Jackson, Lauren M

AbstractBackgroundYoung‐onset multiple system atrophy (YOMSA) is defined as the onset of multiple system atrophy (MSA) before the age of 40 years old. YOMSA is rare and there is much uncertainty of the phenotype and natural history in patients with YOMSA.ObjectiveThe objective is to evaluate the characteristics and disease course of patients with YOMSA.MethodsWe retrospectively reviewed medical records of patients with MSA who were evaluated at all Mayo Clinic sites from 1998 to 2021. We identified patients with YOMSA and evaluated clinical characteristics, autonomic function testing results, and disease course.ResultsOf 1496 patients with a diagnosis of clinically probable or clinically established MSA, 20 patients had YOMSA. The median age of onset was 39.1 (interquartile range [IQR] = 37.1, 40.1) years; 13 patients (65%) were male. MSA‐parkinsonism was the most common subtype (65%). The median duration of symptom onset to YOMSA diagnosis was 4.9 (IQR = 3.7, 9) years. At the time of medical record review, 17 patients were deceased with a median survival of 8.3 (IQR = 7, 10.9) years. Univariate analysis showed that initial onset of autonomic failure predicted unfavorable survival (hazard ratio = 2.89, P = 0.04) compared to those who presented with motor impairment only at onset. At the time of YOMSA diagnosis, composite autonomic severity score was available in 19 patients with a median of 5 (IQR = 4, 6.5).ConclusionsYOMSA resembles MSA in most aspects including phenotype and prognosis, although the diagnosis is usually delayed. The presence of autonomic failure at symptom onset may be a poor predictor for survival.

The course of patients with multiple system atrophy (MSA) who undergo deep brain stimulation (DBS) is unclear. In a retrospective review of 1,496 patients with MSA evaluated at our institutions from 1998–2021, 12 patients underwent DBS; 9 had a diagnosis of Parkinson’s disease at the time of surgery. Nine patients reported initial improvement in at least one symptom and 7 experienced overall worsening following DBS. All patients had at least one red flag sign or symptom suggesting atypical parkinsonism prior to surgery. Considering overall poor outcomes of DBS in MSA, we recommend careful consideration of red flags in patient selection.

OBJECTIVE One of the key metrics that is used to predict the likelihood of success of MR-guided focused ultrasound (MRgFUS) thalamotomy is the overall calvarial skull density ratio (SDR). However, this measure does not fully predict the sonication parameters that would be required or the technical success rates. The authors aimed to assess other skull characteristics that may also contribute to technical success. METHODS The authors retrospectively studied consecutive patients with essential tremor who were treated by MRgFUS at their center between 2017 and 2021. They evaluated the correlation between the different treatment parameters, particularly maximum power and energy delivered, with a range of patients’ skull metrics and demographics. Machine learning algorithms were applied to investigate whether sonication parameters could be predicted from skull density metrics alone and whether including combined local transducer SDRs with overall calvarial SDR would increase model accuracy. RESULTS A total of 62 patients were included in the study. The mean age was 77.1 (SD 9.2) years, and 78% of treatments (49/63) were performed in males. The mean SDR was 0.51 (SD 0.10). Among the evaluated metrics, SDR had the highest correlation with the maximum power used in treatment (ρ = −0.626, p < 0.001; proportion of local SDR values ≤ 0.8 group also had ρ = +0.626, p < 0.001) and maximum energy delivered (ρ = −0.680, p < 0.001). Machine learning algorithms achieved a moderate ability to predict maximum power and energy required from the local and overall SDRs (accuracy of approximately 80% for maximum power and approximately 55% for maximum energy), and high ability to predict average maximum temperature reached from the local and overall SDRs (approximately 95% accuracy). CONCLUSIONS The authors compared a number of skull metrics against SDR and showed that SDR was one of the best indicators of treatment parameters when used alone. In addition, a number of other machine learning algorithms are proposed that may be explored to improve its accuracy when additional data are obtained. Additional metrics related to eventual sonication parameters should also be identified and explored.