Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study.

BACKGROUND: An electronic health record (EHR) holds detailed longitudinal information about a patient's health status and general clinical history, a large portion of which is stored as unstructured, free text. Existing approaches to model a patient's trajectory focus mostly on structured data and a subset of single-domain outcomes. This study aims to evaluate the effectiveness of Foresight, a generative transformer in temporal modelling of patient data, integrating both free text and structured formats, to predict a diverse array of future medical outcomes, such as disorders, substances (eg, to do with medicines, allergies, or poisonings), procedures, and findings (eg, relating to observations, judgements, or assessments). METHODS: Foresight is a novel transformer-based pipeline that uses named entity recognition and linking tools to convert EHR document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events, such as disorders, substances, procedures, and findings. The Foresight pipeline has four main components: (1) CogStack (data retrieval and preprocessing); (2) the Medical Concept Annotation Toolkit (structuring of the free-text information from EHRs); (3) Foresight Core (deep-learning model for biomedical concept modelling); and (4) the Foresight web application. We processed the entire free-text portion from three different hospital datasets (King's College Hospital [KCH], South London and Maudsley [SLaM], and the US Medical Information Mart for Intensive Care III [MIMIC-III]), resulting in information from 811 336 patients and covering both physical and mental health institutions. We measured the performance of models using custom metrics derived from precision and recall. FINDINGS: Foresight achieved a precision@10 (ie, of 10 forecasted candidates, at least one is correct) of 0·68 (SD 0·0027) for the KCH dataset, 0·76 (0·0032) for the SLaM dataset, and 0·88 (0·0018) for the MIMIC-III dataset, for forecasting the next new disorder in a patient timeline. Foresight also achieved a precision@10 value of 0·80 (0·0013) for the KCH dataset, 0·81 (0·0026) for the SLaM dataset, and 0·91 (0·0011) for the MIMIC-III dataset, for forecasting the next new biomedical concept. In addition, Foresight was validated on 34 synthetic patient timelines by five clinicians and achieved a relevancy of 33 (97% [95% CI 91-100]) of 34 for the top forecasted candidate disorder. As a generative model, Foresight can forecast follow-on biomedical concepts for as many steps as required. INTERPRETATION: Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. FUNDING: National Health Service Artificial Intelligence Laboratory, National Institute for Health and Care Research Biomedical Research Centre, and Health Data Research UK.

Validity Evidence for the Brief Self-Control Scale in the Spanish Adult Population: A Systematic Study.

The Brief Self-Control Scale (BSCS) has been widely studied across languages and populations. However, research examining the Spanish version is scant and limited to the adolescent population. Our aim here was to provide validity evidence for use of the BSCS with Spanish adults by analyzing and comparing the psychometric properties of different versions of the scale (13-item, 10-item, 9-item, 8-item, and 7-item). Confirmatory factor analysis was used to test the internal structure based on one-factor and two-factor models. Results obtained in a sample of 676 Spanish adults showed adequate fit indices for the two-factor structure of the 9-item, 8-item, and 7-item versions of the BSCS, although only the 9-item and 8-item BSCS were found to be invariant across gender. Item homogeneity and reliability of factor scores for these two versions (9-item and 8-item) were satisfactory. We also provide novel validity evidence based on relationships with indicators of psychological adjustment and wellbeing. Scores on the 9-item and 8-item BSCS correlated with life satisfaction, flourishing, self-esteem, distress, depression, and loneliness, and hence both may be suitable for use in mental health assessment contexts.

Comparison of devices used to measure blood pressure, grip strength and lung function: A randomised cross-over study.

