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Resumen: Objetivo: Principales: determinar la prevalencia y las características de los pacientes con COVID-19, y la incidencia del trastorno mental asociado a la infección y los factores de riesgo. Secundarios: conocer la prevalencia y las características de los afectados de COVID persistente. Diseño: Estudio de incidencia del trastorno mental en los pacientes diagnosticados de COVID-19, y estudio de casos y controles del síndrome de COVID persistente en la submuestra de pacientes diagnosticados de COVID-19, sin antecedente de trastorno mental. Emplazamiento: Atención Primaria de la Región Sanitaria de Lleida. Participantes: Se incluyeron 46.258 pacientes diagnosticados de COVID-19 sin antecedente de trastorno mental. Mediciones principales: La variable dependiente fue el desarrollo de trastorno mental (ansiedad, trastorno del estado de ánimo, trastorno adaptativo e insomnio) en el periodo de estudio (11/03/2020 a 11/03/2022) y las independientes variables clínicas y sociofamiliares. Se analizó su asociación con el desarrollo del trastorno mental mediante el cálculo de las hazard ratio ajustadas a un modelo de regresión logística. Resultados: La edad media ± desviación estándar de los 46.258 pacientes incluidos fue de 43 ± 17,9 años. El 47% eran mujeres. El 3,46% desarrollaron algún trastorno mental (59,20% ansiedad). El síndrome de «COVID persistente» se manifestó más en las mujeres de edad mayor, sin hábitos tóxicos, con obesidad y comorbilidad asociada. Conclusiones: La COVID-19 ha generado impacto en la salud mental de los pacientes. Conocer los factores de riesgo para los trastornos mentales y para el síndrome de «COVID persistente» permitiría al clínico identificar a los pacientes susceptibles de recibir medidas preventivas y evitar su aparición. Abstract: Objective: To determine the prevalence of COVID-19 and the characteristics of infected patients, as well as the prevalence of mental disorders associated with the infection and the associated risk factors. Secondary: to know the prevalence of the long COVID syndrome and the characteristics of this cohort. Design: Incidence study of mental disorder in patients diagnosed with COVID-19 and case-control study of long COVID syndrome in the subsample of patients diagnosed with COVID-19 without a history of mental disorder. Site: Primary care setting in Lleida. Participants: The 46,258 patients diagnosed of COVID-19 without a history of mental disorder were followed for 2 years. Main measurements: The dependent variable was the mental disorder in the period 03/11/2020 to 03/11/2022; and the independent variables were clinical and social variables. Its association with mental disorder was analyzed by calculating the adjusted hazard ratio using a logistic regression model. Results: The average age of 46,258 patients at the beginning of the study was 43 ± 17.9 years. 47% were women. The mental disorder rate for all period was 3.46% (59.20% anxiety). A predictive score was elaborated. The long COVID syndrome was more common in older women, without toxic habits, with obesity and associated comorbidity. Conclusions: COVID-19 has had an impact on the mental health of patients. Knowing the risk factors for developing these mental disorders and the long COVID syndrome would allow the clinician to be able to identify patients at risk in order to establish preventive measures and avoid their appearance.

Abstract Background Understanding the immune response to the SARS-CoV-2 virus is critical for efficient monitoring and control strategies. The ProHEpic-19 cohort provides a fine-grained description of the kinetics of antibodies after SARS-CoV-2 infection with an exceptional resolution over 17 months. Methods We established a cohort of 769 healthcare workers including healthy and infected with SARS-CoV-2 in northern Barcelona to determine the kinetics of the IgM against the nucleocapsid (N) and the IgG against the N and spike (S) of SARS-CoV-2 in infected healthcare workers. The study period was from 5 May 2020 to 11 November 2021.We used non-linear mixed models to investigate the kinetics of IgG and IgM measured at nine time points over 17 months from the date of diagnosis. The model included factors of time, gender, and disease severity (asymptomatic, mild-moderate, severe-critical) to assess their effects and their interactions. Findings 474 of the 769 participants (61.6%) became infected with SARS-CoV-2. Significant effects of gender and disease severity were found for the levels of all three antibodies. Median IgM(N) levels were already below the positivity threshold in patients with asymptomatic and mild-moderate disease at day 270 after the diagnosis, while IgG(N and S) levels remained positive at least until days 450 and 270, respectively. Kinetic modelling showed a general rise in both IgM(N) and IgG(N) levels up to day 30, followed by a decay with a rate depending on disease severity. IgG(S) levels remained relatively constant from day 15 over time. Interpretation IgM(N) and IgG(N, S) SARS-CoV-2 antibodies showed a heterogeneous kinetics over the 17 months. Only the IgG(S) showed a stable increase, and the levels and the kinetics of antibodies varied according to disease severity. The kinetics of IgM and IgG observed over a year also varied by clinical spectrum can be very useful for public health policies around vaccination criteria in adult population. Funding Regional Ministry of Health of the Generalitat de Catalunya (Call COVID19-PoC SLT16_04; NCT04885478).

