricuspid regurgitation.","authors":"Jingnan Zhang,Jiayi Huang,Ran Guo,Wenli Gu,Haochen Xuan,Lin Liu,Guihua Chen,Shi-Tian Guo,Chenxu Wang,Ami Matsumoto,Ching-Yan Zhu,Sun-Nam Chu,Yueran Shi,Yap-Hang Chan,Qingwen Ren,Kai-Hang Yiu","doi":"10.1093/ejhf/xuag057","DOIUrl":"https://doi.org/10.1093/ejhf/xuag057","url":null,"abstract":"AIMSThe natural history of hepatic involvement in tricuspid regurgitation (TR) remains undercharacterized. We aimed to assess the incidence and risk of liver-related outcomes associated with TR severity and to identify the predictors and survival impact of cirrhosis in patients with ≥moderate TR.METHODSFrom 2004 to 2022, adults with documented TR severity were retrospectively included. Liver-related outcomes including cirrhosis, hepatocellular carcinoma (HCC), and liver-related death were followed longitudinally.RESULTSAmong 41 950 patients without prevalent liver disease, 7226 (17.3%) had ≥moderate TR. The median follow-up was 6.4 years. Incidence rates of cirrhosis, HCC and liver-related death in patients with ≥moderate TR were 4.54 [95% confidence interval (CI), 3.89-5.29], 1.25 (95% CI, 0.92-1.68), and 1.33 (95% CI, 0.99-1.77) per 1000 person-years, respectively. Compared with no/trace TR, both moderate and severe TR were associated with an increased risk of cirrhosis, while risks of HCC and liver-related death did not differ statistically across TR severity groups. Among patients with ≥moderate TR, independent predictors of cirrhosis were heart failure with preserved ejection fraction, right ventricular dysfunction, and cholestatic liver injury. Ventricular TR [hazard ratio (HR): 2.33, 95% CI, 1.22-4.43, P = .009] and lead-associated TR (HR: 2.66, 95% CI, 1.16-6.09, P = .02) were associated with a higher risk of cirrhosis than atrial TR. Incident cirrhosis increased all-cause and cardiovascular death risks in patients with ≥moderate TR.CONCLUSIONSSignificant TR is associated with incident cirrhosis, with risk varying by aetiology. The development of cirrhosis portends a worse survival, supporting more comprehensive hepatic evaluation during TR management.","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"53 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147502458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Illusion of Improvement in ATTR-CM: Re-evaluating Informative Missingness and Discordant Endpoints in ATTRibute-CM.","authors":"Zhang Liu,Weiqin Huang","doi":"10.1093/ejhf/xuag089","DOIUrl":"https://doi.org/10.1093/ejhf/xuag089","url":null,"abstract":"","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"34 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machine learning to reveal hidden HFpEF: promise, pitfalls, and next steps.","authors":"Friedrich Koehler,Kieran Docherty,Stefan Störk","doi":"10.1093/ejhf/xuag081","DOIUrl":"https://doi.org/10.1093/ejhf/xuag081","url":null,"abstract":"","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"20 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147490007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marlene A T Vijver,Olivier C Dams,Geert H D Voordes,Robert C Verdonk,Adriaan A Voors,Steven R Goldsmith,Luca Monzo,Nicolas Girerd,Robert Frost,Daniel Burkhoff,Finn Gustafsson,Kevin Duarte,Faiez Zannad,James E Udelson,Dirk J van Veldhuisen
{"title":"Pancreatic Involvement During Acute Heart Failure: Insights from the AVANTI trial.","authors":"Marlene A T Vijver,Olivier C Dams,Geert H D Voordes,Robert C Verdonk,Adriaan A Voors,Steven R Goldsmith,Luca Monzo,Nicolas Girerd,Robert Frost,Daniel Burkhoff,Finn Gustafsson,Kevin Duarte,Faiez Zannad,James E Udelson,Dirk J van Veldhuisen","doi":"10.1093/ejhf/xuag086","DOIUrl":"https://doi.org/10.1093/ejhf/xuag086","url":null,"abstract":"","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"147 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147490045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AIMSDiagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older individuals. We hypothesized that machine learning (ML) approaches could improve