scholarly journals Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram

Author(s):  
Matthias Unterhuber ◽  
Karl-Philipp Rommel ◽  
Karl-Patrik Kresoja ◽  
Julia Lurz ◽  
Jelena Kornej ◽  
...  

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a rapidly growing global health problem. To date, diagnosis of HFpEF is based on clinical, invasive and laboratory examinations. Electrocardiographic findings may vary, and there are no known typical ECG features for HFpEF. Methods This study included two patient cohorts. In the derivation cohort, we included n = 1884 patients who presented with exertional dyspnea or equivalent and preserved ejection fraction (≥50%) and clinical suspicion for coronary artery disease. The ECGs were divided in segments, yielding a total of 77.558 samples. We trained a convolutional neural network (CNN) to classify HFpEF and control patients according to ESC criteria. An external group of 203 volunteers in a prospective heart failure screening program served as validation cohort of the CNN. Results The external validation of the CNN yielded an AUC of 0.80 (95% CI 0.74–0.86) for detection of HFpEF according to ESC criteria, with a sensitivity of 0.99 (CI 0.98–0.99) and a specificity of 0.60 (95% CI 0.56–0.64), with a positive predictive value of 0.68 (95%CI 0.64–0.72) and a negative predictive value of 0.98 (95% CI 0.95–0.99). Conclusion In this study, we report the first deep learning-enabled CNN for identifying patients with HFpEF according to ESC criteria including NT-proBNP measurements in the diagnostic algorithm among patients at risk. The suitability of the CNN was validated on an external validation cohort of patients at risk for developing heart failure, showing a convincing screening performance.

2015 ◽  
Vol 2 (2) ◽  
pp. 76-84 ◽  
Author(s):  
Raoul Stahrenberg ◽  
André Duvinage ◽  
Meinhard Mende ◽  
Götz Gelbrich ◽  
Wiebke auf der Heide ◽  
...  

2021 ◽  
Vol 8 (2) ◽  
pp. 829-841
Author(s):  
Tobias Daniel Trippel ◽  
Meinhard Mende ◽  
Hans‐Dirk Düngen ◽  
Djawid Hashemi ◽  
Johannes Petutschnigg ◽  
...  

Heart ◽  
2022 ◽  
pp. heartjnl-2021-320270
Author(s):  
Yohei Sotomi ◽  
Shungo Hikoso ◽  
Sho Komukai ◽  
Taiki Sato ◽  
Bolrathanak Oeun ◽  
...  

ObjectiveThe pathophysiological heterogeneity of heart failure with preserved ejection fraction (HFpEF) makes the conventional ‘one-size-fits-all’ treatment approach difficult. We aimed to develop a stratification methodology to identify distinct subphenotypes of acute HFpEF using the latent class analysis.MethodsWe established a prospective, multicentre registry of acute decompensated HFpEF. Primary candidates for latent class analysis were patient data on hospital admission (160 features). The patient subset was categorised based on enrolment period into a derivation cohort (2016–2018; n=623) and a validation cohort (2019–2020; n=472). After excluding features with significant missingness and high degree of correlation, 83 features were finally included in the analysis.ResultsThe analysis subclassified patients (derivation cohort) into 4 groups: group 1 (n=215, 34.5%), characterised by arrythmia triggering (especially atrial fibrillation) and a lower comorbidity burden; group 2 (n=77, 12.4%), with substantially elevated blood pressure and worse classical HFpEF echocardiographic features; group 3 (n=149, 23.9%), with the highest level of GGT and total bilirubin and frequent previous hospitalisation for HF and group 4 (n=182, 29.2%), with infection-triggered HF hospitalisation, high C reactive protein and worse nutritional status. The primary end point—a composite of all-cause death and HF readmission—significantly differed between the groups (log-rank p<0.001). These findings were consistent in the validation cohort.ConclusionsThis study indicated the feasibility of clinical application of the latent class analysis in a highly heterogeneous cohort of patients with acute HFpEF. Patients can be divided into 4 phenotypes with distinct patient characteristics and clinical outcomes.Trial registration numberUMIN000021831.


Author(s):  
Li Shen ◽  
Pardeep S. Jhund ◽  
Inder S. Anand ◽  
Peter E. Carson ◽  
Akshay S. Desai ◽  
...  

