Facility-level program components leading to population impact: a coincidence analysis of obesity treatment options within the Veterans Health Administration.

Obesity is a well-established risk factor for increased morbidity and mortality. Comprehensive lifestyle interventions, pharmacotherapy, and bariatric surgery are three effective treatment approaches for obesity. The Veterans Health Administration (VHA) offers all three domains but in different configurations across medical facilities. Study aim was to explore the relationship between configurations of three types of obesity treatments, context, and population impact across VHA using coincidence analysis. This was a cross-sectional analysis of survey data describing weight management treatment components linked with administrative data to compute population impact for each facility. Coincidence analysis was used to identify combinations of treatment components that led to higher population impact. Facilities with higher impact were in the top two quintiles for (1) reach to eligible patients and (2) weight outcomes. Sixty-nine facilities were included in the analyses. The final model explained 88% (29/33) of the higher-impact facilities with 91% consistency (29/32) and was comprised of five distinct pathways. Each of the five pathways depended on facility complexity-level plus factors from one or more of the three domains of weight management: comprehensive lifestyle interventions, pharmacotherapy, and/or bariatric surgery. Three pathways include components from multiple treatment domains. Combinations of conditions formed "recipes" that lead to higher population impact. Our coincidence analyses highlighted both the importance of local context and how combinations of specific conditions consistently and uniquely distinguished higher impact facilities from lower impact facilities for weight management.

Obesity can contribute to increased rates of ill health and earlier death. Proven treatments for obesity include programs that help people improve lifestyle behaviors (e.g., being physically active), medications, and/or bariatric surgery. In the Veterans Health Administration (VHA), all three types of treatments are offered, but not at every medical center­in practice, individual medical centers offer different combinations of treatment options to their patients. VHA medical centers also have a wide range of population impact. We identified high-impact medical centers (centers with the most patients participating in obesity treatment who would benefit from treatment AND that reported the most weight loss for their patients) and examined which treatment configurations led to better population-level outcomes (i.e., higher population impact). We used a novel analysis approach that allows us to compare combinations of treatment components, instead of analyzing them one-by-one. We found that optimal combinations are context-sensitive and depend on the type of center (e.g., large centers affiliated with a university vs. smaller rural centers). We list five different "recipes" of treatment combinations leading to higher population-level impact. This information can be used by clinical leaders to design treatment programs to maximize benefits for their patients.

Interpretability, credibility, and usability of hospital-specific template matching versus regression-based hospital performance assessments; a multiple methods study.

BACKGROUND: Hospital-specific template matching (HS-TM) is a newer method of hospital performance assessment. OBJECTIVE: To assess the interpretability, credibility, and usability of HS-TM-based vs. regression-based performance assessments. RESEARCH DESIGN: We surveyed hospital leaders (January-May 2021) and completed follow-up semi-structured interviews. Surveys included four hypothetical performance assessment vignettes, with method (HS-TM, regression) and hospital mortality randomized. SUBJECTS: Nationwide Veterans Affairs Chiefs of Staff, Medicine, and Hospital Medicine. MEASURES: Correct interpretation; self-rated confidence in interpretation; and self-rated trust in assessment (via survey). Concerns about credibility and main uses (via thematic analysis of interview transcripts). RESULTS: In total, 84 participants completed 295 survey vignettes. Respondents correctly interpreted 81.8% HS-TM vs. 56.5% regression assessments, p < 0.001. Respondents "trusted the results" for 70.9% HS-TM vs. 58.2% regression assessments, p = 0.03. Nine concerns about credibility were identified: inadequate capture of case-mix and/or illness severity; inability to account for specialized programs (e.g., transplant center); comparison to geographically disparate hospitals; equating mortality with quality; lack of criterion standards; low power; comparison to dissimilar hospitals; generation of rankings; and lack of transparency. Five concerns were equally relevant to both methods, one more pertinent to HS-TM, and three more pertinent to regression. Assessments were mainly used to trigger further quality evaluation (a "check oil light") and motivate behavior change. CONCLUSIONS: HS-TM-based performance assessments were more interpretable and more credible to VA hospital leaders than regression-based assessments. However, leaders had a similar set of concerns related to credibility for both methods and felt both were best used as a screen for further evaluation.

