Chronic obstructive pulmonary disease (COPD) as a disease of early aging: evidence from the epiChron cohort

Background: Aging is an important risk factor for most chronic diseases. Patients with COPD develop more comorbidities than non-COPD subjects. We hypothesized that the development of comorbidities characteristically affecting the elderly occur at an earlier age in subjects with the diagnosis of COPD. Methods and findings: We included all subjects carrying the diagnosis of COPD (n = 27,617), and a similar number of age and sex matched individuals without the diagnosis, extracted from the 727,241 records of individuals 40 years and older included in the EpiChron Cohort (Aragon, Spain). We compared the cumulative number of comorbidities, their prevalence and the mortality risk between both groups. Using network analysis, we explored the connectivity between comorbidities and the most influential comorbidities in both groups. We divided the groups into 5 incremental age categories and compared their comorbidity networks. We then selected those comorbidities known to affect primarily the elderly and compared their prevalence across the 5 age groups. In addition, we replicated the analysis in the smokers' subgroup to correct for the confounding effect of cigarette smoking. Subjects with COPD had more comorbidities and died at a younger age compared to controls. Comparison of both cohorts across 5 incremental age groups showed that the number of comorbidities, the prevalence of diseases characteristic of aging and network's density for the COPD group aged 56-65 were similar to those of non-COPD 15 to 20 years older. The findings persisted after adjusting for smoking. Conclusion: Multimorbidity increases with age but in patients carrying the diagnosis of COPD, these comorbidities are seen at an earlier age

Chronic Obstructive Pulmonary Disease (COPD) as a disease of early aging: Evidence from the EpiChron Cohort

Background Aging is an important risk factor for most chronic diseases. Patients with COPD develop more comorbidities than non-COPD subjects. We hypothesized that the development of comorbidities characteristically affecting the elderly occur at an earlier age in subjects with the diagnosis of COPD. Methods and findings We included all subjects carrying the diagnosis of COPD (n = 27, 617), and a similar number of age and sex matched individuals without the diagnosis, extracted from the 727, 241 records of individuals 40 years and older included in the EpiChron Cohort (Aragon, Spain). We compared the cumulative number of comorbidities, their prevalence and the mortality risk between both groups. Using network analysis, we explored the connectivity between comorbidities and the most influential comorbidities in both groups. We divided the groups into 5 incremental age categories and compared their comorbidity networks. We then selected those comorbidities known to affect primarily the elderly and compared their prevalence across the 5 age groups. In addition, we replicated the analysis in the smokers'' subgroup to correct for the confounding effect of cigarette smoking. Subjects with COPD had more comorbidities and died at a younger age compared to controls. Comparison of both cohorts across 5 incremental age groups showed that the number of comorbidities, the prevalence of diseases characteristic of aging and network''s density for the COPD group aged 56-65 were similar to those of non-COPD 15 to 20 years older. The findings persisted after adjusting for smoking. Conclusion Multimorbidity increases with age but in patients carrying the diagnosis of COPD, these comorbidities are seen at an earlier age

Implementation and evaluation of the VA DPP clinical demonstration: protocol for a multi-site non-randomized hybrid effectiveness-implementation type III trial.

BackgroundThe Diabetes Prevention Program (DPP) study showed that lifestyle intervention resulted in a 58% reduction in incidence of type 2 diabetes among individuals with prediabetes. Additional large randomized controlled trials have confirmed these results, and long-term follow-up has shown sustained benefit 10-20 years after the interventions ended. Diabetes is a common and costly disease, especially among Veterans, and despite strong evidence supporting the feasibility of type 2 diabetes prevention, the DPP has not been widely implemented. The first aim of this study will evaluate implementation of the Veterans Affairs (VA) DPP in three VA medical centers. The second aim will assess weight and hemoglobin A1c (A1c) outcomes, and the third aim will determine the cost-effectiveness and budget impact of implementation of the VA DPP from a health system perspective.Methods/designThis partnered multi-site non-randomized systematic assignment study will use a highly pragmatic hybrid effectiveness-implementation type III mixed methods study design. The implementation and administration of the VA DPP will be funded by clinical operations while the evaluation of the VA DPP will be funded by research grants. Seven hundred twenty eligible Veterans will be systematically assigned to the VA DPP clinical demonstration or the usual care VA MOVE!® weight management program. A multi-phase formative evaluation of the VA DPP implementation will be conducted. A theoretical program change model will be used to guide the implementation process and assess applicability and feasibility of the DPP for VA. The Consolidated Framework for Implementation Research (CFIR) will be used to guide qualitative data collection, analysis, and interpretation of barriers and facilitators to implementation. The RE-AIM framework will be used to assess Reach, Effectiveness, Adoption, Implementation, and Maintenance of the VA DPP. Twelve-month weight and A1c change will be evaluated for the VA DPP compared to the VA MOVE!ProgramMediation analyses will be conducted to identify whether program design differences impact outcomes.DiscussionFindings from this pragmatic evaluation will be highly applicable to practitioners who are tasked with implementing the DPP in clinical settings. In addition, findings will determine the effectiveness and cost-effectiveness of the VA DPP in the Veteran population

Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom

Altres ajuts: This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Center (BRC), US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, and IQVIA. The University of Oxford received funding related to this work from the Bill & Melinda Gates Foundation (Investment ID INV016201 and INV-019257). APU has received funding from the Medical Research Council (MRC) [MR/K501256/1, MR/N013468/1] and Fundación Alfonso Martín Escudero (FAME) (APU). VINCI [VA HSR RES 13-457] (SLD, MEM, KEL). JCEL has received funding from the Medical Research Council (MR/K501256/1) and Versus Arthritis (21605). MR is funded by Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant program [grant number: 2017/1630]A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8−40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0−33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies

Predicting early psychiatric readmission with natural language processing of narrative discharge summaries

The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a cohort of individuals admitted to a psychiatric inpatient unit between 1994 and 2012 with a principal diagnosis of major depressive disorder, and extracted inpatient psychiatric discharge narrative notes. Using these data, we trained a 75-topic Latent Dirichlet Allocation (LDA) model, a form of natural language processing, which identifies groups of words associated with topics discussed in a document collection. The cohort was randomly split to derive a training (70%) and testing (30%) data set, and we trained separate support vector machine models for baseline clinical features alone, baseline features plus common individual words and the above plus topics identified from the 75-topic LDA model. Of 4687 patients with inpatient discharge summaries, 470 were readmitted within 30 days. The 75-topic LDA model included topics linked to psychiatric symptoms (suicide, severe depression, anxiety, trauma, eating/weight and panic) and major depressive disorder comorbidities (infection, postpartum, brain tumor, diarrhea and pulmonary disease). By including LDA topics, prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved from baseline (area under the curve 0.618) to baseline+1000 words (0.682) to baseline+75 topics (0.784). Inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort. Topic modeling and related approaches offer the potential to improve prediction using EHRs, if generalizability can be established in other clinical cohorts

Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review

Novel approaches that complement and go beyond evidence-based medicine are required in the domain of chronic diseases, given the growing incidence of such conditions on the worldwide population. A promising avenue is the secondary use of electronic health records (EHRs), where patient data are analyzed to conduct clinical and translational research. Methods based on machine learning to process EHRs are resulting in improved understanding of patient clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, a wealth of clinical histories remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset

Limitations Of Administrative Databases In Orthopaedic Surgery Research: A Study In Obesity And Anemia

The use of national inpatient databases for orthopaedic surgery research has been increasing. However, large databases that rely on administrative data, such as International Classification of Diseases Ninth Revision (ICD-9) codes, may misrepresent patient information, thus affecting the results of studies using this data. The present study uses easily quantified and objective variables of obesity and anemia as example comorbidities to assess the accuracy of ICD-9 codes in the setting of their continued use in orthopaedic surgery database studies. For each study arm, a large inpatient population was obtained from the Yale-New Haven hospital. Each patient\u27s medical record was reviewed, and the presence of ICD-9 discharge codes for obesity and anemia was directly compared to documented body mass index (BMI) and preoperative hematocrit, respectively. ICD-9 discharge codes for both non-morbid obesity and anemia had a sensitivity of just 0.19. The sensitivity of the ICD-9 code for morbid obesity was 0.48. Using obesity and anemia as examples, this study highlights the potential errors inherent to ICD-9 codes. This calls into serious question the utility of administrative databases for research purposes. Moreover, it is likely that these inaccuracies apply to additional variables as well. As database research continues to increase within orthopaedic surgery, it is important to realize that study outcomes can be skewed by data accuracy, and thus should not be blindly accepted simply by virtue of large sample sizes