BACKGROUND: Blood pressure, grip strength and lung function are frequently assessed in longitudinal population studies, but the measurement devices used differ between studies and within studies over time. We aimed to compare measurements ascertained from different commonly used devices. METHODS: We used a randomised cross-over study. Participants were 118 men and women aged 45-74 years whose blood pressure, grip strength and lung function were assessed using two sphygmomanometers (Omron 705-CP and Omron HEM-907), four handheld dynamometers (Jamar Hydraulic, Jamar Plus+ Digital, Nottingham Electronic and Smedley) and two spirometers (Micro Medical Plus turbine and ndd Easy on-PC ultrasonic flow-sensor) with multiple measurements taken on each device. Mean differences between pairs of devices were estimated along with limits of agreement from Bland-Altman plots. Sensitivity analyses were carried out using alternative exclusion criteria and summary measures, and using multilevel models to estimate mean differences. RESULTS: The mean difference between sphygmomanometers was 3.9mmHg for systolic blood pressure (95% Confidence Interval (CI):2.5,5.2) and 1.4mmHg for diastolic blood pressure (95% CI:0.3,2.4), with the Omron HEM-907 measuring higher. For maximum grip strength, the mean difference when either one of the electronic dynamometers was compared with either the hydraulic or spring-gauge device was 4-5kg, with the electronic devices measuring higher. The differences were small when comparing the two electronic devices (difference = 0.3kg, 95% CI:-0.9,1.4), and when comparing the hydraulic and spring-gauge devices (difference = 0.2kg, 95% CI:-0.8,1.3). In all cases limits of agreement were wide. The mean difference in FEV1 between spirometers was close to zero (95% CI:-0.03,0.03), limits of agreement were reasonably narrow, but a difference of 0.47l was observed for FVC (95% CI:0.53,0.42), with the ndd Easy on-PC measuring higher. CONCLUSION: Our study highlights potentially important differences in measurement of key functions when different devices are used. These differences need to be considered when interpreting results from modelling intra-individual changes in function and when carrying out cross-study comparisons, and sensitivity analyses using correction factors may be helpful.

Remote Administration of ADHD-Sensitive Cognitive Tasks: A Pilot Study.

OBJECTIVE: We assessed the feasibility and validity of remote researcher-led administration and self-administration of modified versions of two cognitive tasks sensitive to ADHD, a four-choice reaction time task (Fast task) and a combined Continuous Performance Test/Go No-Go task (CPT/GNG), through a new remote measurement technology system. METHOD: We compared the cognitive performance measures (mean and variability of reaction times (MRT, RTV), omission errors (OE) and commission errors (CE)) at a remote baseline researcher-led administration and three remote self-administration sessions between participants with and without ADHD (n = 40). RESULTS: The most consistent group differences were found for RTV, MRT and CE at the baseline researcher-led administration and the first self-administration, with 8 of the 10 comparisons statistically significant and all comparisons indicating medium to large effect sizes. CONCLUSION: Remote administration of cognitive tasks successfully captured the difficulties with response inhibition and regulation of attention, supporting the feasibility and validity of remote assessments.

Physical multimorbidity, depressive symptoms, and social participation in adults over 50 years of age: findings from the English Longitudinal Study of Ageing.

OBJECTIVES: The aim of the current study was to identify specific patterns of physical multimorbidity and examine how these patterns associated with changes in social participation over time. METHODS: We used latent class analysis to identify clusters of physical multimorbidity in 11,391 older adults. Mixed effects regression models were used to assess associations between physical multimorbidity clusters and changes in social participation over 15 years. RESULTS: Four clusters of physical multimorbidity were identified. All physical multimorbidity clusters were associated with a reduction in cultural engagement (e.g. visits to theatre, cinema, museums) over time, with the strongest association seen in the complex/multisystem cluster (ß = -0.26, 95% CI = -0.38 to -0.15). Similar results emerged for leisure activities. Adjusting for depressive symptoms fully attenuated some associations. All physical multimorbidity clusters were associated with an increase in civic participation over time. CONCLUSIONS: Physical multimorbidity reduced some aspects of social participation over time, with specific combinations of conditions having increased risk of reductions.Supplemental data for this article is available online at http://dx.doi.org/10.1080/13607863.2021.2017847.

Grip Strength Trajectories and Cognition in English and Chilean Older Adults: A Cross-Cohort Study.

