Super and Nonresponders to Catheter Ablation for Atrial Fibrillation: A Quality-of-Life Assessment Using Patient Reported Outcomes.

[Figure: see text].

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.

Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer treatment. Early detection and prevention of cancer therapy-related cardiac dysfunction (CTRCD) play important roles in precision cardio-oncology. Methods and Results This retrospective study included 4309 cancer patients between 1997 and 2018 whose laboratory tests and cardiovascular echocardiographic variables were collected from the Cleveland Clinic institutional electronic medical record database (Epic Systems). Among these patients, 1560 (36%) were diagnosed with at least 1 type of CTRCD, and 838 (19%) developed CTRCD after cancer therapy (de novo). We posited that machine learning algorithms can be implemented to predict CTRCDs in cancer patients according to clinically relevant variables. Classification models were trained and evaluated for 6 types of cardiovascular outcomes, including coronary artery disease (area under the receiver operating characteristic curve [AUROC], 0.821; 95% CI, 0.815-0.826), atrial fibrillation (AUROC, 0.787; 95% CI, 0.782-0.792), heart failure (AUROC, 0.882; 95% CI, 0.878-0.887), stroke (AUROC, 0.660; 95% CI, 0.650-0.670), myocardial infarction (AUROC, 0.807; 95% CI, 0.799-0.816), and de novo CTRCD (AUROC, 0.802; 95% CI, 0.797-0.807). Model generalizability was further confirmed using time-split data. Model inspection revealed several clinically relevant variables significantly associated with CTRCDs, including age, hypertension, glucose levels, left ventricular ejection fraction, creatinine, and aspartate aminotransferase levels. Conclusions This study suggests that machine learning approaches offer powerful tools for cardiac risk stratification in oncology patients by utilizing large-scale, longitudinal patient data from healthcare systems.

Implantable cardioverter-defibrillator FDA safety advisories: Impact on patient mortality and morbidity.

BACKGROUND: A significant proportion of implantable cardioverter-defibrillators (ICDs) have been subject to Food and Drug Administration (FDA) advisories. The impact of device advisories on mortality or patient care is poorly understood. Although estimated risks of ICD generators under advisory are low, dependency on ICD therapies to prevent sudden death justifies the assessment of long-term mortality. OBJECTIVE: To test the association of FDA advisory status with long-term mortality. METHODS: The study was a retrospective, single-center review of clinical outcomes, including device malfunctions, in patients from implantation to either explant or death. Patients with ICDs first implanted at Cleveland Clinic between August 1996 and May 2004 who became subject to FDA advisories on ICD generators were identified. Mortality was determined by using the Social Security Death Index. RESULTS: In 1644 consecutive patients receiving first ICD implants, 704 (43%) became subject to an FDA advisory, of which 172 (10.5%) were class I and 532 (32.3%) were class II. ICDs were explanted before advisory notifications in 14.0% of class I and 10.1% of class II advisories. Among ICDs under advisory, 28 (4.0%) advisory-related and 15 non-advisory- related malfunctions were documented. Over a median follow-up of 70 months, 814 patients died. Kaplan-Meier 5-year survival rate was 65.6% overall, and 64.2, 61.1, and 69.3% in patients with no, class I, and class II advisories, respectively (P = .17). CONCLUSIONS: ICD advisories impacted 43% of the patients. Advisory-related malfunctions affected 4% within the combined advisory group. Based on a conservative management strategy, ICDs under advisory were not associated with increased mortality over a background of significant disease-related mortality.