Margret Leosdottir

Aims: Investigate sex-specific associations between exercise capacity and the risk of all-cause mortality and major adverse cardiovascular events (MACE) after myocardial infarction (MI), using nationwide real-world data. Methods and results: A prospective cohort study using the SWEDEHEART registry. Patients with an MI (2016–20) were included. Exercise capacity (maximal workload in W) was assessed using bicycle ergometer testing performed before exercise-based cardiac rehabilitation, 2–8 weeks after hospital discharge. Outcomes were all-cause mortality and MACE (MI, stroke, and cardiovascular death), identified in national cause-of-death and patient registries (2016–23). Associations between exercise capacity and outcomes were evaluated using adjusted Cox proportional hazard models. The functional form of the association was assessed using restricted cubic splines. There were 15 945 patients (78% men), with a median age of 64 (IQR: 56–70) years, included. During a median follow-up of 3.2 (2.3–4.3) years, 420 deaths and 948 MACE occurred. Higher exercise capacity (per 10-W increase) was associated with an 18% lower hazard of death in men and a 33% lower hazard in women, with a linear association for both sexes. Similarly, MACE hazard decreased by 8% in men and 18% in women per 10-W increase in exercise capacity, with a curvilinear association for men and a linear association for women. Conclusion: Higher exercise capacity measured by routine clinical exercise testing was associated with lower risk of all-cause mortality and recurrent MACE, with stronger associations in women. These findings support the use of exercise capacity testing to identify patients at the highest risk.

Aims: How individual components of cardiac rehabilitation (CR) programmes predict attainment of risk factor targets after myocardial infarction (MI), and their predictive strength relative to patient characteristics, remain unclear. We aimed to identify organizational and patient-level predictors of risk factor target attainment at 1-year post-MI. Methods and results: Organizational structure and processes followed at all 78 CR centres in Sweden were surveyed and merged with patient-level registry data (n = 6755). Predictors of attaining low-density lipoprotein-cholesterol (LDL-C) and blood pressure (BP) targets and smoking abstinence were identified using orthogonal partial least squares discriminant analysis, generating variables of importance for the projection (VIP) values. Variables with VIP > 0.8 were considered meaningful. The strongest predictors for attaining LDL-C and BP targets included availability of psychosocial management, medication adjustment protocols for nurses, continuity in nurse-patient contact, extended opening hours and adequate facilities, and having a medical director at the CR centre. The strongest patient-level predictors of attaining LDL-C and/or BP targets included low baseline LDL-C and having no history of hypertension, respectively, and the patient having participated in exercise-based CR. For smoking abstinence, the strongest organizational predictors were availability of psychosocial management, having a medical director, prescribing nicotine replacement therapy and varenicline, and self-reported team spirit. Patient-level predictors included having participated in exercise-based CR and group education. Conclusion: We identified multiple CR organizational predictors of attaining key risk factor targets after MI. These results may influence the future design of comprehensive CR programmes.

Aims: Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older individuals. We hypothesized that machine learning (ML) approaches could improve diagnostic accuracy compared with HFpEF scores. Methods: We evaluated the diagnostic performance of four supervised ML algorithms (random forest [RF], extreme gradient boosting [XGBoost], support vector machines, and decision trees) to identify HFpEF in individuals aged 60 to 80 years. The models were trained on three derivation cohorts (N = 1474; HFpEF: KaRen, MEDIA cohorts; community-based without HF: Malmö Preventive Project) and validated in two independent cohorts (N = 542; HFpEF: HF-Nancy cohort; community-based without HF: STANISLAS cohort). Performance metrics included accuracy, F-measure, area under the receiver operating characteristic curve (AUC), and C-index. ML models were also compared with HFA-PEFF, H2FPEF, and HFpEF-ABA scores. Results: Among 2017 participants, RF and XGBoost demonstrated the highest diagnostic value, outperforming traditional HFpEF scores (AUC: RF, 0.98; XGBoost, 0.96; HFA-PEFF, 0.86; H2FPEF, 0.79). RF and XGBoost also showed the greatest gain in discriminative capacity among ML algorithms when compared with H2FPEF (ΔC-index: RF +0.20, XGBoost +0.18), HFA-PEFF (ΔC-index: RF +0.12, XGBoost +0.10), and HFpEF-ABA score (ΔC-index: RF +0.17, XGBoost +0.15). Elevated natriuretic peptides were by far the most influential feature in both RF and XGBoost models (36% of model explainability). Conclusions: Machine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems. These findings support the potential integration of ML-based tools into clinical workflows to facilitate earlier identification of HFpEF and prompt initiation of guideline-recommended therapies.

