Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records.

BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital. METHODS: We identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards. RESULTS: The cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%). CONCLUSION: Our HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.

Development of a composite trauma exposure risk index.

The high burden of exposure to chronic life adversities and trauma is quite prevalent, but assessment of this risk burden is uncommon in primary care settings. This calls for a brief, multiple dimensional mental health risk screening tool in primary care settings. We aimed to develop such a screening tool named the University of California, Los Angeles (UCLA) Life Adversities Screener (LADS). Using pooled data across 4 studies from the UCLA Center for Culture, Trauma, and Mental Health Disparities, 5 domains of mental health risk including perceived discrimination, sexual abuse histories, family adversity, intimate partner violence, and trauma histories, were identified. Regression models for depression (Centers for Epidemiology Studies Depression Scale) and posttraumatic stress disorder (Posttraumatic Diagnostic Scale), controlling for demographic factors, were fitted to develop a weighted continuous scale score for the UCLA LADS. Confirmatory factor analysis supported the 5-domain structure, while item response theory endorsed the inclusion of each item. Receiver operating characteristic analysis indicated that the score was predictive for classifying subjects as reaching clinical threshold criteria for either depression (Beck Depression Inventory-II ≥ 14 or Patient Health Questionnaire-9 ≥ 10) or anxiety (Patient Health Questionnaire-13 ≥10). An optimal cut of 0.33 is suggested based on maximizing sensitivity and specificity of the LADS score, identifying patients at high risk for mental health problems. Given its predictive utility and ease of administration, the UCLA LADS could be useful as a screener to identify racial minority individuals in primary care settings who have a high trauma burden, needing more extensive evaluation.