Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease, but the extent of improvement in motor symptoms varies. A tool which accurately predicts patient outcomes based on data available pre‐surgery would be useful for clinical decision‐making and patient expectation management. Such data includes clinical and cognitive measures, neuroimaging, kinematics, functional connectivity and genetics. In this systematic review, we assess predictive models of motor outcomes from DBS. We searched the databases Web of Science, PubMed and Scopus for primary research articles that tested predictions from models based on pre‐surgical data and focused on motor outcomes from DBS for Parkinson's disease. We identified 19 studies fitting these criteria. The studies with high statistical power and generalisability use only clinical data and have limited accuracy. Studies including other types of data, such as magnetic resonance imaging, may have high accuracy but are under‐powered. Predictions are mostly limited to the first year after surgery, subthalamic nucleus‐targeted DBS and sum scores of motor performance. Following the results of the systematic search, we discuss candidate input and output variables and validation strategies to produce predictive models that are ready for translation to clinical practice. Ideally, models would predict scores for several motor domains, over a range of time after surgery, with confidence intervals. They should also be generalisable to clinics worldwide. To achieve this goal, models and datasets should be made publicly available to enable wider validation. These recommendations provide a framework to achieve predictive models for DBS outcomes that can be used clinically.
Maya Wilde, Dimitra Kiakou, E. Bakštein et al.· European Journal of Neurosci...· 0 citations
BACKGROUND
Predicting long-term outcomes in first-episode schizophrenia (FES) remains difficult, despite being especially important early in the illness, when timely intervention is most critical. It also remains unclear how much data from the initial phase of illness is required to improve prognostic accuracy.
METHODS
We analysed 68 FES patients assessed at baseline (V1; mean 0.5 years post-onset), one-year follow-up (V2; mean 1.2 years), and outcome (V3; mean 4.9 years). Elastic-net models were trained to predict three V3 outcomes - negative symptoms (PANSS Negative factor; Wallwork/Fortgang), global functioning (GAF), and quality of life (WHOQOL-BREF psychological domain) - using either V1 predictors alone (23 variables) or V1 + V2 combined (43 variables). Performance was evaluated using nested cross-validation on held-out data.
RESULTS
With V1 + V2 predictors, all three outcomes were predicted at statistically significant levels: PANSS Negative R2 = 0.22 (driven by log(DUP), PANSS Negative at V1/V2, and PANSS Disorganised at V2); WHOQOL-BREF Psychological Health R2 = 0.22 (driven by WHOQOL Psychological Health and GAF at V2); and GAF R2 = 0.14 (driven by GAF, PANSS Positive, WHOQOL Psychological Health at V2, and hospitalisation burden). With V1 predictors alone, only PANSS Negative showed meaningful predictive power (R2 = 0.15); GAF and WHOQOL-BREF did not outperform the intercept-only baseline.
CONCLUSION
Long-term functioning and quality of life in FES cannot be predicted from first-episode data alone; at least one year of follow-up is required, suggesting post-onset changes shape these outcomes. Negative symptoms are an exception: comparatively stable after initial treatment and predictable from baseline, with past symptomatology along with DUP selected as predictors - indicating stronger persistency and predictability than in the other two investigated outcomes.
E. Bakštein, J. Kúdelka, J. Schneider et al.· Schizophrenia Research· 0 citations
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