Características clínicas de reanimações cardiopulmonares intra-hospitalares registradas em prontuário: estudo transversal / Clinical characteristics of in-hospital cardiopulmonary resuscitations recorded in medical records: a cross-sectional study / Características clínicas de las reanimaciones cardiopulmonares intrahospitalarias registradas en las historias clínicas: estudio transversal

Objetivo: identificar características clínicas das paradas cardiopulmonares e reanimações cardiopulmonares ocorridas em ambiente intra-hospitalar. Método: estudo quantitativo, prospectivo e observacional, a partir de informações de prontuários de pacientes submetidos a manobras de reanimação devido à parada cardiopulmonar entre janeiro e dezembro de 2021. Utilizou-se um instrumento baseado nas variáveis do modelo de registro Utstein. Resultados: em 12 meses foram registradas 37 paradas cardiopulmonares. A maioria ocorreu na unidade de terapia intensiva respiratória, com causa clínica mais prevalente hipóxia. 65% dos pacientes foram intubados no atendimento e 57% apresentaram ritmo atividade elétrica sem pulso. A duração da reanimação variou entre menos de cinco a mais de 20 minutos. Como desfecho imediato, 57% sobreviveram. Conclusão: dentre os registros analisados, a maior ocorrência de paradas cardiopulmonares foi na unidade de terapia intensiva respiratória, relacionada à Covid-19. Foram encontrados registros incompletos e ausência de padronização nas condutas.

Objective: identify the clinical characteristics of cardiopulmonary arrests and cardiopulmonary resuscitations in the in-hospital environment. Method: this is a quantitative, prospective and observational study based on information from the medical records of patients who underwent resuscitation maneuvers due to cardiopulmonary arrest between January and December 2021. An instrument based on the variables of the Utstein registration protocol was used. Results: thirty-seven cardiopulmonary arrests were recorded in 12 months. The majority occurred in a respiratory intensive care unit, with hypoxia being the most prevalent clinical cause. Sixty-five percent of the patients were intubated and 57% had pulseless electrical activity. The duration of resuscitation ranged from less than five to more than 20 min. As for the immediate outcome, 57% survived. Conclusion: among the records analyzed, the highest occurrence of cardiopulmonary arrests was in respiratory intensive care units, and they were related to Covid-19. Moreover, incomplete records and a lack of standardization in cardiopulmonary resuscitation procedures were found.

Objetivo: Identificar las características clínicas de paros cardiopulmonares y reanimaciones cardiopulmonares que ocurren en un ambiente hospitalario. Método: estudio cuantitativo, prospectivo y observacional, realizado a partir de información presente en historias clínicas de pacientes sometidos a maniobras de reanimación por paro cardiorrespiratorio entre enero y diciembre de 2021. Se utilizó un instrumento basado en las variables del modelo de registro Utstein. Resultados: en 12 meses se registraron 37 paros cardiopulmonares. La mayoría ocurrió en la unidad de cuidados intensivos respiratorios, la causa clínica más prevalente fue la hipoxia. El 65% de los pacientes fue intubado durante la atención y el 57% presentaba un ritmo de actividad eléctrica sin pulso. La duración de la reanimación varió entre menos de cinco y más de 20 minutos. Como resultado inmediato, el 57% sobrevivió. Conclusión: entre los registros analizados, la mayor cantidad de paros cardiopulmonares se dio en la unidad de cuidados intensivos respiratorios, relacionada con Covid-19. Se encontraron registros incompletos y falta de estandarización en el procedimiento.

Using Routinely Collected Electronic Healthcare Record Data to Investigate Fibrotic Multimorbidity in England.

