Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

This is the final version. Available on open access from Nature Research via the DOI in this recordThis 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.Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain)European Regional Development FundDepartment of Health of the Catalan GovernmentCatalan Governmen

How is COVID-19 affecting patients with obsessive-compulsive disorder? A longitudinal study on the initial phase of the pandemic in a Spanish cohort

Background: Although the consequences of the COVID-19 pandemic on emotional health are evident, little is known about its impact on patients with obsessive-compulsive disorder (OCD). Methods: One hundred and twenty-seven patients with OCD who attended a specialist OCD Clinic in Barcelona, Spain, were assessed by phone from April 27 to May 25, 2020, during the early phase of the pandemic, using the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured interview that collected clinical and sociodemographic information. Results were compared with those for 237 healthy controls from the same geographic area who completed an online survey. Results: Although 65.3% of the patients with OCD described a worsening of their symptoms, only 31.4% had Y-BOCS scores that increased >25%. The risk of getting infected by SARS-CoV2 was reported as a new obsession by 44.8%, but this only became the main obsessive concern in approximately 10% of the patients. Suicide-related thoughts were more frequent among the OCD cohort than among healthy controls. The presence of prepandemic depression, higher Y-BOCS scores, contamination/washing symptoms, and lower perceived social support all predicted a significantly increased risk of OCD worsening. Conclusions: Most patients with OCD appear to be capable of coping with the emotional stress of the COVID-19 outbreak and its consequences during the initial phase of the pandemic. Nevertheless, the current crisis constitutes a risk factor for a significant worsening of symptoms and suicidal ideation. Action is needed to ensure effective and individualized follow-up care for patients with OCD in the COVID-19 era

The genomics of visuospatial neurocognition in obsessive-compulsive disorder: A preliminary GWAS

Background: The study of Obsessive-Compulsive Disorder (OCD) genomics has primarily been tackled by Genome-wide association studies (GWAS), which have encountered troubles in identifying replicable single nucleotide polymorphisms (SNPs). Endophenotypes have emerged as a promising avenue of study in trying to elucidate the genomic bases of complex traits such as OCD.Methods: We analyzed the association of SNPs across the whole genome with the construction of visuospatial information and executive performance through four neurocognitive variables assessed by the Rey-Osterrieth Complex Figure Test (ROCFT) in a sample of 133 OCD probands. Analyses were performed at SNP- and genelevel.Results: No SNP reached genome-wide significance, although there was one SNP almost reaching significant association with copy organization (rs60360940; P = 9.98E-08). Suggestive signals were found for the four variables at both SNP- (P < 1E-05) and gene-levels (P < 1E-04). Most of the suggestive signals pointed to genes and genomic regions previously associated with neurological function and neuropsychological traits. Limitations: Our main limitations were the sample size, which was limited to identify associated signals at a genome-wide level, and the composition of the sample, more representative of rather severe OCD cases than a population-based OCD sample with a broad severity spectrum.Conclusions: Our results suggest that studying neurocognitive variables in GWAS would be more informative on the genetic basis of OCD than the classical case/control GWAS, facilitating the genetic characterization of OCD and its different clinical profiles, the development of individualized treatment approaches, and the improvement of prognosis and treatment response

Dietary α‐Linolenic Acid, Marine ω‐3 Fatty Acids, and Mortality in a Population With High Fish Consumption: Findings From the PREvención con DIeta MEDiterránea (PREDIMED) Study