Purpose: The primary aim of this study was to describe physical fitness in a large real-world cohort of patients entering exercise-based cardiac rehabilitation (EBCR) after first-time myocardial infarction (MI). The secondary aim was to compare the results with clinical reference values. Methods: This registry-based cohort study used data from the Swedish Web-system for Enhancement and Development of Evidence-based care in Heart Disease Evaluated According to Recommended Therapies (SWEDEHEART) post-MI registry between 2016 and 2019. Patients with first-time MI who underwent physiotherapist-led assessments of physical fitness at EBCR entry were included. Exercise capacity was evaluated by a symptom-limited cycle ergometer test and muscular endurance by a unilateral heel-rise test. Results were compared with age-stratified and sex-stratified clinical reference values. Reference values for exercise capacity were based on the Swedish Kalmar dataset, the national standard reference in Sweden since 2014, and reference values for heel-rise performance were based on a Swedish normative dataset of healthy adults. Results: A total of 15 105 patients (mean age 62.5±9.1 years, 78.4% men) were included. Exercise capacity (Watt max from the exercise test) was higher in men than women across age groups, and muscular endurance declined with age, with a steeper age-related decline in women’s heel rise performance. The mean exercise capacity of the study population corresponded to 64.7±27.4% of predicted values in men and 68.4±18.9% in women. Muscular endurance averaged 78.4%±51.0% of reference values in men and 61.7%±43.2% in women. Conclusion: At EBCR entry after a first-time MI, both exercise capacity and muscular endurance were substantially below age-specific and sex-specific reference values. These results underscore the importance of systematic baseline assessments and tailored rehabilitation interventions targeting both exercise capacity and muscular endurance.

Background: Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older adults. While the Heart Failure Association (HFA)-PEFF and H ₂ FPEF Scores offer structured diagnostic approaches, their clinical utility is still debated. This study aims to compare the diagnostic accuracy of HFpEF Scores versus inclusion criteria used in sodium-glucose cotransporter-2 inhibitors (SGLT2i) trials, age-adjusted N-terminal pro B-type natriuretic peptide (NT-proBNP) thresholds and the universal definition of heart failure (HF). Methods: Diagnostic tools were assessed using sex-weighted and age-weighted propensity score adjustment in individuals aged 60–80 years from two established HFpEF cohorts (MEtabolic Road to DIAstolic Heart Failure (MEDIA), n=297; Karolinska-Rennes (KaRen), n=174) and two community-based cohorts without HF (Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux (STANISLAS), n=461; Malmö, n=1030). Results: HFA-PEFF and H ₂ FPEF Scores classified a large proportion of participants in both community-based cohorts (up to 81% in Malmö) and HFpEF cohorts (up to 75% in MEDIA) in the intermediate-likelihood category, requiring further diagnostic evaluation. Their diagnostic discrimination ranged from moderate to good. The universal definition of HF, SGLT2i trial criteria and NT-proBNP age-adjusted thresholds showed diagnostic performance comparable to HFA-PEFF Scores in the HFpEF cohorts and correctly excluded almost all individuals in the community cohorts. The universal definition of HF demonstrated a diagnostic discrimination higher than H ₂ FPEF and comparable to HFA-PEFF, with the most balanced performance in terms of sensitivity and specificity. Conclusions: Using scores, a substantial proportion of HFpEF individuals fall into the intermediate likelihood category, highlighting diagnostic uncertainty. Simpler tools, such as the universal definition of HF, demonstrate comparable or even superior diagnostic and rule-out performances for HFpEF, emphasising the need for more practical and reliable approaches to HFpEF diagnosis.