Background: Electronic healthcare records (EHRs) are used to document diagnoses, symptoms, tests, and prescriptions. Though not primarily collected for research purposes, owing to the size of the data as well as the depth of information collected, they have been used extensively to conduct epidemiological research. The Clinical Practice Research Datalink (CPRD) is an EHR database containing representative data of the UK population with regard to age, sex, race, and social deprivation measures. Fibrotic conditions are characterised by excessive scarring, contributing towards organ dysfunction and eventual organ failure. Fibrosis is associated with ageing as well as many other factors, it is hypothesised that fibrotic conditions are caused by the same underlying pathological mechanism. We calculated the prevalence of fibrotic conditions (as defined in a previous Delphi survey of clinicians) as well as the prevalence of fibrotic multimorbidity (the proportion of people with multiple fibrotic conditions). Methods: We included a random sample of 993,370 UK adults, alive, and enrolled at a UK general practice, providing data to the CPRD Aurum database as of 1st of January 2015. Individuals had to be eligible for linkage to hospital episode statistics (HES) and ONS death registration. We calculated the point prevalence of fibrotic conditions and multi-morbid fibrosis on the 1st of January 2015. Using death records of those who died in 2015, we investigated the prevalence of fibrosis associated death. We explored the most commonly co-occurring fibrotic conditions and determined the settings in which diagnoses were commonly made (primary care, secondary care or after death). Results: The point prevalence of any fibrotic condition was 21.46%. In total, 6.00% of people had fibrotic multimorbidity. Of the people who died in 2015, 34.82% had a recording of a fibrotic condition listed on their death certificate. Conclusion: The key finding was that fibrotic multimorbidity affects approximately 1 in 16 people.

Fibrotic conditions are scarring conditions which impact the way an organ functions and eventually lead to organ failure. We studied routinely collected health data from GPs, hospitals, and death certificates to estimate the percentage of UK adults who had fibrotic diseases. We found that 1 in 5 people had at least one fibrotic disease, and we also found that 1 in 16 people had more than one fibrotic disease.

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.

Adherence of studies involving artificial intelligence in the analysis of ophthalmology electronic medical records to AI-specific items from the CONSORT-AI guideline: a systematic review.

PURPOSE: In the context of ophthalmologic practice, there has been a rapid increase in the amount of data collected using electronic health records (EHR). Artificial intelligence (AI) offers a promising means of centralizing data collection and analysis, but to date, most AI algorithms have only been applied to analyzing image data in ophthalmologic practice. In this review we aimed to characterize the use of AI in the analysis of EHR, and to critically appraise the adherence of each included study to the CONSORT-AI reporting guideline. METHODS: A comprehensive search of three relevant databases (MEDLINE, EMBASE, and Cochrane Library) from January 2010 to February 2023 was conducted. The included studies were evaluated for reporting quality based on the AI-specific items from the CONSORT-AI reporting guideline. RESULTS: Of the 4,968 articles identified by our search, 89 studies met all inclusion criteria and were included in this review. Most of the studies utilized AI for ocular disease prediction (n = 41, 46.1%), and diabetic retinopathy was the most studied ocular pathology (n = 19, 21.3%). The overall mean CONSORT-AI score across the 14 measured items was 12.1 (range 8-14, median 12). Categories with the lowest adherence rates were: describing handling of poor quality data (48.3%), specifying participant inclusion and exclusion criteria (56.2%), and detailing access to the AI intervention or its code, including any restrictions (62.9%). CONCLUSIONS: In conclusion, we have identified that AI is prominently being used for disease prediction in ophthalmology clinics, however these algorithms are limited by their lack of generalizability and cross-center reproducibility. A standardized framework for AI reporting should be developed, to improve AI applications in the management of ocular disease and ophthalmology decision making.

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.

The effects of the first UK lockdown for the COVID-19 pandemic on primary-care-recorded cancer and type-2 diabetes mellitus records: A population-based quasi-experimental time series study.