Abstract Background Sudden death (SD) and pump failure death (PFD) are leading modes of death in heart failure and preserved ejection fraction (HFpEF). Risk stratification for mode-specific death may aid in patient enrichment for new device trials in HFpEF. Methods Models were derived in 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction trial (I-Preserve), using competing risks regression analysis. A series of models were built in a stepwise manner, and were validated in the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved and Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trials. Results The clinical model for SD included older age, men, lower LVEF, higher heart rate, history of diabetes or myocardial infarction, and HF hospitalization within previous 6 months, all of which were associated with a higher SD risk. The clinical model predicting PFD included older age, men, lower LVEF or diastolic blood pressure, higher heart rate, and history of diabetes or atrial fibrillation, all for a higher PFD risk, and dyslipidaemia for a lower risk of PFD. In each model, the observed and predicted incidences were similar in each risk subgroup, suggesting good calibration. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.71 (95% CI 0.68–0.75) and 0.78 (95% CI 0.75–0.82), respectively. Both models were robust in external validation. Adding ECG and biochemical parameters, model performance improved little in the derivation cohort but decreased in validation. Including NT-proBNP substantially increased discrimination of the SD model, and simplified the PFD model with marginal increase in discrimination. Conclusions The clinical models can predict risks for SD and PFD separately with good discrimination and calibration in HFpEF and are robust in external validation. Adding NT-proBNP further improved model performance. These models may help to identify high-risk individuals for device intervention in future trials. Clinical trial registration I-Preserve: ClinicalTrials.gov NCT00095238; TOPCAT: ClinicalTrials.gov NCT00094302; CHARM-Preserved: ClinicalTrials.gov NCT00634712. Graphic abstract


2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
R Sakhi ◽  
D A M J Theuns ◽  
D Cosgun ◽  
M Michels ◽  
A F L Schinkel ◽  
...  

Abstract Background Currently, the eligibility for a subcutaneous implantable defibrillator (S-ICD) system relies on a pre-implant vector screening based on the automated screening tool (AST). Objective To determine 12-lead ECG characteristics associated with eligibility for an S-ICD in a heterogeneous population at risk for sudden cardiac death (SCD). The goal is to determine patient eligibility for S-ICD using the standard 12-lead ECG, thereby avoiding additional AST screening. Methods We prospectively evaluated the eligibility for an S-ICD in 254 consecutive patients at risk for SCD. We identified 12-lead ECG parameters which were independently associated with AST passing (≥1 vector) using multivariable logistical regression analysis in our derivation cohort. The final model was tested in a separate validation cohort. Results The overall passing rate was 92% in our derivation cohort. Independent 12-lead ECG characteristics associated with AST passing were QRS≤130 ms, absence of QRS/T discordance in lead II and R/T-ratio ≥3.5 in lead II (Table). Eighty-three of 254 patients (33%) fulfilled these three criteria and had a passing rate of 100%. Of the validation cohort, 37 of 60 patients (62%) fulfilled all three criteria and also had a passing rate of 100%. The interobserver agreement for applying the ECG model was 90% (Cohen's Kappa=0.80). Table 1 Variables Univariable Multivariable OR (95% CI) P-value OR (95% CI) P-value QRS ≤130 ms 9.65 (3.66–25.43) <0.01 8.09 (2.88–22.77) <0.01 QTc ≤450 ms 3.33 (1.18–9.54) 0.02 Absence of T-wave inversion in lead I 2.74 (1.03–7.25) 0.04 Absence of T-wave inversion in lead II 3.65 (1.29–10.33) 0.02 Absence of QRS/T-wave discordance in lead II 5.05 (1.98–12.92) <0.01 4.19 (1.49–11.74) <0.01 Absence of QRS/T-wave discordance in lead aVF 3.95 (1.53–10.19) <0.01 R/T-ratio ≥3.5 in lead II 3.58 (1.27–10.01) 0.02 4.21 (1.27–13.95) 0.02 R/T-ratio ≥3.5 in lead aVF 3.16 (1.18–8.42) 0.02 OR = odds ratio; CI = confidence interval. Figure 1 Conclusion Using the standard 12-lead ECG, we developed a simple screening model with a high specificity for S-ICD eligibility. Our results suggest that patients who fulfill the three ECG criteria do not need additional AST-screening. Therefore, we developed a simple flowchart to determine eligibility for an S-ICD that can be easily implemented in daily clinical practice (Figure).


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 140-140 ◽  
Author(s):  
Darko Antic ◽  
Natasa Milic ◽  
Biljana Mihaljevic ◽  
Bruce Cheson ◽  
Mayur Narkhede ◽  
...  

