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.

Opioid prescription and risk of atrial fibrillation in younger veterans.

BACKGROUND: Opioids may play a part in the development of atrial fibrillation (AF). Understanding the relationship between opioid exposure and AF can help providers better assess the risk and benefits of prescribing opioids. OBJECTIVE: To assess the incidence of AF as a function of prescribed opioids and opioid type. DESIGN: We performed unadjusted and adjusted time-updated Cox regressions to assess the association between opioid exposure and incident AF. PARTICIPANTS: The national study sample was comprised of Veterans enrolled in the Veterans Health Administration (VHA) who served in support of post-9/11 operations. MAIN MEASURES: The main predictor of interest was prescription opioid exposure, which was treated as a time-dependent variable. The first was any opioid exposure (yes/no). Secondary was opioid type. The outcome, incident AF, was identified through ICD-9-CM diagnostic codes at any primary care visit after the baseline period. KEY RESULTS: A total of 609,763 veterans (mean age: 34 years and 13.24% female) were included in our study. Median follow-up time was 4.8 years. Within this cohort, 124,395 veterans (20.40%) were prescribed an opioid. A total of 1,455 Veterans (0.24%) were diagnosed with AF. In adjusted time-updated Cox regressions, the risk of incident AF was higher in the veterans prescribed opioids (hazard ratio [HR]: 1.47; 95% confidence interval [CI]: 1.38-1.57). In adjusted time-updated Cox regressions, both immunomodulating and nonimmunomodulating opioid type was associated with increased risk of incident AF (HR: 1.40; 95% CI: 1.25-1.57 and HR: 1.49; 95% CI: 1.39-1.60), compared to no opioid use, respectively. CONCLUSIONS: Our findings suggest opioid prescription may be a modifiable risk factor for the development of AF.

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.

Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration.

BACKGROUND: Survival in pancreatic ductal adenocarcinoma (PDAC) remains poor due to late diagnosis. Electronic Health Records (EHRs) can be used to study this rare disease, but validated algorithms to identify PDAC in the United States EHRs do not currently exist. AIMS: To develop and validate an algorithm using Veterans Health Administration (VHA) EHR data for the identification of patients with PDAC. METHODS: We developed two algorithms to identify patients with PDAC in the VHA from 2002 to 2023. The algorithms required diagnosis of exocrine pancreatic cancer in either ≥ 1 or ≥ 2 of the following domains: (i) the VA national cancer registry, (ii) an inpatient encounter, or (iii) an outpatient encounter in an oncology setting. Among individuals identified with ≥ 1 of the above criteria, a random sample of 100 were reviewed by three gastroenterologists to adjudicate PDAC status. We also adjudicated fifty patients not qualifying for either algorithm. These patients died as inpatients and had alkaline phosphatase values within the interquartile range of patients who met ≥ 2 of the above criteria for PDAC. These expert adjudications allowed us to calculate the positive and negative predictive value of the algorithms. RESULTS: Of 10.8 million individuals, 25,533 met ≥ 1 criteria (PPV 83.0%, kappa statistic 0.93) and 13,693 individuals met ≥ 2 criteria (PPV 95.2%, kappa statistic 1.00). The NPV for PDAC was 100%. CONCLUSIONS: An algorithm incorporating readily available EHR data elements to identify patients with PDAC achieved excellent PPV and NPV. This algorithm is likely to enable future epidemiologic studies of PDAC.

Temporal and Geographic Patterns of Documentation of Sexual Orientation and Gender Identity Keywords in Clinical Notes.

OBJECTIVE: Disclosure of sexual orientation and gender identity correlates with better outcomes, yet data may not be available in structured fields in electronic health record data. To gain greater insight into the care of sexual and gender-diverse patients in the Veterans Health Administration (VHA), we examined the documentation patterns of sexual orientation and gender identity through extraction and analyses of data contained in unstructured electronic health record clinical notes. METHODS: Salient terms were identified through authoritative vocabularies, the research team's expertise, and frequencies, and the use of consistency in VHA clinical notes. Term frequencies were extracted from VHA clinical notes recorded from 2000 to 2018. Temporal analyses assessed usage changes in normalized frequencies as compared with nonclinical use, relative growth rates, and geographic variations. RESULTS: Over time most terms increased in use, similar to Google ngram data, especially after the repeal of the "Don't Ask Don't Tell" military policy in 2010. For most terms, the usage adoption consistency also increased by the study's end. Aggregated use of all terms increased throughout the United States. CONCLUSION: Term usage trends may provide a view of evolving care in a temporal continuum of changing policy. These findings may be useful for policies and interventions geared toward sexual and gender-diverse individuals. Despite the lack of structured data, the documentation of sexual orientation and gender identity terms is increasing in clinical notes.

