Patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM) have a substantially increased risk for major cardiovascular events and mortality. Increasing physical activity and improving a healthy diet may effectively reduce cardiovascular risk factors; however, the effects are often transient. In a multicenter, 1:1 randomized controlled trial including 502 patients with combined CHD and T2DM (68 ± 8 years; 84% men), we assessed the effects of a home-based telemedicine-supported lifestyle intervention (exercise training, nutritional recommendations and health literacy training) with regular individualized feedback versus usual care. The study met its primary endpoint of reduced glycated hemoglobin after 6 months in favor of the lifestyle intervention group (mean between-group difference in the complete-case analysis (n = 197 and n = 193), −0.13% (95% confidence interval, −0.25 to −0.01), P = 0.04). When individualized feedback and health literacy training were discontinued after 6 months (while other telemedicine tools were maintained), no statistically significant between-group differences were observed at 12 months. At 12 months, 31 patients (6.2%) had a major adverse cardiovascular event (lifestyle intervention, n = 20 (8.0%); usual care, n = 11 (4.4%); P = 0.15), with the main reason being hospitalization for angina or revascularization (lifestyle intervention, n = 15; usual care, n = 8). There were five deaths (lifestyle intervention, n = 2; usual care, n = 3), none of which were categorized as related to the intervention. However, three events that resulted in hospitalization were categorized as potentially related to the intervention (decompensation of heart failure, vertebral disc prolapse and inguinal hernia). In conclusion, a home-based lifestyle intervention with telemedicine support showed modest effects in patients with CHD and T2DM. ClinicalTrials.gov registration: NCT03835923.

The new 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes¹ mainly focuses on two aspects (Figure 1).

Background: In Europe, more than 15 million people live with heart failure (HF). It imposes an enormous social, organizational and economic burden. As a reaction to impending impact on healthcare provision, different country-specific structures for HF-care have been established. The aim of this report is to provide an overview and compare the HF-care approaches of Germany, Ireland, the Netherlands and the UK, and to open the possibility of learning from each other’s experience. Methods: A mixed methods approach was implemented that included a literature analysis, interviews and questionnaires with HF-patients and caregivers, and expert interviews with representatives from healthcare, health service research and medical informatics. Results: The models of HF-care in all countries analyzed are based on the European Society of Cardiology guidelines for diagnosis and treatment of HF. Even though the HF-models differed in design and implementation in practice, key challenges were similar: (i) unequal distribution of care between urban and rural areas, (ii) long waiting times, (iii) unequal access to and provision of healthcare services, (iv) information and communication gaps and (v) inadequate implementation and financing of digital applications. Conclusion: Although promising approaches exist to structure and improve HF-care, across the four countries, implementation was reluctant to embrace novel methods. A lack of financial resources and insufficient digitalization making it difficult to adopt new concepts. Integration of HF-nurses seems to be an effective way of improving current models of HF-care. Digital solutions offer further opportunities to overcome communication and coordination gaps and to strengthen self-management skills.

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at −24 h and 0.88 at −48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.

PURPOSE: Intensive treatment protocols for aggressive hematologic malignancies harbor a high risk of serious clinical complications, such as infections. Current techniques of monitoring vital signs to detect such complications are cumbersome and often fail to diagnose them early. Continuous monitoring of vital signs and physical activity by means of an upper arm medical wearable allowing 24/7 streaming of such parameters may be a promising alternative. METHODS: This single-arm, single-center observational trial evaluated symptom-related patient-reported outcomes and feasibility of a wearable-based remote patient monitoring. All wearable data were reviewed retrospectively and were not available to the patient or clinical staff. A total of 79 patients (54 inpatients and 25 outpatients) participated and received standard-of-care treatment for a hematologic malignancy. In addition, the wearable was continuously worn and self-managed by the patient to record multiple parameters such as heart rate, oxygen saturation, and physical activity. RESULTS: Fifty-one patients (94.4%) in the inpatient cohort and 16 (64.0%) in the outpatient cohort reported gastrointestinal symptoms (diarrhea, nausea, and emesis), pain, dyspnea, or shivering in at least one visit. With the wearable, vital signs and physical activity were recorded for a total of 1,304.8 days. Recordings accounted for 78.0% (63.0-88.5; median [interquartile range]) of the potential recording time for the inpatient cohort and 84.6% (76.3-90.2) for the outpatient cohort. Adherence to the wearable was comparable in both cohorts, but decreased moderately over time during the trial. CONCLUSION: A high adherence to the wearable was observed in patients on intensive treatment protocols for a hematologic malignancy who experience high symptom burden. Remote patient monitoring of vital signs and physical activity was demonstrated to be feasible and of primarily sufficient quality.

The standardization of clinical data elements (CDEs) aims to ensure consistent and comprehensive patient information across various healthcare systems. Existing methods often falter when standardizing CDEs of varying representation and complex structure, impeding data integration and interoperability in clinical research. This paper presents CDE-Mapper, a framework that combines a retrieval-augmented generation strategy with large language models to automate the alignment of CDEs with controlled vocabularies. Our modular approach features query decomposition to manage varying levels of CDEs complexity, integrates expert-defined rules within prompt engineering, and employs in-context learning alongside multiple retriever components to resolve terminological ambiguities. In addition, we propose a knowledge reservoir validated by a human-in-loop approach, achieving accurate concept linking for future applications while minimizing computational costs. For four diverse datasets, CDE-Mapper achieved an average of 7.2% higher accuracy improvement compared to baseline methods. This work highlights the potential of advanced language models in improving data harmonization and significantly advancing capabilities in clinical decision support systems and research.