Frailty and Cognitive Function in Middle-Aged and Older Adults With Congenital Heart Disease.

BACKGROUND: Life expectancy of patients with congenital heart disease (CHD) has increased rapidly, resulting in a growing and aging population. Recent studies have shown that older people with CHD have higher morbidity, health care use, and mortality. To maintain longevity and quality of life, understanding their evolving medical and psychosocial challenges is essential. OBJECTIVES: The authors describe the frailty and cognitive profile of middle-aged and older adults with CHD to identify predictor variables and to explore the relationship with hospital admissions and outpatient visits. METHODS: Using a cross-sectional, multicentric design, we included 814 patients aged ≥40 years from 11 countries. Frailty phenotype was determined using the Fried method. Cognitive function was assessed by the Montreal Cognitive Assessment. RESULTS: In this sample, 52.3% of patients were assessed as robust, 41.9% as prefrail, and 5.8% as frail; 38.8% had cognitive dysfunction. Multinomial regression showed that frailty was associated with older age, female sex, higher physiologic class, and comorbidities. Counterintuitively, patients with mild heart defects were more likely than those with complex lesions to be prefrail. Patients from middle-income countries displayed more prefrailty than those from higher-income countries. Logistic regression demonstrated that cognitive dysfunction was related to older age, comorbidities, and lower country-level income. CONCLUSIONS: Approximately one-half of included patients were (pre-)frail, and more than one-third experienced cognitive impairment. Frailty and cognitive dysfunction were identified in patients with mild CHD, indicating that these concerns extend beyond severe CHD. Assessing frailty and cognition routinely could offer valuable insights into this aging population.

Hitting the (bio)mark part 1: selecting and measuring biomarkers in cardiovascular research.

Cardiovascular studies, including nursing research, frequently integrate biomarkers for diagnostic, prognostic, monitoring and therapeutic insights. However, effective utilization of biomarker data demands careful consideration. In the study design phase, researchers must select biomarkers that align with study objectives while considering resources and logistical factors. Additionally, a nuanced understanding of disease pathophysiology and biomarker characteristics is needed. During data collection, suitable experimental conditions and assays need to be defined. Whether researchers opt to manage these steps internally or outsource some, a comprehensive understanding of biomarker selection and experiments remains crucial. In this article, part 1 of 2, we provide an overview of considerations for the design to measurement phases of biomarker studies.

Hitting the (bio)mark part 2: analyzing, interpreting, and reporting biomarker data in cardiovascular research.

Incorporating biomarkers into cardiovascular studies, including nursing research, is a common approach when identifying underlying mechanisms and providing targets for intervention. However, effective utilization of biomarker data demands careful consideration. In the analysis, interpretation, and reporting phase, there are many facets to consider, including non-normality of the data, normalization procedures, and potential confounding influences of other clinical data. Furthermore, as many studies focus on patient-reported outcomes, it is important that the analysis and interpretation of biomarkers in relation to patient-reported outcomes is rigorous and reproducible. In this article, part 2 of 2, we provide an overview of considerations for the analysis, interpretation, and reporting phases of biomarker studies. We also provide an example of these steps.

Biological Age in Congenital Heart Disease-Exploring the Ticking Clock.

Over the past 50 years, there has been a major shift in age distribution of patients with congenital heart disease (CHD) thanks to significant advancements in medical and surgical treatment. Patients with CHD are, however, never cured and face unique challenges throughout their lives. In this review, we discuss the growing data suggesting accelerated aging in this population. Adults with CHD are more often and at a younger age confronted with age-related cardiovascular complications such as heart failure, arrhythmia, and coronary artery disease. These can be related to the original birth defect, complications of correction, or any residual defects. In addition, and less deductively, more systemic age-related complications are seen earlier, such as renal dysfunction, lung disease, dementia, stroke, and cancer. The occurrence of these complications at a younger age makes it imperative to further map out the aging process in patients across the spectrum of CHD. We review potential feasible markers to determine biological age and provide an overview of the current data. We provide evidence for an unmet need to further examine the aging paradigm as this stresses the higher need for care and follow-up in this unique, newly aging population. We end by exploring potential approaches to improve lifespan care.

The Potential and Limitations of Mobile Health and Insertable Cardiac Monitors in the Detection of Atrial Fibrillation in Cryptogenic Stroke Patients: Preliminary Results From the REMOTE Trial.

Aim: This paper presents the preliminary results from the ongoing REMOTE trial. It aims to explore the opportunities and hurdles of using insertable cardiac monitors (ICMs) and photoplethysmography-based mobile health (PPG-based mHealth) using a smartphone or smartwatch to detect atrial fibrillation (AF) in cryptogenic stroke and transient ischemic attack (TIA) patients. Methods and Results: Cryptogenic stroke or TIA patients (n = 39) received an ICM to search for AF and were asked to use a blinded PPG-based mHealth application for 6 months simultaneously. They were randomized to smartphone or smartwatch monitoring. In total, 68,748 1-min recordings were performed using PPG-based mHealth. The number of mHealth recordings decreased significantly over time in both smartphone and smartwatch groups (p < 0.001 and p = 0.002, respectively). Insufficient signal quality was more frequently observed in smartwatch (43.3%) compared to smartphone recordings (17.8%, p < 0.001). However, when looking at the labeling of the mHealth recordings on a patient level, there was no significant difference in signal quality between both groups. Moreover, the use of a smartwatch resulted in significantly more 12-h periods (91.4%) that were clinically useful compared to smartphone users (84.8%) as they had at least one recording of sufficient signal quality. Simultaneously, continuous data was collected from the ICMs, resulting in approximately 6,660,000 min of data (i.e., almost a 100-fold increase compared to mHealth). The ICM algorithm detected AF and other cardiac arrhythmias in 10 and 19 patients, respectively. However, these were only confirmed after adjudication by the remote monitoring team in 1 (10%) and 5 (26.3%) patients, respectively. The confirmed AF was also detected by PPG-based mHealth. Conclusion: Based on the preliminary observations, our paper illustrates the potential as well as the limitations of PPG-based mHealth and ICMs to detect AF in cryptogenic stroke and TIA patients in four elements: (i) mHealth was able to detect AF in a patient in which AF was confirmed on the ICM; (ii) Even state-of-the-art ICMs yielded many false-positive AF registrations; (iii) Both mHealth and ICM still require physician revision; and (iv) Blinding of the mHealth results impairs compliance and motivation.