Endoscopist-directed sedation rarely fails: implications for the value of anesthesia assistance for routine GI endoscopy.

OBJECTIVES: Use of anesthesia-assisted (AA) sedation for routine gastrointestinal (GI) endoscopy has increased markedly. Clinical uncertainty about which patients are most likely to benefit from AA sedation contributes to this increased use. We aimed to estimate the prevalence of failed endoscopist-directed sedation and to identify patients at elevated risk of failing standard sedation. STUDY DESIGN: Retrospective longitudinal study of national Veterans Health Administration (VA) data of all patients who underwent esophagogastroduodenoscopy and/or colonoscopy in 2009-2013. METHODS: Using multivariable logistic regression, we sought to identify patient and procedural risk factors for failed sedation. Failed sedation cases were identified electronically and validated by chart review. RESULTS: Of 302,247 standard sedation procedures performed at VA facilities offering AA sedation, we identified 313 cases of failed sedation (prevalence, 0.10%). None of the factors found to be associated with increased risk of failed sedation (eg, high-dose opioid use, younger age) had an odds ratio greater than 3. Even among the highest-risk patients (top decile), the prevalence of failed sedation was only 0.29%. CONCLUSIONS: Failed sedation among patients undergoing routine outpatient GI endoscopy with standard sedation is very rare, even among patients at highest risk. This suggests that concerns regarding failed sedation due to commonly cited factors such as chronic opioid use and obesity do not justify forgoing standard sedation in favor of AA sedation in most patients. It also suggests that use of AA sedation is generally unnecessary. Reinstatement of endoscopist-directed sedation, rather than AA sedation, as the default sedation standard is warranted to reduce low-value care and prevent undue financial burdens on patients.

Hospital-specific Template Matching for Benchmarking Performance in a Diverse Multihospital System.

BACKGROUND: Hospital-specific template matching is a newer method of hospital performance measurement that may be fairer than regression-based benchmarking. However, it has been tested in only limited research settings. OBJECTIVE: The objective of this study was to test the feasibility of hospital-specific template matching assessments in the Veterans Affairs (VA) health care system and determine power to detect greater-than-expected 30-day mortality. RESEARCH DESIGN: Observational cohort study with hospital-specific template matching assessment. For each VA hospital, the 30-day mortality of a representative subset of hospitalizations was compared with the pooled mortality from matched hospitalizations at a set of comparison VA hospitals treating sufficiently similar patients. The simulation was used to determine power to detect greater-than-expected mortality. SUBJECTS: A total of 556,266 hospitalizations at 122 VA hospitals in 2017. MEASURES: A number of comparison hospitals identified per hospital; 30-day mortality. RESULTS: Each hospital had a median of 38 comparison hospitals (interquartile range: 33, 44) identified, and 116 (95.1%) had at least 20 comparison hospitals. In total, 8 hospitals (6.6%) had a significantly lower 30-day mortality than their benchmark, 5 hospitals (4.1%) had a significantly higher 30-day mortality, and the remaining 109 hospitals (89.3%) were similar to their benchmark. Power to detect a standardized mortality ratio of 2.0 ranged from 72.5% to 79.4% for a hospital with the fewest (6) versus most (64) comparison hospitals. CONCLUSIONS: Hospital-specific template matching may be feasible for assessing hospital performance in the diverse VA health care system, but further refinements are needed to optimize the approach before operational use. Our findings are likely applicable to other large and diverse multihospital systems.

Replicating prediction algorithms for hospitalization and corticosteroid use in patients with inflammatory bowel disease.

