Trajectories of chronic multimorbidity patterns in older patients: MTOP study.

BACKGROUND: Multimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years. METHODS: A retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group. RESULTS: 3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories. CONCLUSIONS: In a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning. TRIAL REGISTRATION NUMBER: NCT05717309.

Multimorbidity patterns in COVID-19 patients and their relationship with infection severity: MRisk-COVID study.

BACKGROUND: Several chronic conditions have been identified as risk factors for severe COVID-19 infection, yet the implications of multimorbidity need to be explored. The objective of this study was to establish multimorbidity clusters from a cohort of COVID-19 patients and assess their relationship with infection severity/mortality. METHODS: The MRisk-COVID Big Data study included 14 286 COVID-19 patients of the first wave in a Spanish region. The cohort was stratified by age and sex. Multimorbid individuals were subjected to a fuzzy c-means cluster analysis in order to identify multimorbidity clusters within each stratum. Bivariate analyses were performed to assess the relationship between severity/mortality and age, sex, and multimorbidity clusters. RESULTS: Severe infection was reported in 9.5% (95% CI: 9.0-9.9) of the patients, and death occurred in 3.9% (95% CI: 3.6-4.2). We identified multimorbidity clusters related to severity/mortality in most age groups from 21 to 65 years. In males, the cluster with highest percentage of severity/mortality was Heart-liver-gastrointestinal (81-90 years, 34.1% severity, 29.5% mortality). In females, the clusters with the highest percentage of severity/mortality were Diabetes-cardiovascular (81-95 years, 22.5% severity) and Psychogeriatric (81-95 years, 16.0% mortality). CONCLUSION: This study characterized several multimorbidity clusters in COVID-19 patients based on sex and age, some of which were found to be associated with higher rates of infection severity/mortality, particularly in younger individuals. Further research is encouraged to ascertain the role of specific multimorbidity patterns on infection prognosis and identify the most vulnerable morbidity profiles in the community. TRIAL REGISTRATION: NCT04981249. Registered 4 August 2021 (retrospectively registered).

Primary and Community Care Transformation in Post-COVID Era: Nationwide General Practitioner Survey.

Introduction: The health emergency caused by COVID-19 has led to substantial changes in the usual working system of primary healthcare centers and in relations with users. The Catalan Society of Family and Community Medicine designed a survey that aimed to collect the opinions and facilitate the participation of its partners on what the future work model of general practitioners (GPs) should look like post-COVID-19. Methodology: Online survey of Family and Community Medicine members consisting of filiation data, 22 Likert-type multiple-choice questions grouped in five thematic axes, and a free text question. Results: The number of respondents to the questionnaire was 1051 (22.6% of all members): 83.2% said they spent excessive time on bureaucratic tasks; 91.8% were against call center systems; 66% believed that home care is the responsibility of every family doctor; 77.5% supported continuity of care as a fundamental value of patient-centered care; and >90% defended the contracting of complementary tests and first hospital visits from primary healthcare (PHC). Conclusions: The survey responses describe a strong consensus on the identity and competencies of the GP and on the needs of and the threats to the PHC system. The demand for an increase in health resources, greater professional leadership, elimination of bureaucracy, an increase in the number of health professionals, and greater management autonomy, are the axes towards which a new era in PHC should be directed.