A consensus-based electronic medical record template for pediatric morphea patient visits.

Morphea, also known as localized scleroderma, is an inflammatory sclerosing disorder of uncertain pathogenesis that affects the skin and underlying tissues. In the pediatric population, the disease often runs a chronic course with a high risk for irreversible sequelae; as such, patients often require long-term monitoring. The objective of this study is to develop a multi-center, consensus-based electronic medical record template for pediatric morphea patient visits using a modified Delphi method of iterative surveys. By facilitating consistent data collection and interpretation across medical centers and patient populations, this template may improve patient care for pediatric patients with morphea.

Social Determinants of Health Mediate Racial Disparities in Cardiovascular Disease in Men With Prostate Cancer.

Background: Cardiovascular disease (CVD) is a significant cause of morbidity and mortality in men with prostate cancer; however, data on racial disparities in CVD outcomes are limited. Objectives: We quantified the disparities in CVD according to self-identified race and the role of the structural social determinants of health in mediating disparities in prostate cancer patients. Methods: A retrospective cohort study of 3,543 prostate cancer patients treated with systemic androgen deprivation therapy (ADT) between 2008 and 2021 at a quaternary, multisite health care system was performed. The multivariable adjusted association between self-reported race (Black vs White) and incident major adverse cardiovascular events (MACE) after ADT initiation was evaluated using cause-specific proportional hazards. Mediation analysis determined the role of theme-specific and overall social vulnerability index (SVI) in explaining the racial disparities in CVD outcomes. Results: Black race was associated with an increased hazard of MACE (HR: 1.38; 95% CI: 1.16-1.65; P < 0.001). The association with Black race was strongest for incident heart failure (HR: 1.79; 95% CI: 1.32-2.43), cerebrovascular disease (HR: 1.98; 95% CI: 1.37-2.87), and peripheral artery disease (HR: 1.76; 95% CI: 1.26-2.45) (P < 0.001). SVI, specifically the socioeconomic status theme, mediated 98% of the disparity in MACE risk between Black and White patients. Conclusions: Black patients are significantly more likely to experience adverse CVD outcomes after systemic ADT compared with their White counterparts. These disparities are mediated by socioeconomic status and other structural determinants of health as captured by census tract SVI. Our findings motivate multilevel interventions focused on addressing socioeconomic vulnerability.

Stroke Risk Reduction in Migraine Patients Using Propranolol: Evidence from Two Large-Scale Real-World Data Analyses.

Background: Propranolol, a non-selective beta-blocker, is commonly used for migraine prevention, but its impact on stroke risk among migraine patients remains controversial. Using two large electronic health records-based datasets, we examined stroke risk differences between migraine patients with- and without- documented use of propranolol. Methods: This retrospective case-control study utilized EHR data from the Vanderbilt University Medical Center (VUMC) and the All of Us Research Program. Migraine patients were first identified based on the International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria using diagnosis codes. Among these patients, cases were defined as those with a primary diagnosis of stroke following the first diagnosis of migraine, while controls had no stroke after their first migraine diagnosis. Logistic regression models, adjusted for potential factors associated with stroke risk, assessed the association between propranolol use and stroke risk, stratified by sex and migraine subtype. A Cox proportional hazards regression model was used to estimate the hazard ratio (HR) for stroke risk at 1, 2, 5, and 10 years from baseline. Results: In the VUMC database, 378 cases and 15,209 controls were identified, while the All of Us database included 267 cases and 6,579 controls. Propranolol significantly reduced stroke risk in female migraine patients (VUMC: OR=0.52, p=0.006; All of Us: OR=0.39, p=0.007), but not in males. The effect was more pronounced for ischemic stroke and in females with migraines without aura (MO) (VUMC: OR=0.60, p=0.014; All of Us: OR=0.28, p=0.006). The Cox model showed lower stroke rates in propranolol-treated female migraine patients at 1, 2, 5, and 10 years (VUMC: HR=0.06-0.55, p=0.0018-0.085; All of Us: HR=0.23, p=0.045 at 10 years). Conclusions: Propranolol is associated with a significant reduction in stroke risk, particularly ischemic stroke, among female migraine without aura patients. These findings suggest that propranolol may benefit stroke prevention in high-risk populations.

Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records.

Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases. Methods: We examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity, F-score, area under the receiver operating characteristic curve (AUROC), and area under the precision and recall curve (AUPRC). The deep learning model was further evaluated for its ability to classify AAV cases at the patient-level, compared with rule-based algorithms in 2,000 randomly chosen samples. Results: Datasets I, II, and III comprised 6,000, 3,008, and 7,500 note sections, respectively. Deep learning achieved the highest AUROC in all three datasets, with scores of 0.983, 0.991, and 0.991. The deep learning approach also had among the highest PPVs across the three datasets (0.941, 0.954, and 0.800, respectively). In a test cohort of 2,000 cases, the deep learning model achieved a PPV of 0.262 and an estimated sensitivity of 0.975. Compared to the best rule-based algorithm, the deep learning model identified six additional AAV cases, representing 13% of the total. Conclusion: The deep learning model effectively classifies clinical note sections for AAV diagnosis. Its application to EHR notes can potentially uncover additional cases missed by traditional rule-based methods.

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data.

BACKGROUND: Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data. METHODS: 2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups. RESULTS: Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF. CONCLUSIONS: Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.

Data Flow Construction and Quality Evaluation of Electronic Source Data in Clinical Trials: Pilot Study Based on Hospital Electronic Medical Records in China.

Background: The traditional clinical trial data collection process requires a clinical research coordinator who is authorized by the investigators to read from the hospital's electronic medical record. Using electronic source data opens a new path to extract patients' data from electronic health records (EHRs) and transfer them directly to an electronic data capture (EDC) system; this method is often referred to as eSource. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness. At the same time, improved data collection efficiency reduces clinical trial costs. Objective: This study aims to explore how to extract clinical trial-related data from hospital EHR systems, transform the data into a format required by the EDC system, and transfer it into sponsors' environments, and to evaluate the transferred data sets to validate the availability, completeness, and accuracy of building an eSource dataflow. Methods: A prospective clinical trial study registered on the Drug Clinical Trial Registration and Information Disclosure Platform was selected, and the following data modules were extracted from the structured data of 4 case report forms: demographics, vital signs, local laboratory data, and concomitant medications. The extracted data was mapped and transformed, deidentified, and transferred to the sponsor's environment. Data validation was performed based on availability, completeness, and accuracy. Results: In a secure and controlled data environment, clinical trial data was successfully transferred from a hospital EHR to the sponsor's environment with 100% transcriptional accuracy, but the availability and completeness of the data could be improved. Conclusions: Data availability was low due to some required fields in the EDC system not being available directly in the EHR. Some data is also still in an unstructured or paper-based format. The top-level design of the eSource technology and the construction of hospital electronic data standards should help lay a foundation for a full electronic data flow from EHRs to EDC systems in the future.

The Impact of the COVID-19 Pandemic on Pattern of Antibiotic and Opioid Prescriptions by Dentists in Alberta, Canada.

OBJECTIVES: After the shutdown of most dental services during the COVID-19 lockdown, the oral health community was concerned about an increase in prescribing opioids and antibiotics by dentists due to patients' limited access to dental offices. Therefore, the objective of this study was to investigate the impact of COVID-19 pandemic on the pattern of antibiotic and opioid prescriptions by dentists in Alberta, Canada. METHODS: Data obtained from the Tracked Prescription Program were divided into antibiotics and opioids. Time periods were outlined as pre-, during-, and postlockdown (phase 1 and 2). For the number of prescriptions and average supply, each monthly average was compared to the corresponding prelockdown monthly average, using descriptive analysis. Time series analyses were conducted using regression analyses with an autoregressive error model. Data were trained and tested on monthly observations before lockdown and predicted for during- and postlockdown. RESULTS: A total of 1.1 million antibiotics and 400,000 opioids dispense were tracked. Decreases in the number of prescriptions during lockdown presented for antibiotics (n = 24,933 vs. 18,884) and opioids (n = 8892 vs. 6051). Average supplies (days) for the antibiotics (n = 7.10 vs. 7.55) and opioids (n = 3.92 vs. 4.05) were higher during the lockdown period. In the trend analyses, the monthly number of antibiotic and opioid prescriptions showed the same pattern and decreased during lockdown. CONCLUSION: The COVID-19 pandemic altered the trends of prescribing antibiotics and opioids by dentists. The full impact of COVID-19 pandemic on the population's oral health in light of changes in prescribing practices by dentists during and after lockdown warrants further investigation.

