Predicting Incident Adenocarcinoma of the Esophagus or Gastric Cardia Using Machine Learning of Electronic Health Records.

BACKGROUND & AIMS: Tools that can automatically predict incident esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using electronic health records to guide screening decisions are needed. METHODS: The Veterans Health Administration (VHA) Corporate Data Warehouse was accessed to identify Veterans with 1 or more encounters between 2005 and 2018. Patients diagnosed with EAC (n = 8430) or GCA (n = 2965) were identified in the VHA Central Cancer Registry and compared with 10,256,887 controls. Predictors included demographic characteristics, prescriptions, laboratory results, and diagnoses between 1 and 5 years before the index date. The Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) tool was developed and internally validated using simple random sampling imputation and extreme gradient boosting, a machine learning method. Training was performed in 50% of the data, preliminary validation in 25% of the data, and final testing in 25% of the data. RESULTS: K-ECAN was well-calibrated and had better discrimination (area under the receiver operating characteristic curve [AuROC], 0.77) than previously validated models, such as the Nord-Trøndelag Health Study (AuROC, 0.68) and Kunzmann model (AuROC, 0.64), or published guidelines. Using only data from between 3 and 5 years before index diminished its accuracy slightly (AuROC, 0.75). Undersampling men to simulate a non-VHA population, AUCs of the Nord-Trøndelag Health Study and Kunzmann model improved, but K-ECAN was still the most accurate (AuROC, 0.85). Although gastroesophageal reflux disease was strongly associated with EAC, it contributed only a small proportion of gain in information for prediction. CONCLUSIONS: K-ECAN is a novel, internally validated tool predicting incident EAC and GCA using electronic health records data. Further work is needed to validate K-ECAN outside VHA and to assess how best to implement it within electronic health records.

Yield of Repeat Endoscopy for Barrett's Esophagus After Normal Index Endoscopy.

INTRODUCTION: Guidelines suggest 1-time screening with esophagogastroduodenoscopy (EGD) for Barrett's esophagus (BE) in individuals at an increased risk of esophageal adenocarcinoma (EAC). We aimed to estimate the yield of repeat EGD performed at prolonged intervals after a normal index EGD. METHODS: We conducted a national retrospective analysis within the U S Veterans Health Administration, identifying patients with a normal index EGD between 2003 and 2009 who subsequently had a repeat EGD. We tabulated the proportion with a new diagnosis of BE, EAC, or esophagogastric junction adenocarcinoma (EGJAC) and conducted manual chart review of a sample. We fitted logistic regression models for the odds of a new diagnosis of BE/EAC/EGJAC. RESULTS: We identified 71,216 individuals who had a repeat EGD between 1 and 16 years after an index EGD without billing or cancer registry codes for BE/EAC/EGJAC. Of them, 4,088 had a new billing or cancer registry code for BE/EAC/EGJAC after the repeat EGD. On manual review of a stratified sample, most did not truly have new BE/EAC/EGJAC. A longer duration between EGD was associated with greater odds of a new diagnosis (adjusted odds ratio [aOR] for each 5 years 1.31; 95% confidence interval [CI] 1.19-1.44), particularly among those who were younger during the index EGD (ages 19-29 years: aOR 3.92; 95% CI 1.24-12.4; ages 60-69 years: aOR 1.19; 95% CI 1.01-1.40). DISCUSSION: The yield of repeat EGD for BE/EAC/EGJAC seems to increase with time after a normal index EGD, particularly for younger individuals. Prospective studies are warranted to confirm these findings.

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!

The Impact of Community Care Referral on Time to Surgery for Veterans With Carpal Tunnel Syndrome.

BACKGROUND: The US Department of Veterans Affairs (VA) enacted policies offering Veterans care in the community, aiming to improve access challenges. However, the impact of receipt of community care on wait times for Veterans receiving surgical care is poorly understood. OBJECTIVES: To compare wait times for surgery for Veterans with carpal tunnel syndrome who receive VA care plus community care (mixed care) and those who receive care solely within the VA (VA-only). RESEARCH DESIGN: Retrospective cohort study. SUBJECTS: Veterans undergoing carpal tunnel release (CTR) between January 1, 2010 and December 31, 2016. MEASURES: Our primary outcome was time from primary care physician (PCP) referral to CTR. RESULTS: Of the 29,242 Veterans undergoing CTR, 23,330 (79.8%) received VA-only care and 5912 (20.1%) received mixed care. Veterans receiving mixed care had significantly longer time from PCP referral to CTR (median mixed care: 378 days; median VA-only care: 176 days, P<0.001). After controlling for patient and facility covariates, mixed care was associated with a 37% increased time from PCP referral to CTR (adjusted hazard ratio, 0.63; 95% confidence interval, 0.61-0.65). Each additional service provided in the community was associated with a 23% increase in time to surgery (adjusted hazard ratio, 0.77; 95% confidence interval, 0.76-0.78). CONCLUSIONS: VA-only care was associated with a shorter time to surgery compared with mixed care. Moreover, there were additional delays for each service received in the community. With likely increases in Veterans seeking community care, strategies must be used to identify and mitigate sources of delay through the spectrum of care between referral and definitive treatment.

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.