Patterns of Intimate Partner Violence Among Veterans: A Latent Class Analysis.

The majority of intimate partner violence (IPV) research is unidirectional, focusing on IPV use (i.e., perpetration) or experience (i.e., victimization). However, when IPV use and experience data are simultaneously included in analyses, bidirectional IPV often emerges as a common IPV pattern. The objective of this study was to examine patterns of IPV use and experience, risk factors that may be associated with these patterns, and potential gender differences within a sample of post-9/11 Veterans. This study included a national sample of post-9/11 Veterans (N = 1,150; 50.3% women) who completed self-report measures at two timepoints. We performed a latent class analysis (LCA) to determine the appropriate number of IPV classes, conducted sensitivity analyses, and examined factors potentially associated with IPV class membership. We identified three distinct classes of IPV: Low to no IPV, Bidirectional Psychological IPV, and Bidirectional Multiform IPV. Men and women reported similar rates of IPV use and experience, and there were no gender differences in the LCA model. However, race and ethnicity, employment status, children in the household, marital status, child abuse or witnessing family violence, lifetime physical assault, posttraumatic stress symptoms, and binge drinking were differentially associated with class membership. This study extends existing knowledge on patterns of IPV among Veterans and factors associated with these patterns. Bidirectional IPV was the most common IPV pattern, underscoring the importance of examining IPV use and experience concurrently within research and clinical samples, and developing comprehensive IPV screening and treatment strategies that incorporate bidirectional IPV in work to advance relationship health and safety among Veterans.

Risk of Serious Adverse Gastrointestinal Events with Potassium Binders in Hospitalized Patients: A National Study.

BACKGROUND: Concerns about serious adverse gastrointestinal (GI) events with sodium polystyrene sulfonate (SPS) led to development of two new potassium binders, patiromer and sodium zirconium cyclosilicate (SZC), for treatment of hyperkalemia. OBJECTIVE: To compare risk of intestinal ischemia/thrombosis or other serious GI events associated with SPS, patiromer, or SZC in hospitalized patients. DESIGN: Retrospective cohort study. PARTICIPANTS: National sample of 3,144,960 veterans hospitalized 2016-2022 in the U.S. Department of Veterans Affairs Healthcare System. MAIN MEASURES: Demographics, comorbidities, medications and outcomes were ascertained from the VA Corporate Data Warehouse. Exposures were SPS, patiromer, SZC. Outcomes were 30-day intestinal ischemia/thrombosis, and a composite of intestinal ischemia/thrombosis, peptic ulcer/perforation or bowel resection/ostomy. KEY RESULTS: Potassium binders were used during 39,270 (1.3%) hospitalizations: SPS = 30,040 (1.0%), patiromer = 3,750 (0.1%), and SZC = 5,520 (0.2%). Intestinal ischemia/thrombosis occurred with 106/30,040 (0.4%) SPS, 12/3750 (0.3%) patiromer and 24/5520 (0.4%) SZC, vs. 6998/3,105,650 (0.2%) without potassium binder. Adjusted odds ratios (aOR) were 1.40 [95% CI, 1.16 to 1.69] with SPS, 1.36 [CI, 0.79 to 2.36] with patiromer, and 1.78 [CI, 1.21 to 2.63] with SZC exposures. Composite GI adverse events occurred with 754/30,040 (2.5%) SPS, 96/3750 (2.6%) patiromer, 2.6% SZC, vs. 144/5520 (2.4%) without binder; aOR were 1.00 [CI, 0.94 to 1.08] with SPS, 1.08 [CI, 0.89 to 1.32] with patiromer, and 1.08 [CI, 0.93 to 1.27] with SZC exposures. No statistical difference in intestinal ischemia/thrombosis between each new agent and SPS was seen (p = 0.274 for SPS vs. SZC; p = 0.916 for SPS vs. patiromer). CONCLUSION: Risk of intestinal ischemia/thrombosis or other serious adverse GI events was low and did not differ across three potassium-binding drugs.

Implementation and Impact of Intimate Partner Violence Screening Expansion in the Veterans Health Administration: Protocol for a Mixed Methods Evaluation.

