Contribution of Potentially Inappropriate Medications to Polypharmacy-Associated Risk of Mortality in Middle-Aged Patients: A National Cohort Study.

BACKGROUND: The role of potentially inappropriate medications (PIMs) in mortality has been studied among those 65 years or older. While middle-aged individuals are believed to be less susceptible to the harms of polypharmacy, PIMs have not been as carefully studied in this group. OBJECTIVE: To estimate PIM-associated risk of mortality and evaluate the extent PIMs explain associations between polypharmacy and mortality in middle-aged patients, overall and by sex and race/ethnicity. DESIGN: Observational cohort study. SETTING: Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. PARTICIPANTS: Patients aged 41 to 64 who received a chronic medication (continuous use of ≥ 90 days) between October 1, 2008, and September 30, 2017. MEASUREMENT: Patients were followed for 5 years until death or end of study period (September 30, 2019). Time-updated polypharmacy and hyperpolypharmacy were defined as 5-9 and ≥ 10 chronic medications, respectively. PIMs were identified using the Beers criteria (2015) and were time-updated. Cox models were adjusted for demographic, behavioral, and clinical characteristics. RESULTS: Of 733,728 patients, 676,935 (92.3%) were men, 479,377 (65.3%) were White, and 156,092 (21.3%) were Black. By the end of follow-up, 104,361 (14.2%) patients had polypharmacy, 15,485 (2.1%) had hyperpolypharmacy, and 129,992 (17.7%) were dispensed ≥ 1 PIM. PIMs were independently associated with mortality (HR 1.11, 95% CI 1.04-1.18). PIMs also modestly attenuated risk of mortality associated with polypharmacy (HR 1.07, 95% CI 1.03-1.11 before versus HR 1.05, 95% CI 1.01-1.09 after) and hyperpolypharmacy (HR 1.18, 95% CI 1.09-1.28 before versus HR 1.12, 95% CI 1.03-1.22 after). Patterns varied when stratified by sex and race/ethnicity. LIMITATIONS: The predominantly male VA patient population may not represent the general population. CONCLUSION: PIMs were independently associated with increased mortality, and partially explained polypharmacy-associated mortality in middle-aged people. Other mechanisms of injury from polypharmacy should also be studied.

Identification of hepatic steatosis among persons with and without HIV using natural language processing.

BACKGROUND: Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potentially identify hepatic steatosis systematically within large clinical repositories of imaging reports. We validated the performance of an NLP algorithm for the identification of SLD in clinical imaging reports and applied this tool to a large population of people with and without HIV. METHODS: Patients were included in the analysis if they enrolled in the Veterans Aging Cohort Study between 2001 and 2017, had an imaging report inclusive of the liver, and had ≥2 years of observation before the imaging study. SLD was considered present when reports contained the terms "fatty," "steatosis," "steatotic," or "steatohepatitis." The performance of the SLD NLP algorithm was compared to a clinical review of 800 reports. We then applied the NLP algorithm to the first eligible imaging study and compared patient characteristics by SLD and HIV status. RESULTS: NLP achieved 100% sensitivity and 88.5% positive predictive value for the identification of SLD. When applied to 26,706 eligible Veterans Aging Cohort Study patient imaging reports, SLD was identified in 72.2% and did not significantly differ by HIV status. SLD was associated with a higher prevalence of metabolic comorbidities, alcohol use disorder, and hepatitis B and C, but not HIV infection. CONCLUSIONS: While limited to those undergoing radiologic study, the NLP algorithm accurately identified SLD in people with and without HIV and offers a valuable tool to evaluate the determinants and consequences of hepatic steatosis.

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.

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.

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.

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.

Contemporary Management and Outcomes of Veterans Hospitalized With Alcohol Withdrawal: A Multicenter Retrospective Cohort Study.

