European heart journal. Digital health最新文献

Aims: Echocardiographic strain imaging reflects myocardial deformation and is a sensitive measure of cardiac function and wall-motion abnormalities. Deep learning (DL) algorithms could automate the interpretation of echocardiographic strain imaging.

Methods and results: We developed and trained an automated DL-based algorithm for left ventricular (LV) strain measurements in an internal dataset. Global longitudinal strain (GLS) was validated externally in (i) a real-world Taiwanese cohort of participants with and without heart failure (HF), (ii) a core-lab measured dataset from the multinational prevalence of microvascular dysfunction-HF and preserved ejection fraction (PROMIS-HFpEF) study, and regional strain in (iii) the HMC-QU-MI study of patients with suspected myocardial infarction. Outcomes included measures of agreement [bias, mean absolute difference (MAD), root-mean-squared-error (RMSE), and Pearson's correlation (R)] and area under the curve (AUC) to identify HF and regional wall-motion abnormalities. The DL workflow successfully analysed 3741 (89%) studies in the Taiwanese cohort, 176 (96%) in PROMIS-HFpEF, and 158 (98%) in HMC-QU-MI. Automated GLS showed good agreement with manual measurements (mean ± SD): -18.9 ± 4.5% vs. -18.2 ± 4.4%, respectively, bias 0.68 ± 2.52%, MAD 2.0 ± 1.67, RMSE = 2.61, R = 0.84 in the Taiwanese cohort; and -15.4 ± 4.1% vs. -15.9 ± 3.6%, respectively, bias -0.65 ± 2.71%, MAD 2.19 ± 1.71, RMSE = 2.78, R = 0.76 in PROMIS-HFpEF. In the Taiwanese cohort, automated GLS accurately identified patients with HF (AUC = 0.89 for total HF and AUC = 0.98 for HF with reduced ejection fraction). In HMC-QU-MI, automated regional strain identified regional wall-motion abnormalities with an average AUC = 0.80.

Conclusion: DL algorithms can interpret echocardiographic strain images with similar accuracy as conventional measurements. These results highlight the potential of DL algorithms to democratize the use of cardiac strain measurements and reduce time-spent and costs for echo labs globally.

Aims: In Brugada syndrome (BrS), with spontaneous or ajmaline-induced coved ST elevation, epicardial electro-anatomic potential duration maps (epi-PDMs) were detected on a right ventricle (RV) outflow tract (RVOT), an arrhythmogenic substrate area (AS area), abolished by epicardial-radiofrequency ablation (EPI-AS-RFA). Novel CineECG, projecting 12-lead electrocardiogram (ECG) waveforms on a 3D heart model, previously localized depolarization forces in RV/RVOT in BrS patients. We evaluate 12-lead ECG and CineECG depolarization/repolarization changes in spontaneous type-1 BrS patients before/after EPI-AS-RFA, compared with normal controls.

Methods and results: In 30 high-risk BrS patients (93% males, age 37 + 9 years), 12-lead ECGs and epi-PDMs were obtained at baseline, early after EPI-AS-RFA, and late follow-up (FU) (2.7-16.1 months). CineECG estimates temporo-spatial localization during depolarization (Early-QRS and Terminal-QRS) and repolarization (ST-Tpeak, Tpeak-Tend). Differences within BrS patients (baseline vs. early after EPI-AS-RFA vs. late FU) were analysed by Wilcoxon signed-rank test, while differences between BrS patients and 60 age-sex-matched normal controls were analysed by the Mann-Whitney test. In BrS patients, baseline QRS and QTc durations were longer and normalized after EPI-AS-ATC (151 ± 15 vs. 102 ± 13 ms, P < 0.001; 454 ± 40 vs. 421 ± 27 ms, P < 0.000). Baseline QRS amplitude was lower and increased at late FU (0.63 ± 0.26 vs. 0.84 ± 13 ms, P < 0.000), while Terminal-QRS amplitude decreased (0.24 ± 0.07 vs. 0.08 ± 0.03 ms, P < 0.000). At baseline, CineECG depolarization/repolarization wavefront prevalently localized in RV/RVOT (Terminal-QRS, 57%; ST-Tpeak, 100%; and Tpeak-Tend, 61%), congruent with the AS area on epi-PDM. Early after EPI-AS-RFA, RV/RVOT localization during depolarization disappeared, as Terminal-QRS prevalently localized in the left ventricle (LV, 76%), while repolarization still localized on RV/RVOT [ST-Tpeak (44%) and Tpeak-Tend (98%)]. At late FU, depolarization/repolarization forces prevalently localized in the LV (Terminal-QRS, 94%; ST-Tpeak, 63%; Tpeak-Tend, 86%), like normal controls.