Growing evidence about the link between cognitive and physical decline suggests the early changes in physical functioning as a potential biomarker for cognitive impairment. Thus, we compared grip-strength trajectories over 12-16 years in three groups classified according to their cognitive status (two stable patterns, normal and impaired cognitive performance, and a declining pattern) in two representative UK and Chilean older adult samples. The samples consisted of 7069 UK (ELSA) and 1363 Chilean participants (ALEXANDROS). Linear Mixed models were performed. Adjustments included socio-demographics and health variables. The Declined and Impaired group had significantly lower grip-strength at baseline when compared to the Non-Impaired. In ELSA, the Declined and Impaired showed a faster decline in their grip strength compared to the Non-Impaired group but differences disappeared in the fully adjusted models. In ALEXANDROS, the differences were only found between the Declined and Non-Impaired and they were partially attenuated by covariates. Our study provides robust evidence of the association between grip strength and cognitive performance and how socio-economic factors might be key to understanding this association and their variability across countries. This has implications for future epidemiological research, as hand-grip strength measurements have the potential to be used as an indicator of cognitive performance.

Exploring the Association between Vitamin D and Changes in Cognitive Function in Chilean Older Adults: Evidence from the ALEXANDROS Cohort Study.

Background: The increasing aging of the population with the consequent increase of age-associated cognitive disorders pose the challenge of controlling its preventable risk factors, among which vitamin D deficit is a putative factor. Thus, our objective is to explore the association between vitamin D and cognitive performance in a cohort study of community-dwelling Chilean older people. Material and Methods: Cohort study of 955 (69.7% female), community-dwelling older Chileans free of cognitive impairment from the Alexandros cohorts, with 25(OH)D measurement at baseline. Cognitive Function was evaluated with the Mini Mental State Examination (MMSE) short-form questionnaire. Plasma levels of 25(OH)D were classified as Normal > 30 ng/mL Insufficiency 20−29 ng/mL, Deficiency 20−12 ng/mL and Severe Deficiency < 12 ng/mL. Penalized regressions models were made to assess associations. Results: Mean age of the sample was 66.6 + 4.5 years, with 8.5 + 4.7 years of education. After a mean follow-up of 9.6 years, 54 new cases of Mild Cognitive Impairment (MCI)were identified (Incidence density rate = 5.9 per 1000 person/years). Mean vitamin D plasma levels were lower in people with MCI than in the normal cognitive ones (23.0 + 12.75 vs. 28.35 + 15.17 ng/mL, p < 0.01). In the fully adjusted model only severe deficiency of vitamin D was associated with MCI (RR = 2.33; 95% CI: (1.03−5.26). Conclusions: In this longitudinal study, our results confirm that low Vitamin D is a risk factor for MCI, and that people with severe deficiency have more than double the risk of MCI people with normal Vitamin D levels. Considering the high frequency of vitamin D deficiency in older people, and its preventability, these results are very valuable for future public health programmes.

Measurement Invariance and Validity of the Satisfaction With Life Scale in Informal Caregivers.

BACKGROUND: The number of informal caregivers within ageing population is increasing and there is a growing research interest to promote their well-being, and therefore there is a need for adequate measurement tools. We aim to provide validity evidence of the Satisfaction with Life Scale (SWLS) in a representative sample of British older adults, including measurement invariance across caregivers and non-caregivers. METHOD: Data was drawn from English Longitudinal Study of Ageing (ELSA). The sample consisted of 3,754 caregivers and 4,036 non-caregivers. The structure and measurement invariance were tested through a confirmatory factor analysis. Reliability and validity evidence based on relationships with other variables were also analysed. RESULTS: Our results supported the one-factor structure of the SWLS, CFI = .996; NNFI = .993; RMSEA = 0.081, and measurement invariance across caregiving status. McDonald´s omega was .93. Scores on the SWLS were positively correlated with quality of life, positive social support, and self-reported health, and negatively with loneliness, depression, negative social support, difficulties in activities and instrumental activities of daily living, and number of health conditions. CONCLUSIONS: These findings provide new psychometric evidence to support the use of the SWLS in research which focuses on caregivers and on the comparison with non-caregiver samples.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study.