BackgroundMultimorbidity 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. ObjectiveThis study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. MethodsLongitudinal 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. ResultsThe 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 change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. ConclusionsOur results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.

Abstract Objective To create an electronic frailty index (eFRAGICAP) using electronic health records (EHR) in Catalunya (Spain) and assess its predictive validity with a two-year follow-up of the outcomes: homecare need, institutionalization and mortality in the elderly. Additionally, to assess its concurrent validity compared to other standardized measures: the Clinical Frailty Scale (CFS) and the Risk Instrument for Screening in the Community (RISC). Methods The eFRAGICAP was based on the electronic frailty index (eFI) developed in United Kingdom, and includes 36 deficits identified through clinical diagnoses, prescriptions, physical examinations, and questionnaires registered in the EHR of primary health care centres (PHC). All subjects > 65 assigned to a PHC in Barcelona on 1st January, 2016 were included. Subjects were classified according to their eFRAGICAP index as: fit, mild, moderate or severe frailty. Predictive validity was assessed comparing results with the following outcomes: institutionalization, homecare need, and mortality at 24 months. Concurrent validation of the eFRAGICAP was performed with a sample of subjects (n = 333) drawn from the global cohort and the CFS and RISC. Discrimination and calibration measures for the outcomes of institutionalization, homecare need, and mortality and frailty scales were calculated. Results 253,684 subjects had their eFRAGICAP index calculated. Mean age was 76.3 years (59.5% women). Of these, 41.1% were classified as fit, and 32.2% as presenting mild, 18.7% moderate, and 7.9% severe frailty. The mean age of the subjects included in the validation subsample (n = 333) was 79.9 years (57.7% women). Of these, 12.6% were classified as fit, and 31.5% presented mild, 39.6% moderate, and 16.2% severe frailty. Regarding the outcome analyses, the eFRAGICAP was good in the detection of subjects who were institutionalized, required homecare assistance, or died at 24 months (c-statistic of 0.841, 0.853, and 0.803, respectively). eFRAGICAP was also good in the detection of frail subjects compared to the CFS (AUC 0.821) and the RISC (AUC 0.848). Conclusion The eFRAGICAP has a good discriminative capacity to identify frail subjects compared to other frailty scales and predictive outcomes.

Background: The changes in shield strategies, treatments, emergence variants, and healthcare pathways might shift the profile and outcome of patients hospitalized with COVID-19 in successive waves of the outbreak. Methods: We retrospectively analysed the characteristics and in-hospital outcomes of all patients admitted with COVID-19 in eight university hospitals of Catalonia (North-East Spain) between Feb 28, 2020 and Feb 28, 2021. Using a 7-joinpoint regression analysis, we split admissions into four waves. The main hospital outcomes included 30-day mortality and admission to intensive care unit (ICU). Findings: The analysis included 17,027 subjects admitted during the first wave (6800; 39.9%), summer wave (1807; 10.6%), second wave (3804; 22.3%), and third wave (4616; 27.1%). The highest 30-day mortality rate was reported during the first wave (17%) and decreased afterwards, remaining stable at 13% in the second and third waves (overall 30% reduction); the lowest mortality was reported during the summer wave (8%, 50% reduction). ICU admission became progressively more frequent during successive waves. In Cox regression analysis, the main factors contributing to differences in 30-day mortality were the epidemic wave, followed by gender, age, diabetes, chronic kidney disease, and neoplasms. Interpretation: Although in-hospital COVID-19 mortality remains high, it decreased substantially after the first wave and is highly dependent of patient's characteristics and ICU availability. Highest mortality reductions occurred during a wave characterized by younger individuals, an increasingly frequent scenario as vaccination campaigns progress. Funding: This work did not receive specific funding.

