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Literature record

Clinical predictors of freezing of gait in patients with Parkinson's disease: A systematic review.

PMID 40101322 | DOI 10.1016/j.clineuro.2025.108848 · Clinical neurology and neurosurgery · 2025

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BACKGROUND: Freezing of gait (FOG) is a debilitating motor symptom of Parkinson's disease (PD) that markedly impacts patients' quality of life. This review aims to identify clinical predictors of FOG to facilitate early prediction and future interventions. METHODS: A systematic review adhering to PRISMA guidelines was conducted. Comprehensive searches in PubMed, EBSCO, and Web of Science yielded 1761 records. After removing duplicates, 1558 records were screened by title and abstract, and 92 full-text articles were assessed. Nine studies met the inclusion criteria and were qualitatively synthesized. RESULTS: Our systematic review indicates that higher baseline MDS-UPDRS scores, which reflect greater disease severity, alongside elevated doses and early use of levodopa, are predictive of FOG in patients with PD. Additionally, higher Postural Instability and Gait Disorder (PIGD) scores, motor fluctuations, and lower limb disease onset further increase the risk of FOG. Other factors associated with an increased risk of FOG include older age, longer disease duration, anxiety, hyposmia, cognitive deficits, and sleep disorders. Furthermore, decreased step initiation duration when using visual cues serves as a predictor for the development of FOG. Early treatment with amantadine, selegiline, and dopamine agonists may help reduce the risk of developing FOG. CONCLUSION: A combination of motor and non-motor factors predicts the development of FOG. Understanding FOG predictors is crucial for developing future therapeutics and personalized management plans, enabling targeted interventions and improved outcomes.

Validated evidence

TypeEntitySource evidenceConfidenceExtractor
phenotypeParkinson disease“Clinical predictors of freezing of gait in patients with Parkinson's disease: A systematic review.”0.93phenotype_alias_lexicon_v2