Treatment of hypertension during pregnancy: a cohort of pregnancy episodes from the SIDIAP database, Catalonia, Spain.

Introduction: Hypertension during pregnancy is one of the most frequent causes of maternal and fetal morbimortality. Perinatal and maternal death and disability rates have decreased by 30%, but hypertension during pregnancy has increased by approximately 10% in the last 30 years. This research aimed to describe the pharmacological treatment and pregnancy outcomes of pregnancies with hypertension. Methods: We carried out an observational cohort study from the Information System for the Development of Research in Primary Care (SIDIAP) database. Pregnancy episodes with hypertension (ICD-10 codes for hypertension, I10-I15 and O10-O16) were identified. Antihypertensives were classified according to the ATC WHO classification: ß-blocking agents (BBs), calcium channel blockers (CCBs), agents acting on the renin-angiotensin system (RAS agents), diuretics, and antiadrenergic agents. Exposure was defined for hypertension in pregnancies with ≥2 prescriptions during the pregnancy episode. Descriptive statistics for diagnoses and treatments were calculated. Results: In total, 4,839 pregnancies with hypertension diagnosis formed the study cohort. There were 1,944 (40.2%) pregnancies exposed to an antihypertensive medication. There were differences in mother's age, BMI, and alcohol intake between pregnancies exposed to antihypertensive medications and those not exposed. BBs were the most used (n = 1,160 pregnancy episodes; 59.7%), followed by RAS agents (n = 825, 42.4%), and CCBs were the least used (n = 347, 17.8%). Discussion: Pregnancies involving hypertension were exposed to antihypertensive medications, mostly BBs. We conduct a study focused on RAS agent use during pregnancy and its outcomes in the offspring.

Reducing diagnostic delays in acute hepatic porphyria using health records data and machine learning.

BACKGROUND: Acute hepatic porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of 15 years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery. We sought to train and characterize models for identifying patients with AHP. METHODS: This diagnostic study used structured and notes-based EHR data from 2 centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into 2 cohorts (referral and diagnosis) and used to develop models that predict (1) who will be referred for testing of acute porphyria, among those who presented with abdominal pain (a cardinal symptom of AHP), and (2) who will test positive, among those referred. The referral cohort consisted of 747 patients referred for testing and 99 849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. The case cohort was 81% female and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs. Our primary outcome was the F-score on an outcome-stratified test set. RESULTS: The best center-specific referral models achieved an F-score of 86%-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥10% probability of referral, ≥50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years. CONCLUSIONS: ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed.

COPD Exacerbations Before and During COVID-19 in France, Germany, Italy, the UK and the US.

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) were reported less frequently during the COVID-19 pandemic. We report real-world data on COPD exacerbation rates before and during this pandemic. Methods: Exacerbation patterns were analysed using electronic medical records or claims data of patients with COPD before (2017-2019) and during the COVID-19 pandemic (2020 through early 2022) in France, Germany, Italy, the United Kingdom and the United States. Data from each country were analysed separately. The proportions of patients with COPD receiving maintenance treatment were also estimated. Results: The proportion of patients with exacerbations fell 45-78% across five countries in 2020 versus 2019. Exacerbation rates in most countries were reduced by >50% in 2020 compared with 2019. The proportions of patients with an exacerbation increased in most countries in 2021. Across each country, seasonal exacerbation increases seen during autumn and winter in pre-pandemic years were absent during the first year of the pandemic. The percentage of patients filling COPD prescriptions across each country increased by 4.53-22.13% in 2019 to 9.94-34.17% in 2021. Conclusion: Early, steep declines in exacerbation rates occurred in 2020 versus 2019 across all five countries and were accompanied by a loss of the seasonal pattern of exacerbation.

Association of patient photographs and reduced retract-and-reorder events.