Background: Epidemiological evidence suggests a cardioprotective role of α‐linolenic acid (ALA), a plant‐derived ω‐3 fatty acid. It is unclear whether ALA is beneficial in a background of high marine ω‐3 fatty acids (long‐chain n‐3 polyunsaturated fatty acids) intake. In persons at high cardiovascular risk from Spain, a country in which fish consumption is customarily high, we investigated whether meeting the International Society for the Study of Fatty Acids and Lipids recommendation for dietary ALA (0.7% of total energy) at baseline was related to all‐cause and cardiovascular disease mortality. We also examined the effect of meeting the society's recommendation for long‐chain n‐3 polyunsaturated fatty acids (≥500 mg/day). Methods and Results: We longitudinally evaluated 7202 participants in the PREvención con DIeta MEDiterránea (PREDIMED) trial. Multivariable‐adjusted Cox regression models were fitted to estimate hazard ratios. ALA intake correlated to walnut consumption (r=0.94). During a 5.9‐y follow‐up, 431 deaths occurred (104 cardiovascular disease, 55 coronary heart disease, 32 sudden cardiac death, 25 stroke). The hazard ratios for meeting ALA recommendation (n=1615, 22.4%) were 0.72 (95% CI 0.56–0.92) for all‐cause mortality and 0.95 (95% CI 0.58–1.57) for fatal cardiovascular disease. The hazard ratios for meeting the recommendation for long‐chain n‐3 polyunsaturated fatty acids (n=5452, 75.7%) were 0.84 (95% CI 0.67–1.05) for all‐cause mortality, 0.61 (95% CI 0.39–0.96) for fatal cardiovascular disease, 0.54 (95% CI 0.29–0.99) for fatal coronary heart disease, and 0.49 (95% CI 0.22–1.01) for sudden cardiac death. The highest reduction in all‐cause mortality occurred in participants meeting both recommendations (hazard ratio 0.63 [95% CI 0.45–0.87]). Conclusions: In participants without prior cardiovascular disease and high fish consumption, dietary ALA, supplied mainly by walnuts and olive oil, relates inversely to all‐cause mortality, whereas protection from cardiac mortality is limited to fish‐derived long‐chain n‐3 polyunsaturated fatty acids. Clinical Trial Registration URL: http://www.Controlled-trials.com/. Unique identifier: ISRCTN35739639

Aplicación de la terapia de presion negativa

La utilización de la presión negativa ha sido y continúa siendo una buena y eficaz herramienta de trabajo, no sólo en el tratamiento de las heridas posquirúrgicas (drenajes abdominales, vaciamiento ganglionar), sino también en patologías médicas como (aspiraciones gástricas, neumotórax, etc.). El trabajo que presentamos esta basado en la terapia con presión negativa de distintas patologías tanto en heridas agudas como crónicas, teniendo unos excelentes resultados y una considerable casuística, ya que el estudio se ha realizado con 60 pacientes de nuestro hospital con un total de 65 heridas. Se ha realizado tanto en heridas crónicas como en intervenciones donde el postoperatorio ha desencadenado en dehiscencias. Se presenta un caso clínico

Aplicación de la terapia de presión negativa

La utilización de la presión negativa ha sido y continúa siendo una buena y eficaz herramienta de trabajo, no sólo en el tratamiento de las heridas posquirúrgicas (drenajes abdominales, vaciamiento ganglionar), sino también en patologías médicas como (aspiraciones gástricas, neumotórax, etc.). El trabajo que presentamos esta basado en la terapia con presión negativa de distintas patologías tanto en heridas agudas como crónicas, teniendo unos excelentes resultados y una considerable casuística, ya que el estudio se ha realizado con 60 pacientes de nuestro hospital con un total de 65 heridas. Se ha realizado tanto en heridas crónicas como en intervenciones donde el postoperatorio ha desencadenado en dehiscencias. Se presenta un caso clínico.Enfermerí

Cancer and the risk of COVID-19 diagnosis, hospitalisation, and death: a population-based multi-state cohort study including 4,618,377 adults in Catalonia, Spain

