Development and validation of a novel scoring system integrating MIBG scintigraphy and SPECT imaging for differentiating Parkinson's disease.

Background: Early and accurate differentiation of Parkinson's disease (PD) from other parkinsonian syndromes (PS) is crucial for appropriate management and prognostication. This study aimed to develop and evaluate a diagnostic model and a simplified scoring system combining clinical features, with parameters from cardiac Methods: This retrospective study included 102 patients clinically diagnosed with PD and 71 patients with PS, based on their final diagnostic classification after follow-up. Data on demographic characteristics, clinical features, MIBG scintigraphy (15-min and 4-h heart-to-mediastinum (H/M) ratios, 4-h clearance rate), and SPECT findings (categorized as no uptake, uniform, or non-uniform) were collected. Patients with missing SPECT data were excluded. Univariate analyses were performed, and variables with Results: The final logistic regression model identified 4-h H/M ratio (OR 0.109, 95% CI 0.033-0.358), 4-h clearance rate (OR 4.500, 95% CI 1.030-19.651), and SPECT findings as significant predictors of PD. The logistic model achieved an area under the ROC curve (AUC) of 0.810 (95% CI 0.744-0.876), with a sensitivity of 81.4% and specificity of 70.4%. A derived combined score (ranging from 3 to 9 points) demonstrated an AUC of 0.800 (95% CI 0.736-0.864), with a sensitivity of 78.4% and specificity of 70.4% at a cutoff of 5 points. Conclusion: A combination of MIBG scintigraphy parameters and SPECT imaging provides good diagnostic accuracy for differentiating PD from PS. The developed logistic regression model and the simplified scoring system offer promising tools for clinical practice, potentially improving diagnostic precision. Further prospective validation in larger, diverse cohorts is warranted.