INTRODUCTION: Previous work had shown that machine learning models can predict inflammatory bowel disease (IBD)-related hospitalizations and outpatient corticosteroid use based on patient demographic and laboratory data in a cohort of United States Veterans. This study aimed to replicate this modeling framework in a nationally representative cohort. METHODS: A retrospective cohort design using Optum Electronic Health Records (EHR) were used to identify IBD patients, with at least 12 months of follow-up between 2007 and 2018. IBD flare was defined as an inpatient/emergency visit with a diagnosis of IBD or an outpatient corticosteroid prescription for IBD. Predictors included demographic and laboratory data. Logistic regression and random forest (RF) models were used to predict IBD flare within 6 months of each visit. A 70% training and 30% validation approach was used. RESULTS: A total of 95,878 patients across 780,559 visits were identified. Of these, 22,245 (23.2%) patients had at least one IBD flare. Patients were predominantly White (87.7%) and female (57.1%), with a mean age of 48.0 years. The logistic regression model had an area under the receiver operating curve (AuROC) of 0.66 (95% CI: 0.65-0.66), sensitivity of 0.69 (95% CI: 0.68-0.70), and specificity of 0.74 (95% CI: 0.73-0.74) in the validation cohort. The RF model had an AuROC of 0.80 (95% CI: 0.80-0.81), sensitivity of 0.74 (95% CI: 0.73-0.74), and specificity of 0.72 (95% CI: 0.72-0.72) in the validation cohort. Important predictors of IBD flare in the RF model were the number of previous flares, age, potassium, and white blood cell count. CONCLUSION: The machine learning modeling framework was replicated and results showed a similar predictive accuracy in a nationally representative cohort of IBD patients. This modeling framework could be embedded in routine practice as a tool to distinguish high-risk patients for disease activity.

Facility-level conditions leading to higher reach: a configurational analysis of national VA weight management programming.

BACKGROUND: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach. METHODS: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of "new" MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. RESULTS: One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47). CONCLUSIONS: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE!

Association of Anti-Tumor Necrosis Factor Therapy With Mortality Among Veterans With Inflammatory Bowel Disease.

Importance: Inflammatory bowel disease (IBD) is commonly treated with corticosteroids and anti-tumor necrosis factor (TNF) drugs; however, medications have well-described adverse effects. Prior work suggests that anti-TNF therapy may reduce all-cause mortality compared with prolonged corticosteroid use among Medicare and Medicaid beneficiaries with IBD. Objective: To examine the association between use of anti-TNF or corticosteroids and all-cause mortality in a national cohort of veterans with IBD. Design, Setting, and Participants: This cohort study used a well-established Veteran's Health Administration cohort of 2997 patients with IBD treated with prolonged corticosteroids (≥3000-mg prednisone equivalent and/or ≥600 mg of budesonide within a 12-month period) and/or new anti-TNF therapy from January 1, 2006, to October 1, 2015. Data were analyzed between July 1, 2019, and December 31, 2020. Exposures: Use of corticosteroids or anti-TNF. Main Outcomes and Measures: The primary end point was all-cause mortality as defined by the Veterans Health Administration vital status file. Marginal structural modeling was used to compare associations between anti-TNF therapy or corticosteroid use and all-cause mortality. Results: A total of 2997 patients (2725 men [90.9%]; mean [SD] age, 50.0 [17.4] years) were included in the final analysis, 1734 (57.9%) with Crohn disease (CD) and 1263 (42.1%) with ulcerative colitis (UC). All-cause mortality was 8.5% (n = 256) over a mean (SD) of 3.9 (2.3) years' follow-up. At cohort entry, 1836 patients were new anti-TNF therapy users, and 1161 were prolonged corticosteroid users. Anti-TNF therapy use was associated with a lower likelihood of mortality for CD (odds ratio [OR], 0.54; 95% CI, 0.31-0.93) but not for UC (OR, 0.33; 95% CI, 0.10-1.10). In a sensitivity analysis adjusting prolonged corticosteroid users to include patients receiving corticosteroids within 90 to 270 days after initiation of anti-TNF therapy, the OR for UC was statistically significant, at 0.33 (95% CI, 0.13-0.84), and the OR for CD was 0.55 (95% CI, 0.33-0.92). Conclusions and Relevance: This study suggests that anti-TNF therapy may be associated with reduced mortality compared with long-term corticosteroid use among veterans with CD, and potentially among those with UC.