Characteristics Associated with Spirometry Guideline Adherence in VA Patients Hospitalized with Chronic Obstructive Pulmonary Disease.

BACKGROUND: The Global Initiative for Chronic Obstructive Lung Disease (GOLD) recommends at least annual spirometry for patients with chronic obstructive pulmonary disease (COPD). Since spirometry acquisition is variable in clinical practice, identifying characteristics associated with annual spirometry may inform strategies to improve care for patients with COPD. METHODS: We included veterans hospitalized for COPD at Veterans Health Administration (VHA) facilities from 10/2012 to 09/2015. Our primary outcome was spirometry within 1 year of COPD hospitalization. Patient demographics, health factors, and comorbidities as well as practice and geographic variables were identified using Corporate Data Warehouse; provider characteristics were obtained from the Survey of Healthcare Experiences of Patients. We used logistic regression with a random intercept to account for potential clustering within facilities. RESULTS: Spirometry was completed 1 year before or after hospitalization for 20,683/38,148 (54.2%) veterans across 114 facilities. Patients with spirometry were younger, (mean=67.2 years (standard deviation (SD)=9.3) vs. 69.4 (10.3)), more likely non-white (21.3% vs. 19.7%), and more likely to have comorbidities (p<0.0001 for asthma, depression, and post-traumatic stress disorder). Pulmonary clinic visit was most strongly associated with spirometry (odds ratio (OR)=3.14 [95% confidence interval 2.99-3.30]). There was no association for facility complexity. In a secondary analysis including provider-level data (3862 patients), results were largely unchanged. There was no association between primary care provider age, gender, or type (physician vs. advanced practice registered nurse vs. physician assistant) and spirometry. CONCLUSION: In a cohort of high-risk COPD patients, just over half completed spirometry within 1 year of hospitalization. Pulmonary clinic visit was most strongly associated with 1-year spirometry, though provider variables were not. Spirometry completion for high-risk COPD patients remains suboptimal and strategies to improve post-hospitalization care for patients not seen in pulmonary clinic should be developed to ensure guideline concordant care.

Personalized Digital Health Information to Substantiate Human-Delivered Exercise Support for Adults With Type 1 Diabetes.

OBJECTIVE: Pilot-test personalized digital health information to substantiate human-delivered exercise support for adults with type 1 diabetes (T1D). DESIGN: Single-group, 2-week baseline observation, then 10-week intervention with follow-up observation. SETTING: Community-based sample participating remotely with physician oversight. PARTICIPANTS: Volunteers aged 18 to 65 years with T1D screened for medical readiness for exercise intervention offerings. N = 20 enrolled, and N = 17 completed all outcomes with 88% to 91% biosensor adherence. INTERVENTION: Feedback on personalized data from continuous glucose monitoring (CGM), its intersection with other ecological data sets (exercise, mood, and sleep), and other informational and motivational elements (exercise videos, text-based exercise coach, and self-monitoring diary). MAIN OUTCOME MEASURES: Feasibility (use metrics and assessment completion), safety (mild and severe hypoglycemia, and diabetic ketoacidosis), acceptability (system usability scale, single items, and interview themes), and standard clinical and psychosocial assessments. RESULTS: Participants increased exercise from a median of 0 (Interquartile range, 0-21) to 64 (20-129) minutes per week ( P = 0.001, d = 0.71) with no severe hypoglycemia or ketoacidosis. Body mass index increased (29.5 ± 5.1 to 29.8 ± 5.4 kg/m 2 , P = 0.02, d = 0.57). Highest satisfaction ratings were for CGM use (89%) and data on exercise and its intersection with CGM and sleep (94%). Satisfaction was primarily because of improved exercise management behavioral skills, although derived motivation was transient. CONCLUSIONS: The intervention was feasible, safe, and acceptable. However, there is a need for more intensive, sustained support. Future interventions should perform analytics upon the digital health information and molecular biomarkers (eg, genomics) to make exercise support tools that are more personalized, automated, and intensive than our present offerings.