Summary: 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. Funding: Instituto de Salud Carlos III through PI19/00535, and the PFIS Grant FI20/00040 (Co-funded by European Regional Development Fund/European Social Fund).

Abstract 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.

The co-occurrence of chronic diseases in the same person increases the risk of negative health events. Here authors show that grouping people based on their underlying disease patterns helps to identify homogeneous groups of people with similar needs and prognosis, facilitating personalized approaches.

Abstract Background The implementation of individual clinical practice guidelines in patients with multimorbidity often results in polypharmacy. Our aim was to analyse medication use according to longitudinal multimorbidity patterns (MP) and determine during a 5-year period (2012–16) which MP are associated with abnormal liver and kidney function in primary care patients over 65 years of age living in Catalonia. Methods Design: Longitudinal study (years 2012 to 2016) based on the electronic health records contained in Information System for Research in Primary Care database of the Catalan Institute of Health (SIDIAP). Variables: age, sex, MP, medication and polypharmacy (drug exposure obtained from the Pharmacy Invoice Registry). Medicines were classified in accordance with the Anatomical Therapeutic Chemical Classification System (ATC). Glomerular filtration rate was used to determine abnormal kidney function, and serum levels of alkaline phosphatase, alanine transaminase and gamma-glutamyl transpeptidase were used to diagnose abnormal liver function. Statistics: For medication use in MP, we calculated annual mean packages of each drug in each MP, and observed/expected ratios were obtained by dividing mean packages in the cluster by mean packages of the same drug in the overall population. Logistic regression models were fitted to estimate the association between MP at baseline and abnormal kidney and liver function tests during follow up. Results Nine hundred sixteen thousand six hundred nineteen patients were included, and 743,827 completed the follow up. We identified one polypharmacy profile per MP, and concluded that the most prescribed drugs in each pattern corresponded to the diseases overrepresented in that specific MP. The median of drugs ranged from 3 (Cluster 1 - Non-Specific) to 8 (Cluster 10 - Multisystem Pattern). Abnormal kidney function was most commonly observed in the Cluster 4 - Cardio-Circulatory and Renal (Odds Ratio [OR] 2.19; Confidence interval [CI] 95% 2.15–2.23) and Cluster 3 - Minority Metabolic Autoimmune-Inflammatory (OR 2.16; CI 95% 2.12–2.20) MP. A higher risk of abnormal liver function was observed in the Cluster 8 - Digestive (OR 3.39; CI 95% 3.30–3.49), and Cluster 4 - Cardio-Circulatory and Renal (OR 1.96; CI 95% 1.91–2.02) MP. Conclusions A higher risk of abnormal kidney and liver function was observed in specific MP. The long-term characterisation of MP and polypharmacy illustrates the burden of chronic multimorbidity and polypharmacy in the elderly population.

(1) Introduction: Cardiovascular disease is associated with high mortality, especially in older people. This study aimed to characterize the evolution of combined multimorbidity and polypharmacy patterns in older people with different cardiovascular disease profiles. (2) Material and methods: This longitudinal study drew data from the Information System for Research in Primary Care in people aged 65 to 99 years with profiles of cardiovascular multimorbidity. Combined patterns of multimorbidity and polypharmacy were analysed using fuzzy c-means clustering techniques and hidden Markov models. The prevalence, observed/expected ratio, and exclusivity of chronic diseases and/or groups of these with the corresponding medication were described. (3) Results: The study included 114,516 people, mostly men (59.6%) with a mean age of 78.8 years and a high prevalence of polypharmacy (83.5%). The following patterns were identified: Mental, behavioural, digestive and cerebrovascular; Neuropathy, autoimmune and musculoskeletal; Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological; Non-specific; Multisystemic; Respiratory, cardiovascular, behavioural and genitourinary; Diabetes and ischemic cardiopathy; and Cardiac. The prevalence of overrepresented health problems and drugs remained stable over the years, although by study end, cohort survivors had more polypharmacy and multimorbidity. Most people followed the same pattern over time; the most frequent transitions were from Non-specific to Mental, behavioural, digestive and cerebrovascular and from Musculoskeletal, mental, behavioural, genitourinary, digestive and dermatological to Non-specific. (4) Conclusions: Eight combined multimorbidity and polypharmacy patterns, differentiated by sex, remained stable over follow-up. Understanding the behaviour of different diseases and drugs can help design individualised interventions in populations with clinical complexity.