BACKGROUND: Intimate partner violence (IPV) is a significant public health problem with far-reaching consequences. The health care system plays an integral role in the detection of and response to IPV. Historically, the majority of IPV screening initiatives have targeted women of reproductive age, with little known about men's IPV screening experiences or the impact of screening on men's health care. The Veterans Health Administration (VHA) has called for an expansion of IPV screening, providing a unique opportunity for a large-scale evaluation of IPV screening and response across all patient populations. OBJECTIVE: In this protocol paper, we describe the recently funded Partnered Evaluation of Relationship Health Innovations and Services through Mixed Methods (PRISM) initiative, aiming to evaluate the implementation and impact of the VHA's IPV screening and response expansion, with a particular focus on identifying potential gender differences. METHODS: The PRISM Initiative is guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) and Consolidated Framework for Implementation Research (CFIR 2.0) frameworks. We will use mixed methods data from 139 VHA facilities to evaluate the IPV screening expansion, including electronic health record data and qualitative interviews with patients, clinicians, and national IPV program leadership. Quantitative data will be analyzed using a longitudinal observational design with repeated measurement periods at baseline (T0), year 1 (T1), and year 2 (T2). Qualitative interviews will focus on identifying multilevel factors, including potential implementation barriers and facilitators critical to IPV screening and response expansion, and examining the impact of screening on patients and clinicians. RESULTS: The PRISM initiative was funded in October 2023. We have developed the qualitative interview guides, obtained institutional review board approval, extracted quantitative data for baseline analyses, and began recruitment for qualitative interviews. Reports of progress and results will be made available to evaluation partners and funders through quarterly and end-of-year reports. All data collection and analyses across time points are expected to be completed in June 2026. CONCLUSIONS: Findings from this mixed methods evaluation will provide a comprehensive understanding of IPV screening expansion at the VHA, including the implementation and impact of screening and the scope of IPV detected in the VHA patient population. Moreover, data generated by this initiative have critical policy and clinical practice implications in a national health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/59918.

Electronic Health Record Concordance with Survey-Reported Military Sexual Trauma Among Younger Veterans: Associations with Health Care Utilization and Mental Health Diagnoses.

Introduction: Military sexual trauma (MST) is more common among post-9/11 Veterans and women versus older Veterans and men. Despite mandatory screening, the concordance of electronic health record (EHR) documentation and survey-reported MST, and associations with health care utilization and mental health diagnoses, are unknown for this younger group. Materials and Methods: Veterans' Health Administration (VHA) EHR (2001-2021) were merged with data from the observational, nationwide WomenVeterans Cohort Study (collected 2016-2020, n = 1058; 51% women). Experiencing MST was defined as positive endorsement of sexual harassment and/or assault. From the EHR, we derived Veterans' number of primary care and mental health visits in the initial two years of VHA care and diagnoses of posttraumatic stress disorder (PTSD), depression, and anxiety. First, the concordance of EHR MST screening and survey-reported MST was compared. Next, multivariate analyses tested the cross-sectional associations of EHR screening and survey-reported MST with Veterans' health care utilization, and compared the likelihood of PTSD, depression, and anxiety diagnoses by MST group, while covarying demographics and service-related characteristics. With few MST cases among men, multivariate analyses were only pursued for women. Results: Overall, 29% of women and 2% of men screened positive for MST in the EHR, but 64% of women and 9% of men had survey-reported MST. Primary care utilization was similar between women with concordant, positive MST reports in the EHR and survey versus those with survey-reported MST only. Women with survey-reported MST only were less likely to have a PTSD or depression diagnosis than those with concordant, positive MST reports. There was no group difference in women's likelihood of anxiety. Conclusions: EHR MST documentation is discordant for many post-9/11 Veterans-both for men and women. Improving MST screening and better supporting MST disclosure are each critical to provide appropriate and timely care for younger Veterans, particularly women.

Leveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration.

BACKGROUND: Residential mobility, or a change in residence, can influence health care utilization and outcomes. Health systems can leverage their patients' residential addresses stored in their electronic health records (EHRs) to better understand the relationships among patients' residences, mobility, and health. The Veteran Health Administration (VHA), with a unique nationwide network of health care systems and integrated EHR, holds greater potential for examining these relationships. METHODS: We conducted a cross-sectional analysis to examine the association of sociodemographics, clinical conditions, and residential mobility. We defined residential mobility by the number of VHA EHR residential addresses identified for each patient in a 1-year period (1/1-12/31/2018), with 2 different addresses indicating one move. We used generalized logistic regression to model the relationship between a priori selected correlates and residential mobility as a multinomial outcome (0, 1, ≥2 moves). RESULTS: In our sample, 84.4% (n=3,803,475) veterans had no move, 13.0% (n=587,765) had 1 move, and 2.6% (n=117,680) had ≥2 moves. In the multivariable analyses, women had greater odds of moving [aOR=1.11 (95% CI: 1.10,1.12) 1 move; 1.27 (1.25,1.30) ≥2 moves] than men. Veterans with substance use disorders also had greater odds of moving [aOR=1.26 (1.24,1.28) 1 move; 1.77 (1.72,1.81) ≥2 moves]. DISCUSSION: Our study suggests about 16% of veterans seen at VHA had at least 1 residential move in 2018. VHA data can be a resource to examine relationships between place, residential mobility, and health.

