Risk and Predictors of Esophageal and Stomach Cancers in HIV-Infected Veterans: A Matched Cohort Study.

BACKGROUND: To evaluate the risks of esophageal and stomach carcinomas in people living with HIV (PLWH) compared with the general population and risk factors for these cancers in PLWH. SETTING: Retrospective cohort study in the Veterans Health Administration. METHODS: We compared incidence rates for esophageal and stomach cancers in 44,075 HIV-infected male veterans with those in a matched HIV-uninfected cohort (N = 157,705; 4:1 matched on age and HIV-index date). We used Cox regression models to estimate Hazard ratios (HRs) and 95% confidence intervals (CIs) for associations with HIV infection and for cancer risk factors in PLWH. RESULTS: In unadjusted models, HIV infection was associated with increased risks of esophageal squamous cell carcinoma (ESCC; HR, 2.21; 95% CI: 1.47 to 3.13) and gastric cardia cancer (HR, 1.69; 95% CI: 1.00 to 2.85) but associated with lower risk of esophageal adenocarcinoma (EAC; HR, 0.48; 95% CI: 0.31 to 0.74). After adjusting for age, race/ethnicity, smoking and alcohol use, HIV infection remained statistically significantly associated with elevated risk for ESCC [adjusted hazard ratio (aHR), 1.58; 95% CI: 1.02 to 2.47], especially among HIV-infected patients with CD4 count ≤200 (aHR, 2.20; 95% CI: 1.35 to 3.60). HIV infection was not associated with risks of EAC (aHR, 0.82; 95% CI: 0.53 to 1.26), gastric cardia (aHR, 0.80; 95% CI: 0.33 to 1.94), or noncardia (aHR, 1.06; 95% CI: 0.61 to 1.84) cancers. Risk factors for these cancers in HIV-infected patients were otherwise similar to those in general population (eg, Helicobacter pylori for gastric noncardia cancer). CONCLUSION: HIV-infected individuals with low CD4 count are at highest risk for ESCC, but HIV infection was not independently associated with EAC or gastric cancer after adjusting for confounders.

Risk of Hepatocellular Cancer in Patients With Non-Alcoholic Fatty Liver Disease.

BACKGROUND & AIMS: There are limited data on the risk of hepatocellular cancer (HCC) in patients with non-alcoholic fatty liver disease (NAFLD). We aimed to estimate the risk of incident HCC among patients with NAFLD. METHODS: We conducted a retrospective cohort study from a total of 130 facilities in the Veterans Health Administration. Patients with NAFLD diagnosed between January 1, 2004 and December 31, 2008 were included and followed until HCC diagnosis, death, or December 31, 2015. We also identified a sex- and age-matched control cohort without NAFLD. We ascertained all new HCC cases from the Central Cancer Registry and manual chart reviews. We calculated incidence rates for HCC by NAFLD status, as well as in subgroups of NAFLD patients. We used competing risk models to compare the risk of HCC in patients with NAFLD vs those without NAFLD. We reviewed electronic medical records of all HCC cases that developed in NAFLD patients without cirrhosis. RESULTS: We compared 296,707 NAFLD patients with 296,707 matched controls. During 2,382,289 person-years [PYs] of follow-up, 490 NAFLD patients developed HCC (0.21/1000 PYs). HCC incidence was significantly higher among NAFLD patients vs controls (0.02/1000 PYs; hazard ratio, 7.62; 95% confidence interval, 5.76-10.09). Among patients with NAFLD, those with cirrhosis had the highest annual incidence of HCC (10.6/1000 PYs). Among patients with NAFLD cirrhosis, HCC risk ranged from 1.6 to 23.7 per 1000 PYs based on other demographic characteristics; risk of HCC was the highest in older Hispanics with cirrhosis. In medical record reviews, 20% of NAFLD patients with HCC had no evidence of cirrhosis. CONCLUSIONS: Risk of HCC was higher in NAFLD patients than that observed in general clinical population. Most HCC cases in NAFLD developed in patients with cirrhosis. The absolute risk of HCC was higher than the accepted thresholds for HCC surveillance for most patients with NAFLD cirrhosis.

Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing.

INTRODUCTION: Natural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records. METHODS: We utilized this technique to develop algorithms capable of "reading" full-text radiology reports to accurately identify the presence of fatty liver disease. Abdominal ultrasound, computerized tomography, and magnetic resonance imaging reports were retrieved from the Veterans Affairs Corporate Data Warehouse from a random national sample of 652 patients. Radiographic fatty liver disease was determined by manual review by two physicians and verified with an expert radiologist. A split validation method was utilized for algorithm development. RESULTS: For all three imaging modalities, the algorithms could identify fatty liver disease with >90% recall and precision, with F-measures >90%. DISCUSSION: These algorithms could be used to rapidly screen patient records to establish a large cohort to facilitate epidemiological and clinical studies and examine the clinic course and outcomes of patients with radiographic hepatic steatosis.