Dalal G, Wright SJ, Vass CM, Davison NJ, Vander Stichele G, Smith CH, Griffiths CEM, Payne K, PSORT consortium. Patient preferences for stratified medicine in psoriasis: a discrete choice experiment. Br J Dermatol. 2021 May 15. doi: 10.1111/bjd.20482.

BACKGROUND: New technologies have enabled the potential for stratified medicine in psoriasis. It is important to understand patients' preferences to enable the informed introduction of stratified medicine which is likely to involve a number of individual tests that could be collated into a prescribing-algorithm for biologic selection to be used in clinical practice.

OBJECTIVE: To quantify patient preferences for an algorithm-based approach to prescribing biologics ('biologic-calculator') in psoriasis.

METHODS: An online survey comprising a discrete choice experiment (DCE) was conducted to elicit the preferences of two purposive samples of adults living with psoriasis in the UK, identified from a psoriasis patient organisation (Psoriasis Association) and an online-panel provider (Dynata). Respondents chose between two biologic-calculators and conventional prescribing described using five attributes: treatment delay; positive and negative predictive values; risk of infection; cost-saving to the NHS. Each participant selected their preferred alternative from six hypothetical choice-sets. Additional data including socio-demographic characteristics were collected. Choice data were analysed using conditional logit and fully correlated random-parameters logit models.

Data from 212 respondents (Psoriasis Association = 67; Dynata = 145) were analysed. The signs of all estimated coefficients were consistent with a priori expectations. Respondents had a strong preference for high predictive accuracy and avoiding serious infection but there was evidence of systematic differences in preferences between the samples.

CONCLUSION: This study indicates that individuals with psoriasis would value a biologic-calculator and suggested that such a biologic-calculator should have sufficient accuracy to predict future response and risk of serious infection from the biologic.

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