Gender Differences in Guideline-Directed Medical Therapy for Cardiovascular Disease Among Young Veterans.

BACKGROUND: There is an increasing burden of cardiovascular disease, including coronary artery disease (CAD) and heart failure (HF), among women Veterans. Clinical practice guidelines recommend multiple pharmacotherapies that can reduce risk of mortality and adverse cardiovascular outcomes. OBJECTIVE: To determine if there are disparities in the use of guideline-directed medical therapy by gender among Veterans with incident CAD and HF. DESIGN: Retrospective. PARTICIPANTS: Veterans (934,504; 87.8% men and 129,469; 12.2% women) returning from Operations Enduring Freedom, Iraqi Freedom, and New Dawn. MAIN MEASURES: Differences by gender in the prescription of Class 1, Level of Evidence A guideline-directed medical therapy among patients who developed incident CAD and HF at 30 days, 90 days, and 12 months after diagnosis. For CAD, medications included statins and antiplatelet therapy. For HF, medications included beta-blockers and renin-angiotensin-aldosterone system inhibitors. KEY RESULTS: Overall, women developed CAD and HF at a younger average age than men (mean 45.8 vs. 47.7 years, p<0.001; and 43.7 vs. 45.4 years, p<0.02, respectively). In the 12 months following a diagnosis of incident CAD, the odds of a woman receiving a prescription for at least one CAD drug was 0.85 (95% confidence interval [CI], 0.68-1.08) compared to men. In the 12 months following a diagnosis of incident HF, the odds of a woman receiving at least one HF medication was 0.54 (95% CI, 0.37-0.79) compared to men. CONCLUSIONS: Despite guideline recommendations, young women Veterans have approximately half the odds of being prescribed guideline-directed medical therapy within 1-year after a diagnosis of HF. These results highlight the need to develop targeted strategies to minimize gender disparities in CVD care to prevent adverse outcomes in this young and growing population.

Joint longitudinal trajectories of pain intensity and opioid prescription in veterans with back pain.

PURPOSE: We describe pain intensity and opioid prescription jointly over time in Veterans with back pain to better understand their relationship. METHODS: We performed a retrospective cohort study on electronic health record data from 117 126 Veterans (mean age 49.2 years) diagnosed with back pain in 2015. We used latent class growth analysis to jointly model pain intensity (0-10 scores) and opioid prescriptions over 2 years to identify classes of individuals similar in their trajectory of pain and opioid over time. Multivariable multinomial logit models assessed sociodemographic and clinical predictors of class membership. RESULTS: We identified six trajectory classes: a "no pain/no opioid" class (22.2%), a "mild pain/no opioid" class (45.0%), a "moderate pain/no opioid" class (24.6%), a "moderate, decreasing pain/decreasing opioid" class (3.3%), a "moderate pain/high opioid" class (2.6%), and a "moderate, increasing pain/increasing opioid" class (2.3%). Among those in moderate pain classes, being white (vs. non-white) and older were associated with higher odds of being prescribed opioids. Veterans with mental health diagnoses had increased odds of being in the painful classes versus "no pain/no opioid" class. CONCLUSION: We found distinct patterns in the long-term joint course of pain and opioid prescription in Veterans with back pain. Understanding these patterns and associated predictors may help with development of targeted interventions for patients with back pain.

Risk Factors Associated with Healthcare Utilization for Spine Pain.

