Arruda-Olson, Adelaide

Background Cardiovascular disease (CVD) is a leading cause of death in cancer survivors. Predicting CVD risk in this population remains challenging. Risk prediction models that use machine learning algorithms could provide an objective method for accurate risk prediction to facilitate the prevention and management of CVD in cancer survivors. We evaluated previously tested machine learning algorithms and logistic regression (regularized by machine learning methods), in addition to testing and validating newer, more complex machine learning algorithms, for CVD prediction in cancer survivors. Methods This multicenter study used a database of 3835 multiracial cancer survivors with 89 clinical, laboratory, and echocardiographic features over 20 years. Models were trained using repeated random and time‐split samples and tested on a separate cohort of 329 patients. Model performance was assessed using the area under the receiver operating characteristic curve. Results Regularized logistic regression achieved an area under the receiver operating characteristic curve of 0.845 (heart failure), 0.783 (atrial fibrillation), 0.792 (coronary artery disease), and 0.806 (composite CVD). These are comparable to 0.837 and 0.848 for heart failure, using Bayesian additive regression tree and random forest as more advanced machine learning models, respectively. De novo composite CVD (post‐cancer diagnosis) was also predicted with an area under the receiver operating characteristic curve of 0.826 using regularized logistic regression, compared with 0.735 and 0.802 using decision tree and random forest, respectively. Conclusions Regularized logistic regression and advanced machine learning models demonstrated similar predictive performance, with institutional transferability. These tools may support risk stratification and prevention strategies in cardio‐oncology using longitudinal data. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05377320.

Abstract Aims Our aim was to assess the clinical and prognostic significance of the left ventricular (LV) hypercontractile phenotype (HP) in hypertrophic cardiomyopathy (HCM), which until now remains unclear. Methods and results We enrolled 1533 HCM patients (age 51 ± 15 years, 965 males, 63%) with ejection fraction (EF) ≥ 50%, referred for rest transthoracic echocardiography (TTE) in 27 laboratories from 13 countries. Two-dimensional volumetric TTE assessment included LV outflow tract gradient (LVOTG), LV EF, and LV force (systolic blood pressure + LVOTG/ESV, mmHg/mL). HP was defined as the highest quartile of the force (> 7.32 mmHg/mL). Survival analysis was performed in a subset of 1200 patients with follow-up information. Compared with non-HP patients, HP showed higher heart rate (72 ± 14 vs. 67 ± 14 beats per minute, P < 0.001), lower stroke volume (50 ± 22 vs. 67 ± 22 mL, P < 0.001), larger left atrial volume index (44 ± 16 vs. 40 ± 16 mL/m2, P = 0.011) and higher coronary flow velocity in the mid-distal left anterior descending artery (n = 325, 41 ± 12 vs. 37 ± 14 cm/s, P = 0.021). Force was moderately related to LV EF (r = 0.48, P < 0.001) and weakly to LVOTG (r = 0.36, P < 0.001). During a median follow-up of 87 months (interquartile range 45–143 months), 131 all-cause deaths occurred. At multivariable Cox analysis, a force > 7.32 mmHg (the fourth quartile for analyzed patients) was associated with a hazard ratio of 1.44 (95% Confidence intervals 1.00–2.07) for all-cause death, independently of LVOTG and LV EF. Conclusion HP in HCM is associated with a disadvantageous systemic, cardiac and coronary hemodynamic profile as well as lower survival in the long-term.

A 34-year-old man was referred for progressive angina and exertional dyspnoea refractory to medical therapy, with a presumptive diagnosis of hypertrophic cardiomyopathy (HCM). Transthoracic echocardiography (TTE) revealed asymmetric septal hypertrophy without systolic anterior motion of the mitral valve leaflet and with no dynamic left ventricular outflow tract (LVOT) obstruction. However, the LVOT velocity was elevated at rest as well as with provocation, without the characteristic late peaking obstruction seen in HCM. Focused TTE to evaluate for suspected fixed obstruction demonstrated a subaortic membrane 2.2 cm below the aortic valve. Coronary CT angiography confirmed the presence of the subaortic membrane and was negative for concomitant coronary artery disease. Surgical resection of the subaortic membrane and septal myectomy resulted in significant symptomatic relief and lower LVOT velocities on postoperative TTE. This case reminds the clinician to carefully evaluate for alternative causes of LVOT obstruction, especially subaortic membrane, as a cause of symptoms mimicking HCM.

Objective Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR. Methods Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups. Results Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate–severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74–0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age. Conclusions Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.

Background: Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. Methods: Consecutive patients (4/8/2015 – 12/31/2020) undergoing rest and postexercise ankle–brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 – 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. Results: Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92–0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91–0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). Conclusion: An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.