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Background Several chronic diseases accelerate cognitive decline; however, it is still unknown how different patterns of multimorbidity influence individuals' trajectories across the cognitive continuum. Objectives We aimed to investigate the impact of multimorbidity and of specific multimorbidity patterns on the transitions across cognitive stages (normal cognition, cognitive impairment, no dementia [CIND], dementia) and death. Methods We included 3122 dementia-free individuals from the Swedish National study on Aging and Care in Kungsholmen. Using fuzzy c-means cluster analysis, multimorbid participants were classified into mutually exclusive groups characterized by commonly coexisting chronic diseases. Participants were followed up to 18 years to detect incident CIND, dementia, or death. Transition hazard ratios (HRs), life expectancies, and time spent in different cognitive stages were estimated using multistate Markov models. Results At baseline, five multimorbidity patterns were identified: neuropsychiatric, cardiovascular, sensory impairment/cancer, respiratory/metabolic/musculoskeletal, and unspecific. Compared to the unspecific pattern, the neuropsychiatric and sensory impairment/cancer ones showed reduced hazards of reverting from CIND to normal cognition (HR 0.53, 95% CI 0.33-0.85 and HR 0.60, 95% CI 0.39-0.91). Participants in the cardiovascular pattern exhibited an increased hazard of progression from CIND to dementia (HR 1.70, 95% CI 1.15-2.52) and for all transitions to death. Subjects with the neuropsychiatric and cardiovascular patterns showed reduced life expectancy at age 75, with an anticipation of CIND (up to 1.6 and 2.2 years, respectively) and dementia onset (up to 1.8 and 3.3 years, respectively). Conclusions Multimorbidity patterns differentially steer individual trajectories across the cognitive continuum of older adults and may be used as a risk stratification tool.

Background: Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. Objective: This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. Methods: Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d’Informació pel Desenvolupament de la Investigació a l’Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. Results: The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty., The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded in 2019 under the Health Strategy Action 2013-2016, within the National Research Programme oriented to Societal Challenges, within the Technical, Scientific and Research National Plan 2013-2016 (reference PI19/00535), and the PFIS Grant FI20/00040, co-funded with European Union ERDF (European Regional Development Fund) funds., Peer Reviewed, Postprint (published version)

Background: The evolution of multimorbidity patterns during aging is still an under-researched area. We lack evidence concerning the time spent by older adults within one same multimorbidity pattern, and their transitional probability across different patterns when further chronic diseases arise. The aim of this study is to fill this gap by exploring multimorbidity patterns across decades of age in older adults, and longitudinal dynamics among these patterns. Methods: Longitudinal study based on the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) on adults ≥60 years (N=3,363). Hidden Markov Models were applied to model the temporal evolution of both multimorbidity patterns and individuals' transitions over a 12-year follow-up. Findings: Within the study population (mean age 76.1 years, 66.6% female), 87.2% had ≥2 chronic conditions at baseline. Four longitudinal multimorbidity patterns were identified for each decade. Individuals in all decades showed the shortest permanence time in an Unspecific pattern lacking any overrepresented diseases (range: 4.6-10.9 years), but the pattern with the longest permanence time varied by age. Sexagenarians remained longest in the Psychiatric-endocrine and sensorial pattern (15.4 years); septuagenarians in the Neuro-vascular and skin-sensorial pattern (11.0 years); and octogenarians and beyond in the Neuro-sensorial pattern (8.9 years). Transition probabilities varied across decades, sexagenarians showing the highest levels of stability. Interpretation: Our findings highlight the dynamism and heterogeneity underlying multimorbidity by quantifying the varying permanence times and transition probabilities across patterns in different decades. With increasing age, older adults experience decreasing stability and progressively shorter permanence time within one same multimorbidity pattern.