OBJECTIVE: This study examined potential risk factors associated with healthcare utilization among patients with spine (i.e., neck and back) pain. METHODS: A two-stage sampling approach examined spine pain episodes of care among veterans with a yearly outpatient visit for six consecutive years. Descriptive and bivariate statistics, followed by logistic regression analyses, examined baseline characteristics of veterans with new episodes of care who either continued or discontinued spine pain care. A multivariable logistic regression model examined correlates associated with seeking continued spine pain care. RESULTS: Among 331,908 veterans without spine pain episodes of care during the 2-year baseline observation period, 16.5% (n = 54,852) had a new episode of care during the following 2-year observation period. Of those 54,852 veterans, 37,025 had an outpatient visit data during the final 2-year follow-up period, with 53.7% (n = 19,865) evidencing continued spine pain care. Those with continued care were more likely to be overweight or obese, non-smokers, Army veterans, have higher education, and had higher rates of diagnoses of all medical and mental health conditions examined at baseline. Among several important findings, women had 13% lower odds of continued care during the final 2-year observation period, OR 0.87 (0.81, 0.95). CONCLUSIONS: A number of important demographics and clinical correlates were associated with increased likelihood of seeking new and continued episodes of care for spine pain; however, further examination of risk factors associated with healthcare utilization for spine pain is indicated.

Longitudinal Analysis of Persistent Postconcussion Symptoms, Probable TBI, and Intimate Partner Violence Perpetration Among Veterans.

OBJECTIVE: To determine veterans' intimate partner violence (IPV) perpetration following report of traumatic brain injury (TBI) and persistent postconcussion symptoms (PPCSs). SETTING: Five Department of Veterans Affairs (VA) medical centers. PARTICIPANTS: Veterans with nonmissing data on main measures, resulting in N = 1150 at baseline and N = 827 at follow-up. DESIGN: Prospective cohort study with secondary data analysis of self-reported TBI, PPCSs, and IPV perpetration, controlling for common predictors of IPV, including binge drinking, marijuana use, pain intensity, and probable posttraumatic stress disorder. MAIN MEASURES: VA TBI Screening Tool to assess for probable TBI and PPCSs; Conflict Tactics Scale-Revised (CTS-2S) to assess for IPV perpetration. RESULTS: Almost half (48%) of participants reported IPV perpetration at follow-up. Both probable TBI and higher PPCSs at baseline were associated with overall IPV perpetration and more frequent IPV perpetration at follow-up. Only PPCSs significantly predicted IPV perpetration after controlling for common predictors of IPV perpetration. Neither probably TBI nor PPCSs predicted frequency of IPV perpetration. CONCLUSION: When considered alongside common risk factors for IPV perpetration, PPCS was uniquely associated with the likelihood of IPV perpetration in this veteran sample. Given post-9/11 veterans' elevated risk for head injury, findings emphasize the distinctive value of PPCSs in understanding risk for IPV perpetration. We recommend increased assessment for PPCSs in clinical practice among veterans enrolled in VA care and highlight several important areas for future research and intervention development.

Posttraumatic Stress Disorder, Antidepressant Use, and Hemorrhagic Stroke in Young Men and Women: A 13-Year Cohort Study.

BACKGROUND AND PURPOSE: Antidepressants are commonly prescribed for posttraumatic stress disorder (PTSD) and may increase the risk of bleeding, including hemorrhagic stroke. METHODS: We prospectively examined independent effects of PTSD, selective serotonin and norepinephrine reuptake inhibitors (SSRI and SNRI) on the risk of incident hemorrhagic stroke in a nationwide sample of 1.1 million young and middle-aged veterans. Time-varying multivariate Cox models were used to examine hemorrhagic stroke risk by PTSD status and use of SSRI or SNRI while adjusting for demographics, lifestyle factors, stroke, and psychiatric comorbidities. Sensitivity analyses controlled for health care utilization. RESULTS: During 13 years of follow-up (2.14 years on average), 507 patients (12% women) suffered a hemorrhagic stroke. The overall incidence rate was 1.70 events per 10 000-person years. In unadjusted models, PTSD was associated with an 82% greater risk of new-onset hemorrhagic stroke (hazard ratio [HR], 1.82 [95% CI, 1.48-2.24]), SSRI use was associated with a >2-fold risk (HR, 2.02 [95% CI, 1.66-2.57]), and SNRI use was associated with a 52% greater risk (HR, 1.52 [95% CI, 1.08-2.16]). In fully adjusted models, effects of PTSD and SNRI were attenuated (adjusted HR, 1.03 [95% CI, 0.81-1.34]; adjusted HR, 1.19 [95% CI, 0.83-1.71]), but SSRI use remained associated with a 45% greater risk of hemorrhagic stroke (adjusted HR, 1.45 [95% CI, 1.13-1.85]). Hypertension, drug abuse, and alcohol abuse were also associated with increased stroke risk. Nonobesity and being non-Hispanic were protective factors. In sensitivity analyses, health care utilization was a small but significant predictor of stroke. CONCLUSIONS: In the largest known investigation of PTSD and antidepressant-associated risk for hemorrhagic stroke in young adults, use of SSRIs, but neither PTSD nor SNRIs were independently associated with incident stroke. SNRIs may be preferable for treating PTSD and comorbid conditions, although pursuing other modifiable risk factors and non-pharmacological treatments for PTSD also remains essential.

