Continuous Subcutaneous Versus Intestinal Levodopa Infusion for Parkinson's Disease: A Real‐World, Monocentric, Observational Study and Critical Review

BACKGROUND: Subcutaneous foslevodopa-foscarbidopa (SCFF) is a novel, non-surgical dopaminergic infusion therapy for better controlling motor fluctuations in advanced Parkinson's disease (PD). However, there are scarce real-world data on efficacy, adverse events and comparisons with other infusion strategies. OBJECTIVES: Here, we aimed to provide real-world, observational data on treatment of advanced PD with SCFF infusion and compare and review its performance versus intestinal dopaminergic treatment strategies. METHODS: We retrospectively collected monocentric data from patients with advanced PD treated with either SCFF (n = 58) or levodopa-carbidopa(-entacapone) intestinal gel (n = 70). We extracted efficacy and adverse events in a real-world setting and systematically reviewed the available literature for comparison. RESULTS: One-third of patients on SCFF withdrew from treatment within 4 weeks. Though generally deemed effective by both clinician and patient, there was a significant mismatch amid clinician (89%) and patient (74%) as per global clinical impression. Correspondingly, patients commonly withdrew due to preference rather than adverse events. Similar results were found for intestinal gel treated patients (89% vs. 70%). Comparison with intestinal levodopa & literature revealed that dose adjustments and adverse events in pump-based therapies for PD are overall common, yet not systematically managed. CONCLUSION: Conclusively, our data suggest real-world efficacy for SCFF in controlling motor fluctuations. However, there are significant dropout rates, side effects and patient-clinician disagreement in global efficacy estimation. Comparison with intestinal infusion and literature revealed that pump-based therapies lack structured management. We recommend the establishment of systematic guidelines for pump-based therapies in advanced PD and provide a first troubleshooting algorithm for treating clinicians.