Liver international : official journal of the International Association for the Study of the Liver · 2026
PSC-DM: A Validated Clinical Diagnostic Model for Distinguishing Primary From Secondary Sclerosing Cholangitis.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Scaravaglio Miki, Motta Rodrigo V, Cristoferi Laura, Maino Cesare, Marini Alberto, Nofit Eugenia, Pesatori Eugenia, Gallo Camilla, Bolis Francesca, D'Amato Daphne, Curto Armando, Malinverno Federica, Gerussi Alessio, Viganò Raffaella, Viganò Mauro, Rossi Stephen J, Kowdley Kris V, Invernizzi Pietro, Nardi Alessandra, Carbone Marco, Culver Emma L
Аннотация
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.
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.