Inpatient and Discharge Fluoroquinolone Prescribing in Veterans Affairs Hospitals Between 2014 and 2017.

BACKGROUND: Between 2007 and 2015, inpatient fluoroquinolone use declined in US Veterans Affairs (VA) hospitals. Whether fluoroquinolone use at discharge also declined, in particular since antibiotic stewardship programs became mandated at VA hospitals in 2014, is unknown. METHODS: In this retrospective cohort study of hospitalizations with infection between January 1, 2014, and December 31, 2017, at 125 VA hospitals, we assessed inpatient and discharge fluoroquinolone (ciprofloxacin, levofloxacin, moxifloxacin) use as (a) proportion of hospitalizations with a fluoroquinolone prescribed and (b) fluoroquinolone-days per 1000 hospitalizations. After adjusting for illness severity, comorbidities, and age, we used multilevel logit and negative binomial models to assess for hospital-level variation and longitudinal prescribing trends. RESULTS: Of 560219 hospitalizations meeting inclusion criteria as hospitalizations with infection, 37.4% (209602/560219) had a fluoroquinolone prescribed either during hospitalization (32.5%, 182337/560219) or at discharge (19.6%, 110003/560219). Hospitals varied appreciably in inpatient, discharge, and total fluoroquinolone use, with 71% of hospitals in the highest prescribing quartile located in the Southern United States. Nearly all measures of fluoroquinolone use decreased between 2014 and 2017, with the largest decreases found in inpatient fluoroquinolone and ciprofloxacin use. In contrast, there was minimal decline in fluoroquinolone use at discharge, which accounted for a growing percentage of hospitalization-related fluoroquinolone-days (52.0% in 2014; 61.3% by 2017). CONCLUSIONS: Between 2014 and 2017, fluoroquinolone use decreased in VA hospitals, largely driven by decreased inpatient fluoroquinolone (especially ciprofloxacin) use. Fluoroquinolone prescribing at discharge, as well as levofloxacin prescribing overall, is a growing target for stewardship.

Construction and Use of Body Weight Measures from Administrative Data in a Large National Health System: A Systematic Review.

OBJECTIVE: Administrative data are increasingly used in research and evaluation yet lack standardized guidelines for constructing measures using these data. Body weight measures from administrative data serve critical functions of monitoring patient health, evaluating interventions, and informing research. This study aimed to describe the algorithms used by researchers to construct and use weight measures. METHODS: A structured, systematic literature review of studies that constructed body weight measures from the Veterans Health Administration was conducted. Key information regarding time frames and time windows of data collection, measure calculations, data cleaning, treatment of missing and outlier weight values, and validation processes was collected. RESULTS: We identified 39 studies out of 492 nonduplicated records for inclusion. Studies parameterized weight outcomes as change in weight from baseline to follow-up (62%), weight trajectory over time (21%), proportion of participants meeting weight threshold (46%), or multiple methods (28%). Most (90%) reported total time in follow-up and number of time points. Fewer reported time windows (54%), outlier values (51%), missing values (34%), or validation strategies (15%). CONCLUSIONS: A high variability in the operationalization of weight measures was found. Improving methods to construct clinical measures will support transparency and replicability in approaches, guide interpretation of findings, and facilitate comparisons across studies.

Developing a template matching algorithm for benchmarking hospital performance in a diverse, integrated healthcare system.