A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness.

OBJECTIVES: We evaluated methods for preparing electronic health record data to reduce bias before applying artificial intelligence (AI). METHODS: We created methods for transforming raw data into a data framework for applying machine learning and natural language processing techniques for predicting falls and fractures. Strategies such as inclusion and reporting for multiple races, mixed data sources such as outpatient, inpatient, structured codes, and unstructured notes, and addressing missingness were applied to raw data to promote a reduction in bias. The raw data was carefully curated using validated definitions to create data variables such as age, race, gender, and healthcare utilization. For the formation of these variables, clinical, statistical, and data expertise were used. The research team included a variety of experts with diverse professional and demographic backgrounds to include diverse perspectives. RESULTS: For the prediction of falls, information extracted from radiology reports was converted to a matrix for applying machine learning. The processing of the data resulted in an input of 5,377,673 reports to the machine learning algorithm, out of which 45,304 were flagged as positive and 5,332,369 as negative for falls. Processed data resulted in lower missingness and a better representation of race and diagnosis codes. For fractures, specialized algorithms extracted snippets of text around keywork "femoral" from dual x-ray absorptiometry (DXA) scans to identify femoral neck T-scores that are important for predicting fracture risk. The natural language processing algorithms yielded 98% accuracy and 2% error rate The methods to prepare data for input to artificial intelligence processes are reproducible and can be applied to other studies. CONCLUSION: The life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. When applying artificial intelligence methods, input data must be prepared optimally to reduce algorithmic bias, as biased output is harmful. Building AI-ready data frameworks that improve efficiency can contribute to transparency and reproducibility. The roadmap for the application of AI involves applying specialized techniques to input data, some of which are suggested here. This study highlights data curation aspects to be considered when preparing data for the application of artificial intelligence to reduce bias.

Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis.

Objectives: Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a deep neural network (DNN) to examine suicidal death risk factors among US Veterans. Methods: Data on 8.8 million veterans with visits between 2010 and 2017 was used. A case-control study was performed, and suicide death risk was analyzed by a DNN. Feature impacts and interactions on the outcome were evaluated. Results: The crude suicide mortality rate was higher in LGBT patients. However, after adjusting for over 200 risk and protective factors, known LGBT status was associated with reduced risk compared to LGBT-Unknown status. Among LGBT patients, black, female, married, and older Veterans have a higher risk, while Veterans of various religions have a lower risk. Conclusion: Our results suggest that disclosed LGBT status is not directly associated with an increase suicide death risk, however, other factors (e.g., depression and anxiety caused by stigma) are associated with suicide death risks.

Blood Pressure Control and Maintenance in a Prospective Cohort of Younger Veterans: Roles of Sex, Race, Ethnicity, and Social Determinants of Health.

Background: Proactive blood pressure (BP) management is particularly beneficial for younger Veterans, who have a greater prevalence and earlier onset of cardiovascular disease than non-Veterans. It is unknown what proportion of younger Veterans achieve and maintain BP control after hypertension onset and if BP control differs by demographics and social deprivation. Methods: Electronic health records were merged from Veterans who enrolled in VA care 10/1/2001-9/30/2017 and met criteria for hypertension - first diagnosis or antihypertensive fill. BP control (140/90 mmHg), was estimated 1, 2, and 5 years post-hypertension documentation, and characterized by sex, race, and ethnicity. Adjusted logistic regressions assessed likelihood of BP control by these demographics and with the Social Deprivation Index (SDI). Results: Overall, 17% patients met criteria for hypertension (n=198,367; 11% of women, median age 41). One year later, 59% of men and 65% of women achieved BP control. After adjustment, women had a 72% greater odds of BP control than men, with minimal change over 5 years. Black adults had a 22% lower odds of BP control than White adults. SDI did not significantly change these results. Conclusions: In the largest study of hypertension in younger Veterans, 41% of men and 35% of women did not have BP control after 1 year, and BP control was consistently better for women through 5 years. Thus, the first year of hypertension management portends future, long-term BP control. As social deprivation did not affect BP control, the VA system may protect against disadvantages observed in the general U.S. population.

