Insight assessment in psychosis remains challenging in practice-oriented research.

To develop and validate a proxy measure for insight based on information from electronic health records (EHR). For that purpose, we used data on the Scale to Assess Unawareness of Mental Disorder (SUMD) and data from EHR notes of patients in an early psychosis intervention programme (Programa de Atención a Fases Iniciales de Psicosis, Santander, Spain).

Junior and senior clinicians examined 134 clinical notes from 106 patients to explore criterion and content validity between SUMD and a clinician-rated proxy measure, using three SUMD items.

In terms of criterion validity, SUMD scores correlated with the proxy (r = 0.61, P < 0.001), even after adjusting for the following confounders: type of psychotic disorder, clinical remission status and rater experience (r = 0.58, P < 0.001); and the proxy predicted good insight status (odds ratio 20.95, 95% CI 7.32–59.91, P < 0.001). Regarding content validity, the three main SUMD subscores correlated with the proxy (r = 0.55–0.60, P < 0.005). There were no significant differences in age, gender or other clinical variables, i.e. discriminant validity, and the proxy significantly correlated with validated psychometric instruments, i.e. external validity. Intraclass correlation coefficient (i.e. interrater reliability) was 0.88 (95% CI 0.59–1.00, P < 0.05).

This SUMD-based proxy measure was shown to have good to excellent validity and reliability, which may offer a reliable and efficient alternative for assessing insight in real-world clinical practice, EHR-based research and management. Future studies should explore its applicability across different healthcare contexts and its potential for automation, using natural language-processing techniques.

Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.

This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Initially prescribed for schizophrenia and psychosis, antipsychotics are increasingly prescribed for other indications. Since the late 1990s, prescribing shifted from first-generation to second-generation antipsychotics.

To examine overall initiation and prevalence of antipsychotic drug prescribing in UK primary care from 1995 to 2018, stratified by gender.

Cohort studies using UK anonymised electronic primary care data from IQVIA Medical Research Data, including over 790 general practices and registered individuals aged 18–99 years.

Antipsychotic drug initiation was stable in the late 1990s, at 6–7/1000 person-years at risk (PYAR) in men and 9–11/1000 PYAR in women. From 2001, initiation declined, stabilising from 2005 onward at 4/1000 PYAR in men and 4–5/1000 PYAR in women. Prevalence remained consistent from 1995 to 2018: 12/1000 in men and 14/1000 in women by 2018. Initiation and prevalence were higher in women than men, but increased with age in both genders: (18–39 v. 80–99 years; incidence rate ratio (IRR) 4.85, 95% CI 4.75–4.95 in men; IRR 5.90, 95% CI 5.78–6.02 in women; prevalence rate ratio (PRR) 2.22, 95% CI 2.19–2.25 in men; PRR 4.28, 95% CI 4.24–4.33 in women). Initiation and prevalence were greater in individuals with greater socioeconomic deprivation (Townsend score of 5 v. 1; IRR 2.69, 95% CI 2.64–2.75 in men; IRR 2.19, 95% CI 2.15–2.24 in women; PRR 3.87, 95% CI 3.82–3.92 in men; PRR 2.80, 95% CI 2.77–2.83 in women).

Antipsychotic drug initiation decreased after 2001, stabilising from 2005 onward. Prevalence remained relatively consistent throughout the study period. Women had higher initiation and prevalence than men. However, both genders showed increased prescribing with age and socioeconomic deprivation.

Adults with mood and/or anxiety disorders have increased risks of comorbidities, chronic treatments and polypharmacy, increasing the risk of drug–drug interactions (DDIs) with antidepressants.

To use primary care records from the UK Biobank to assess DDIs with citalopram, the most widely prescribed antidepressant in UK primary care.

We classified drugs with pharmacokinetic or pharmacodynamic DDIs with citalopram, then identified prescription windows for these drugs that overlapped with citalopram prescriptions in UK Biobank participants with primary care records. We tested for associations of DDI status (yes/no) with sociodemographic and clinical characteristics and with cytochrome 2C19 activity, using univariate tests, then fitted multivariable models for variables that reached Bonferroni-corrected significance.

In UK Biobank primary care data, 25 508 participants received citalopram prescription(s), among which 11 941 (46.8%) had at least one DDI, with an average of 1.96 interacting drugs. The drugs most commonly involved were proton pump inhibitors (40% of co-prescription instances). Individuals with DDIs were more often female and older, had more severe and less treatment-responsive depression, and had higher rates of psychiatric and physical disorders. In the multivariable models, treatment resistance and markers of severity (e.g. history of suicidal and self-harm behaviours) were strongly associated with DDIs, as well as comorbidity with cardiovascular disorders. Cytochrome 2C19 activity was not associated with the occurrence of DDIs.

