Global prevalence and incidence of Young Onset Parkinson's disease: A systematic review and meta-analysis.

BACKGROUND: There is a lack of enough evidence regarding the epidemiology of Young-onset Parkinson's disease (YOPD) which is needed by clinicians and healthcare policymakers. AIM: Herein, in this systematic review and meta-analysis, we aimed to estimate the global prevalence and incidence rates of YOPD. METHODS: We searched the literature in PubMed, Scopus, and Web of Science in May 2022. We included retrospective, prospective, cross-sectional observational population-based studies that reported the prevalence or incidence of PD in individuals younger than 40 years with known diagnostic criteria. RESULTS: After two-step screening, 50 studies were eligible to be included in our study. The age-standardized prevalence of YOPD was 10.2 per 100,000 persons globally while it was 14.7 per 100,000 population in European countries. Age-standardized prevalence estimates for 5-year age bands showed that the YOPD prevalence estimates varied from 6.1 per 100,000 population in the group aged 20-24 to 16.1 per 100,000 population in the group aged 35-39. Also, the age-standardized incidence of YOPD was 1.3 per 100,000 person-years population worldwide and 1.2 per 100,000 person-years in the European population. CONCLUSION: Based on this systematic review and meta-analysis, the overall prevalence of YOPD is 10.2 per 100,000 population, although estimates of the prevalence and incidence in low-income countries remain scarce. To improve monitoring and certain diagnoses of YOPD, healthcare providers and policymakers should be aware that much more effective tools are required.

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis.

BACKGROUND: Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS. METHODS: PubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17. RESULTS: A total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84-0.93) and 0.87 (95% CI: 0.81-0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5-11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08-0.19), and the diagnostic odds ratio was 56 (95% CI: 25-125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76-0.98) and 0.80 (95% CI: 0.53-0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6-14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02-0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606). CONCLUSION: ML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.