Relationship Between Pain and LGBT Status Among Veterans in Care in a Retrospective Cross-Sectional Cohort.

Background: Pain assessment is performed in many healthcare systems, such as the Veterans Health Administration, but prior studies have not assessed whether pain screening varies in sexual and gender minority populations that include individuals who identify as lesbian, gay, bisexual, and/or transgender (LGBT). Objective: The purpose of this study was to evaluate pain screening and reported pain of LGBT Veterans compared to non-LGBT Veterans. Methods: Using a retrospective cross-sectional cohort, data from the Corporate Data Warehouse, a national repository with clinical/administrative data, were analyzed. Veterans were classified as LGBT using natural language processing. We used a robust Poisson model to examine the association between LGBT status and binary outcomes of pain screening, any pain, and persistent pain within one year of entry in the cohort. All models were adjusted for demographics, mental health, substance use, musculoskeletal disorder(s), and number of clinic visits. Results: There were 1,149,486 Veterans (218,154 (19%) classified as LGBT) in our study. Among LGBT Veterans, 94% were screened for pain compared to 89% among those not classified as LGBT (non-LGBT) Veterans. In adjusted models, LGBT Veterans' probability of being screened for pain compared to non-LGBT Veterans was 2.5% higher (95% CI 2.3%, 2.6%); risk of any pain was 2.1% lower (95% CI 1.6%, 2.6%); and there was no significant difference between LGBT and non-LGBT Veterans in persistent pain (RR = 1.00, 95% CI (0.99, 1.01), p = 0.88). Conclusions: In a nationwide sample, LGBT Veterans were more likely to be screened for pain but had lower self-reported pain scores, though adjusted differences were small. It was notable that transgender and Black Veterans reported the greatest pain. Reasons for these findings require further investigation.

Screening for Intimate Partner Violence Experience and Use in the Veterans Health Administration.

Importance: The practice of screening women for intimate partner violence (IPV) in health care settings has been a critical part of responding to this major public health problem. Yet, IPV prevention would be enhanced with detection efforts that extend beyond screening for IPV experiences to identifying those who use violence in relationships as well. Objective: To determine rates of IPV experiences and use (ie, among perpetrators of IPV) and factors associated with disclosures among adult patients seeking mental health services at the Veterans Health Administration. Design, Setting, and Participants: This cross-sectional study used electronic medical record data drawn from a quality improvement initiative at 5 Veterans Health Administration medical centers conducted between November 2021 and February 2022 to examine IPV disclosures following concurrent screening for IPV experience and use. Participants included patients engaged in mental health services. Data were analyzed in April and May 2023. Exposure: Mental health clinicians were trained to screen for IPV experience and use concurrently and instructed to screen all patients encountered through routine mental health care visits during a 3-month period. Main Outcomes and Measures: Outcomes of interest were past-year prevalence of IPV use and experience, sociodemographic characteristics, and clinical diagnoses among screened patients. Results: A total of 200 patients were offered IPV screening. Of 155 participants (mean [SD] age, 52.45 [15.65] years; 124 [80.0%] men) with completed screenings, 74 (47.7%) denied past-year IPV experience and use, 76 (49.0%) endorsed past-year IPV experience, and 72 (46.4%) endorsed past-year IPV use, including 67 participants (43.2%) who reported IPV experience and use concurrently; only 9 participants (5.8%) endorsed unidirectional IPV experiences and 5 participants (3.2%) endorsed unidirectional IPV use. Patients who reported past-year IPV experience and use were younger than those who denied IPV (experience: mean difference, -7.34 [95% CI, 2.51-12.17] years; use: mean difference, -7.20 [95% CI, 2.40-12.00] years). Patients with a posttraumatic stress disorder diagnosis were more likely to report IPV use (43 patients [59.7%]) than those without a posttraumatic stress disorder diagnosis (29 patients [40.3%]; odds ratio, 2.14; [95% CI, 1.12-4.06]). No other demographic characteristics or clinical diagnoses were associated with IPV use or experience. Conclusions and Relevance: In this cross-sectional study of IPV rates and associated factors, screening for IPV found high rates of both IPV experience and use among patients receiving mental health care. These findings highlight the benefit of screening for IPV experience and use concurrently across gender and age. Additionally, the associations found between PTSD and IPV use underscore the importance of strengthening and developing additional targeted treatment for IPV.

