Lower comorbidity scores and severity levels in Veterans Health Administration hospitals: a cross-sectional study.

BACKGROUND: Previous studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change. METHODS: Using repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans' admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics. RESULTS: Following adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)). CONCLUSIONS: More effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Validation of the Melanoma Institute of Australia's Sentinel Lymph Node Biopsy Risk Prediction Tool for Cutaneous Melanoma.

BACKGROUND: For patients with cutaneous melanoma, sentinel lymph node biopsy (SLNB) is used to stage regional lymph nodes pathologically and inform prognosis, treatment, and surveillance. To reduce unnecessary surgeries, predictive tools aim to identify those at lowest risk for node-positive disease. The Melanoma Institute of Australia (MIA)'s Prediction Tool for Sentinel Node Metastasis Risk estimates risk of a positive SLNB using patient age and primary melanoma Breslow depth, histologic subtype, ulceration, mitotic rate, and lymphovascular invasion. METHODS: A single-institution validation was performed of the MIA Calculator with 982 cutaneous melanoma patients that included all relevant clinicopathologic factors and SLNB pathology outcomes. The study evaluated discrimination via receiver operating characteristic (ROC) curves, calibration via calibration plots, and clinical utility via decision curve analysis of the MIA model in various subgroups. The data were fit to MIA model parameters via a generalized linear model to assess the odds ratio of parameters in our dataset. RESULTS: The Calculator demonstrated limited discrimination based on ROC curves (C-statistic, 0.709) and consistently underestimated risk of SLN positivity. It did not provide a net benefit over SLNB performed on all patients or reduce unnecessary procedures in the risk domain of 0% to 16%. Compared with the original development and validation cohorts, the current study cohort had thinner tumors and a larger proportion of acral melanomas. CONCLUSIONS: The Calculator generally underestimated SLN positivity risk, including assessment in patients who would be counseled to forego SLNB based on a predicted risk lower than 5%. Recognition of the tool's current limitations emphasizes the need to refine it further for use in medical decision-making.

Hospital and Patient Factors Affecting Veterans' Hospital Choice.

Veterans enrolled in the Veterans Affairs (VA) health care system gained greater access to non-VA care beginning in 2014. We examined hospital and Veteran characteristics associated with hospital choice. We conducted a longitudinal study of elective hospitalizations 2011 to 2017 in 11 states and modeled patients' choice of VA hospital, large non-VA hospital, or small non-VA hospital in conditional logit models. Patients had higher odds of choosing a hospital with an academic affiliation, better patient experience rating, location closer to them, and a more common hospital type. Patients who were male, racial/ethnic minorities, had higher VA enrollment priority, and had a mental health comorbidity were more likely than other patients to choose a VA hospital than a non-VA hospital. Our findings suggest that patients respond to certain hospital attributes. VA hospitals may need to maintain or achieve high levels of quality and patient experience to attract or retain patients in the future.

Estimating remaining life expectancy in veterans with basal cell carcinoma using an automated electronic health record scoring system: A retrospective cohort study.

BACKGROUND: Active surveillance may be considered for low-risk basal cell carcinomas (BCCs) in patients with limited life expectancy; however, estimates of life expectancy are not readily available. Veterans Health Administration's Care Assessment Need (CAN) score may address this problem. OBJECTIVE: We examined the CAN score's performance in predicting 1-, 3-, and 5-year mortality in US veterans with BCC. METHODS: This retrospective cohort study used national Veterans Health Administration's electronic medical record data. The CAN score's performance in the prediction of mortality in veterans with BCC was evaluated based on tests of goodness-of-fit, discrimination, and calibration. RESULTS: For 54,744 veterans with BCC treatment encounters between 2013 and 2018, the CAN score performed well in the prediction of mortality based on multiple tests. A threshold CAN score of 90 had a positive predictive value of 55% for 3-year mortality, clinically useful in identifying patients with intermediate-term survival. LIMITATIONS: The study relied upon the combination of diagnosis codes and procedure codes to identify BCC cases. CONCLUSION: The CAN score has the potential to improve the quality of cancer care for veterans by providing clinicians with an estimate of life expectancy and facilitating conversations in cases where active surveillance can be considered.

