PSC
‐
DM
: A Validated Clinical Diagnostic Model for Distinguishing Primary From Secondary Sclerosing Cholangitis
Miki Scaravaglio, Rodrigo V. Motta, Laura Cristoferi, Cesare Maino, Alberto Marini, Eugenia Nofit, Eugenia Pesatori, Camilla Gallo, Francesca Bolis, Daphne D'Amato, Armando Curto, Federica Malinverno, Alessio Gerussi, Raffaella Viganò, Mauro Viganò, Stephen J. Rossi, Kris V. Kowdley, Pietro Invernizzi, Alessandra Nardi, Marco Carbone, Emma L. Culver ABSTRACT
Background and Aims
Primary sclerosing cholangitis (PSC) is a rare, progressive cholangiopathy for which diagnosis remains challenging because of the absence of disease‐specific markers and the presence of secondary causes of sclerosing cholangitis (SSC) that closely mimic its clinical presentation. Diagnostic uncertainty can delay appropriate management and add to the psychological burden experienced by patients. This study aimed to identify clinical parameters that support the early and accurate differentiation of PSC from SSC.
Methods
We conducted a multicentre retrospective study of adults with an established diagnosis of PSC or SSC. Independent predictors of PSC were identified using multivariable logistic regression and externally validated in an independent cohort.
Results
In the derivation cohort ( n = 234, 155 PSC, 79 SSC), six variables were independently associated with PSC diagnosis, including younger age at presentation ( p < 0.0001), presence of IBD ( p < 0.0001), absence of prior hepato‐pancreato‐biliary surgery ( p < 0.0001), autoimmune comorbidities ( p = 0.0038), family history of autoimmune diseases ( p = 0.0086), and absence of pancreatic abnormalities ( p = 0.0338). These variables were used to develop the PSC diagnostic model (PSC‐DM). In the external validation cohort ( n = 142, 98 PSC, 44 SSC), the AUROC of PSC‐DM was 0.95 (95% CI 0.91–0.98). In an exploratory analysis, PSC‐DM significantly improved non‐expert diagnostic accuracy (94.3% versus 78.6%, p = 0.0023).
Conclusions
PSC‐DM is a simple, externally validated clinical prediction model that accurately distinguishes PSC from SSC using routinely available clinical variables. It may support initial diagnostic assessment in non‐specialist settings, facilitate timely referral to expert centres and improve patient selection for clinical trials.