Background: Wrong-patient order entry (WPOE) is a potentially dangerous medical error. It remains unknown if patient photographs reduce WPOE in the pediatric inpatient population. Materials and Methods: Order sessions from a single pediatric hospital system were examined for retract-and-reorder (RAR) events, a surrogate WPOE measure. We determined the association of patient photographs with the proportion of order sessions resulting in a RAR event, adjusted for patient, provider, and ordering context. Results: In multivariable analysis, the presence of a patient photo in the electronic health record was associated with 40% lower odds of a RAR event (aOR: 0.60, 95% CI: 0.48-0.75), while cardiac and ICU contexts had higher RAR frequency (aOR: 2.12, 95% CI: 1.69-2.67 and 2.05, 95% CI: 1.71-2.45, respectively). Discussion and Conclusion: Patient photos were associated with lower odds of RAR events in the pediatric inpatient setting, while high acuity locations may be at higher risk. Patient photographs may reduce WPOE without interruptions.

Inferring Population HIV Viral Load From a Single HIV Clinic's Electronic Health Record: Simulation Study With a Real-World Example.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV. OBJECTIVE: A given HIV clinic's electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure. METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware. RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate. CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic's EHR without the resource-intensive elucidation of an informative prior.

Recurrent urinary tract infections and prophylactic antibiotic use in women: cross-sectional study.

BACKGROUND: Despite the considerable morbidity caused by recurrent UTIs (rUTIs), and the wider personal and public health implications from frequent antibiotic use, few studies adequately describe the prevalence and characteristics of women with rUTIs or those who use prophylactic antibiotics. AIM: To describe the prevalence, characteristics, and urine profiles of women with rUTIs with and without prophylactic antibiotic use in Welsh primary care. DESIGN AND SETTING: Retrospective cross-sectional study in Welsh General Practice using the SAIL Databank. METHOD: We describe the characteristics of women aged ≥18 years with rUTIs or using prophylactic antibiotics from 2010-2020, and associated urine culture results from 2015 - 2020. RESULTS: 6.0% of women (n=92,213) had rUTIs, and 1.7% (n=26,862) were prescribed prophylactic antibiotics. Only 49% of prophylactic antibiotic users met the definition of rUTIs before initiation. 81% of women with rUTIs had a urine culture result in the preceding 12 months with high rates of resistance to trimethoprim and amoxicillin. 64% of women taking prophylactic antibiotics had a urine culture result before initiation, and 18% (n=320) of women prescribed trimethoprim had resistance to it on the antecedent sample. CONCLUSION: A substantial proportion of women had rUTIs or incident prophylactic antibiotic use. However, 64% of women had urine cultured before starting prophylaxis. There was a high proportion of cultured bacteria resistant to two antibiotics used for rUTI prevention and evidence of resistance to the prescribed antibiotic. More frequent urine cultures for rUTI diagnosis and before prophylactic antibiotic initiation could better inform antibiotic choices.

Assessing the quality of emergency department data for injury surveillance in Victoria, Australia: a comparative analysis of two Victorian hospital data sources.

OBJECTIVE: The emergency department (ED) is pivotal in treating serious injuries, making it a valuable source for population-based injury surveillance. In Victoria, information that is relevant to injury surveillance is collected in the Victorian Emergency Minimum Dataset (VEMD). This study aims to assess the data quality of the VEMD as an injury data source by comparing it with the Victorian Admitted Episodes Dataset (VAED). DESIGN: A retrospective observational study of administrative healthcare data. SETTING AND PARTICIPANTS: VEMD and VAED data from July 2014 to June 2019 were compared. Including only hospitals contributing to both datasets, cases that (1) arrived at the ED and (2) were subsequently admitted, were selected. RESULTS: While the overall number of cases was similar, VAED outnumbered VEMD cases (414 630 vs 404 608), suggesting potential under-reporting of injuries in the ED. Age-related differences indicated a relative under-representation of older individuals in the VEMD. Injuries caused by falls or transport, and intentional injuries were relatively under-reported in the VEMD. CONCLUSIONS: Injury cases were more numerous in the VAED than in the VEMD even though the number is expected to be equal based on case selection. Older patients were under-represented in the VEMD; this could partly be attributed to patients being admitted for an injury after they presented to the ED with a non-injury ailment. The patterns of under-representation described in this study should be taken into account in ED-based injury incidence reporting.

Agreement between patient-reported and clinically documented symptoms of acute myeloid leukemia: Study protocol.