OBJECTIVES: Few studies describe contemporary alcohol withdrawal management in hospitalized settings or review current practices considering the guidelines by the American Society of Addiction Medicine (ASAM). METHODS: We conducted a retrospective cohort study of patients hospitalized with alcohol withdrawal on medical or surgical wards in 19 Veteran Health Administration (VHA) hospitals between October 1, 2018, and September 30, 2019. Demographic and comorbidity data were obtained from the Veteran Health Administration Corporate Data Warehouse. Inpatient management and hospital outcomes were obtained by chart review. Factors associated with treatment duration and complicated withdrawal were examined. RESULTS: Of the 594 patients included in this study, 51% were managed with symptom-triggered therapy alone, 26% with fixed dose plus symptom-triggered therapy, 10% with front loading regimens plus symptom-triggered therapy, and 3% with fixed dose alone. The most common medication given was lorazepam (87%) followed by chlordiazepoxide (33%), diazepam (14%), and phenobarbital (6%). Symptom-triggered therapy alone (relative risk [RR], 0.68; 95% confidence interval [CI], 0.57-0.80) and front loading with symptom-triggered therapy (RR, 0.75; 95% CI, 0.62-0.92) were associated with reduced treatment duration. Lorazepam (RR, 1.20; 95% CI, 1.02-1.41) and phenobarbital (RR, 1.28; 95% CI, 1.06-1.54) were associated with increased treatment duration. Lorazepam (adjusted odds ratio, 4.30; 95% CI, 1.05-17.63) and phenobarbital (adjusted odds ratio, 6.51; 95% CI, 2.08-20.40) were also associated with complicated withdrawal. CONCLUSIONS: Overall, our results support guidelines by the ASAM to manage patients with long-acting benzodiazepines using symptom-triggered therapy. Health care systems that are using shorter acting benzodiazepines and fixed-dose regimens should consider updating alcohol withdrawal management pathways to follow ASAM recommendations.

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.

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.

Changes in opioid prescribing in veterans with headache during the COVID-19 pandemic: A regression discontinuity in time analysis.

OBJECTIVE: To determine changes in opioid prescribing among veterans with headaches during the coronavirus disease of 2019 (COVID-19) pandemic by comparing the stay-at-home phase (March 15 to May 30, 2020) and the reopening phase (May 31 to December 31, 2020). BACKGROUND: Opioid prescribing for chronic pain has declined substantially since 2016; however, changes in opioid prescribing during the COVID-19 pandemic among veterans with headaches remain unknown. METHODS: This retrospective cohort study utilized regression discontinuity in time and difference-in-differences design to analyze veterans aged ≥18 years with a previous diagnosis of headache disorders and an outpatient visit to the Veterans Health Administration (VHA) during the study period. We measured the weekly number of opioid prescriptions, the number of days supplied, the daily dose in morphine milligram equivalents (MMEs), and the number of prescriptions with ≥50 morphine equivalent daily doses (MEDD). RESULTS: A total of 81,376 veterans were analyzed with 589,950 opioid prescriptions. The mean (SD) age was 51.6 (13.5) years, 57,242 (70.3%) were male, and 53,464 (65.7%) were White. During the pre-pandemic period, 323.6 opioid prescriptions (interquartile range 292.1-325.8) were dispensed weekly, with an median (IQR) of 24.1 (24.0-24.4) days supplied and 31.8 (31.2-32.5) MMEs. Transition to stay-at-home was associated with a 7.7% decrease in the number of prescriptions (incidence rate ratio [IRR] 1.077, 95% confidence interval [CI] 0.866-0.984) and a 9.8% increase in days supplied (IRR 1.098, 95% CI 1.078-1.119). Similar trends were observed during the reopening period. Subgroup analysis among veterans on long-term opioid therapy also revealed 1.7% and 1.4% increases in days supplied during the stay-at-home (IRR 1.017, 95% CI 1.009-1.025) and reopening phase (IRR 1.014, 95% CI 1.007-1.021); however, changes in the total number of prescriptions, MME/day, or the number of prescriptions >50 MEDD were insignificant. CONCLUSION: Prescription opioid access was maintained for veterans within VHA during the pandemic. The de-escalation of opioid prescribing observed prior to the pandemic was not seen in our study.

Trajectory of blood pressure after initiating anti-calcitonin gene-related peptide treatment of migraine: a target trial emulation from the veterans health administration.