Use of Proton Pump Inhibitors Increases Risk of Incident Kidney Stones.

BACKGROUND AND AIMS: Proton pump inhibitors (PPIs) are widely prescribed and have effects on gut ion absorption and urinary ion concentrations. PPIs might therefore protect against or contribute to development of kidney stones. We investigated the association between PPI use and kidney stones. METHODS: We performed a retrospective study using data from the Women's Veteran's Cohort Study, which comprised men and women, from October 1, 1999 through September 30, 2017. We collected data from 465,891 patients on PPI usage over time, demographics, laboratory results, comorbidities, and medication usage. Time-varying Cox proportional hazards and propensity matching analyses determined risk of PPI use and incident development of kidney stones. Use of histamine-2 receptor antagonists (H2RAs) was measured and levothyroxine use was a negative control exposure. RESULTS: PPI use was associated with kidney stones in the unadjusted analysis, with PPI use as a time-varying variable (hazard ratio [HR], 1.74; 95% CI, 1.67-1.82), and persisted in the adjusted analysis (HR, 1.46; CI, 1.38-1.55). The association was maintained in a propensity score-matched subset of PPI users and nonusers (adjusted HR, 1.25; CI 1.19-1.33). Increased dosage of PPI was associated with increased risk of kidney stones (HR, 1.11; CI, 1.09-1.14 for each increase in 30 defined daily doses over a 3-month period). H2RAs were also associated with increased risk (adjusted HR, 1.47; CI 1.31-1.64). We found no association, in adjusted analysis, of levothyroxine use with kidney stones (adjusted HR, 1.06; CI 0.94-1.21). CONCLUSIONS: In a large cohort study of veterans, we found PPI use to be associated with a dose-dependent increase in risk of kidney stones. H2RA use also has an association with risk of kidney stones, so acid suppression might be an involved mechanism. The effect is small and should not change prescribing for most patients.

Does pulmonary subspecialty referral from primary care affect the adherence to vaccination recommendations in COPD patients?

The importance of vaccinations for COPD patients has been previously described. However, there is still a gap between guideline recommendations and the implementation of preventive care delivery for these patients. Specially, the rise of SARS-CoV-2 pandemic has made the significance of vaccination adherence more critical to address. Our study showed that referral to pulmonary clinic is associated with increased odds of receiving influenza (OR = 1.97, [95% CI 1.07, 3.65]) and pneumococcal vaccinations (PCV13 OR = 3.55, [1.47, 8.54]; PPSV23 OR = 4.92, [1.51, 16.02]). These data suggest that partnerships between primary care physicians and pulmonologists can potentially improve the vaccination rates for patients with COPD.

Documentation of Shared Decisionmaking in the Emergency Department.