Template matching is a proposed approach for hospital benchmarking, which measures performance based on matching a subset of comparable patient hospitalizations from each hospital. We assessed the ability to create the required matched samples and thus the feasibility of template matching to benchmark hospital performance in a diverse healthcare system.Nationwide Veterans Affairs (VA) hospitals, 2017.Observational cohort study.We used administrative and clinical data from 668,592 hospitalizations at 134 VA hospitals in 2017. A standardized template of 300 hospitalizations was selected, and then 300 hospitalizations were matched to the template from each hospital.There was substantial case-mix variation across VA hospitals, which persisted after excluding small hospitals, hospitals with primarily psychiatric admissions, and hospitalizations for rare diagnoses. Median age ranged from 57 to 75 years across hospitals; percent surgical admissions ranged from 0.0% to 21.0%; percent of admissions through the emergency department, 0.1% to 98.7%; and percent Hispanic patients, 0.2% to 93.3%. Characteristics for which there was substantial variation across hospitals could not be balanced with any matching algorithm tested. Although most other variables could be balanced, we were unable to identify a matching algorithm that balanced more than ∼20 variables simultaneously.We were unable to identify a template matching approach that could balance hospitals on all measured characteristics potentially important to benchmarking. Given the magnitude of case-mix variation across VA hospitals, a single template is likely not feasible for general hospital benchmarking.

The Association of Medications and Vaccination with Risk of Pneumonia in Inflammatory Bowel Disease.

BACKGROUND: Patients with inflammatory bowel disease (IBD) are at increased risk for pneumonia, and corticosteroids are reported to amplify this risk. Less is known about the impact of corticosteroid-sparing IBD therapies on pneumonia risk or the efficacy of pneumococcal vaccination in reducing all-cause pneumonia in real-world IBD cohorts. METHODS: We performed a population-based study using an established Veterans Health Administration cohort of 29,957 IBD patients. We identified all patients who developed bacterial pneumonia. Cox survival analysis was used to determine the association of corticosteroids at study entry and as a time-varying covariate, corticosteroid-sparing agents (immunomodulators and antitumor necrosis-alpha [TNF] inhibitors), and pneumococcal vaccination with the development of all-cause pneumonia. RESULTS: Patients with IBD who received corticosteroids had a greater risk of pneumonia when controlling for age, gender, and comorbidities (hazard ratio [HR] 2.21; 95% confidence interval [CI], 1.90-2.57 for prior use; HR = 3.42; 95% CI, 2.92-4.01 for use during follow-up). Anti-TNF inhibitors (HR 1.52; 95% CI, 1.02-2.26), but not immunomodulators (HR 0.91; 95% CI, 0.77-1.07), were associated with a small increase in pneumonia. A history of pneumonia was strongly associated with subsequent pneumonia (HR = 4.41; 95% CI, 3.70-5.27). Less than 15% of patients were vaccinated against pneumococcus, and this was not associated with a reduced risk of pneumonia (HR = 1.02; 95% CI, 0.80-1.30) in this cohort. CONCLUSION: In a large US cohort, corticosteroids were confirmed to increase pneumonia risk. Tumor necrosis-alpha inhibitors were associated with a smaller increase in the risk of pneumonia. Surprisingly, pneumococcal vaccination did not reduce all-cause pneumonia in this population, though few patients were vaccinated.

Increased Cardiovascular Disease Risk in Veterans With Mental Illness.