BACKGROUND: The mobility of an individual measured by phone-collected location data has been found to be associated with depression; however, the longitudinal relationships (the temporal direction of relationships) between depressive symptom severity and phone-measured mobility have yet to be fully explored. OBJECTIVE: We aimed to explore the relationships and the direction of the relationships between depressive symptom severity and phone-measured mobility over time. METHODS: Data used in this paper came from a major EU program, called the Remote Assessment of Disease and Relapse-Major Depressive Disorder, which was conducted in 3 European countries. Depressive symptom severity was measured with the 8-item Patient Health Questionnaire (PHQ-8) through mobile phones every 2 weeks. Participants' location data were recorded by GPS and network sensors in mobile phones every 10 minutes, and 11 mobility features were extracted from location data for the 2 weeks prior to the PHQ-8 assessment. Dynamic structural equation modeling was used to explore the longitudinal relationships between depressive symptom severity and phone-measured mobility. RESULTS: This study included 2341 PHQ-8 records and corresponding phone-collected location data from 290 participants (age: median 50.0 IQR 34.0, 59.0) years; of whom 215 (74.1%) were female, and 149 (51.4%) were employed. Significant negative correlations were found between depressive symptom severity and phone-measured mobility, and these correlations were more significant at the within-individual level than the between-individual level. For the direction of relationships over time, Homestay (time at home) (φ=0.09, P=.01), Location Entropy (time distribution on different locations) (φ=-0.04, P=.02), and Residential Location Count (reflecting traveling) (φ=0.05, P=.02) were significantly correlated with the subsequent changes in the PHQ-8 score, while changes in the PHQ-8 score significantly affected (φ=-0.07, P<.001) the subsequent periodicity of mobility. CONCLUSIONS: Several phone-derived mobility features have the potential to predict future depression, which may provide support for future clinical applications, relapse prevention, and remote mental health monitoring practices in real-world settings.

Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach.

Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.

Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register.

OBJECTIVES: The first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD). DESIGN: Observational study. SETTING: Secondary mental healthcare services from South London PARTICIPANTS: Our maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018. MEASURES: We designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale. RESULTS: Physical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses. CONCLUSIONS: Our data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.

Cognition in informal caregivers: evidence from an English population study.

BACKGROUND AND OBJECTIVES: The relationship between caregiving and cognition remains unclear. We investigate this association comparing four cognitive tasks and exploring the role of potential explanatory pathways such as healthy behaviours (healthy caregiver hypothesis) and depression (stress process model). RESEARCH DESIGN AND METHODS: Respondents were from English Longitudinal Study of Ageing (ELSA) (N = 8910). Cognitive tasks included immediate and delayed word recall, verbal fluency and serial 7 subtraction. Series of hierarchical linear regressions were performed. Adjustments included socio-demographics, health related variables, health behaviours and depression. RESULTS: Being a caregiver was positively associated with immediate and delayed recall, verbal fluency but not with serial 7. For immediate and delayed recall, these associations were partially attenuated when adjusting for health behaviours, and depression. For verbal fluency, associations were partially attenuated when adjusting for depression but fully attenuated when adjusting for health behaviours. No associations were found for serial 7. DISCUSSION AND IMPLICATIONS: Our findings show that caregivers have higher level of memory and executive function compared to non-caregivers. For memory, we found that although health behaviours and depression can have a role in this association, they do not fully explain it. However, health behaviours seem to have a clear role in the association with executive function. Public health and policy do not need to target specifically cognitive function but other areas as the promotion of healthy behaviours and psychological adjustment such as preventing depression and promoting physical activity in caregivers.

Measurement Invariance and Validity of the Satisfaction With Life Scale in Informal Caregivers / Invarianza Métrica y Validez de la Escala de Satisfacción Vital en Cuidadores Informales

Background: The number of informal caregivers within ageing populationis increasing and there is a growing research interest to promote their well-being, and therefore there is a need for adequate measurement tools. Weaim to provide validity evidence of the Satisfaction with Life Scale (SWLS)in a representative sample of British older adults, including measurementinvariance across caregivers and non-caregivers. Method: Data wasdrawn from English Longitudinal Study of Ageing (ELSA). The sampleconsisted of 3,754 caregivers and 4,036 non-caregivers. The structureand measurement invariance were tested through a confirmatory factoranalysis. Reliability and validity evidence based on relationships withother variables were also analysed. Results: Our results supported the one-factor structure of the SWLS, CFI = .996; NNFI = .993; RMSEA = 0.081,and measurement invariance across caregiving status. McDonald’s omegawas .93. Scores on the SWLS were positively correlated with quality oflife, positive social support, and self-reported health, and negatively withloneliness, depression, negative social support, difficulties in activities andinstrumental activities of daily living, and number of health conditions.Conclusions: These findings provide new psychometric evidence tosupport the use of the SWLS in research which focuses on caregivers andon the comparison with non-caregiver samples.