STUDY OBJECTIVE: While patient-centered communication and shared decisionmaking are increasingly recognized as vital aspects of clinical practice, little is known about their characteristics in real-world emergency department (ED) settings. We constructed a natural language processing tool to identify patient-centered communication as documented in ED notes and to describe visit-level, site-level, and temporal patterns within a large health system. METHODS: This was a 2-part study involving (1) the development and validation of an natural language processing tool using regular expressions to identify shared decisionmaking and (2) a retrospective analysis using mixed effects logistic regression and trend analysis of shared decisionmaking and general patient discussion using the natural language processing tool to assess ED physician and advanced practice provider notes from 2013 to 2020. RESULTS: Compared to chart review of 600 ED notes, the accuracy rates of the natural language processing tool for identification of shared decisionmaking and general patient discussion were 96.7% (95% CI 94.9% to 97.9%) and 88.9% (95% confidence interval [CI] 86.1% to 91.3%), respectively. The natural language processing tool identified shared decisionmaking in 58,246 (2.2%) and general patient discussion in 590,933 (22%) notes. From 2013 to 2020, natural language processing-detected shared decisionmaking increased 300% and general patient discussion increased 50%. We observed higher odds of shared decisionmaking documentation among physicians versus advanced practice providers (odds ratio [OR] 1.14, 95% CI 1.07 to 1.23) and among female versus male patients (OR 1.13, 95% CI 1.11 to 1.15). Black patients had lower odds of shared decisionmaking (OR 0.8, 95% CI 0.84 to 0.88) compared with White patients. Shared decisionmaking and general patient discussion were also associated with higher levels of triage and commercial insurance status. CONCLUSION: In this study, we developed and validated an natural language processing tool using regular expressions to extract shared decisionmaking from ED notes and found multiple potential factors contributing to variation, including social, demographic, temporal, and presentation characteristics.

DSM-5 eating disorder prevalence, gender differences, and mental health associations in United States military veterans.

OBJECTIVE: Little is known about prevalence estimates of new and revised DSM-5 eating disorders diagnoses in general, and especially among high-risk, underserved and diverse eating disorder populations. The aim of the current study was to determine prevalence, gender differences and correlates of DSM-5 eating disorders in veterans. METHOD: Iraq and Afghanistan war era veterans (N = 1,121, 51.2% women) completed the Eating Disorder Diagnostic Scale-5 and validated measures of eating pathology and mental health between July 2014 and September 2019. RESULTS: Overall more women than men (32.8% vs. 18.8%, p < .001) reported symptoms consistent with a DSM-5 eating disorder. Prevalence estimates (women vs. men) for the specific diagnoses were: Anorexia Nervosa (AN; 0.0% vs. 0.0%), Bulimia Nervosa (BN; 6.1% vs. 3.5%), Binge-Eating Disorder (BED; 4.4% vs. 2.9%), Atypical AN (AAN; 13.6% vs. 4.9%), Subclinical BN (0.0% vs. 0.2%), Subclinical BED (1.4% vs. 0.6%), Purging Disorder (2.1% vs. 0.7%), and Night Eating Syndrome (NES; 5.2% vs. 6.0%). Women were more likely to have BN or AAN, and there was no difference for BED or NES among genders. The eating disorder group had a higher mean BMI, and significantly greater eating pathology and mental health symptoms than the non-eating disorder group. DISCUSSION: Approximately one-third of women, and one-fifth of men, reported symptoms consistent with a DSM-5 eating disorder diagnosis. These high prevalence estimates across genders, and associated mental health concerns, suggest an urgent need to better understand and address eating disorders in military and veteran populations.

Correlates of Manual Therapy and Acupuncture Use Among Rural Patients Seeking Conventional Pain Management: A Cross-sectional Study.

OBJECTIVE: In this cross-sectional study, we examined correlates of manual therapy (spinal manipulation, massage therapy) and/or acupuncture use in a population engaging in conventional pain care in West Virginia. METHODS: Participants were patients (aged 18+ years) from 4 Appalachian pain and rheumatology clinics. Of those eligible (N = 343), 88% completed an anonymous survey including questions regarding health history, pain distress (Short Form Global Pain Scale), prescription medications, and current use of complementary health approaches for pain management. We used age-adjusted logistic regression to assess the relation of sociodemographic, lifestyle, and health-related factors to use of manual therapies and/or acupuncture for pain (complete-case N = 253). RESULTS: The majority of participants were white (92%), female (56%), and middle aged (mean age, 54.8 ± 13.4 years). Nearly all reported current chronic pain (94%), and 56% reported ≥5 comorbidities (mean, 5.6 ± 3.1). Manual therapy and/or acupuncture was used by 26% of participants for pain management (n = 66). Current or prior opioid use was reported by 37% of those using manual therapies. Manual therapy and/or acupuncture use was significantly elevated in those using other complementary health approaches (adjusted odds ratio, 3.0; 95% confidence interval, 1.5-5.8). Overall Short Form Global Pain Scale scores were not significantly associated with use of manual therapies and/or acupuncture after adjustment (adjusted odds ratio per 1-point increase, 1.01; 95% confidence interval, 1.00-1.03). CONCLUSION: We found no evidence for an association of pain-related distress and use of manual therapies and/or acupuncture, but identified a strong association with use of dietary supplements and mind-body therapies. Larger studies are needed to further examine these connections in the context of clinical outcomes and cost-effectiveness in rural adults given their high pain burden and unique challenges in access to care.