The high frequency of DDIs with citalopram in fragile groups confirms the need for careful consideration before prescribing and periodic re-evaluation.

Physical health checks in primary care for people with severe mental illness ((SMI) defined as schizophrenia, bipolar disorders and non-organic psychosis) aim to reduce health inequalities. Patients who decline or are deemed unsuitable for screening are removed from the denominator used to calculate incentivisation, termed exception reporting.

To describe the prevalence of, and patient characteristics associated with, exception reporting in patients with SMI.

We identified adult patients with SMI from the UK Clinical Practice Research Datalink (CPRD), registered with a general practice between 2004 and 2018. We calculated the annual prevalence of exception reporting and investigated patient characteristics associated with exception reporting, using logistic regression.

Of 193 850 patients with SMI, 27.7% were exception reported from physical health checks at least once. Exception reporting owing to non-response or declining screening increased over the study period. Patients of Asian or Black ethnicity (Asian: odds ratio 0.72, 95% CI 0.65–0.80; Black: odds ratio 0.86, 95% CI 0.76–0.97; compared with White) and women (odds ratio 0.90, 95% CI 0.88–0.92) had a reduced odds of being exception reported, whereas patients diagnosed with ‘other psychoses’ (odds ratio 1.19, 95% CI 1.15–1.23; compared with bipolar disorder) had increased odds. Younger patients and those diagnosed with schizophrenia were more likely to be exception reported owing to informed dissent.

Exception reporting was common in people with SMI. Interventions are required to improve accessibility and uptake of physical health checks to improve physical health in people with SMI.

Electronic Health Records (EHR) analysis is pivotal in advancing medical research. Numerous real-world EHR data providers offer data access through exported datasets. While enabling profound research possibilities, exported EHR data requires quality control and restructuring for meaningful analysis. Challenges arise in medical events (e.g., diagnoses or procedures) sequence analysis, which provides critical insights into conditions, treatments, and outcomes progression. Identifying causal relationships, patterns, and trends requires a more complex approach to data mining and preparation.

This paper introduces EHRchitect – an application written in Python that addresses the quality control challenges by automating dataset transformation, facilitating the creation of a clean, formatted, and optimized MySQL database (DB), and sequential data extraction according to the user’s configuration.

The tool creates a clean, formatted, and optimized DB, enabling medical event sequence data extraction according to users’ study configuration. Event sequences encompass patients’ medical events in specified orders and time intervals. The extracted data are presented as distributed Parquet files, incorporating events, event transitions, patient metadata, and events metadata. The concurrent approach allows effortless scaling for multi-processor systems.

EHRchitect streamlines the processing of large EHR datasets for research purposes. It facilitates extracting sequential event-based data, offering a highly flexible framework for configuring event and timeline parameters. The tool delivers temporal characteristics, patient demographics, and event metadata to support comprehensive analysis. The developed tool significantly reduces the time required for dataset acquisition and preparation by automating data quality control and simplifying event extraction.

A substantial subset of patients with major depressive disorder (MDD) experience treatment-resistant depression (TRD), typically defined as failure to respond to at least two sequential antidepressant trials at adequate dose and length.

To examine clinical and service-level associations of TRD, and the experiences of people with TRD and clinicians involved in their care within a large, diverse National Health Service trust in the UK.

This mixed-methods study integrated quantitative analysis of electronic health records with thematic analysis of semi-structured interviews. Chi-squared tests and one-way analysis of variance were used to assess associations between lines of antidepressant treatments and sociodemographic and clinical variables, and binary logistic regression was used to identify associations of TRD status.

Nearly half (48%) of MDD patients met TRD criteria, with 36.9% having trialled ≥4 antidepressant treatments. People with TRD had higher rates of recurrent depression (odds ratio = 1.24, 95% CI: 1.05–1.45, P = 0.008), comorbid anxiety disorders (odds ratio = 1.21, 95% CI: 1.03–1.41, P = 0.019), personality disorders (odds ratio=1.35, 95% CI: 1.10–1.65, P = 0.003), self-harm (odds ratio = 1.76, 95% CI: 1.06–2.93, P = 0.029) and cardiovascular diseases (odds ratio = 1.46, 95% CI: 1.02–2.07, P = 0.0374). Greater treatment resistance was linked to increased economic inactivity and functional loss. Qualitative findings revealed severe emotional distress and frustration with existing treatments, as well as organisational and illness-related barriers to effective care.