Outcomes of Veterans Treated in Veterans Affairs Hospitals vs Non-Veterans Affairs Hospitals.

Importance: Many veterans enrolled in the Veterans Affairs (VA) health care system have access to non-VA care through insurance and VA-purchased community care. Prior comparisons of VA and non-VA hospital outcomes have been limited to subpopulations. Objective: To compare outcomes for 6 acute conditions in VA and non-VA hospitals for younger and older veterans using VA and all-payer discharge data. Design, Setting, and Participants: This cohort study used a repeated cross-sectional analysis of hospitalization records for acute myocardial infarction (AMI), coronary artery bypass graft (CABG), gastrointestinal (GI) hemorrhage, heart failure (HF), pneumonia, and stroke. Participants included VA enrollees from 11 states at VA and non-VA hospitals from 2012 to 2017. Analysis was conducted from July 1, 2022, to October 18, 2023. Exposures: Treatment in VA or non-VA hospital. Main Outcome and Measures: Thirty-day mortality, 30-day readmission, length of stay (LOS), and costs. Average treatment outcomes of VA hospitals were estimated using inverse probability weighted regression adjustment to account for selection into hospitals. Models were stratified by veterans' age (aged less than 65 years and aged 65 years and older). Results: There was a total of 593 578 hospitalizations and 414 861 patients with mean (SD) age 75 (12) years, 405 602 males (98%), 442 297 hospitalizations of non-Hispanic White individuals (75%) and 73 155 hospitalizations of non-Hispanic Black individuals (12%) overall. VA hospitalizations had a lower probability of 30-day mortality for HF (age ≥65 years, -0.02 [95% CI, -0.03 to -0.01]) and stroke (age <65 years, -0.03 [95% CI, -0.05 to -0.02]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.03]). VA hospitalizations had a lower probability of 30-day readmission for CABG (age <65 years, -0.04 [95% CI, -0.06 to -0.01]; age ≥65 years, -0.05 [95% CI, -0.07 to -0.02]), GI hemorrhage (age <65 years, -0.04 [95% CI, -0.06 to -0.03]), HF (age <65 years, -0.05 [95% CI, -0.07 to -0.03]), pneumonia (age <65 years, -0.04 [95% CI, -0.06 to -0.03]; age ≥65 years, -0.03 [95% CI, -0.04 to -0.02]), and stroke (age <65 years, -0.11 [95% CI, -0.13 to -0.09]; age ≥65 years, -0.13 [95% CI, -0.16 to -0.10]) but higher probability of readmission for AMI (age <65 years, 0.04 [95% CI, 0.01 to 0.06]). VA hospitalizations had a longer mean LOS and higher costs for all conditions, except AMI and stroke in younger patients. Conclusions and Relevance: In this cohort study of veterans, VA hospitalizations had lower mortality for HF and stroke and lower readmissions, longer LOS, and higher costs for most conditions compared with non-VA hospitalizations with differences by condition and age group. There were tradeoffs between better outcomes and higher resource use in VA hospitals for some conditions.

Differences in use of Veterans Health Administration and non-Veterans Health Administration hospitals by rural and urban Veterans after access expansions.