BACKGROUND: COVID-19 disrupted consulting behaviour, healthcare delivery and cancer diagnostic services. This study quantifies the cancer incidence coded in UK general practice electronic health records and deviations from historical trends after the March 2020 national lockdown. For comparison, we study the coded incidence of type-2 diabetes mellitus, which is diagnosed almost entirely within primary care. METHODS: Poisson interrupted time series models investigated the coded incidence of diagnoses in adults aged ≥ 18 years in the Clinical Practice Research Datalink before (01/03/2017-29/02/2020) and after (01/03/2020-28/02/2022) the first lockdown. Datasets were stratified by age, sex, and general practice per 28-day aggregation period. Models captured incidence changes associated with lockdown, both immediately and over time based on historical trends. RESULTS: We studied 189,457 incident cancer and 191,915 incident diabetes records in 1480 general practices over 52,374,197 person-years at risk. During 01/03/2020-28/02/2022, there were fewer incident records of cancer (n = 22,199, 10.49 %, 10.44-10.53 %) and diabetes (n = 15,709, 7.57 %, 7.53-7.61 %) than expected. Within cancers, impacts ranged from no effect (e.g. unknown primary, pancreas, and ovary), to small effects for lung (n = 773, 3.11 %, 3.09-3.13 % fewer records) and female breast (n = 2686, 6.77 %, 6.73-6.81 %), to the greatest effect for bladder (n = 2874, 31.15 %, 31.00-31.31 %). Diabetes and cancer records recovered maximally to 86 % (95 %CI 80.3-92.7 %) and 74 % (95 %CI 70.3-78.6 %) in July 2021 and May 2021, respectively, of their expected values, declining again until the study end. CONCLUSION: The "missing" cancer and diabetes diagnoses in primary care may comprise delayed or missed diagnoses, reduced incidence associated with excess deaths from COVID-19, and potentially increased non-coded recording of diagnoses. Future validation studies must quantify the concordance between primary care and National Cancer Registration Data and Hospital Episode Statistics over the pandemic era.

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.

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.

A Cross-Sectional Study on Whether Comprehensively Gathering Information From Medical Records Is Useful for the Collection of Operational Characteristics.

This study tests whether comprehensively gathering information from medical records is useful for developing clinical decision support systems using Bayes' theorem. Using a single-center cross-sectional study, we retrospectively extracted medical records of 270 patients aged ≥16 years who visited the emergency room at the Tokyo Metropolitan Tama Medical Center with a chief complaint of experiencing headaches. The medical records of cases were analyzed in this study. We manually extracted diagnoses, unique keywords, and annotated keywords, classifying them as either positive or negative. Cross tables were created, and the proportion of combinations for which the likelihood ratios could be calculated was evaluated. Probability functions for the appearance of new unique keywords were modeled, and theoretical values were calculated. We extracted 623 unique keywords, 26 diagnoses, and 6,904 annotated keywords. Likelihood ratios could be calculated only for 276 combinations (1.70%), of which 24 (0.15%) exhibited significant differences. The power function+constant was the best fit for new unique keywords. The increase in the number of combinations after increasing the number of cases indicated that while it is theoretically possible to comprehensively gather information from medical records in this way, doing so presents difficulties related to human costs. It also does not necessarily solve the fundamental issues with medical informatics or with developing clinical decision support systems. Therefore, we recommend using methods other than comprehensive information gathering with Bayes' theorem as the classifier to develop such systems.

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.

An increase in global daily precipitation records in response to global warming based on reanalysis and observations.

Understanding trends in extreme precipitation events in the context of global warming is critical for assessing climate change impacts. This study employs a novel methodology developed by Giorgi and Ciarlo (2022) to analyze record-breaking daily precipitation events from 1980 to 2020, utilizing three reanalysis products (ERA5, MERRA-2, and JRA-55) and one global observation dataset (MSWEP). Our results indicate a consistent and statistically significant increase in record-breaking precipitation events globally, with variations across different latitude bands and between land and ocean areas. This trend is evident in all datasets, with the most substantial increases observed over oceans in ERA5 and over land in JRA and MERRA. Notably, the Southern Hemisphere shows mixed results, with some regions displaying negative trends. This study highlights the increasing frequency of extreme precipitation events, supporting the hypothesis of intensified hydrological cycles under a warming climate. Our findings enhance understanding of precipitation extremes and underscore the importance of regional analyses in climate impact studies. Future work could extend these findings to formal attribution studies linking observed trends directly to anthropogenic climate change.

In recent decades, observations have shown changes in how often and how intensely it rains, which can be linked to global warming. Our study analyses record-breaking rainfall events, i.e. days when rainfall reaches unprecedented highs, in different observational and reanalysis records for the last 40 years. We use a new method to compare daily rainfall records with the values that would be expected in stable climate conditions, i.e. without warming. Our findings show that extreme rainfall events have become more frequent around the world. This trend is predominant across various latitudinal regions and over oceans and land, though there are some differences depending on the location. Notably, the increase in record rainfall events is more consistent across the oceans than the continental regions, with some of the latter showing negative trends in the southern hemisphere. This conclusion has important implications for how we prepare for and manage flooding and other related natural disasters in the future.