TRD is characterised by increasing mental and physical morbidity and functional decline, with individuals experiencing barriers to effective care. Improved pathways, service structures and more effective biological and psychological interventions are needed.

Exposure to maternal mental illness during foetal development may lead to altered development, resulting in permanent changes in offspring functioning.

To assess whether there is an association between prenatal maternal psychiatric disorders and offspring behavioural problems in early childhood, using linked health administrative data and the Australian Early Development Census from New South Wales, Australia.

The sample included all mother–child pairs of children who commenced full-time school in 2009 in New South Wales, and met the inclusion criteria (N = 69 165). Univariable logistic regression analysis assessed unadjusted associations between categories of maternal prenatal psychiatric disorders with indicators of offspring behavioural problems. Multivariable logistic regression adjusted the associations of interest for psychiatric categories and a priori selected covariates. Sensitivity analyses included adjusting the final model for primary psychiatric diagnoses and assessing association of interest for effect modification by child's biological gender.

Children exposed in the prenatal period to maternal psychiatric disorders had greater odds of being developmentally vulnerable in their first year of school. Children exposed to maternal anxiety disorders prenatally had the greatest odds for behavioural problems (adjusted odds ratio 1.98; 95% CI 1.43–2.69). A statistically significant interaction was found between child biological gender and prenatal hospital admissions for substance use disorders, for emotional subdomains, aggression and hyperactivity/inattention.

Children exposed to prenatal maternal mental illness had greater odds for behavioural problems, independent of postnatal exposure. Those exposed to prenatal maternal anxiety were at greatest risk, highlighting the need for targeted interventions for, and support of, families with mental illness.

Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large language models (LLMs) to detect suicide risk from medical text in psychiatric care.

To extract information about suicidality status from the admission notes in electronic health records (EHRs) using privacy-sensitive, locally hosted LLMs, specifically evaluating the efficacy of Llama-2 models.

We compared the performance of several variants of the open source LLM Llama-2 in extracting suicidality status from 100 psychiatric reports against a ground truth defined by human experts, assessing accuracy, sensitivity, specificity and F1 score across different prompting strategies.

A German fine-tuned Llama-2 model showed the highest accuracy (87.5%), sensitivity (83.0%) and specificity (91.8%) in identifying suicidality, with significant improvements in sensitivity and specificity across various prompt designs.

The study demonstrates the capability of LLMs, particularly Llama-2, in accurately extracting information on suicidality from psychiatric records while preserving data privacy. This suggests their application in surveillance systems for psychiatric emergencies and improving the clinical management of suicidality by improving systematic quality control and research.

Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.

We conducted a PubMed search using “SDOH” and “EHR” Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.

Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Society of Thoracic Surgeons Congenital Heart Surgery Database is the largest congenital heart surgery database worldwide but does not provide information beyond primary episode of care. Linkage to hospital electronic health records would capture complications and comorbidities along with long-term outcomes for patients with CHD surgeries. The current study explores linkage success between Society of Thoracic Surgeons Congenital Heart Surgery Database and electronic health record data in North Carolina and Georgia.

The Society of Thoracic Surgeons Congenital Heart Surgery Database was linked to hospital electronic health records from four North Carolina congenital heart surgery using indirect identifiers like date of birth, sex, admission, and discharge dates, from 2008 to 2013. Indirect linkage was performed at the admissions level and compared to two other linkages using a “direct identifier,” medical record number: (1) linkage between Society of Thoracic Surgeons Congenital Heart Surgery Database and electronic health records from a subset of patients from one North Carolina institution and (2) linkage between Society of Thoracic Surgeons data from two Georgia facilities and Georgia’s CHD repository, which also uses direct identifiers for linkage.

Indirect identifiers successfully linked 79% (3692/4685) of Society of Thoracic Surgeons Congenital Heart Surgery Database admissions across four North Carolina hospitals. Direct linkage techniques successfully matched Society of Thoracic Surgeons Congenital Heart Surgery Database to 90.2% of electronic health records from the North Carolina subsample. Linkage between Society of Thoracic Surgeons and Georgia’s CHD repository was 99.5% (7,544/7,585).

Linkage methodology was successfully demonstrated between surgical data and hospital-based electronic health records in North Carolina and Georgia, uniting granular procedural details with clinical, developmental, and economic data. Indirect identifiers linked most patients, consistent with similar linkages in adult populations. Future directions include applying these linkage techniques with other data sources and exploring long-term outcomes in linked populations.