BACKGROUND: Calcitonin gene-related peptide (CGRP) is involved in migraine pathophysiology and blood pressure regulation. Although clinical trials have established the cardio-cerebrovascular safety profile of anti-CGRP treatment, limited high-quality real-world evidence exists on its long-term effects on blood pressure (BP). To address this gap, we examined the safety of anti-CGRP treatment on BP in patients with migraine headache in the Veterans Health Administration (VHA). METHODS: We emulated a target trial of patients who initiated anti-CGRP treatment or topiramate for migraine prevention between May 17th, 2018 and February 28th, 2023. We calculated stabilized inverse probability weights to balance between groups and then used weighted linear mixed-effect models to estimate the systolic and diastolic BP changes over the study period. For patients without hypertension at baseline, we estimated the cumulative incidence of hypertension using Kaplan-Meier curve. We also used weight mixed-effect Poisson model to estimate the number of antihypertension medications for patients with hypertension at baseline. RESULTS: This analysis included 69,589 patients and 554,437 blood pressure readings. of these, 18,880 patients received anti-CGRP treatment, and they were more likely to be women, have a chronic migraine diagnosis and higher healthcare utilization than those received topiramate. Among patients without hypertension at baseline, we found no significant differences in systolic BP changes over the four-year follow-up between anti-CGRP (slope [standard error, SE] = 0.48[0.06]) and topiramate treated patients (slope[SE] = 0.39[0.04]). The incidence of hypertension was similar for anti-CGRP and topiramate group (4.4 vs 4.3 per 100 person-years). Among patients with hypertension at baseline who initiated anti-CGRP treatment, we found a small but persistent effect on exacerbating hypertension during the first four years of treatment, as evidenced by a significant annual 3.7% increase in the number of antihypertensive medications prescribed (RR = 1.037, 95%CI 1.025-1.048). CONCLUSIONS: Our findings suggest that anti-CGRP treatment is safe regarding blood pressure in patients without hypertension. However, for those with baseline hypertension, anti-CGRP treatment resulted in a small but persistent increase in the number of antihypertensives, indicating an exacerbation of hypertension. Future studies are needed to evaluate the cardio-cerebrovascular safety of anti-CGRP treatment beyond the first four years.

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.

Calcitonin Gene-Related Peptide Monoclonal Antibodies and Risk of SARS-CoV-2 Infection and Severe COVID-19 Outcomes Among Veterans With Migraine Disorder.

Importance: Calcitonin gene-related peptide (CGRP), a neuropeptide involved in migraine pathophysiology, is also a key neuroimmune modulator. CGRP antagonists may help mitigate the hyperinflammatory response observed in patients with COVID-19; however, findings from the literature are contradictory, and to date, no study has investigated the safety and effectiveness of CGRP antagonists against COVID-19. Objective: To evaluate the association between CGRP monoclonal antibody (mAb) treatment and risk of SARS-CoV-2 infection and sequela hospitalization, requiring supplemental oxygen, use of mechanical ventilation, or death. Design, Setting, and Participants: This retrospective cohort study analyzed the electronic health records of US veterans aged 18 to 65 years who were diagnosed with migraine disorder and were at risk of COVID-19 between January 20, 2020, and May 19, 2022. Exposure: Initiation of CGRP mAbs. Main Outcomes and Measures: The main outcome was cumulative incidence of SARS-CoV-2 infection. Odds of 30-day hospitalization, requiring supplemental oxygen, use of mechanical ventilation, or death were secondary outcomes. Results: Among 8 178 652 eligible person-trials (354 294 veterans), 9992 (mean [SD] age, 46.0 [9.5] years; 53.9% male) initiated CGRP mAbs and 8 168 660 (mean [SD] age, 46.6 [10.2] years; 65.7% male) did not initiate CGRP mAbs. Over a 28-month follow-up period, 1247 initiators (12.5%) and 780 575 noninitiators (9.6%) tested positive for SARS-CoV-2. After censoring persons who deviated from treatment, the incidence was 7.4 cases per 1000 person-months among initiators and 6.9 per 1000 person-months among noninitiators. The inverse probability-weighted observational analogs of intention-to-treat and per-protocol hazard ratios were 0.95 (95% CI, 0.89-1.01) and 0.93 (95% CI, 0.86-1.02), respectively. No significant differences in the likelihood of hospitalization (odds ratio [OR], 0.93; 95% CI, 0.62-1.41), requiring supplemental oxygen (OR, 0.77; 95% CI, 0.45-1.30), use of mechanical ventilation (OR, 0.85; 95% CI, 0.26-2.84), or death (OR, 0.67; 95% CI, 0.09-5.23) were observed between CGRP mAb initiators and noninitiators who tested positive for SARS-CoV-2. Conclusions and Relevance: In this cohort study, CGRP mAb treatment was not associated with positive SARS-CoV-2 test results or risk of severe COVID-19 outcomes, suggesting that CGRP mAbs may be used for migraine prevention during the COVID-19 pandemic. Given the few events of requiring supplemental oxygen, use of mechanical ventilation, and death, replication analysis in a larger sample of patients later in the course of disease is warranted.

Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.

PURPOSE: Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region-of-interest-based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status. METHODS: We performed a cross-sectional study to evaluate the performance of ALARM within noncontrast abdominal CT images from a sample of patients with and without HIV in the US Veterans Health Administration. We evaluated the ability of ALARM to identify moderate-to-severe hepatic steatosis, defined by mean absolute liver attenuation <40 Hounsfield units (HU), compared to manual radiologist assessment. RESULTS: Among 120 patients (51 PWH) who underwent noncontrast abdominal CT, moderate-to-severe hepatic steatosis was identified in 15 (12.5%) persons via ALARM and 12 (10%) by radiologist assessment. Percent agreement between ALARM and radiologist assessment of absolute liver attenuation <40 HU was 95.8%. Sensitivity, specificity, positive predictive value, and negative predictive value of ALARM were 91.7% (95%CI, 51.5%-99.8%), 96.3% (95%CI, 90.8%-99.0%), 73.3% (95%CI, 44.9%-92.2%), and 99.0% (95%CI, 94.8%-100%), respectively. No differences in performance were observed by HIV status. CONCLUSIONS: ALARM demonstrated excellent accuracy for moderate-to-severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real-world studies to evaluate medications associated with steatosis and assess differences by HIV status.

Racial, Ethnic, and Rural Disparities in US Veteran COVID-19 Vaccine Rates.

Background: Race, ethnicity, and rurality-related disparities in coronavirus disease 2019 (COVID-19) vaccine uptake have been documented in the United States (US). Objective: We determined whether these disparities existed among patients at the Department of Veterans Affairs (VA), the largest healthcare system in the US. Design Settings Participants Measurements: Using VA Corporate Data Warehouse data, we included 5,871,438 patients (9.4% women) with at least one primary care visit in 2019 in a retrospective cohort study. Each patient was assigned a single race/ethnicity, which were mutually exclusive, self-reported categories. Rurality was based on 2019 home address at the zip code level. Our primary outcome was time-to-first COVID-19 vaccination between December 15, 2020-June 15, 2021. Additional covariates included age (in years), sex, geographic region (North Atlantic, Midwest, Southeast, Pacific, Continental), smoking status (current, former, never), Charlson Comorbidity Index (based on ≥1 inpatient or two outpatient ICD codes), service connection (any/none, using standardized VA-cutoffs for disability compensation), and influenza vaccination in 2019-2020 (yes/no). Results: Compared with unvaccinated patients, those vaccinated (n=3,238,532; 55.2%) were older (mean age in years vaccinated=66.3, (standard deviation=14.4) vs. unvaccinated=57.7, (18.0), p<.0001)). They were more likely to identify as Black (18.2% vs. 16.1%, p<.0001), Hispanic (7.0% vs. 6.6% p<.0001), or Asian American/Pacific Islander (AA/PI) (2.0% vs. 1.7%, P<.0001). In addition, they were more likely to reside in urban settings (68.0% vs. 62.8, p<.0001). Relative to non-Hispanic White urban Veterans, the reference group for race/ethnicity-urban/rural hazard ratios reported, all urban race/ethnicity groups were associated with increased likelihood for vaccination except American Indian/Alaskan Native (AI/AN) groups. Urban Black groups were 12% more likely (Hazard Ratio (HR)=1.12 [CI 1.12-1.13]) and rural Black groups were 6% more likely to receive a first vaccination (HR=1.06 [1.05-1.06]) relative to white urban groups. Urban Hispanic, AA/PI and Mixed groups were more likely to receive vaccination while rural members of these groups were less likely (Hispanic: Urban HR=1.17 [1.16-1.18], Rural HR=0.98 [0.97-0.99]; AA/PI: Urban HR=1.22 [1.21-1.23], Rural HR=0.86 [0.84-0.88]). Rural White Veterans were 21% less likely to receive an initial vaccine compared with urban White Veterans (HR=0.79 [0.78-0.79]). AI/AN groups were less likely to receive vaccination regardless of rurality: Urban HR=0.93 [0.91-0.95]; AI/AN-Rural HR=0.76 [0.74-0.78]. Conclusions: Urban Black, Hispanic, and AA/PI Veterans were more likely than their urban White counterparts to receive a first vaccination; all rural race/ethnicity groups except Black patients had lower likelihood for vaccination compared with urban White patients. A better understanding of disparities and rural outreach will inform equitable vaccine distribution.