BACKGROUND: Although previous studies have demonstrated an association between various mental illnesses and cardio-cerebrovascular disease (CVD) risk, few have compared the strength of association between different mental illnesses and CVD risk. METHODS AND RESULTS: We assessed the association of psychiatric diagnoses (psychosis, bipolar disorder, depression, anxiety, and posttraumatic stress disorder) with major CVD outcomes (CVD events and CVD mortality) over 5 years, using a national primary prevention cohort of military veterans receiving care in the Department of Veterans Affairs. Data were linked from the Department of Veterans Affairs, Centers for Medicare and Medicaid Services, and Centers for Disease Control and Prevention National Death Index databases. We used multiple logistic regression to examine how the presence of a psychiatric diagnosis at baseline (2005-2009) was associated with CVD outcomes over the next 5 years (January 1, 2010, to December 31, 2014) stratified by sex, adjusting for other psychiatric diagnoses, as well as age, race, conventional CVD risk factors as calculated by the Veterans Affairs Risk Score-CVD, and antipsychotic and anticonvulsant/mood stabilizer medication prescriptions. Approximately 1.52 million men and over 94 000 women met our inclusion criteria. In the fully adjusted model, among men, we found that depression, psychosis, and bipolar disorder were predictive of both CVD events and CVD mortality, with psychosis having the largest effect size (eg, adjusted odds ratio, 1.48; CI, 1.41-1.56; P<0.001 for psychosis and CVD mortality). Among women, only psychosis and bipolar disorder were predictive of both CVD events and CVD mortality, again with psychosis having the largest effect size (eg, adjusted odds ratio, 1.97; CI, 1.52-2.57; P<0.001 for psychosis and CVD mortality). Anxiety was associated with only CVD mortality in men, and depression was associated with only CVD events in women. CONCLUSIONS: Consistent with the hypothesis that chronic stress leads to greater CVD risk, multiple mental illnesses were associated with an increased risk of CVD outcomes, with more severe mental illnesses (eg, primary psychotic disorders) having the largest effect sizes even after controlling for other psychiatric diagnoses, conventional CVD risk factors, and psychotropic medication use.

Intensive Glucose Control in Patients with Type 2 Diabetes - 15-Year Follow-up.

BACKGROUND: We previously reported that a median of 5.6 years of intensive as compared with standard glucose lowering in 1791 military veterans with type 2 diabetes resulted in a risk of major cardiovascular events that was significantly lower (by 17%) after a total of 10 years of combined intervention and observational follow-up. We now report the full 15-year follow-up. METHODS: We observationally followed enrolled participants (complete cohort) after the conclusion of the original clinical trial by using central databases to identify cardiovascular events, hospitalizations, and deaths. Participants were asked whether they would be willing to provide additional data by means of surveys and chart reviews (survey cohort). The prespecified primary outcome was a composite of major cardiovascular events, including nonfatal myocardial infarction, nonfatal stroke, new or worsening congestive heart failure, amputation for ischemic gangrene, and death from cardiovascular causes. Death from any cause was a prespecified secondary outcome. RESULTS: There were 1655 participants in the complete cohort and 1391 in the survey cohort. During the trial (which originally enrolled 1791 participants), the separation of the glycated hemoglobin curves between the intensive-therapy group (892 participants) and the standard-therapy group (899 participants) averaged 1.5 percentage points, and this difference declined to 0.2 to 0.3 percentage points by 3 years after the trial ended. Over a period of 15 years of follow-up (active treatment plus post-trial observation), the risks of major cardiovascular events or death were not lower in the intensive-therapy group than in the standard-therapy group (hazard ratio for primary outcome, 0.91; 95% confidence interval [CI], 0.78 to 1.06; P = 0.23; hazard ratio for death, 1.02; 95% CI, 0.88 to 1.18). The risk of major cardiovascular disease outcomes was reduced, however, during an extended interval of separation of the glycated hemoglobin curves (hazard ratio, 0.83; 95% CI, 0.70 to 0.99), but this benefit did not continue after equalization of the glycated hemoglobin levels (hazard ratio, 1.26; 95% CI, 0.90 to 1.75). CONCLUSIONS: Participants with type 2 diabetes who had been randomly assigned to intensive glucose control for 5.6 years had a lower risk of cardiovascular events than those who received standard therapy only during the prolonged period in which the glycated hemoglobin curves were separated. There was no evidence of a legacy effect or a mortality benefit with intensive glucose control. (Funded by the VA Cooperative Studies Program; VADT ClinicalTrials.gov number, NCT00032487.).

Template matching for benchmarking hospital performance in the veterans affairs healthcare system.

