Predictors of Health-Related Quality of Life Among Women Participating in an Appointment-Based Cardiac Rehabilitation Program.

BACKGROUND: An alternative patient-centered appointment-based cardiac rehabilitation (CR) program has led to significant improvements in health outcomes for patients with cardiovascular disease. However, less is known about the effects of this approach on health-related quality of life (HRQoL), particularly for women. OBJECTIVE: We examined the effects of a patient-centered appointment-based CR program on HRQoL by sex and examined predictors of HRQoL improvements specifically for women. METHODS: Data were used from an urban single-center CR program at Yale New Haven Health (2012-2017). We collected information on patient demographics, socioeconomic status, and clinical characteristics. The Outcome Short-Form General Health Survey (SF-36) was used to measure HRQoL. We evaluated sex differences in SF-36 scores using t tests and used a multivariate linear regression model to examine predictors of improvements in HRQoL (total SF-36 score) for women. RESULTS: A total of 1530 patients with cardiovascular disease (23.7% women, 4.8% Black; mean age, 64 ± 10.8 years) were enrolled in the CR program. Women were more likely to be older, Black, and separated, divorced, or widowed. Although women had lower total SF-36 scores on CR entry, there was no statistically significant difference in CR adherence or total SF-36 score improvements between sexes. Women who were employed and those with chronic obstructive pulmonary disease were more likely to have improvements in total SF-36 scores. CONCLUSION: Both men and women participating in an appointment-based CR program achieved significant improvements in HRQoL. This approach could be a viable alternative to conventional CR to optimize secondary outcomes for patients.

Automated HEART score determination via ChatGPT: Honing a framework for iterative prompt development.

Objectives: This study presents a design framework to enhance the accuracy by which large language models (LLMs), like ChatGPT can extract insights from clinical notes. We highlight this framework via prompt refinement for the automated determination of HEART (History, ECG, Age, Risk factors, Troponin risk algorithm) scores in chest pain evaluation. Methods: We developed a pipeline for LLM prompt testing, employing stochastic repeat testing and quantifying response errors relative to physician assessment. We evaluated the pipeline for automated HEART score determination across a limited set of 24 synthetic clinical notes representing four simulated patients. To assess whether iterative prompt design could improve the LLMs' ability to extract complex clinical concepts and apply rule-based logic to translate them to HEART subscores, we monitored diagnostic performance during prompt iteration. Results: Validation included three iterative rounds of prompt improvement for three HEART subscores with 25 repeat trials totaling 1200 queries each for GPT-3.5 and GPT-4. For both LLM models, from initial to final prompt design, there was a decrease in the rate of responses with erroneous, non-numerical subscore answers. Accuracy of numerical responses for HEART subscores (discrete 0-2 point scale) improved for GPT-4 from the initial to final prompt iteration, decreasing from a mean error of 0.16-0.10 (95% confidence interval: 0.07-0.14) points. Conclusion: We established a framework for iterative prompt design in the clinical space. Although the results indicate potential for integrating LLMs in structured clinical note analysis, translation to real, large-scale clinical data with appropriate data privacy safeguards is needed.

Young Women With Acute Myocardial Infarction: Risk Prediction Model for 1-Year Hospital Readmission.

Background: Although young women ( aged ≤ 55 years) are at higher risk than similarly aged men for hospital readmission within 1 year after an acute myocardial infarction (AMI), no risk prediction models have been developed for them. The present study developed and internally validated a risk prediction model of 1-year post-AMI hospital readmission among young women that considered demographic, clinical, and gender-related variables. Methods: We used data from the US Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients (VIRGO) study (n = 2007 women), a prospective observational study of young patients hospitalized with AMI. Bayesian model averaging was used for model selection and bootstrapping for internal validation. Model calibration and discrimination were respectively assessed with calibration plots and area under the curve. Results: Within 1-year post-AMI, 684 women (34.1%) were readmitted to the hospital at least once. The final model predictors included: any in-hospital complication, baseline perceived physical health, obstructive coronary artery disease, diabetes, history of congestive heart failure, low income ( < $30,000 US), depressive symptoms, length of hospital stay, and race (White vs Black). Of the 9 retained predictors, 3 were gender-related. The model was well calibrated and exhibited modest discrimination (area under the curve = 0.66). Conclusions: Our female-specific risk model was developed and internally validated in a cohort of young female patients hospitalized with AMI and can be used to predict risk of readmission. Whereas clinical factors were the strongest predictors, the model included several gender-related variables (ie, perceived physical health, depression, income level). However, discrimination was modest, indicating that other unmeasured factors contribute to variability in hospital readmission risk among younger women.