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.

Agreement between Clinical Frailty Scale-scores based on information from patient interviews and Clinical Frailty Scale-scores based on information from medical records - a cross sectional study.

INTRODUCTION: Frailty is an age-related condition with increased risk for adverse health outcomes. Assessing frailty according to the Clinical Frailty Scale (CFS) based on data from medical records is useful for previously unassessed patients, but the validity of such scores in exclusively geriatric populations and in patients with dementia is relatively unknown. METHODS: Patients admitted for the first time to one of two geriatric wards at Örebro University hospital between January 1st - December 31st, 2021, were included in this study if they had been appointed a CFS-score by anamnestic interview (CFSI) at admission. CFS scores based on medical records (CFSR) were appointed by a single medical student, who was blinded to the CFSI score. Score-agreement was evaluated with quadratic weighted Cohen's kappa (κ). RESULTS: In total, 145 patients between the age of 55-101 were included in the study. The CFSR and CFSI scores agreed perfectly in 102 cases (0.7, 95% CI 0.65-0.77). There was no significant difference regarding age, sex, comorbidity, or number of patients diagnosed with dementia between the patients with complete agreement and the patients whose scores did not agree. Agreement between the scores was substantial, κ = 0.66, 95% CI 0.53-0.80. CONCLUSIONS: CFS scores based on information from medical records can be generated with substantial agreement to CFS scores based on in-person anamnestic interviews. A dementia diagnosis does not influence the agreement between the scores. Therefore, these scores are a useful tool for assessing frailty in geriatric patients who previously lack a frailty assessment, both in clinical practice and future research. The results support previous findings, but larger studies are warranted.

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.

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.

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.

Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.

INTRODUCTION AND OBJECTIVES: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model. PATIENTS AND METHODS: n=940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n=528 MASLD patients. RESULTS: In-sample model performance achieved AUROC curve 0.74-0.90 (95% CI: 0.72-0.94), sensitivity 64%-82%, specificity 75%-92% and Positive Predictive Value (PPV) 94%-98%. Out-of-sample model validation had AUROC 0.70-0.86 (95% CI: 0.67-0.90), sensitivity 69%-70%, specificity 96%-97% and PPV 75%-77%. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days. CONCLUSIONS: A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.

Prospective validation of a mobile health application for blood pressure management in patients with hypertensive disorders of pregnancy: study protocol for a randomized controlled trial.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) pose significant risks to both maternal and fetal health, contributing to global morbidity and mortality. Management of HDP is complex, particularly because of concerns regarding potential negative effects on utero-placental circulation and limited therapeutic options due to fetal safety. Our study investigates whether blood pressure monitoring through a mobile health (mHealth) application can aid in addressing the challenges of blood pressure management in pregnant individuals with HDP. Additionally, we aim to assess whether this intervention can improve short-term maternal and fetal outcomes and potentially mitigate long-term cardiovascular consequences. METHODS: This prospective, randomized, single-center trial will include 580 pregnant participants who meet the HDP criteria or who have a heightened risk of pregnancy-related hypertension due to factors such as multiple pregnancies, obesity, diabetes, or a history of HDP in prior pregnancies leading to preterm birth. Participants will be randomized to either the mHealth intervention group or the standard care group. The primary endpoint is the difference in systolic blood pressure from enrollment to 1 month after childbirth. The secondary endpoints include various blood pressure parameters, obstetric outcomes, body mass index trajectory, step counts, mood assessment, and drug adherence. CONCLUSIONS: This study emphasizes the potential of mHealth interventions, such as the Heart4U application, to improve blood pressure management in pregnant individuals with HDP. By leveraging technology to enhance engagement, communication, and monitoring, this study aims to positively impact maternal, fetal, and postpartum outcomes associated with HDP. This innovative approach demonstrates the potential of personalized technology-driven solutions for managing complex health conditions. TRIAL REGISTRATION: ClinicalTrials.gov NCT05995106. Registered on 16 August 2023.