Comparing hospital performance in a health system is traditionally done with multilevel regression models that adjust for differences in hospitals' patient case-mix. In contrast, "template matching" compares outcomes of similar patients at different hospitals but has been used only in limited patient settings.Our objective was to test a basic template matching approach in the nationwide Veterans Affairs healthcare system (VA), compared with a more standard regression approach.We performed various simulations using observational data from VA electronic health records whereby we randomly assigned patients to "pseudo hospitals," eliminating true hospital level effects. We randomly selected a representative template of 240 patients and matched 240 patients on demographic and physiological factors from each pseudo hospital to the template. We varied hospital performance for different simulations such that some pseudo hospitals negatively impacted patient mortality.Electronic health record data of 460,213 hospitalizations at 111 VA hospitals across the United States in 2015.We assessed 30-day mortality at each pseudo hospital and identified lowest quintile hospitals by template matching and regression. The regression model adjusted for predicted 30-day mortality (as a measure of illness severity).Regression identified the lowest quintile hospitals with 100% accuracy compared with 80.3% to 82.0% for template matching when systematic differences in 30-day mortality existed.The current standard practice of risk-adjusted regression incorporating patient-level illness severity was better able to identify lower-performing hospitals than the simplistic template matching algorithm.

Veterans Affairs patient database (VAPD 2014-2017): building nationwide granular data for clinical discovery.

BACKGROUND: To study patient physiology throughout a period of acute hospitalization, we sought to create accessible, standardized nationwide data at the level of the individual patient-facility-day. This methodology paper summarizes the development, organization, and characteristics of the Veterans Affairs Patient Database 2014-2017 (VAPD 2014-2017). The VAPD 2014-2017 contains acute hospitalizations from all parts of the nationwide VA healthcare system with daily physiology including clinical data (labs, vitals, medications, risk scores, etc.), intensive care unit (ICU) indicators, facility, patient, and hospitalization characteristics. METHODS: The VA data structure and database organization represents a complex multi-hospital system. We define a single-site hospitalization as one or more consecutive stays with an acute treating specialty at a single facility. The VAPD 2014-2017 is structured at the patient-facility-day level, where every patient-day in a hospital is a row with separate identification variables for facility, patient, and hospitalization. The VAPD 2014-2017 includes daily laboratory, vital signs, and inpatient medication. Such data were validated and verified through lab value range and comparison with patient charts. Sepsis, risk scores, and organ dysfunction definitions were standardized and calculated. RESULTS: We identified 565,242 single-site hospitalizations (SSHs) in 2014; 558,060 SSHs in 2015; 553,961 SSHs in 2016; and 550,236 SSHs in 2017 at 141 VA hospitals. The average length of stay was four days for all study years. In-hospital mortality decreased from 2014 to 2017 (1.7 to 1.4%), 30-day readmission rates increased from 15.3% in 2014 to 15.6% in 2017; 30-day mortality also decreased from 4.4% in 2014 to 4.1% in 2017. From 2014 to 2017, there were 107,512 (4.8%) of SSHs that met the Center for Disease Control and Prevention's Electronic Health Record-based retrospective definition of sepsis. CONCLUSION: The VAPD 2014-2017 represents a large, standardized collection of granular data from a heterogeneous nationwide healthcare system. It is also a direct resource for studying the evolution of inpatient physiology during both acute and critical illness.

Variation in Laboratory Test Naming Conventions in EHRs Within and Between Hospitals: A Nationwide Longitudinal Study.

BACKGROUND: Electronic health records provide clinically rich data for research and quality improvement work. However, the data are often unstructured text, may be inconsistently recorded and extracted into centralized databases, making them difficult to use for research. OBJECTIVES: We sought to quantify the variation in how key laboratory measures are recorded in the Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) across hospitals and over time. We included 6 laboratory tests commonly drawn within the first 24 hours of hospital admission (albumin, bilirubin, creatinine, hemoglobin, sodium, white blood cell count) from fiscal years 2005-2015. RESULTS: We assessed laboratory test capture for 5,454,411 acute hospital admissions at 121 sites across the VA. The mapping of standardized laboratory nomenclature (Logical Observation Identifiers Names and Codes, LOINCs) to test results in CDW varied within hospital by laboratory test. The relationship between LOINCs and laboratory test names improved over time; by FY2015, 109 (95.6%) hospitals had >90% of the 6 laboratory tests mapped to an appropriate LOINC. All fields used to classify test results are provided in an Appendix (Supplemental Digital Content 1, http://links.lww.com/MLR/B635). CONCLUSIONS: The use of electronic health record data for research requires assessing data consistency and quality. Using laboratory test results requires the use of both unstructured text fields and the identification of appropriate LOINCs. When using data from multiple facilities, the results should be carefully examined by facility and over time to maximize the capture of data fields.

