RESUMO
BACKGROUND AND AIMS: Non-invasive variceal risk stratification systems have not been validated in patients with hepatocellular carcinoma (HCC), which presents logistical barriers for patients in the setting of systemic HCC therapy. We aimed to develop and validate a non-invasive algorithm for the prediction of varices in patients with unresectable HCC. METHODS: We performed a retrospective cohort study in 21 centers in the US including adult patients with unresectable HCC and Child Pugh A5-B7 cirrhosis diagnosed between 2007 and2019. We included patients who completed an esophagogastroduodonoscopy (EGD) within 12 months of index imaging but prior to HCC treatment. We divided the cohort into a 70:30 training set and validation set, with the goal of maximizing negative predictive value (NPV) to avoid EGD in low-risk patients. RESULTS: We included 707 patients (median age 64.6 years, 80.6% male and 74.0% White). Median time from HCC diagnosis to EGD was 47 (IQR: 114) days, with 25.0% of patients having high-risk varices. A model using clinical variables alone achieved a NPV of 86.3% in the validation cohort, while a model integrating clinical and imaging variables had an NPV 97.4% in validation. The clinical and imaging model would avoid EGDs in over half of low-risk patients while misclassifying 7.7% of high-risk patients. CONCLUSION: A model incorporating clinical and imaging data can accurately predict the absence of high-risk varices in patients with HCC and avoid EGD in many low-risk patients prior to the initiation of systemic therapy, thus expediting their care and avoiding treatment delays.
RESUMO
OBJECTIVE: We aimed to describe preliminary dietary intake results using DietID™ for dietary assessment during pregnancy. A sub-sample of participants in the Research Enterprise to Advance Children's Health (REACH) prospective birth cohort from Detroit, MI received a unique web link to complete the DietID™ assessment multiple times during pregnancy. We present results for the first dietary assessment completed during pregnancy by each participant. DietID™ uses an image-based algorithm to estimate nutrient intake, dietary patterns, and diet quality and provides immediate results to participants. Descriptive statistics were used to summarize participant characteristics, nutrient intakes, dietary patterns, diet quality, and participant-rated accuracy of individual dietary assessment results. Differences in diet parameters were assessed by participant race with an independent t-test. RESULTS: Participants (n = 84) identified as majority Black (n = 47; 56%), reflective of the source population. Mean (SD) maternal age and gestational age at dietary assessment were 32 (5.6) years and 14.3 (4.8) weeks, respectively. Mean dietary quality, as reported in the DietID™ data output as the Healthy Eating Index (HEI), was 68 (range 12-98; higher scores indicate higher diet quality) and varied significantly between Black (mean [SD] 61 [23]) and White (mean [SD] 81 [19]) race (p < 0.01). Mean participant-rated accuracy of individual dietary assessment results was high at 87% on a scale of 0-100% ("not quite right" to "perfect"; range 47-100%).