Jackson, Lauren

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.

Abstract Background In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. Methods Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. Results Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1,152 patients have been evaluated with an overall solve rate of 14.1% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. Conclusion Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

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.