Incident Musculoskeletal Conditions Among Men and Women Veterans Returning From Deployment.

BACKGROUND: Military service confers an increased risk for musculoskeletal (MSK) injury among women and men Veterans. OBJECTIVE: The objective of this study was to determine the prevalence of MSK conditions at first visit to Veterans Affairs (VA), and the incidence rates of new MSK conditions in women and men Veterans with and without a baseline MSK condition. DESIGN: A cohort study including Veterans whose end of last deployment was between October 1, 2001 and October 1, 2015. SUBJECTS: A total of 765,465 Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn Veterans. MAIN OUTCOME MEASURES: Prevalent and incident MSK conditions identified through the International Classification of Diseases, ninth Revision, Clinical Modification diagnostic codes. RESULTS: Twenty-six percent of women and 29% of men present to the VA with a MSK condition. In those without an MSK diagnosis at baseline, the unadjusted rate of developing at least 1 MSK condition was 168 and 180 per 1000 person-year [hazard ratio (HR)=0.94; 95% confidence interval (CI)=0.92-0.95] in women and men. Women were more likely to develop newly diagnosed MSK conditions of the hip (HR=1.9; 95% CI=1.83-1.98) or the ankle/foot (HR=1.17; 95% CI=1.15-1.20) and less likely to develop MSK conditions of the upper extremity (HR=0.75; 95% CI=0.73-0.78), knee (HR=0.87; 95% CI=0.86-0.89), and spine (HR=0.94; 95% CI=0.93-0.96). In those with prevalent MSK conditions at baseline, the rate of developing a second MSK condition was higher in women than men (151 and 133/1000 person-year; HR=1.13; 95% CI=1.11-1.15). CONCLUSIONS: A high proportion of Veterans present to the VA with MSK conditions. Women are less likely to develop conditions related to the upper extremities, spine or knee, and more likely to have conditions of the hip or ankle/foot.

Association of prescription opioids and incident cardiovascular risk factors among post-9/11 Veterans.

Reports indicate that long-term opioid therapy is associated with cardiovascular disease (CVD). Using VA electronic health record data, we measured the impact of opioid use on the incidence of modifiable CVD risk factors. We included Veterans whose encounter was between October 2001 to November 2014. We identified Veterans without CVD risk factors during our baseline period, defined as the date of first primary care visit plus 365 days. The main exposure was opioid prescriptions (yes/no, long-term (i.e. ≥90 days) vs no opioid, and long-term vs short-term (i.e. <90 days)), which was time-updated yearly from the end of the baseline period to February 2015. The main outcome measures were incident CVD risk factors (hypertension, dyslipidemia, diabetes, obesity, and current smoking). After excluding prevalent CVD risk factors, we identified 308,015 Veterans. During the first year of observation, 12,725 (4.1%) Veterans were prescribed opioids, including 2028 (0.6%) with long-term exposure. Compared to patients without opioid use, Veterans with opioid use were more likely to have CVD risk factors. Those with long-term exposure were at higher risk of having hypertension (adjusted average hazards ratio [HR] 1.45, 99% confidence interval [CI] 1.33-1.59), dyslipidemia (HR 1.45, 99% CI 1.35-156), diabetes (HR 1.30, 99% CI 1.07-1.57), current smoking status (HR 1.34, 99% CI 1.24-1.46), and obesity (HR 1.22, 99% CI 1.12-1.32). Compared to short-term exposure, long-term had higher risk of current smoking status (HR 1.12, 99% CI 1.01-1.24). These findings suggest potential benefit to screening and surveillance of CVD risk factors for patients prescribed opioids, especially long-term opioid therapy.