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Introduction Congenital cytomegalovirus (cCMV) is the leading cause of non-genetic sensorineural hearing loss and one of the main causes of neurological disability. Despite this, no universal screening programme for cCMV has been implemented in Spain. A recent study has shown that early treatment with valaciclovir, initiated in the first trimester and before the onset of signs in the fetus, reduces the risk of fetal infection. This finding favours the implementation of a universal screening programme for cCMV.The aim of this study is to evaluate the performance of a universal screening programme for cCMV during the first trimester of pregnancy in a primary care setting.Methods and analysis This is an observational multicentre cohort study. The study will be conducted in four primary care settings from the Northern Metropolitan Barcelona area and three related hospitals and will last 3 years and will consist of a recruitment period of 18 months.In their first pregnancy visit, pregnant women will be offered to add a CMV serology test to the first trimester screening tests. Pregnant women with primary infection will be referred to the reference hospital, where they will continue treatment and follow-up according to the clinical protocol of the referral hospital, which includes treatment with valacyclovir. A CMV-PCR will be performed at birth on newborns of mothers with primary infection, and those who are infected will undergo neonatal follow-up for at least 12 months of life.For the analysis, the acceptance rate, the prevalence of primary CMV infections and the CMV seroprevalence in the first trimester of pregnancy will be studied.Ethics and dissemination Ethical approval was obtained from the University Institute Foundation for Primary Health Care Research Jordi Gol i Gurina Ethics Committee 22/097-P dated 27 April 2022.

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.

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.