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.