Abstract Introduction Lymphoma patients are at increased risk of thromboembolic events (TE), however, thromboprophylaxis in these patients is largely under utilized. Actual guidelines recommend different models for thromboembolic risk estimation in cancer patients. Proposed models are of limited use in lymphoma patients as their development is not based on specific characteristics for this patient population. Previously, we developed and internally validated a simple model, based on individual clinical and laboratory patient characteristics that would classify lymphoma patients at risk for a TE. The variables independently associated with the risk for thromboembolism were: previous venous and/or arterial events, mediastinal involvement, BMI>30 kg/m2, reduced mobility, extranodal localization, development of neutropenia and hemoglobin level < 100g/L. For patients classified at risk in derivation cohort (n=1236), the model revealed positive predictive value of 25.1%, negative predictive value of 98.5%, sensitivity of 75.4%, and specificity of 87.5%. The diagnostic performance measures retained similar values in the internal validation cohort (n=584). The aim of this study was to perform external validation of the previously developed thrombosis lymphoma (Throly) score. Methods The study population included patients with a confirmed diagnosis of non-Hodgkin lymphoma (NHL), Hodgkin lymphoma (HL), and chronic lymphocytic leukemia (CLL)/ small lymphocytic lymphoma (SLL) from 8 lymphoma centers from USA, France, Spain, Croatia, Austria, Switzerland, Macedonia, and Jordan. During 2015 to 2016, data were prospectively collected for venous TE events from time of diagnosis to 3 months after the last cycle of therapy for newly diagnosed and relapsed patients who had completed a minimum of one chemotherapy cycle. The score development and validation were done according to TRIPOD suggested guidelines. Sensitivity analyses were carried out to test the model robustness to possible different settings, according to in/out patient settings and according to different countries included. Results External validation cohort included 1723 patients, similar to the developed group and consisted of 467 indolent NHL, 647 aggressive NHL, 235 CLL/SLL and 366 HL patients, out of which 121 (7%) patients developed venous thromboembolic events. For patients classified at risk in external validation cohort, the model resulted in positive and negative predictive values of 17% and 93%, respectively. Based on new available information from this large prospective cohort study this model was revised to include the following variables: diagnosis/clinical stage, previous VTE, reduced mobility, hemoglobin level < 100g/L and presence of vascular devices. In the new score we divided patients in two groups: low risk patients, score value ≤ 2; and high risk patients, score value > 2. For patients classified at risk by the revised model, the model produced positive predictive value of 22%, negative predictive value of 96%, sensitivity of 51%, and specificity of 72%. In sensitivity analysis, the final model proved its robustness in different settings of major importance for lymphoma patients. The final model presented good discrimination and calibration performance. Concordance C statistics was 0.794 (95% CI 0.750-0.837). Conclusions Revised Thrombosis Lymphoma - ThroLy score is more specific for lymphoma patients than any other available score targeting thrombosis risk in solid cancer patients. We included biological characteristic of lymphoma, indolent vs aggressive, as well as data about dissemination of disease, localized vs advanced stage, reflecting specificity of lymphomas comparing to other types of cancer. Also, we pointed out significance of central vascular devices as risk factor having considered the role of vascular damage during insertion as a potential trigger for activation of the clotting cascade. This score is user friendly for daily clinical practice and provides a very good predictive power to identify patients who are candidates for pharmacological thromboprophylaxis. Disclosures Cheson: AbbVie, Roche/Genentech, Pharmacyclics, Acerta, TG Therapeutics: Consultancy. Ghielmini:Roche: Consultancy, Honoraria, Research Funding, Speakers Bureau. Jaeger:Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; AOP Orphan: Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; MSD: Research Funding; Bioverativ: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Honoraria; Mundipharma: Membership on an entity's Board of Directors or advisory committees; Takeda-Millenium: Membership on an entity's Board of Directors or advisory committees; Takeda-Millenium: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees; Infinity: Membership on an entity's Board of Directors or advisory committees.


2021 ◽  
Vol 8 ◽  
Author(s):  
Blanka Morvai-Illés ◽  
Nóra Polestyuk-Németh ◽  
István Adorján Szabó ◽  
Magdolna Monoki ◽  
Luna Gargani ◽  
...  

Background: Heart failure with preserved ejection fraction (HFpEF) is a growing healthcare burden, and its prevalence is steadily increasing. Lung ultrasound (LUS) is a promising screening and prognostic tool in the heart failure population. However, more information on its value in predicting outcome is needed.Aims: The aim of our study was to assess the prognostic performance of LUS B-lines compared to traditional and novel clinical and echocardiographic parameters and natriuretic peptide levels in patients with newly diagnosed HFpEF in an ambulatory setting.Methods: In our prospective cohort study, all ambulatory patients with clinical suspicion of HFpEF underwent comprehensive echocardiography, lung ultrasound and NT-proBNP measurement during their first appointment at our cardiology outpatient clinic. Our endpoint was a composite of worsening heart failure symptoms requiring hospitalization or loop diuretic dose escalation and death.Results: We prospectively enrolled 75 consecutive patients with HFpEF who matched our inclusion and exclusion criteria. We detected 11 events on a 26 ± 10-months follow-up. We found that the predictive value of B-lines is similar to the predictive value of NT-proBNP (AUC 0.863 vs. 0.859), with the best cut-off at &gt;15 B-lines. Having more B-lines than 15 significantly increased the likelihood of adverse events with a hazard ratio of 20.956 (p = 0.004). The number of B-lines remained an independent predictor of events at multivariate modeling. Having more than 15 B-lines lines was associated with a significantly worse event-free survival (Log-rank: 16.804, p &lt; 0.001).Conclusion: The number of B-lines seems to be an independent prognostic factor for adverse outcomes in HFpEF. Since it is an easy-to-learn, feasible and radiation-free method, it may add substantial value to the commonly used diagnostic and risk stratification models.


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