The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).

We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group.

Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers’ unmet needs.

Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.

The progression of long-term diabetes complications has led to a decreased quality of life. Our objective was to evaluate the adverse outcomes associated with diabetes based on a patient’s clinical profile by utilizing a multistate modeling approach.

This was a retrospective study of diabetes patients seen in primary care practices from 2013 to 2017. We implemented a five-state model to examine the progression of patients transitioning from one complication to having multiple complications. Our model incorporated high dimensional covariates from multisource data to investigate the possible effects of different types of factors that are associated with the progression of diabetes.

The cohort consisted of 10,596 patients diagnosed with diabetes and no previous complications associated with the disease. Most of the patients in our study were female, White, and had type 2 diabetes. During our study period, 5928 did not develop complications, 3323 developed microvascular complications, 1313 developed macrovascular complications, and 1129 developed both micro- and macrovascular complications. From our model, we determined that patients had a 0.1334 [0.1284, .1386] rate of developing a microvascular complication compared to 0.0508 [0.0479, .0540] rate of developing a macrovascular complication. The area deprivation index score we incorporated as a proxy for socioeconomic information indicated that patients who reside in more disadvantaged areas have a higher rate of developing a complication compared to those who reside in least disadvantaged areas.

Our work demonstrates how a multistate modeling framework is a comprehensive approach to analyzing the progression of long-term complications associated with diabetes.

Concern that self-harm and mental health conditions are increasing in university students may reflect widening access to higher education, existing population trends and/or stressors associated with this setting.

To compare population-level data on self-harm, neurodevelopmental and mental health conditions between university students and non-students with similar characteristics before and during enrolment.

This cohort study linked electronic records from the Higher Education Statistics Agency for 2012–2018 to primary and secondary healthcare records. Students were undergraduates aged 18 to 24 years at university entry. Non-students were pseudo-randomly selected based on an equivalent age distribution. Logistic regressions were used to calculate odds ratios. Poisson regressions were used to calculate incidence rate ratios (IRR).

The study included 96 760 students and 151 795 non-students. Being male, self-harm and mental health conditions recorded before university entry, and higher deprivation levels, resulted in lower odds of becoming a student and higher odds of drop-out from university. IRRs for self-harm, depression, anxiety, autism spectrum disorder (ASD), drug use and schizophrenia were lower for students. IRRs for self-harm, depression, attention-deficit hyperactivity disorder, ASD, alcohol use and schizophrenia increased more in students than in non-students over time. Older students experienced greater risk of self-harm and mental health conditions, whereas younger students were more at risk of alcohol use than non-student counterparts.

Mental health conditions in students are common and diverse. While at university, students require person-centred stepped care, integrated with local third-sector and healthcare services to address specific conditions.

The serotonin 4 receptor (5-HT4R) is a promising target for the treatment of depression. Highly selective 5-HT4R agonists, such as prucalopride, have antidepressant-like and procognitive effects in preclinical models, but their clinical effects are not yet established.

To determine whether prucalopride (a 5-HT4R agonist and licensed treatment for constipation) is associated with reduced incidence of depression in individuals with no past history of mental illness, compared with anti-constipation agents with no effect on the central nervous system.

Using anonymised routinely collected data from a large-scale USA electronic health records network, we conducted an emulated target trial comparing depression incidence over 1 year in individuals without prior diagnoses of major mental illness, who initiated treatment with prucalopride versus two alternative anti-constipation agents that act by different mechanisms (linaclotide and lubiprostone). Cohorts were matched for 121 covariates capturing sociodemographic factors, and historical and/or concurrent comorbidities and medications. The primary outcome was a first diagnosis of major depressive disorder (ICD-10 code F32) within 1 year of the index date. Robustness of the results to changes in model and population specification was tested. Secondary outcomes included a first diagnosis of six other neuropsychiatric disorders.

Treatment with prucalopride was associated with significantly lower incidence of depression in the following year compared with linaclotide (hazard ratio 0.87, 95% CI 0.76–0.99; P = 0.038; n = 8572 in each matched cohort) and lubiprostone (hazard ratio 0.79, 95% CI 0.69–0.91; P < 0.001; n = 8281). Significantly lower risks of all mood disorders and psychosis were also observed. Results were similar across robustness analyses.

These findings support preclinical data and suggest a role for 5-HT4R agonists as novel agents in the prevention of major depression. These findings should stimulate randomised controlled trials to confirm if these agents can serve as a novel class of antidepressant within a clinical setting.