Effects of Severe Hypoglycemia on Cardiovascular Outcomes and Death in the Veterans Affairs Diabetes Trial.

OBJECTIVE: To determine the risk factors for severe hypoglycemia and the association between severe hypoglycemia and serious cardiovascular adverse events and cardiovascular and all-cause mortality in the Veterans Affairs Diabetes Trial (VADT). RESEARCH DESIGN AND METHODS: This post hoc analysis of data from the VADT included 1,791 military veterans (age 60.5 ± 9.0 years) with suboptimally controlled type 2 diabetes (HbA1c 9.4 ± 2.0%) of 11.5 ± 7.5 years disease duration with or without known cardiovascular disease and additional cardiovascular risk factors. Participants were randomized to intensive (HbA1c <7.0%) versus standard (HbA1c <8.5%) glucose control. RESULTS: The rate of severe hypoglycemia in the intensive treatment group was 10.3 per 100 patient-years compared with 3.7 per 100 patient-years in the standard treatment group (P < 0.001). In multivariable analysis, insulin use at baseline (P = 0.02), proteinuria (P = 0.009), and autonomic neuropathy (P = 0.01) were independent risk factors for severe hypoglycemia, and higher BMI was protective (P = 0.017). Severe hypoglycemia within the past 3 months was associated with an increased risk of serious cardiovascular events (P = 0.032), cardiovascular mortality (P = 0.012), and total mortality (P = 0.024). However, there was a relatively greater increased risk for total mortality in the standard group compared with the intensive group (P = 0.019). The association between severe hypoglycemia and cardiovascular events increased significantly as overall cardiovascular risk increased (P = 0.012). CONCLUSIONS: Severe hypoglycemic episodes within the previous 3 months were associated with increased risk for major cardiovascular events and cardiovascular and all-cause mortality regardless of glycemic treatment group assignment. Standard therapy further increased the risk for all-cause mortality after severe hypoglycemia.

Correction: Corticosteroid Use and Complications in a US Inflammatory Bowel Disease Cohort.

[This corrects the article DOI: 10.1371/journal.pone.0158017.].

Using Veterans Affairs Corporate Data Warehouse to identify 30-day hospital readmissions.

Hospital readmission is a key metric of hospital quality, such as for comparing Veterans Affairs (VA) hospitals to private sector hospitals. To calculate readmission rates, one must first identify individual hospitalizations. However, in the VA Corporate Data Warehouse (CDW), data are organized by "bedded stays," that is, any stay in a healthcare facility where a patient is provided a bed, not hospitalizations. Thus, CDW data poses several challenges to identifying hospitalizations including: (1) bedded stays include both non-acute inpatient stays (i.e. nursing home, mental health) and acute inpatient hospital care; (2) transfers between VA facilities appear as separate bedded stays; and (3) VA care may also be fragmented by non-VA care. Thus, we sought to develop a rigorous method to identify acute hospitalizations using the VA CDW. We examined all VA bedded stays with an admission date in 2009. Non-acute portions of a stay were dropped. VA to VA transfers were merged when consecutive discharge and admission dates were within one calendar day. Finally, hospitalizations that occurred in a non-VA facility were merged. The 30-day readmission rate was calculated at each step of the algorithm to demonstrate the impact. The total number of VA medical hospitalizations in 2009 with live discharges was 505,861. The 30-day readmission rate after adjusting for VA to VA transfers and incorporating non-VA care was 18.3% (95% confidence interval (CI): 18.2, 18.4%).