BACKGROUND:Spontaneous coronary artery dissection (SCAD) is associated with substantial psychological sequelae. Although previous studies have identified individual risk factors for poor mental health post-SCAD, none has examined whether protective and vulnerability factors cluster together. This study aimed to identify distinct post-SCAD psychological profiles. METHOD:A cross-sectional survey of 263 SCAD survivors recruited from the Victor Chang Cardiac Research Institute Arteriopathies and SCAD Cohort (VASC). Participants completed measures of anxiety (GAD-7), depression (PHQ-9), cardiac distress (CDI-SF), health-related quality of life (SF-12), resilience (CD-RISC), social support (ESSI), patient activation (PAM-13), and post-traumatic growth (PTGI), and provided sociodemographic and medical data. K-Prototypes cluster analysis identified subgroups. Between-cluster differences were examined using chi-square tests and ANOVAs. RESULTS:Clustering identified three psychological recovery profiles. Cluster 1 (Psychologically Vulnerable; 39%) comprised younger-to-mid-aged survivors characterised by low resilience, low patient activation, financial strain, and limited social support. Cluster 2 (Established Copers; 32%) were older with moderate-to-high resilience, higher cardiac rehabilitation (CR) attendance, and low financial strain. Cluster 3 (High-Resource Recoverers; 29%), the youngest group, exhibited the highest resilience, patient activation, and post-traumatic growth. Cluster solution validity was supported by convergent evidence across five complementary indices. CONCLUSIONS:Psychological recovery after SCAD is heterogeneous and not necessarily age dependent. Younger survivors who are financially strained and socially unsupported are most vulnerable to poor recovery whereas, in a novel finding, those with favourable personal and social conditions cope well. Among older survivors, financial security and CR attendance may be protective. Interventions should prioritise resilience building, patient engagement, and strengthening social support.
AIMS:To analyse French-language public online conversations about tetralogy of Fallot (TOF), with emphasis on patient and parent concerns, emotional tone, and information needs relevant to congenital cardiovascular care. METHODS AND RESULTS:We conducted a retrospective observational study using the Observatoire Social du Patient database, which captures anonymised public posts from French-language forums and social media. Posts published between December 2014 and November 2024 containing terms related to TOF were extracted and analysed using natural language processing, thematic ontologies, and sentiment analysis. A total of 1291 messages from 360 unique authors were included. Parents represented 63% of authors and generated 84% of posts; women accounted for 89% of contributors. Parents most often discussed daily life, care events, and psychological state, whereas patients more frequently discussed healthcare events, physical activity, and long-term outcomes. Negative emotions, especially anxiety, fear, and uncertainty, were common around diagnosis and repeated interventions, although hope and trust in care were also present. Posts frequently contained medical terminology, suggesting substantial disease knowledge. However, 328 posts (25%) included questions, mainly about the number and timing of operations, prognosis, daily life, and pregnancy. CONCLUSION:Social media testimonies reveal persistent psychosocial and informational needs among people affected by TOF. Integrating these patient and parent voices into routine congenital cardiac care may support more anticipatory communication, psychosocial support, and patient-centred digital education.
Attending the ACNAP2026 Congress for the first time was a highly valuable and inspiring experience. I particularly appreciated the opportunity to gain insights into different healthcare systems and to learn more about the various roles of healthcare professionals across Europe and other continents. The congress offered a broad scientific programme with poster sessions, workshops and symposia covering various topics in cardiovascular care. I focused on sessions closely related to my clinical and research interests, particularly personalised exercise prescription, cardiovascular rehabilitation and patient engagement. Three aspects were especially relevant to my work. First, the interactive workshop 'Let the Games Begin: Games for Heart' by Aseel Berglund [1] provided insights into the potential role of gamification in improving health outcomes among patients with cardiovascular disease. Second, the workshop 'Precision in Motion: A Practical Workshop on Personalised Exercise Prescription', led by Stefan Tino Kulnik and Dominique Hansen, offered practical perspectives on individualised exercise prescription. These concepts were further explored during the symposium 'From Prescription to Performance: Tailoring Exercise and Motivational Skills for Optimal Outcomes', where Stefan Tino Kulnik [2], Dominique Hansen [3], and Jennifer Jones [4] presented evidence-based approaches to exercise prescription and strategies to support long-term adherence in cardiovascular rehabilitation. Third, I had the opportunity to present my poster entitled 'Effects of Intensive Early Physiotherapy Intervention after Myocardial Infarction on the Functional Capacity Assessed with the 6-Minute Walk Test' [5]. Sharing our work with an international audience was a valuable experience and stimulated constructive discussions with experts in the field.