Background: Prevalence of both multimorbidity and frailty increases with age, but more evidence is needed to elucidate their relationship and their association with other health-related outcomes. We analysed the dynamics of both conditions as people age and calculate the associated risk of death, nursing home admission, and need for home care. Methods: Data were drawn from the primary care electronic health records of a longitudinal cohort of people aged 65 or older in Catalonia in 2010–2019. Frailty and multimorbidity were measured using validated instruments (eFRAGICAP, a cumulative deficit model; and SNAC-K, respectively), and their longitudinal evolution was described. Cox regression models accounted for the competing risk of death and adjusted by sex, socioeconomical status, and time-varying age, alcohol and smoking. Findings: We included 1 456 052 patients. Prevalence of multimorbidity was consistently high regardless of age, while frailty almost quadrupled from 65 to 99 years. Frailty worsened and also changed with age: up to 84 years, it was more related to concurrent diseases, and afterwards, to frailty-related deficits. While concurrent diseases contributed more to mortality, frailty-related deficits increased the risk of institutionalisation and the need for home care. Interpretation: The nature of people’s multimorbidity and frailty vary with age, as does their impact on health status. People become frailer as they age, and their frailty is more characterised by disability and other symptoms than by diseases. Mortality is most associated with the number of comorbidities, whereas frailty-related deficits are associated with needing specialised care., The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded on the 2019 call under the Health Strategy Action 2013−2016, within the National Research Programme oriented to Societal Challenges,within the Technical, Scientific and Innovation Research National Plan 2013−2016, (reference PI19/00535), and the PFIS Grant FI20/00040, co-funded with European Union ERDF (European Regional Development Fund) funds. The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of this work., Peer Reviewed, Postprint (published version)

Background: the aim of this study was to examine the cross-sectional and longitudinal associations of different multimorbidity patterns with physical frailty in older adults. Methods: we used data from the Swedish National study on Aging and Care in Kungsholmen to generate a physical frailty measure, and clusters of participants with similar multimorbidity patterns were identified through fuzzy c-means cluster analyses. The cross-sectional association (n= 2,534) between multimorbidity clusters and physical frailty was measured through logistic regression analyses. Six- (n= 2,122) and 12-year (n= 2,140) longitudinal associations were determined through multinomial logistic regression analyses. Results: six multimorbidity patterns were identified at baseline: psychiatric diseases; cardiovascular diseases, anaemia and dementia; sensory impairments and cancer; metabolic and sleep disorders; musculoskeletal, respiratory and gastrointestinal diseases; and an unspecific pattern lacking any overrepresented diseases. Cross-sectionally, each pattern was associated with physical frailty compared with the unspecific pattern. Over 6 years, the psychiatric diseases (relative risk ratio [RRR]: 3.04; 95% confidence intervals [CI]: 1.59-5.79); cardiovascular diseases, anaemia and dementia (RRR 2.25; 95% CI: 1.13-4.49) and metabolic and sleep disorders (RRR 1.99; 95% CI: 1.25-3.16) patterns were associated with incident physical frailty. The cardiovascular diseases, anaemia and dementia (RRR: 4.81; 95% CI: 1.59-14.60); psychiatric diseases (RRR 2.62; 95% CI: 1.45-4.72) and sensory impairments and cancer (RRR 1.87; 95% CI: 1.05-3.35) patterns were more associated with physical frailty, compared with the unspecific pattern, over 12 years. Conclusions: we found that older adults with multimorbidity characterised by cardiovascular and neuropsychiatric disease patterns are most susceptible to developing physical frailty.

Introduction: We investigate dementia risk in older adults with different disease patterns and explore the role of inflammation and apolipoprotein E (APOE) genotype. Methods: A total of 2,478 dementia-free participants with two or more chronic diseases (ie, multimorbidity) part of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) were grouped according to their multimorbidity patterns and followed to detect clinical dementia. The potential modifier effect of C-reactive protein (CRP) and apolipoprotein E (APOE) genotype was tested through stratified analyses. Results: People with neuropsychiatric, cardiovascular, and sensory impairment/cancer multimorbidity had increased hazards for dementia compared to the unspecific (Hazard ration (HR) 1.66, 95% confidence interval [CI] 1.13-2.42; 1.61, 95% CI 1.17-2.29; 1.32, 95% CI 1.10-1.71, respectively). Despite the lack of statistically significant interaction, high CRP increased dementia risk within these patterns, and being APOE epsilon 4 carriers heightened dementia risk for neuropsychiatric and cardiovascular multimorbidity. Discussion: Individuals with neuropsychiatric, cardiovascular, and sensory impairment/cancer patterns are at increased risk for dementia and APOE epsilon 4, and inflammation may further increase the risk. Identifying such high-risk groups might allow tailored interventions for dementia prevention.

