Remote monitoring of pacemakers and defibrillators: Effective and safe in Brazil?

Background: The remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has become a common method of in-home monitoring and follow-up in high-income countries given its effectiveness, safety, convenience, and the possibility of early intervention. However, in Brazil, RM is still underutilized. Objectives: This observational study aims to demonstrate our experience of using RM in Brazil and the predictive factors of RM of CIED follow-up in Brazil. Methods: This was a prospective cohort study of patients with a CIED. Event rates are reported and clinical responses to those findings and outcomes based on the detection of RM. A logistic regression model was performed to identify predictors of more events, with P < .05 for statistical significance. Results: This study evaluated consecutive 119 patients: 30.2% with pacemakers, 42.8% with implantable cardioverter-defibrillator, 22.7% with cardiac resynchronization therapy (CRT) with defibrillator, and 3.3% with CRT with pacemaker. Events were detected in 63.9% of the cases in 29.5 ± 23 months of follow-up. The outcomes found were that 44.5% needed elective evaluation in medical treatment and 23.5% needed immediate evaluation in therapy. Logistic regression analysis showed that the groups with CRT or CRT with defibrillator (75.0%), reduced ejection fraction (76.5%), and New York Heart Association functional class ≥II (75.0%) had the highest RM event rates. Conclusions: RM proved to be effective and safe in the follow-up of patients with CIEDs in Brazil, allowing early interventions and facilitating therapeutic management.

Residual analysis for linear mixed models.

Residuals are frequently used to evaluate the validity of the assumptions of statistical models and may also be employed as tools for model selection. For standard (normal) linear models, for example, residuals are used to verify homoscedasticity, linearity of effects, presence of outliers, normality and independence of the errors. Similar uses may be envisaged for three types of residuals that emerge from the fitting of linear mixed models. We review some of the residual analysis techniques that have been used in this context and propose a standardization of the conditional residual useful to identify outlying observations and clusters. We illustrate the procedures with a practical example.