AIM: To describe our methods to compare patient-reported symptoms of acute myeloid leukemia and the corresponding documentation by healthcare providers in the electronic health record. BACKGROUND: Patients with acute myeloid leukemia experience many distressing symptoms, particularly related to chemotherapy. The timely recognition and provision of evidence-based interventions to manage these symptoms can improve outcomes. However, lack of standardized formatting for symptom documentation within electronic health records leads to challenges for clinicians when accessing and comprehending patients' symptom information, as it primarily exists in narrative forms in various parts of the electronic health record. This variability raises concerns about over- or under-reporting of symptoms. Consistency between patient-reported symptoms and clinician's symptom documentation is important for patient-centered symptom management, but little is known about the degree of agreement between patient reports and their documentation. This is a detailed description of the study's methodology, procedures and design to determine how patient-reported symptoms are similar or different from symptoms documented in electronic health records by clinicians. DESIGN: Exploratory, descriptive study. METHODS: Forty symptoms will be assessed as patient-reported outcomes using the modified version of the Memorial Symptom Assessment Scale. The research team will annotate symptoms from the electronic health record (clinical notes and flowsheets) corresponding to the 40 symptoms. The degree of agreement between patient reports and electronic health record documentation will be analyzed using positive and negative agreement, kappa statistics and McNemar's test. CONCLUSION: We present innovative methods to comprehensively compare the symptoms reported by acute myeloid leukemia patients with all available electronic health record documentation, including clinical notes and flowsheets, providing insights into symptom reporting in clinical practice. IMPACT: Findings from this study will provide foundational understanding and compelling evidence, suggesting the need for more thorough efforts to assess patients' symptoms. Methods presented in this paper are applicable to other symptom-intensive diseases.

Experiences from patients in mental healthcare accessing their electronic health records: results from a cross-national survey in Estonia, Finland, Norway, and Sweden.

BACKGROUND: Patients' online record access (ORA) enables patients to read and use their health data through online digital solutions. One such solution, patient-accessible electronic health records (PAEHRs) have been implemented in Estonia, Finland, Norway, and Sweden. While accumulated research has pointed to many potential benefits of ORA, its application in mental healthcare (MHC) continues to be contested. The present study aimed to describe MHC users' overall experiences with national PAEHR services. METHODS: The study analysed the MHC-part of the NORDeHEALTH 2022 Patient Survey, a large-scale multi-country survey. The survey consisted of 45 questions, including demographic variables and questions related to users' experiences with ORA. We focused on the questions concerning positive experiences (benefits), negative experiences (errors, omissions, offence), and breaches of security and privacy. Participants were included in this analysis if they reported receiving mental healthcare within the past two years. Descriptive statistics were used to summarise data, and percentages were calculated on available data. RESULTS: 6,157 respondents were included. In line with previous research, almost half (45%) reported very positive experiences with ORA. A majority in each country also reported improved trust (at least 69%) and communication (at least 71%) with healthcare providers. One-third (29.5%) reported very negative experiences with ORA. In total, half of the respondents (47.9%) found errors and a third (35.5%) found omissions in their medical documentation. One-third (34.8%) of all respondents also reported being offended by the content. When errors or omissions were identified, about half (46.5%) reported that they took no action. There seems to be differences in how patients experience errors, omissions, and missing information between the countries. A small proportion reported instances where family or others demanded access to their records (3.1%), and about one in ten (10.7%) noted that unauthorised individuals had seen their health information. CONCLUSIONS: Overall, MHC patients reported more positive experiences than negative, but a large portion of respondents reported problems with the content of the PAEHR. Further research on best practice in implementation of ORA in MHC is therefore needed, to ensure that all patients may reap the benefits while limiting potential negative consequences.

Treatment drop-in in a contemporary cohort used to derive cardiovascular risk prediction equations.

BACKGROUND: No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment. METHODS: Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined. RESULTS: A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds). CONCLUSIONS: CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: a performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy's rule-based and machine learning-based methods.