Objectives To estimate the frequency of chronic conditions and geriatric syndromes in older patients admitted to hospital because of an exacerbation of their chronic conditions, and to identify multimorbidity clusters in these patients.Design Multicentre, prospective cohort study.Setting Internal medicine or geriatric services of five general teaching hospitals in Spain.Participants 740 patients aged 65 and older, hospitalised because of an exacerbation of their chronic conditions between September 2016 and December 2018.Primary and secondary outcome measures Active chronic conditions and geriatric syndromes (including risk factors) of the patient, a score about clinical management of chronic conditions during admission, and destination at discharge were collected, among other variables. Multimorbidity patterns were identified using fuzzy c-means cluster analysis, taking into account the clinical management score. Prevalence, observed/expected ratio and exclusivity of each chronic condition and geriatric syndrome were calculated for each cluster, and the final solution was approved after clinical revision and discussion among the research team.Results 740 patients were included (mean age 84.12 years, SD 7.01; 53.24% female). Almost all patients had two or more chronic conditions (98.65%; 95% CI 98.23% to 99.07%), the most frequent were hypertension (81.49%, 95% CI 78.53% to 84.12%) and heart failure (59.86%, 95% CI 56.29% to 63.34%). The most prevalent geriatric syndrome was polypharmacy (79.86%, 95% CI 76.82% to 82.60%). Four statistically and clinically significant multimorbidity clusters were identified: osteoarticular, psychogeriatric, cardiorespiratory and minor chronic disease. Patient-level variables such as sex, Barthel Index, number of chronic conditions or geriatric syndromes, chronic disease exacerbation 3 months prior to admission or destination at discharge differed between clusters.Conclusions In older patients admitted to hospital because of the exacerbation of chronic health problems, it is possible to define multimorbidity clusters using soft clustering techniques. These clusters are clinically relevant and could be the basis to reorganise healthcare circuits or processes to tackle the increasing number of older, multimorbid patients.Trial registration number NCT02830425.

Abstract Background Older adults suffer from various chronic conditions which make them particularly vulnerable. The proper management of multiple drug use is therefore crucial. The aim of our study was to describe drug prescription and medication patterns in this population. Methods A cross-sectional study in Barcelona (Spain) using electronic health records from 50 primary healthcare centres. Participants were aged 65 to 94 years, presenting multimorbidity (≥2 chronic diseases), and had been prescribed at least 1 drug for 6 months or longer during 2009. We calculated the prevalence of prescribed drugs and identified medication patterns using multiple correspondence analysis and k-means clustering. Analyses were stratified by sex and age (65–79, 80–94 years). Results We studied 164,513 patients (66.8% women) prescribed a median of 4 drugs (interquartile range [IQR] = 3–7) in the 65–79 age-group and 6 drugs (IQR = 4–8) in the 80–94 age-group. A minimum of 45.9% of patients aged 65–79 years, and 61.8% of those aged 80–94 years, were prescribed 5 or more drugs. We identified 6 medication patterns, a non-specific one and 5 encompassing 8 anatomical groups (alimentary tract and metabolism, blood, cardiovascular, dermatological, musculo-skeletal, neurological, respiratory, and sensory organ). Conclusions Drug prescription is widespread among the elderly. Six medication patterns were identified, 5 of which were related to one or more anatomical group, with associations among drugs from different systems. Overall, guidelines do not accurately reflect the situation of the elderly multimorbid, new strategies for managing multiple drug uses are needed to optimize prescribing in these patients.