Performance of a modified fracture risk assessment tool for fragility fracture prediction among older veterans living with HIV.

OBJECTIVE: Fragility fractures (fractures) are a critical outcome for persons aging with HIV (PAH). Research suggests that the fracture risk assessment tool (FRAX) only modestly estimates fracture risk among PAH. We provide an updated evaluation of how well a 'modified FRAX' identifies PAH at risk for fractures in a contemporary HIV cohort. DESIGN: Cohort study. METHODS: We used data from the Veterans Aging Cohort Study to evaluate veterans living with HIV, aged 50+ years, for the occurrence of fractures from 1 January 2010 through 31 December 2019. Data from 2009 were used to evaluate the eight FRAX predictors available to us: age, sex, BMI, history of previous fracture, glucocorticoid use, rheumatoid arthritis, alcohol use, and smoking status. These predictor values were then used to estimate participant risk for each of two types of fractures (major osteoporotic and hip) over the subsequent 10 years in strata defined by race/ethnicity using multivariable logistic regression. RESULTS: Discrimination for major osteoporotic fracture was modest [Blacks: area under the curve (AUC) 0.62; 95% confidence interval (CI) 0.62, 0.63; Whites: AUC 0.61; 95% CI 0.60, 0.61; Hispanic: AUC 0.63; 95% CI 0.62, 0.65]. For hip fractures, discrimination was modest to good (Blacks: AUC 0.70; 95% CI 0.69, 0.71; Whites: AUC 0.68; 95% CI 0.67, 0.69]. Calibration was good in all models across all racial/ethnic groups. CONCLUSION: Our 'modified FRAX' exhibited modest discrimination for predicting major osteoporotic fracture and slightly better discrimination for hip fracture. Future studies should explore whether augmentation of this subset of FRAX predictors results in enhanced prediction of fractures among PAH.

Advance directive screening among veterans with incident heart failure: Comparisons among people aging with and without HIV.

BACKGROUND: Heart failure (HF) is common among people aging with HIV (PWH) and without HIV (PWoH). Despite the poor prognosis for HF, advance directives (AD) completion is low but has not been compared among PWH and PWoH. OBJECTIVES: Determine the prevalence and predictors of AD screening among PWH and PWoH with incident HF. METHODS: We included Veterans with an incident HF diagnosis code from 2013-2018 in the Veterans Aging Cohort Study (VACS) without prior AD screening. Health records were reviewed for AD screening note titles within -30 days to 1-year post-HF diagnosis. Analyses were stratified by HIV status. Trends in annual AD screening were evaluated with the Cochran-Mantel-Haenszel test. The associations of AD screening with demographics, disease severity (Charlson Comorbidity Index, VACS 2.0 Index), and healthcare encounters (cardiology, palliative care, hospitalization) were evaluated with Cox proportional hazards regression. RESULTS: HF was diagnosed in 4516 Veterans (28.2% PWH, 71.8% PWoH). Annual AD screening rates increased in both groups (Ptrend<0.0001) and aggregate rates were higher among PWH than PWoH (53.5% vs. 48.2%, p=.001). In both groups, the likelihood of AD screening increased with greater disease severity, palliative care contact, and hospitalization (HR range=1.04-3.32, all p≤.02) but not with cardiology contact (p≥.53). CONCLUSIONS: AD screening rates after incident HF remain suboptimal but increased over time and were higher in PWH. Future quality improvement and implementation efforts should aim for universal AD screening with incident HF diagnosis, initiated by providers skilled in discussing AD, including in the cardiology subspecialty setting.

Complementary and Integrative Health Approaches and Pain Care Quality in the Veterans Health Administration Primary Care Setting: A Quasi-Experimental Analysis.