diagnostic accuracy compared with HFpEF scores.METHODSWe evaluated the diagnostic performance of four supervised ML algorithms (random forest [RF], extreme gradient boosting [XGBoost], support vector machines, and decision trees) to identify HFpEF in individuals aged 60 to 80 years. The models were trained on three derivation cohorts (N = 1474; HFpEF: KaRen, MEDIA cohorts; community-based without HF: Malmö Preventive Project) and validated in two independent cohorts (N = 542; HFpEF: HF-Nancy cohort; community-based without HF: STANISLAS cohort). Performance metrics included accuracy, F-measure, area under the receiver operating characteristic curve (AUC), and C-index. ML models were also compared with HFA-PEFF, H2FPEF, and HFpEF-ABA scores.RESULTSAmong 2017 participants, RF and XGBoost demonstrated the highest diagnostic value, outperforming traditional HFpEF scores (AUC: RF, 0.98; XGBoost, 0.96; HFA-PEFF, 0.86; H2FPEF, 0.79). RF and XGBoost also showed the greatest gain in discriminative capacity among ML algorithms when compared with H2FPEF (ΔC-index: RF +0.20, XGBoost +0.18), HFA-PEFF (ΔC-index: RF +0.12, XGBoost +0.10), and HFpEF-ABA score (ΔC-index: RF +0.17, XGBoost +0.15). Elevated natriuretic peptides were by far the most influential feature in both RF and XGBoost models (36% of model explainability).CONCLUSIONSMachine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems. These findings support the potential integration of ML-based tools into clinical workflows to facilitate earlier identification of HFpEF and prompt initiation of guideline-recommended therapies.
{"title":"Comparative diagnostic performance of machine learning models and traditional scores for HFpEF in older adults.","authors":"Luca Monzo,Olivier Huttin,Emmanuel Bresso,Kevin Duarte,Cecilia Linde,Lars H Lund,Camilla Hage,Erwan Donal,Martin Magnusson,Peter Nilsson,Margret Leosdottir,Erwan Bozec,Guillaume Baudry,Faiez Zannad,Nicolas Girerd","doi":"10.1093/ejhf/xuag039","DOIUrl":"https://doi.org/10.1093/ejhf/xuag039","url":null,"abstract":"AIMSDiagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older individuals. We hypothesized that machine learning (ML) approaches could improve diagnostic accuracy compared with HFpEF scores.METHODSWe evaluated the diagnostic performance of four supervised ML algorithms (random forest [RF], extreme gradient boosting [XGBoost], support vector machines, and decision trees) to identify HFpEF in individuals aged 60 to 80 years. The models were trained on three derivation cohorts (N = 1474; HFpEF: KaRen, MEDIA cohorts; community-based without HF: Malmö Preventive Project) and validated in two independent cohorts (N = 542; HFpEF: HF-Nancy cohort; community-based without HF: STANISLAS cohort). Performance metrics included accuracy, F-measure, area under the receiver operating characteristic curve (AUC), and C-index. ML models were also compared with HFA-PEFF, H2FPEF, and HFpEF-ABA scores.RESULTSAmong 2017 participants, RF and XGBoost demonstrated the highest diagnostic value, outperforming traditional HFpEF scores (AUC: RF, 0.98; XGBoost, 0.96; HFA-PEFF, 0.86; H2FPEF, 0.79). RF and XGBoost also showed the greatest gain in discriminative capacity among ML algorithms when compared with H2FPEF (ΔC-index: RF +0.20, XGBoost +0.18), HFA-PEFF (ΔC-index: RF +0.12, XGBoost +0.10), and HFpEF-ABA score (ΔC-index: RF +0.17, XGBoost +0.15). Elevated natriuretic peptides were by far the most influential feature in both RF and XGBoost models (36% of model explainability).CONCLUSIONSMachine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems. These findings support the potential integration of ML-based tools into clinical workflows to facilitate earlier identification of HFpEF and prompt initiation of guideline-recommended therapies.","PeriodicalId":164,"journal":{"name":"European Journal of Heart Failure","volume":"80 1","pages":""},"PeriodicalIF":18.2,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147483389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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