Li J, Lu M, Zhou Y, Bowlus CL, Lindor K, Rodriguez-Watson C, Romanelli RJ, Haller IV, Anderson H, VanWormer JJ, Boscarino JA, Schmidt MA, Daida YG, Sahota A, Vincent J, Wu KH, Trudeau S, Rupp LB, Melkonian C, and Gordon SC. Dynamic Risk Prediction of Response to Ursodeoxycholic Acid Among Patients with Primary Biliary Cholangitis in the USA. Dig Dis Sci 2021.
Digestive diseases and sciences
BACKGROUND: Ursodeoxycholic acid (UDCA) remains the first-line therapy for primary biliary cholangitis (PBC); however, inadequate treatment response (ITR) is common. The UK-PBC Consortium developed the modified UDCA Response Score (m-URS) to predict ITR (using alkaline phosphatase [ALP] > 1.67 times the upper limit of normal [*ULN]) at 12 months post-UDCA initiation). Using data from the US-based Fibrotic Liver Disease Consortium, we assessed the m-URS in our multi-racial cohort. We then used a dynamic modeling approach to improve prediction accuracy.
METHODS: Using data collected at the time of UDCA initiation, we assessed the m-URS using the original formula; then, by calibrating coefficients to our data, we also assessed whether it remained accurate when using Paris II criteria for ITR. Next, we developed and validated a dynamic risk prediction model that included post-UDCA initiation laboratory data.
RESULTS: Among 1578 patients (13% men; 8% African American, 9% Asian American/American Indian/Pacific Islander; 25% Hispanic), the rate of ITR was 27% using ALP > 1.67*ULN and 45% using Paris II criteria. M-URS accuracy was "very good" (AUROC = 0.87, sensitivity = 0.62, and specificity = 0.82) for ALP > 1.67*ULN and "moderate" (AUROC = 0.74, sensitivity = 0.57, and specificity = 0.70) for Paris II. Our dynamic model significantly improved accuracy for both definitions of ITR (ALP > 1.67*ULN: AUROC = 0.91; Paris II: AUROC = 0.81); specificity approached 100%. Roughly 9% of patients in our cohort were at the highest risk of ITR.
CONCLUSIONS: Early identification of patients who will not respond to UDCA treatment using a dynamic prediction model based on longitudinal, repeated risk factor measurements may facilitate earlier introduction of adjuvant treatment.
ePub ahead of print