Antecedentes: el número de cuidadores informalesmayores está aumentando y existe un creciente interés en la investigaciónsobre su bienestar, para lo cual es necesario disponer de instrumentosadecuados de medida. Se pretende proporcionar evidencias de validez dela Escala de Satisfacción Vital (SWLS) en una muestra representativa demayores de la población británica, incluyendo invarianza factorial entrecuidadores y no cuidadores. Método: los datos se extrajeron del EnglishLongitudinal Study of Ageing (ELSA). Participaron 3.754 cuidadores y4.036 no cuidadores. La estructura y la invarianza factorial se evaluaronmediante análisis factorial confirmatorio. También se analizaron la fiabilidady la validez basada en la relación con otras variables. Resultados:los resultados apoyaron la estructura unifactorial del SWLS, CFI = .996; NNFI= .993; RMSEA = 0.081, y su invarianza factorial entre cuidadores y nocuidadores. El omega de McDonald fue .93. Se encontraron correlacionespositivas con calidad de vida, apoyo social positivo y salud percibida, ynegativas con soledad, depresión, apoyo social negativo, dificultades enactividades y actividades instrumentales de la vida diaria y problemas desalud. Conclusiones: los hallazgos proporcionan nueva evidencia empíricaque apoya el uso del SWLS en investigación centrada en cuidadores.

Investigating the association between physical health comorbidities and disability in individuals with severe mental illness.

BACKGROUND: Research suggests that an increased risk of physical comorbidities might have a key role in the association between severe mental illness (SMI) and disability. We examined the association between physical multimorbidity and disability in individuals with SMI. METHODS: Data were extracted from the clinical record interactive search system at South London and Maudsley Biomedical Research Centre. Our sample (n = 13,933) consisted of individuals who had received a primary or secondary SMI diagnosis between 2007 and 2018 and had available data for Health of Nations Outcome Scale (HoNOS) as disability measure. Physical comorbidities were defined using Chapters II-XIV of the International Classification of Diagnoses (ICD-10). RESULTS: More than 60 % of the sample had complex multimorbidity. The most common organ system affected were neurological (34.7%), dermatological (15.4%), and circulatory (14.8%). All specific comorbidities (ICD-10 Chapters) were associated with higher levels of disability, HoNOS total scores. Individuals with musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders were found to be associated with significant difficulties associated with more than five HoNOS domains while others had a lower number of domains affected. CONCLUSIONS: Individuals with SMI and musculoskeletal, skin/dermatological, respiratory, endocrine, neurological, hematological, or circulatory disorders are at higher risk of disability compared to those who do not have those comorbidities. Individuals with SMI and physical comorbidities are at greater risk of reporting difficulties associated with activities of daily living, hallucinations, and cognitive functioning. Therefore, these should be targeted for prevention and intervention programs.

Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study.

BACKGROUND: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. METHODS: Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. RESULTS: We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79-0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76-0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74-0.88] and KCH AUROC 0.72 [0.68-0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. CONCLUSIONS: The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.

Pre-existing cardiovascular disease rather than cardiovascular risk factors drives mortality in COVID-19.

