Many medical applications may benefit from data-driven patient grouping, particularly when optimized using outcome information. These patient outcomes are usually multifaceted (not captured by a single score) and are frequently presented in the form of time-to-event data (such as time to death or cancer recurrence). For this purpose, we introduce MESA-LVQ, a novel Multi-Event Survival Analysis target-informed clustering method based on the recently proposed SurvivalLVQ method. MESA-LVQ enables patient clustering informed by multiple survival analysis outcomes simultaneously. We apply this approach in the context of time-to-worsening in multiple sclerosis, specifically investigating Kurtzke’s functional system scores (sensory, brainstem, sphincteric, pyramidal, cerebral, visual, cerebellar) and their aggregate score (the Expanded Disability Status Scale). To evaluate the approach, we use the publicly available MSOAC(Data used in the preparation of this article were obtained from the Multiple Sclerosis Outcome Assessments Consortium (MSOAC). As such, the investigators within MSOAC contributed to the design and implementation of the MSOAC Placebo database and/or provided placebo data, but did not participate in the analysis of the data or the writing of this report.) Placebo database from the Critical Path Institute. After preprocessing and inclusion criteria, 2340 patients and 36 features remain, along with the 8 time-to-worsening events defined above. We show that MESA-LVQ is able to find a more valid, coherent, and interpretable clustering than single-event SurvivalLVQ. All modeling and experiment code is available at https://github.com/robbedhondt/MESALVQ .