Multimorbidity is common in over 50 percent of people over the age of 65, and can be a major factor in recovery from chronic illnesses. In addition, the use of multiple medications simultaneously is increasingly common in the population, and in some cases the effects derived from this consumption may go beyond the main purpose. This thesis studies the effect of drug consumption and multimorbidity on breast cancer survival at 12, 24, 36 and 48 months from the first diagnosis. Six different machine learning algorithms have been used to build predictive models. After an analysis, Extreme Gradient Boost has been selected as the main model due to its performance. The results obtained for 36 and 48 months, in which the tables contain a proportion of dead patients higher than 27%, have been very satisfactory, with metrics AUC, F1 score and accuracy higher than 91% and recall higher than 88%. In the cases of 12 and 24 months the results have been satisfactory in the AUC metrics (>93%), but between 57 and 78% in the others.

Background: The aim of this study is to identify clusters of older persons based on their multimorbidity patterns and to analyze differences among clusters according to sociodemographic, lifestyle, clinical, and functional characteristics. Methods: We analyzed data from the Swedish National Study on Aging and Care in Kungsholmen on 2,931 participants aged 60 years and older who had at least two chronic diseases. Participants were clustered by the fuzzy c-means cluster algorithm. A disease was considered to be associated with a given cluster when the observed/expected ratio was >= 2 or the exclusivity was >= 25%. Results: Around half of the participants could be classified into five clinically meaningful clusters: respiratory and musculoskeletal diseases (RESP-MSK) 15.7%, eye diseases and cancer (EYE-CANCER) 10.7%, cognitive and sensory impairment (CNS-IMP) 10.6%, heart diseases (HEART) 9.3%, and psychiatric and respiratory diseases (PSY-RESP) 5.4%. Individuals in the CNS-IMP cluster were the oldest, with the worst function and more likely to live in a nursing home; those in the HEART cluster had the highest number of co-occurring diseases and drugs, and they exhibited the highest mean values of serum creatinine and C-reactive protein. The PSY-RESP cluster was associated with higher levels of alcoholism and neuroticism. The other half of the cohort was grouped in an unspecific cluster, which was characterized by gathering the youngest individuals, with the lowest number of co-occurring diseases, and the best functional and cognitive status. Conclusions: The identified multimorbidity patterns provide insight for setting targets for secondary and tertiary preventative interventions and for designing care pathways for multimorbid older people.

The presence of multiple chronic conditions (i.e., multimorbidity) increases the risk of hospitalisation in older adults. We aimed to examine the association between different multimorbidity patterns and unplanned hospitalisations over 5 years. To that end, 2,250 community-dwelling individuals aged 60 years and older from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K) were studied. Participants were grouped into six multimorbidity patterns using a fuzzy c-means cluster analysis. The associations between patterns and outcomes were tested using Cox models and negative binomial models. After 5 years, 937 (41.6%) participants experienced at least one unplanned hospitalisation. Compared to participants in the unspecific multimorbidity pattern, those in the cardiovascular diseases, anaemia and dementia pattern, the psychiatric disorders pattern and the metabolic and sleep disorders pattern presented with a higher hazard of first unplanned hospitalisation (hazard ratio range: 1.49-2.05; p < 0.05 for all), number of unplanned hospitalisations (incidence rate ratio (IRR) range: 1.89-2.44; p < 0.05 for all), in-hospital days (IRR range: 1.91-3.61; p < 0.05 for all), and 30-day unplanned readmissions (IRR range: 2.94-3.65; p < 0.05 for all). Different multimorbidity patterns displayed a differential association with unplanned hospital care utilisation. These findings call for a careful primary care follow-up of older adults with complex multimorbidity patterns.