BACKGROUND: The relative association between cardiovascular (CV) risk factors, such as diabetes and hypertension, established CV disease (CVD), and susceptibility to CV complications or mortality in COVID-19 remains unclear. METHODS: We conducted a cohort study of consecutive adults hospitalised for severe COVID-19 between 1st March and 30th June 2020. Pre-existing CVD, CV risk factors and associations with mortality and CV complications were ascertained. RESULTS: Among 1721 patients (median age 71 years, 57% male), 349 (20.3%) had pre-existing CVD (CVD), 888 (51.6%) had CV risk factors without CVD (RF-CVD), 484 (28.1%) had neither. Patients with CVD were older with a higher burden of non-CV comorbidities. During follow-up, 438 (25.5%) patients died: 37% with CVD, 25.7% with RF-CVD and 16.5% with neither. CVD was independently associated with in-hospital mortality among patients < 70 years of age (adjusted HR 2.43 [95% CI 1.16-5.07]), but not in those ≥ 70 years (aHR 1.14 [95% CI 0.77-1.69]). RF-CVD were not independently associated with mortality in either age group (< 70 y aHR 1.21 [95% CI 0.72-2.01], ≥ 70 y aHR 1.07 [95% CI 0.76-1.52]). Most CV complications occurred in patients with CVD (66%) versus RF-CVD (17%) or neither (11%; p < 0.001). 213 [12.4%] patients developed venous thromboembolism (VTE). CVD was not an independent predictor of VTE. CONCLUSIONS: In patients hospitalised with COVID-19, pre-existing established CVD appears to be a more important contributor to mortality than CV risk factors in the absence of CVD. CVD-related hazard may be mediated, in part, by new CV complications. Optimal care and vigilance for destabilised CVD are essential in this patient group. Trial registration n/a.

Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.

Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: (a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; (b) a feature-rich annotation interface for customizing and training IE models; and (c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets (F1:0.448-0.738 vs 0.429-0.650). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ∼8.8B words from ∼17M clinical records and further fine-tuning with ∼6K clinician annotated examples. We show strong transferability (F1 > 0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.

Measurement invariance of the Phubbing Scale across 20 countries.

Mobile phone addiction is a robust phenomenon observed throughout the world. The social aspect of mobile phone use is crucial; therefore, phubbing is a part of the mobile phone addiction phenomenon. Phubbing is defined as ignoring an interlocutor by glancing at one's mobile phone during a face-to-face conversation. The main aim of this study was to investigate how the Phubbing Scale (containing 10 items) might vary across countries, and between genders. Data were collected in 20 countries: Belarus, Brazil, China, Croatia, Ecuador, India, Israel, Italy, Netherlands, Pakistan, Poland, Portugal, Serbia, Slovakia, Slovenia, Spain, Turkey, UK, Ukraine and USA. The mean age across the sample (N = 7696, 65.8% women, 34.2% men) was 25.32 years (SD = 9.50). The cross-cultural invariance of the scale was investigated using multigroup confirmatory factor analyses (MGCFA) as well as the invariance analyses. Additionally, data from each country were assessed individually via confirmatory factor analyses (CFAs). We obtained two factors, based on only eight of the items: (a) communication disturbances and (b) phone obsession. The 8 items Phubbing Scale.

A multidimensional measure of polypharmacy for older adults using the Health and Retirement Study.

Polypharmacy is commonly defined based on the number of medications taken concurrently using standard cut-offs, but several studies have highlighted the need for a multidimensional assessment. We developed a multidimensional measure of polypharmacy and compared with standard cut-offs. Data were extracted for 2141 respondents of the 2007 Prescription Drug Survey, a sub-study of the Health Retirement Study. Latent classes were identified based on multiple indicators of polypharmacy, including quantity, temporality and risk profile. A four-class model was selected based on fit statistics and clinical interpretability: 'High risk, long-term' (Class 1), 'Low risk, long-term' (Class 2), 'High risk, short-term' (Class 3), and 'High risk for drug interactions, medium-term, regular' (Class 4). Classes differed regarding sex, cohabitation, disability and multimorbidity. Participants in the 'low risk' class tended to be male, cohabitating, and reported fewer health conditions, compared to 'high risk' classes. Polypharmacy classes were compared to standard cut-offs (5+ or 9+ medications) in terms of overlap and mortality risk. The three 'high risk' classes overlapped with the groups concurrently taking 5+ and 9+ medications per month. However, the multidimensional measure further differentiated individuals in terms of risk profile and temporality of medication taking, thus offering a richer assessment of polypharmacy.

Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study.

BACKGROUND: Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE: The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS: Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. RESULTS: We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS: We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.