Insomnia and Early Incident Atrial Fibrillation: A 16-Year Cohort Study of Younger Men and Women Veterans.

Background There is growing consideration of sleep disturbances and disorders in early cardiovascular risk, including atrial fibrillation (AF). Obstructive sleep apnea confers risk for AF but is highly comorbid with insomnia, another common sleep disorder. We sought to first determine the association of insomnia and early incident AF risk, and second, to determine if AF onset is earlier among those with insomnia. Methods and Results This retrospective analysis used electronic health records from a cohort study of US veterans who were discharged from military service since October 1, 2001 (ie, post-9/11) and received Veterans Health Administration care, 2001 to 2017. Time-varying, multivariate Cox proportional hazard models were used to examine the independent contribution of insomnia diagnosis to AF incidence while serially adjusting for demographics, lifestyle factors, clinical comorbidities including obstructive sleep apnea and psychiatric disorders, and health care utilization. Overall, 1 063 723 post-9/11 veterans (Mean age=28.2 years, 14% women) were followed for 10 years on average. There were 4168 cases of AF (0.42/1000 person-years). Insomnia was associated with a 32% greater adjusted risk of AF (95% CI, 1.21-1.43), and veterans with insomnia showed AF onset up to 2 years earlier. Insomnia-AF associations were similar after accounting for health care utilization (adjusted hazard ratio [aHR], 1.27 [95% CI, 1.17-1.39]), excluding veterans with obstructive sleep apnea (aHR, 1.38 [95% CI, 1.24-1.53]), and among those with a sleep study (aHR, 1.26 [95% CI, 1.07-1.50]). Conclusions In younger adults, insomnia was independently associated with incident AF. Additional studies should determine if this association differs by sex and if behavioral or pharmacological treatment for insomnia attenuates AF risk.

Implementer report: ICD-10 code F44.5 review for functional seizure disorder.

OBJECTIVE: The study aimed to measure the validity of International Classification of Diseases, 10th Edition (ICD-10) code F44.5 for functional seizure disorder (FSD) in the Veterans Affairs Connecticut Healthcare System electronic health record (VA EHR). METHODS: The study used an informatics search tool, a natural language processing algorithm and a chart review to validate FSD coding. RESULTS: The positive predictive value (PPV) for code F44.5 was calculated to be 44%. DISCUSSION: ICD-10 introduced a specific code for FSD to improve coding validity. However, results revealed a meager (44%) PPV for code F44.5. Evaluation of the low diagnostic precision of FSD identified inconsistencies in the ICD-10 and VA EHR systems. CONCLUSION: Information system improvements may increase the precision of diagnostic coding by clinicians. Specifically, the EHR problem list should include commonly used diagnostic codes and an appropriately curated ICD-10 term list for 'seizure disorder,' and a single ICD code for FSD should be classified under neurology and psychiatry.

Identifying suicide documentation in clinical notes through zero-shot learning.

Background and Aims: In deep learning, a major difficulty in identifying suicidality and its risk factors in clinical notes is the lack of training samples given the small number of true positive instances among the number of patients screened. This paper describes a novel methodology that identifies suicidality in clinical notes by addressing this data sparsity issue through zero-shot learning. Our general aim was to develop a tool that leveraged zero-shot learning to effectively identify suicidality documentation in all types of clinical notes. Methods: US Veterans Affairs clinical notes served as data. The training data set label was determined using diagnostic codes of suicide attempt and self-harm. We used a base string associated with the target label of suicidality to provide auxiliary information by narrowing the positive training cases to those containing the base string. We trained a deep neural network by mapping the training documents' contents to a semantic space. For comparison, we trained another deep neural network using the identical training data set labels, and bag-of-words features. Results: The zero-shot learning model outperformed the baseline model in terms of area under the curve, sensitivity, specificity, and positive predictive value at multiple probability thresholds. In applying a 0.90 probability threshold, the methodology identified notes documenting suicidality but not associated with a relevant ICD-10-CM code, with 94% accuracy. Conclusion: This method can effectively identify suicidality without manual annotation.

Intimate partner violence among lesbian, gay, and bisexual veterans.