Conclusion: CineECG and 12-lead ECG showed a complex temporo-spatial perturbation of both depolarization and repolarization in BrS patients, prevalently localized in RV/RVOT, progressively normalizing after epicardial ablation.

Aims: Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.

Methods and results: Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.

Conclusion: Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.

Registration: Clinicaltrial.gov identifier is NCT02188355.

Aims: Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD).

Methods and results: In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods.

Conclusion: The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement.

Aims: Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.

Methods and results: We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.

Conclusion: Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10^-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital i

Aims: We evaluated a self-care intervention with a novel mobile application (app) in chronic heart failure (HF) patients. To facilitate patient-centred care in HF management, we developed a self-care support mobile app to boost HF patients' optimal self-care.

Methods and results: We conducted a multicentre, randomized, controlled study evaluating the feasibility of the self-care support mobile app designed for use by HF patients. The app consists of a self-monitoring assistant, education, and automated alerts of possible worsening HF. The intervention group received a tablet personal computer (PC) with the self-care support app installed, and the control group received a HF diary. All patients performed self-monitoring at home for 2 months. Their self-care behaviours were evaluated by the European Heart Failure Self-Care Behaviour Scale. We enrolled 24 outpatients with chronic HF (ages 31-78 years; 6 women, 18 men) who had a history of HF hospitalization. During the 2 month study period, the intervention group (n = 13) showed excellent adherence to the self-monitoring of each vital sign, with a median [interquartile range (IQR)] ratio of self-monitoring adherence for blood pressure, body weight, and body temperature at 100% (92-100%) and for oxygen saturation at 100% (91-100%). At 2 months, the intervention group's self-care behaviour score was significantly improved compared with the control group (n = 11) [median (IQR): 16 (16-22) vs. 28 (20-36), P = 0.02], but the HF Knowledge Scale, the General Self-Efficacy Scale, and the Short Form-8 Health Survey scores did not differ between the groups.

Conclusion: The novel mobile app for HF is feasible.

[This corrects the article DOI: 10.1093/ehjdh/ztad029.].

Aims: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.

Methods and results: De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals.

Conclusion: Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.

Aims: A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity.

Methods and results: The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements. We included data from a cohort of 405 257 participants aged 37-73 years and trained various machine learning and deep learning models on different subsets of risk factors to predict CVD incidence. Each of the models was trained on the complete set of predictors and subsets where each category was excluded. The results were benchmarked against QRISK3. The findings highlight that (i) leveraging a more comprehensive medical history substantially improves model performance. Relative to QRISK3, the best performing models improved the discrimination by 3.78% and improved precision by 1.80%. (ii) Both model- and data-centric approaches are necessary to improve predictive performance. The benefits of using a comprehensive history of diseases were far more pronounced when a neural sequence model, BEHRT, was used. This highlights the importance of the temporality of medical events that existing clinical risk models fail to capture. (iii) Besides the history of diseases, socioeconomic factors and measurements had small but significant independent contributions to the predictive performance.

Conclusion: These findings emphasize the need for considering broad determinants and novel modelling approaches to enhance CVD incidence prediction.