E145 Developing and externally validating a multivariable prediction model to predict the risk of developing psoriatic arthritis in adults newly diagnosed with psoriasis in primary care: an observational cohort study

Abstract Background/Aims Detection of people with psoriasis at increased risk for developing psoriatic arthritis (PsA) is essential to aid early diagnosis and treatment. However, there are limitations of the existing clinical prediction models: none have been externally validated and most are based on studies with modest sample sizes. We aimed to develop and externally validate a multivariable prediction model to predict the risk of developing PsA in adults newly diagnosed with psoriasis in primary care. Methods A retrospective observational cohort study using primary care electronic health record data was performed from 2000-2024. The exposure cohort was adults with an incident diagnosis of psoriasis. Outcome was a diagnosis of PsA. Time at risk for the model was five years. The Clinical Practice Research Datalink (CPRD) GOLD was used for developing and internally validating the model. The Health Improvement Network (THIN) databases from UK, France, Spain, Italy, Romania, and Belgium were used for external validation. Extreme Gradient Boosting was used to build the model. Results Table 1 summarises the results from model development and external validation. The discrimination in the train and test datasets in CPRD GOLD showed that the model performed well above the threshold. There was good calibration for the train dataset in CPRD GOLD. However, there was poor calibration in the test dataset: the model underpredicted the outcome. There was also poor calibration in the THIN databases used for external validation. The predictors with the highest discriminatory power were age and visit occurrence to the GP in the one year prior to their psoriasis diagnosis. Conclusion This is the first clinical prediction model for predicting PsA developed using clinical markers available in primary care that has been externally validated. The model performs well in the dataset it was developed in (similar to other published models). However, it did not perform well when externally validated. It could be that further granular data not available in primary care data and other markers (e.g., genetic) are needed to predict PsA. The Health initiatives in Psoriasis and PsOriatic arthritis ConsoRTium European States (HIPPOCRATES) consortium is aiming to address this through the HIPPOCRATES Prospective Observational Study (HPOS). Disclosure A. Vivekanantham: None. M. Pineda Moncusi: None. E. Burn: None. S. Khalid: None. D. Prieto Alhambra: Consultancies; DPA has provided consultancy services, with fees paid to the University, for UCB Biopharma. Honoraria; DPA is a member of the Board of the EHDEN Foundation and has received grants from the European Medicines Agency and the Innovative Medicines Initiative. Grants/research support; DPA’s department has received grants from Amgen, Chiesi-Taylor, Gilead, Lilly, Janssen, Novartis, and UCB Biopharma. Other; Janssen has funded or supported training programmes organised by DPA’s department. L.C. Coates: Consultancies; LCC worked as a paid consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer and UCB. Grants/research support; LCC received grants/research support from AbbVie, Amgen, Celgene, Eli Lilly, Novartis and Pfizer. Other; LCC has been paid as a speaker for AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Medac, Novartis, Pfizer and UCB.