The present study describes intimate partner violence (IPV) perpetration and victimization alongside theoretically associated variables in a sample of lesbian, gay, and bisexual veterans. We conducted bivariate analyses (chi-square tests and independent t test) to examine whether the frequencies of IPV perpetration and victimization varied by demographic characteristics, military sexual trauma, alcohol use, and mental health symptoms. Out of the 69 lesbian, gay, and bisexual (LGB) veterans who answered the questions on IPV, 16 (23.2%) reported some form of IPV victimization in the past year, and 38 (55.1%) reported past-year perpetration. Among the 43 veterans who reported psychological IPV, roughly half (48.9%) reported bidirectional psychological IPV, 39.5% reported perpetration only, and 11.6% reported victimization only. LGB veterans who reported bidirectional psychological IPV in their relationships were younger and reported greater symptoms of posttraumatic stress disorder symptoms and depression. The results presented here call for universal screening of IPV perpetration and victimization to both accurately assess and ultimately intervene among all veterans. Inclusive interventions are needed for all genders and sexual orientations, specifically interventions that do not adhere to gendered assumptions of perpetrators and victims. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding Veterans' intimate partner violence use and patterns of healthcare utilization.

OBJECTIVE: To understand the association between Veterans' healthcare utilization and intimate partner violence (IPV) use (i.e., perpetration) in order to (1) identify conditions comorbid with IPV use and (2) inform clinical settings to target for IPV use screening, intervention, and provider training. DATA SOURCES AND STUDY SETTING: We examined survey data from a national sample of 834 Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn (OEF/OIF/OND) Veterans. STUDY DESIGN: We assessed associations between past-year IPV use and medical treatment, health issues, and use of Veterans Health Administration (VA) and non-VA services using chi-square tests and logistic regression. DATA COLLECTION/EXTRACTION METHODS: Data were derived from the Department of Defense OEF/OIF/OND Roster. Surveys were sent to all women Veterans and a random sample of men from participating study sites. PRINCIPAL FINDINGS: Half (49%) of the Veterans who reported utilizing VA healthcare in the past year indicated using IPV. Q values using a 5% false discovery rate indicated that Veterans who used IPV were more likely than Veterans who did not use IPV to have received treatment for post-traumatic stress disorder (PTSD; 39% vs. 27%), chronic sleep problems (36% vs. 26%), anxiety or depression (44% vs. 36%), severe chronic pain (31% vs. 22%), and stomach or digestive disorders (24% vs. 16%). Veterans who used IPV were also more likely than Veterans who did not use IPV to have received medical treatment in the past year (86% vs. 80%), seen psychiatrists outside VA (39% vs. 20%), and have outpatient healthcare outside VA (49% vs. 41%). IPV use was not related to whether Veterans received care from VA or non-VA providers. CONCLUSIONS: Veterans' IPV use was related to greater utilization of services for mental health, chronic pain, and digestive issues. Future research should examine whether these are risk factors or consequences of IPV use.

Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.

Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.

A Roadmap to Artificial Intelligence (AI): Methods for Designing and Building AI ready Data for Women's Health Studies.

Objectives: Evaluating methods for building data frameworks for application of AI in large scale datasets for women's health studies. Methods: We created methods for transforming raw data to a data framework for applying machine learning (ML) and natural language processing (NLP) techniques for predicting falls and fractures. Results: Prediction of falls was higher in women compared to men. Information extracted from radiology reports was converted to a matrix for applying machine learning. For fractures, by applying specialized algorithms, we extracted snippets from dual x-ray absorptiometry (DXA) scans for meaningful terms usable for predicting fracture risk. Discussion: Life cycle of data from raw to analytic form includes data governance, cleaning, management, and analysis. For applying AI, data must be prepared optimally to reduce algorithmic bias. Conclusion: Algorithmic bias is harmful for research using AI methods. Building AI ready data frameworks that improve efficiency can be especially valuable for women's health.

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.

BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.

Patient Dietary Supplements Use: Do Results from Natural Language Processing of Clinical Notes Agree with Survey Data?

There is widespread use of dietary supplements, some prescribed but many taken without a physician's guidance. There are many potential interactions between supplements and both over-the-counter and prescription medications in ways that are unknown to patients. Structured medical records do not adequately document supplement use; however, unstructured clinical notes often contain extra information on supplements. We studied a group of 377 patients from three healthcare facilities and developed a natural language processing (NLP) tool to detect supplement use. Using surveys of these patients, we investigated the correlation between self-reported supplement use and NLP extractions from the clinical notes. Our model achieved an F1 score of 0.914 for detecting all supplements. Individual supplement detection had a variable correlation with survey responses, ranging from an F1 of 0.83 for calcium to an F1 of 0.39 for folic acid. Our study demonstrated good NLP performance while also finding that self-reported supplement use is not always consistent with the documented use in clinical records.