Objective: Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, and 2 rule-based and machine learning-based methods, namely, scispaCy and medspaCy. Materials and Methods: Phenotypes such as initial cancer stage, initial treatment, evidence of cancer recurrence, and affected organs during recurrence were identified from 13 646 clinical notes for 63 NSCLC patients from Washington University in St. Louis, Missouri. The performance of the GPT-4 model is evaluated against GPT-3.5-turbo, Flan-T5-xxl, Flan-T5-xl, Llama-3-8B, medspaCy, and scispaCy by comparing precision, recall, and micro-F1 scores. Results: GPT-4 achieved higher F1 score, precision, and recall compared to Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, medspaCy, and scispaCy's models. GPT-3.5-turbo performed similarly to that of GPT-4. GPT, Flan-T5, and Llama models were not constrained by explicit rule requirements for contextual pattern recognition. spaCy models relied on predefined patterns, leading to their suboptimal performance. Discussion and Conclusion: GPT-4 improves clinical phenotype identification due to its robust pre-training and remarkable pattern recognition capability on the embedded tokens. It demonstrates data-driven effectiveness even with limited context in the input. While rule-based models remain useful for some tasks, GPT models offer improved contextual understanding of the text, and robust clinical phenotype extraction.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

BACKGROUND: Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults. METHODS: Medline and Embase were searched via Ovid until January 2023. We included studies describing the development or validation of multivariable prediction models of falls in older adults (60+). Both risk of bias and reporting quality were assessed using the PROBAST and TRIPOD, respectively. RESULTS: We included and reviewed 28 relevant studies, describing 30 prediction models (23 cohort-based and 7 RCD-based), and external validation of two existing models (one cohort-based and one RCD-based). The median sample sizes for cohort-based and RCD-based studies were 1365 [interquartile range (IQR) 426-2766] versus 90 441 (IQR 56 442-128 157), and the ranges of fall rates were 5.4% to 60.4% versus 1.6% to 13.1%, respectively. Discrimination performance was comparable between cohort-based and RCD-based models, with the respective area under the receiver operating characteristic curves ranging from 0.65 to 0.88 versus 0.71 to 0.81. The median number of predictors in cohort-based final models was 6 (IQR 5-11); for RCD-based models, it was 16 (IQR 11-26). All but one cohort-based model had high bias risks, primarily due to deficiencies in statistical analysis and outcome determination. CONCLUSIONS: Cohort-based models to predict falls in older adults in the community are plentiful. RCD-based models are yet in their infancy but provide comparable predictive performance with no additional data collection efforts. Future studies should focus on methodological and reporting quality.

Impact of Social Vulnerability Index on the Development of Severe Visual Impairment or Blindness from Glaucoma.

PURPOSE: To evaluate whether geocoded social risk factor data predict the development of severe visual impairment or blindness due to glaucoma during follow-up using a large electronic health record (EHR) database. DESIGN: Cohort study. METHODS: Patients diagnosed with open-angle glaucoma (OAG) at a tertiary care institution. All eyes had glaucomatous visual field defects at baseline. Sociodemographic and ocular data were extracted from EHR, including age, gender, self-reported race and ethnicity, insurance status, OAG type, prior glaucoma laser or surgery, baseline disease severity using Hodapp-Anderson-Parrish criteria, mean intraocular pressure (IOP) during follow-up, and central corneal thickness. Social vulnerability index (SVIndex) data at the census tract level were obtained using geocoded patient residences. Mixed-effects Cox proportional hazard models were completed to assess for the development of severe visual impairment or blindness during follow-up, defined as BCVA ≤20/200 at the last two clinic visits or standard automated perimetry (SAP) mean deviation (MD) ≤-22dB confirmed on two tests. RESULTS: A total of 4,046 eyes from 2,826 patients met inclusion criteria and were followed for an average of 4.3±2.2 years. Severe visual impairment or blindness developed in 79 eyes (2.0%) from 76 patients (2.7%) after an average of 3.4±1.8 years, leading to an incidence rate of severe visual impairment or blindness of 0.5% per year. Older age (adjusted hazards ratio (HR) 1.36 per decade, p=0.007), residence in areas with higher SVIndex (HR 1.56 per 25% increase, p<0.001), higher IOP during follow-up (HR 3.01 per 5 mmHg increase, p<0.001), and moderate or severe glaucoma at baseline (HR 7.31 and 26.87, p<0.001) were risk factors for developing severe visual impairment or blindness. Concordance index of the model was 0.87. Socioeconomic, minority status/language, and housing type/transportation SVIndex themes were key contributors to developing severe visual impairment or blindness. CONCLUSIONS: Risk factors for developing glaucoma-related severe visual impairment or blindness included older age, elevated IOP during follow-up, moderate or severe disease at baseline, and residence in areas associated with greater social vulnerability. In addition to ocular risk factors, geocoded EHR data regarding social risk factors could help identify patients at high risk of developing glaucoma-related visual impairment.