AIM:Heart failure (HF) poses a growing public health burden, yet conventional risk stratification models fail to capture the multidimensional complexity of acute HF by overlooking nutritional and lifestyle-related factors critical to prognosis. This study aimed to classify patients hospitalized with acute HF into clinically distinct risk subgroups using machine learning and to evaluate their prognostic significance for individualized nursing care. METHODS AND RESULTS:We retrospectively analyzed records of 1,104 patients admitted to the cardiac intensive care unit of a tertiary hospital in Seoul, Korea (2013-2023). Adverse outcome was defined as all-cause mortality or readmission. Random forest identified key predictors, and clustering using Gower distance and Partitioning Around Medoids defined patient groups. Survival was assessed using Kaplan-Meier and Cox proportional-hazards models. Patients had a mean age of 66.4±14.6 years, and 31.3% experienced adverse outcomes (25.6% died, 8.4% were readmitted). Key predictors included nutritional risk index, hemoglobin, creatinine, left ventricular ejection fraction, and age. Three clinically distinct clusters were identified: Cluster 1, Middle-aged unhealthy lifestyle; Cluster 2, Older multimorbidity; and Cluster 3, Malnutrition-renal dysfunction (log-rank p < .001). Compared with Cluster 1, Cluster 3 showed a significantly higher risk of adverse outcomes (HR=1.82, 95% CI 1.33-2.50, p < .001). CONCLUSION:Machine learning-driven clustering identified three HF phenotypes with divergent prognoses, with the malnutrition-renal dysfunction phenotype conferring nearly twofold higher mortality risk. These findings support cluster-specific nursing strategies, including early nutritional risk screening and renal monitoring, to guide individualized care in acute HF.
AIMS:Hospital readmissions following ischaemic stroke remain a serious concern in cardiovascular care, particularly among younger adults whose recovery needs differ from older populations. This study examined predictors of 30-day readmission among stroke survivors aged 19-64 years, with a focus on how different rural-urban classifications influence estimated associations between geography and readmission outcomes. METHODS AND RESULTS:We conducted a retrospective secondary analysis of hospital discharge data from the Healthcare Cost and Utilization Project State Inpatient Databases (2012-15) for six US states. The sample included 85 667 adults aged 19-64 years hospitalized for acute ischaemic stroke. Geographic location was operationalized using multiple rural-urban classifications derived from Urban Influence Codes. Multivariable logistic regression models assessed predictors of planned and unplanned 30-day readmission.Geographic location was not a consistent predictor of readmission across alternative rurality definitions. Living in a large metropolitan area was associated with slightly higher odds of unplanned readmission when compared with other locations (adjusted odds ratio 1.06; 95% CI 1.00-1.12), but this association was not observed when alternative rural-urban classifications were applied. Across models, significant predictors of readmission included age, payer type, length of stay, and number of chronic conditions. CONCLUSION:Associations between geographic location and stroke readmission outcomes varied depending on how rural-urban classifications were defined. These findings highlight the importance of methodological clarity when interpreting geographic effects in secondary analyses of administrative data and have implications for cardiovascular nurses involved in discharge planning and transitional care for younger stroke survivors.
