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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).

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.

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.

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.

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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.