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.
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http://dx.doi.org/10.1186/s12877-024-04925-2 | DOI Listing |
Ann Med
December 2025
Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, Australia.
Background: Most older patients with atrial fibrillation (AF) have comorbidities. However, it is unclear whether specific comorbidity patterns are associated with adverse outcomes. We identified comorbidity patterns and their association with mortality in multimorbid older AF patients with different multidimensional frailty.
View Article and Find Full Text PDFHum Genomics
January 2025
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Richards Building B304, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
Background: Disease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.
View Article and Find Full Text PDFHealth Place
January 2025
University of Edinburgh, Edinburgh, UK. Electronic address:
In the context of population ageing, multimorbidity is an increasingly prevalent public health issue that has a substantial impact on both individuals and healthcare systems. Alongside the literature looking at risk factors at the individual level, there is a growing body of research examining the role of neighbourhoods in the development of multimorbidity. However, most of this work has focused on physical features of place such as air pollution and green space, while social features of place have been largely overlooked.
View Article and Find Full Text PDFComput Biol Med
January 2025
Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.
Multimorbidity, the co-occurrence of multiple chronic conditions within the same individual, is increasing globally. This is a challenge for the single patients, as these individuals are subject to a heavy disease and treatment burden, yet evidence on the epidemiology and consequences of multimorbidity remains underexplored. Historically, studies aiming to understand multimorbidity patterns predominantly utilized cross-sectional data, neglecting the essential temporal dynamics which shape multimorbidity progression.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka.
Background: Caring for older people has become a significant public health concern in Sri Lanka due to the growing aging population. This has placed a heavy burden on family caregivers, particularly those caring for older individuals with multiple chronic conditions. Recognizing this challenge, the present study aimed to evaluate the psychometric properties of the Sinhala version of the 10-item short form of the Burden Scale for Family Caregivers (BSFC-s) and assess caregiver burden and associated factors among caregivers of older people aged over 65 years with multimorbidity.
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