PURPOSE: To examine changes in rural and urban Veterans' utilization of acute inpatient care in Veterans Health Administration (VHA) and non-VHA hospitals following access expansion from the Veterans Choice Act, which expanded eligibility for VHA-paid community hospitalization. METHODS: Using repeated cross-sectional data of VHA enrollees' hospitalizations in 9 states (AZ, CA, CT, FL, LA, MA, NY, PA, and SC) between 2012 and 2017, we compared rural and urban Veterans' probability of admission in VHA and non-VHA hospitals by payer over time for elective and nonelective hospitalizations using multinomial logistic regression to adjust for patient-level sociodemographic features. We also used generalized linear models to compare rural and urban Veterans' travel distances to hospitals. FINDINGS: Over time, the probability of VHA-paid community hospitalization increased more for rural Veterans than urban Veterans. For elective inpatient care, rural Veterans' probability of VHA-paid admission increased from 2.9% (95% CI 2.6%-3.2%) in 2012 to 6.5% (95% CI 5.8%-7.1%) in 2017. These changes were associated with a temporal trend that preceded and continued after the implementation of the Veterans Choice Act. Overall travel distances to hospitalizations were similar over time; however, the mean distance traveled decreased from 39.2 miles (95% CI 35.1-43.3) in 2012 to 32.3 miles (95% CI 30.2-34.4) in 2017 for rural Veterans receiving elective inpatient care in VHA-paid hospitals. CONCLUSIONS: Despite limited access to rural hospitals, these data demonstrate an increase in rural Veterans' use of non-VHA hospitals for acute inpatient care and a small reduction in distance traveled to elective inpatient services.

Comparing the Quality of Ambulatory Surgical Care for Skin Cancer in a Veterans Affairs Clinic and a Fee-For-Service Practice Using Clinical and Patient-Reported Measures.

The Institute of Medicine has identified serious deficiencies in the measurement of cancer care quality, including the effects on quality of life and patient experience. Moreover, comparisons of quality in Veterans Affairs Medical Centers (VA) and other sites are timely now that many Veterans can choose where to seek care. To compare quality of ambulatory surgical care for keratinocyte carcinoma (KC) between a VA and fee-for-service (FFS) practice, we used unique clinical and patient-reported data from a comparative effectiveness study. Patients were enrolled in 1999-2000 and followed for a median of 7.2 years. The practices differed in a few process measures (e.g., median time between biopsy and treatment was 7.5 days longer at VA) but there were no substantial or consistent differences in clinical outcomes or a broad range of patient-reported outcomes. For example, 5-year tumor recurrence rates were equally low (3.6% [2.3-5.5] at VA and 3.4% [2.3-5.1] at FFS), and similar proportions of patients reported overall satisfaction at one year (78% at VA and 80% at FFS, P = 0.69). These results suggest that the quality of care for KC can be compared comprehensively in different health care systems, and suggest that quality of care for KC was similar at a VA and FFS setting.

Oral treatment with γ-aminobutyric acid improves glucose tolerance and insulin sensitivity by inhibiting inflammation in high fat diet-fed mice.

Adipocyte and ß-cell dysfunction and macrophage-related chronic inflammation are critical for the development of obesity-related insulin resistance and type 2 diabetes mellitus (T2DM), which can be negatively regulated by Tregs. Our previous studies and those of others have shown that activation of γ-aminobutyric acid (GABA) receptors inhibits inflammation in mice. However, whether GABA could modulate high fat diet (HFD)-induced obesity, glucose intolerance and insulin resistance has not been explored. Here, we show that although oral treatment with GABA does not affect water and food consumption it inhibits the HFD-induced gain in body weights in C57BL/6 mice. Furthermore, oral treatment with GABA significantly reduced the concentrations of fasting blood glucose, and improved glucose tolerance and insulin sensitivity in the HFD-fed mice. More importantly, after the onset of obesity and T2DM, oral treatment with GABA inhibited the continual HFD-induced gain in body weights, reduced the concentrations of fasting blood glucose and improved glucose tolerance and insulin sensitivity in mice. In addition, oral treatment with GABA reduced the epididymal fat mass, adipocyte size, and the frequency of macrophage infiltrates in the adipose tissues of HFD-fed mice. Notably, oral treatment with GABA significantly increased the frequency of CD4(+)Foxp3(+) Tregs in mice. Collectively, our data indicated that activation of peripheral GABA receptors inhibited the HFD-induced glucose intolerance, insulin resistance, and obesity by inhibiting obesity-related inflammation and up-regulating Treg responses in vivo. Given that GABA is safe for human consumption, activators of GABA receptors may be valuable for the prevention of obesity and intervention of T2DM in the clinic.