The relationship between cancer and COVID-19 infection and severity remains poorly understood. We conducted a population-based cohort study between 1 March and 6 May 2020 describing the associations between cancer and risk of COVID-19 diagnosis, hospitalisation, and COVID-19-related death. Data was obtained from the SIDIAP database, including primary care electronic health records from ~80% of the population in Catalonia, Spain. Cancer was defined as any primary invasive malignancy excluding non-melanoma skin cancer. We estimated adjusted hazard ratios (aHRs) for the risk of COVID-19 (outpatient) clinical diagnosis, hospitalisation (with or without a prior COVID-19 diagnosis) and COVID-19-related death using Cox proportional hazard regressions. Models were estimated for the overall cancer population and by years since cancer diagnosis (<1-year, 1-5-years, ≥5-years), sex, age, and cancer type; and adjusted for age, sex, smoking status, deprivation, and comorbidities. We included 4,618,377 adults, of which 260,667 (5.6%) had a history of cancer. A total of 98,951 individuals (5.5% with cancer) were diagnosed and 6,355 (16.4% with cancer) were directly hospitalised with COVID-19. Of those diagnosed, 6,851 were subsequently hospitalised (10.7% with cancer) and 3,227 died without being hospitalised (18.5% with cancer). Among those hospitalised, 1,963 (22.5% with cancer) died. Cancer was associated with an increased risk of COVID-19 diagnosis (aHR: 1.08; 95% CI [1.05-1.11]); direct COVID-19 hospitalisation (1.33 [1.24-1.43]); and death following hospitalisation (1.12 [1.01-1.25]). These associations were stronger for patients recently diagnosed with cancer, aged <70 years, and with haematological cancers. These patients should be prioritised in COVID-19 vaccination campaigns and continued non-pharmaceutical interventions

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Background and objective As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). Methods We show step-by-step how to implement the analytics pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?’. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Results Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Conclusion Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research

Berta Raventós,1,2,&ast; Sergio Fernández-Bertolín,1,&ast; María Aragón,1 Erica A Voss,3– 5 Clair Blacketer,3– 5 Leonardo Méndez-Boo,6 Martina Recalde,1 Elena Roel,1,2 Andrea Pistillo,1,7 Carlen Reyes,1 Sebastiaan van Sandijk,8 Lars Halvorsen,9 Peter R Rijnbeek,4,5 Edward Burn,1,10 Talita Duarte-Salles1,4 1Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 2Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain; 3Janssen Pharmaceutical Research and Development, Titusville, NJ, USA; 4Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands; 5OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA; 6Sistemes d’Informació dels Serveis d’Atenció Primària (SISAP), Institut Català de la Salut, Barcelona, Spain; 7Universitat Pompeu Fabra, Barcelona, Spain; 8Odysseus Data Services s.r.o., Prague, Czech Republic; 9edenceHealth NV, Kontich, Belgium; 10Centre for Statistics in Medicine, University of Oxford, Oxford, UK&ast;These authors contributed equally to this workCorrespondence: Talita Duarte-Salles, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Gran Via Corts Catalanes, 587 àtic, Barcelona, 08007, Spain, Tel +34935824342, Email [email protected]: The primary aim of this work was to convert the Information System for Research in Primary Care (SIDIAP) from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Our second aim was to provide a descriptive analysis of COVID-19-related outcomes among the general population.Patients and Methods: We mapped patient-level data from SIDIAP to the OMOP CDM and we performed more than 3,400 data quality checks to assess its readiness for research. We established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or tested positive for, hospitalised with, admitted to intensive care units (ICU) with, died with, or vaccinated against COVID-19 up to 30th June 2022.Results: After verifying the high quality of the transformed dataset, we included 5,870,274 individuals in the general population cohort. Of those, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation, 5,642 had an ICU admission, and 11,233 died with COVID-19. A total of 4,584,515 received a COVID-19 vaccine. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised and those who died.Conclusion: We successfully transformed SIDIAP to the OMOP CDM. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19-related outcomes over time were described. The transformed SIDIAP database is a valuable resource that can enable distributed network research in COVID-19 and beyond.Keywords: electronic health records, medical ontologies, secondary data use, common data model, OMO

Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets

Background SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool and has not been externally validated. Methods We followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia. Findings The internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68. Interpretation The results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 should not be used to aid decision making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model