This study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity., This work was supported by a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded for the 2016 call under the Health Strategy Action 2013–2016, within the National Research Program oriented to Societal Challenges, within the Technical, Scientifc and Innovation Research National Plan 2013–2016 ‘[grant number PI16/00639]’, co-funded with European Union ERDF funds (European Regional Development Fund) and the Department of Health of the Catalan Government, in the call corresponding to 2017 for the granting of subsidies from the Strategic Plan for Research in Health (Pla Estratègic de Recerca i Innovació en Salut, PERIS) 2016–2020, modality research oriented to Primary care ‘[grant number SLT002/16/00058]’ and by the Catalan Government ‘[Grant Number AGAUR 2017 SGR 578]., Peer Reviewed, Postprint (published version)

A challenge to solve when analyzing multimorbidity patterns in elderly people is the management of a high number of characteristics associated with each patient. The main variables to study multimorbidity are diseases, however other variables should be considered to better classify the people included in each pattern. Age, sex, social class and medication are frequently used in the typing of each multimorbidity pattern. Subsequently the cardinality of the set of features that characterize a patient is very high and normally, the set is compressed to obtain a patient vector of new variables whose dimension is noticeably smaller than that of the initial set. To minimize the loss of information by compression, traditionally Principal Component Analysis (PCA) based projection techniques have been used, which although they are generally a good option, the projection is linear, which somehow reduces its flexibility and limits the performance. As an alternative to the PCA based techniques, in this paper, it is proposed to use autoencoders, and it is shown the improvement in the obtained multimorbidity patterns from the compressed database, when the registered data on about a million patients (5 years' follow-up) are processed. This work demonstrates that autoencoders retain a larger amount of information in each pattern and results are more consistent with clinical experience than other approaches frequently found in the literature.Clinical relevance- From an epidemiological perspective, the contribution is relevant, since it allows for a more precise analysis of multimorbidity patterns, leading to better approaches to patient health strategies., Work supported by Carlos III Institute of Health, Spanish Ministry of Economy and Competitiveness and ERDF (UE) funds under Grant PI16/00639, and in part by Catalan Government funds under Grants 2017 SGR 578 AGAUR and Strategic Plan for Research in Health PERIS grant number SLT002/16/00058, Peer Reviewed, Postprint (published version)

Background. The axillary lymph nodes (ALNs) in breast cancer patients are the body regions to where tumoral cells most often first disseminate. The tumour immune response is important for breast cancer patient outcome, and some studies have evaluated its involvement in ALN metastasis development. Most studies have focused on the intratumoral immune response, but very few have evaluated the peritumoral immune response. The aim of the present article is to evaluate the immune infiltrates of the peritumoral area and their association with the presence of ALN metastases. Methods. The concentration of 11 immune markers in the peritumoral areas was studied in 149 patients diagnosed with invasive breast carcinoma of no special type (half of whom had ALN metastasis at diagnosis) using tissue microarrays, immunohistochemistry and digital image analysis procedures. The differences in the concentration of the immune response of peritumoral areas between patients diagnosed with and without metastasis in their ALNs were evaluated. A multivariate logistic regression model was developed to identify the clinical-pathological variables and the peritumoral immune markers independently associated with having or not having ALN metastases at diagnosis. Results. No statistically significant differences were found in the concentrations of the 11 immune markers between patients diagnosed with or without ALN metastases. Patients with metastases in their ALNs had a higher histological grade, more lymphovascular and perineural invasion and larger-diameter tumours. The multivariate analysis, after validation by bootstrap simulation, revealed that only tumour diameter (OR = 1.04; 95% CI [1.00-1.07]; p = 0.026), lymphovascular invasion (OR = 25.42; 95% CI [9.57-67.55]; p<0.001) and histological grad