An amendment to this paper has been published and can be accessed via the original article.

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

Summary: Proper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk.

Abstract Background The purpose of this study was to ascertain multimorbidity patterns using a non-hierarchical cluster analysis in adult primary patients with multimorbidity attended in primary care centers in Catalonia. Methods Cross-sectional study using electronic health records from 523,656 patients, aged 45–64 years in 274 primary health care teams in 2010 in Catalonia, Spain. Data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), a population database. Diagnoses were extracted using 241 blocks of diseases (International Classification of Diseases, version 10). Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex. Results The 408,994 patients who met multimorbidity criteria were included in the analysis (mean age, 54.2 years [Standard deviation, SD: 5.8], 53.3% women). Six multimorbidity patterns were obtained for each sex; the three most prevalent included 68% of the women and 66% of the men, respectively. The top cluster included coincident diseases in both men and women: Metabolic disorders, Hypertensive diseases, Mental and behavioural disorders due to psychoactive substance use, Other dorsopathies, and Other soft tissue disorders. Conclusion Non-hierarchical cluster analysis identified multimorbidity patterns consistent with clinical practice, identifying phenotypic subgroups of patients.

Abstract Background Multimorbidity is the coexistence of more than two chronic diseases in the same individual; however, there is no consensus about the best definition. In addition, few studies have described the variability of multimorbidity patterns over time. The aim of this study was to identify multimorbidity patterns and their variability over a 6-year period in patients older than 65 years attended in primary health care. Methods A cohort study with yearly cross-sectional analysis of electronic health records from 50 primary health care centres in Barcelona. Selected patients had multimorbidity and were 65 years of age or older in 2009. Diagnoses (International Classification of Primary Care, second edition) were extracted using O’Halloran criteria for chronic diseases. Multimorbidity patterns were identified using two steps: 1) multiple correspondence analysis and 2) k-means clustering. Analysis was stratified by sex and age group (65–79 and ≥80 years) at the beginning of the study period. Results Analysis of 2009 electronic health records from 190,108 patients with multimorbidity (59.8% women) found a mean age of 71.8 for the 65–79 age group and 84.16 years for those over 80 (Standard Deviation [SD] 4.35 and 3.46, respectively); the median number of chronic diseases was seven (Interquartil range [IQR] 5–10). We obtained 6 clusters of multimorbidity patterns (1 nonspecific and 5 specifics) in each group, being the specific ones: Musculoskeletal, Endocrine-metabolic, Digestive/Digestive-respiratory, Neurological, and Cardiovascular patterns. A minimum of 42.5% of the sample remained in the same pattern at the end of the study, reflecting the stability of these patterns. Conclusions This study identified six multimorbidity patterns per each group, one nonnspecific pattern and five of them with a specific pattern related to an organic system. The multimorbidity patterns obtained had similar characteristics throughout the study period. These data are useful to improve clinical management of each specific subgroup of patients showing a particular multimorbidity pattern.

Objectives The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.Design A cross-sectional study was conducted based on data from electronic health records.Setting 284 primary healthcare centres in Catalonia, Spain (2012).Participants 916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measures We extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.Results Multimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.Conclusions Multimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

Objetivo: Identificar los patrones de multimorbilidad en pacientes de 19-44 años atendidos en atención primaria en Cataluña en el año 2010. Diseño: Estudio descriptivo transversal. Emplazamiento: Doscientos cincuenta y un centros de salud. Participantes: Fueron 530.798 personas con multimorbilidad de 19-44 años. Mediciones principales: La multimorbilidad fue definida como la coexistencia de ≥2 diagnósticos de la Clasificación Internacional de Enfermedades registrados en la historia clínica informatizada. Los patrones de multimorbilidad fueron identificados a través de un análisis jerárquico de clústeres y descritos por sexo y grupos de edad (19-24 y 25-44 años). Resultados: De las 882.708 personas de la población inicial, 530.798 (60,1%) presentaron multimorbilidad. La media de edad fue de 33,0 años (DT: 7,0) y el 53,3% fueron mujeres. La multimorbilidad fue más alta en el grupo de 25-44 años respecto al grupo más joven (60,5 vs. 58,1%, p