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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 (PROs), it is important that the analysis and interpretation of biomarkers in relation to PROs 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. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. 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. [Display omitted] [ABSTRACT FROM AUTHOR]

The article focuses on the ethical dilemmas surrounding the integration of artificial intelligence (AI) into healthcare. Topics include the potential for AI to improve decision-making and patient care in cardiovascular nursing, concerns about the reliability and biases of AI systems, and the need for healthcare professionals to critically engage with AI technologies to protect professional autonomy and patient welfare.