Andrés Gaspar Castillo Sanz

BACKGROUND Population-based risk stratification using routinely collected health data can help identify individuals with greater health and care needs. However, high-risk chronic patients constitute a heterogeneous population, and further characterization of their complexity may support more targeted population health management. OBJECTIVE To describe the sociodemographic, clinical, functional, pharmacological, and primary health care (PHC) utilization profiles of high-risk chronic patients across quartiles of Adjusted Morbidity Group (AMG) complexity and identify factors independently associated with the highest complexity quartile. METHODS This population-based cross-sectional study used routinely collected real-world data from PHC electronic health records and pharmacy dispensing databases of the Madrid Health Service. Adults aged ≥18 years with chronic conditions classified as high-risk according to the AMG stratification tool as of April 30, 2021, were included. Patients were categorized into AMG complexity quartiles, with Q4 representing the highest complexity level. Sociodemographic, clinical, functional, pharmacological, and health care utilization characteristics were compared across complexity levels using descriptive and bivariate analyses. Multivariable logistic regression was used to identify factors independently associated with Q4 complexity. RESULTS A total of 163,188 high-risk chronic patients were included. Compared with patients in the lower complexity quartile, those in Q4 were older (mean age 78.9 vs 75.6 years), had greater functional dependency (31.1% vs 14.2%), immobilization (18.9% vs 8.1%), and caregiver requirements (5.4% vs 2.4%), and had a higher morbidity burden (mean number of conditions 8.93 vs 6.31), medication use (10.0 vs 8.2 medications), and prevalence of polypharmacy (64.3% vs 47.7%). They also had more PHC contacts (29.6 vs 20.8 visits/year). In multivariable analysis, older age (OR 1.024 per year), heart failure (OR 2.874), neoplasm (OR 2.715), chronic kidney disease (OR 2.088), dementia (OR 1.987), caregiver presence (OR 1.393), polypharmacy (OR 1.161), and ≥20 PHC visits/year (OR 1.887) were positively associated with Q4 complexity, whereas female sex (OR 0.846) and greater functional independence (OR 0.669) were associated with lower odds of Q4 complexity (all p<0.001). CONCLUSIONS High-risk chronic patients in the highest AMG complexity quartile showed greater multimorbidity, functional impairment, medication burden, and PHC utilization. AMG complexity quartiles add value beyond high-risk status by identifying complex chronic patients with greater care needs and supporting targeted proactive care in PHC settings. These findings highlight the potential of routinely collected electronic health record and pharmacy data, combined with population-based risk stratification systems, as scalable tools for population health analysis and targeted health care planning in primary care.

MDCG 2019-16 is intended to assist practitioners in compliance with the Medical Device Regulation and the In-Vitro Device Regulation. This paper presents a gap analysis of MDCG 2019-16, identifying key gaps and proposing a robust set of recommendations to enhance the IoMT regulatory framework. This work has been undertaken by a selection of current (2023-2025) projects, all funded under the Horizon Europe call “Enhancing cybersecurity of connected medical devices”: HORIZON-HLTH-2022-IND-13-01, and this paper summarises observations and recommendations from across these projects. There is considerable consensus across the projects in many recommendation themes, notably; linking cybersecurity with patient safety and privacy; keeping the guidelines current; and usage recipes for the guidelines. The paper also suggests toolkit solutions to address some of the recommendations.