Background: Complementary and integrative health (CIH) approaches have been recommended in national and international clinical guidelines for chronic pain management. We set out to determine whether exposure to CIH approaches is associated with pain care quality (PCQ) in the Veterans Health Administration (VHA) primary care setting. Methods: We followed a cohort of 62,721 Veterans with newly diagnosed musculoskeletal disorders between October 2016 and September 2017 over 1-year. PCQ scores were derived from primary care progress notes using natural language processing. CIH exposure was defined as documentation of acupuncture, chiropractic or massage therapies by providers. Propensity scores (PSs) were used to match one control for each Veteran with CIH exposure. Generalized estimating equations were used to examine associations between CIH exposure and PCQ scores, accounting for potential selection and confounding bias. Results: CIH was documented for 14,114 (22.5%) Veterans over 16,015 primary care clinic visits during the follow-up period. The CIH exposure group and the 1:1 PS-matched control group achieved superior balance on all measured baseline covariates, with standardized differences ranging from 0.000 to 0.045. CIH exposure was associated with an adjusted rate ratio (aRR) of 1.147 (95% confidence interval [CI]: 1.142, 1.151) on PCQ total score (mean: 8.36). Sensitivity analyses using an alternative PCQ scoring algorithm (aRR: 1.155; 95% CI: 1.150-1.160) and redefining CIH exposure by chiropractic alone (aRR: 1.118; 95% CI: 1.110-1.126) derived consistent results. Discussion: Our data suggest that incorporating CIH approaches may reflect higher overall quality of care for patients with musculoskeletal pain seen in primary care settings, supporting VHA initiatives and the Declaration of Astana to build comprehensive, sustainable primary care capacity for pain management. Future investigation is warranted to better understand whether and to what degree the observed association may reflect the therapeutic benefits patients actually received or other factors such as empowering provider-patient education and communication about these approaches.

Understanding headache classification coding within the veterans health administration using ICD-9-CM and ICD-10-CM in fiscal years 2014-2017.

OBJECTIVES: Understand the continuity and changes in headache not-otherwise-specified (NOS), migraine, and post-traumatic headache (PTH) diagnoses after the transition from ICD-9-CM to ICD-10-CM in the Veterans Health Administration (VHA). BACKGROUND: Headache is one of the most commonly diagnosed chronic conditions managed within primary and specialty care clinics. The VHA transitioned from ICD-9-CM to ICD-10-CM on October-1-2015. The effect transitioning on coding of specific headache diagnoses is unknown. Accuracy of headache diagnosis is important since different headache types respond to different treatments. METHODS: We mapped headache diagnoses from ICD-9-CM (FY 2014/2015) onto ICD-10-CM (FY 2016/2017) and computed coding proportions two years before/after the transition in VHA. We used queries to determine the change in transition pathways. We report the odds of ICD-10-CM coding associated with ICD-9-CM controlling for provider type, and patient age, sex, and race/ethnicity. RESULTS: Only 37%, 58% and 34% of patients with ICD-9-CM coding of NOS, migraine, and PTH respectively had an ICD-10-CM headache diagnosis. Of those with an ICD-10-CM diagnosis, 73-79% had a single headache diagnosis. The odds ratios for receiving the same code in both ICD-9-CM and ICD-10-CM after adjustment for ICD-9-CM and ICD-10-CM headache comorbidities and sociodemographic factors were high (range 6-26) and statistically significant. Specifically, 75% of patients with headache NOS had received one headache diagnoses (Adjusted headache NOS-ICD-9-CM OR for headache NOS-ICD-10-CM = 6.1, 95% CI 5.89-6.32. 79% of migraineurs had one headache diagnoses, mostly migraine (Adjusted migraine-ICD-9-CM OR for migraine-ICD-10-CM = 26.43, 95% CI 25.51-27.38). The same held true for PTH (Adjusted PTH-ICD-9-CM OR for PTH-ICD-10-CM = 22.92, 95% CI: 18.97-27.68). These strong associations remained after adjustment for specialist care in ICD-10-CM follow-up period. DISCUSSION: The majority of people with ICD-9-CM headache diagnoses did not have an ICD-10-CM headache diagnosis. However, a given diagnosis in ICD-9-CM by a primary care provider (PCP) was significantly predictive of its assignment in ICD-10-CM as was seeing either a neurologist or physiatrist (compared to a generalist) for an ICD-10-CM headache diagnosis. CONCLUSION: When a veteran had a specific diagnosis in ICD-9-CM, the odds of being coded with the same diagnosis in ICD-10-CM were significantly higher. Specialist visit during the ICD-10-CM period was independently associated with all three ICD-10-CM headaches.