Contexte: Bien que les femmes jeunes (≤ 55 ans) présentent un risque plus élevé que les hommes du même âge de réadmission à l'hôpital dans l'année suivant un infarctus aigu du myocarde (IAM), il n'existe pas de modèle de prédiction des risques conçu spécialement pour elles. Dans le cadre de la présente étude, on a créé et validé à l'interne un modèle de prédiction des risques de réadmission à l'hôpital dans l'année suivant un IAM chez les femmes jeunes en tenant compte de variables démographiques, cliniques et associées au genre. Méthodologie: Nous avons utilisé les données de l'étude américaine VIRGO (variation du rétablissement : le rôle du genre dans les résultats des jeunes patientes ayant subi un IAM) (n = 2007 femmes), une étude observationnelle prospective menée auprès de jeunes patientes hospitalisées pour un IAM. Un modèle bayésien d'établissement de la moyenne a été utilisé pour la sélection du modèle et la méthode bootstrap a été utilisée pour la validation interne. L'étalonnage et la discrimination du modèle ont été évalués respectivement au moyen des courbes d'étalonnage et de la surface sous la courbe. Résultats: Dans l'année suivant l'IAM, 684 femmes (34,1 %) ont été réadmises à l'hôpital au moins une fois. Les facteurs prédictifs finaux du modèle sont notamment : toute complication survenue à l'hôpital, l'état de santé physique perçu au départ, la coronaropathie obstructive, le diabète, les antécédents d'insuffisance cardiaque congestive, le faible revenu (< 30 000 $ US), les symptômes dépressifs, la durée du séjour à l'hôpital et l'ethnie (blanc par rapport à noir). Parmi les neuf facteurs prédictifs retenus, trois sont associés au genre. Le modèle est bien étalonné et présente une discrimination modeste (surface sous la courbe = 0,66). Conclusions: Notre modèle de risque propre aux femmes a été conçu et validé à l'interne auprès d'une cohorte de femmes jeunes hospitalisées pour un IAM et peut être utilisé pour prédire le risque de réadmission. Bien que les facteurs cliniques soient les facteurs prédictifs les plus puissants, le modèle inclut plusieurs variables liées au genre (p. ex., état de santé physique perçu, dépression, revenu). Cependant, la discrimination étant modeste, d'autres facteurs non mesurés contribuent à la variabilité du risque de réadmission à l'hôpital chez les femmes plus jeunes.

Interventions Targeted to Address Social Determinants of Health in Ischemic Heart Disease: A Sex- and Gender-Oriented Scoping Review.

The burden of ischemic heart disease (IHD) is a major health problem worldwide. The detrimental effect of gendered (ie, unevenly distributed between female and male) socioeconomic determinants of health (SDOH) on outcomes has been demonstrated, more so in female individuals. Therefore, addressing SDOH is a priority for the care implementation of patients with IHD. We conducted a scoping review to identify the types of SDOH-tailored interventions tested in randomised controlled trials (RCTs) among IHD patients, and whether the reporting of findings was sex-unbiased. We identified 8 SDOH domains: education, physical environment, health care system, economic stability, social support, sexual orientation, culture/language, and systemic racism. A total of 28 RCTs (2 ongoing) were evaluated. Since the 1990s, 26 RCTs have been conducted, mainly in the Middle East and Asia, and addressed only education, physical environment, health care system, and social support. The 77% of studies focused on patient-education interventions, and around 80% on SDOH-based interventions achieved positive effects on a variety of primary outcome(s). Among the limitations of the conducted RCTs, the most relevant were an overall low participation of female and racial/ethnical minority participants, a lack of sex-stratified analyses, and a missing opportunity of tailoring some SDOH interventions relevant for health. The SDOH-tailored interventions tested so far in RCTs, enrolling predominantly male patients and mainly targeting education and health literacy, were effective in improving outcomes among patients with IHD. Future studies should focus on a wider range of SDOH with an adequate representation of female and minority patients who would most benefit from such interventions.

Implementation of an Appointment-Based Cardiac Rehabilitation Approach: A Single-Center Experience.

Background There has been a focus on alternative cardiac rehabilitation (CR) delivery models aimed at improving CR adherence and completion. We examined pre- and post-CR health outcomes, reasons for discharge, and predictors of completion using a patient-driven appointment-based CR approach that uses center-scheduled class start times. Methods and Results Data were used from an urban single-center CR program at Yale New Haven Health (2012-2017) that enrolled 2135 patients. We evaluated pre- and post-CR outcomes (12 weeks) using paired t tests and used a multivariable logistic regression model to examine predictors of CR completion (≥36 sessions) for the overall cardiovascular disease population. The mean age of participants was 65±12 years, 27.9% were women, and 5.1% were Black patients, and patients completed a median of 30 of 36 sessions. Patients achieved significant improvements in health outcomes, including across age and sex subgroups. The primary reason for discharge was completion of all 36 sessions of CR (46.4%). The final logistic regression model contained 12 predictors: age, sex, Black race, marital status, employment, number of physician-reported risk factors, dietary fat intake >30%, obesity, lack of exercise, benign prostatic hyperplasia, and self-reported stress and physical activity. Conclusions We demonstrated that patients participating in an appointment-based CR program achieved significant improvements in health outcomes and across sex/age subgroups. In addition, older individuals were more likely to complete CR. An appointment-based approach could be a viable alternative CR method to aid in optimizing the dose-response benefit of CR for patients with cardiovascular disease.