Evidence map›Paper›PMID 36751867›Full record

ArticleAnnals of clinical and translational neurology2023

A machine learning approach to predict quality of life changes in patients with Parkinson's Disease.

Tyler D Alexander, Chandrasekhar Nataraj, Chengyuan Wu

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Article in Annals of clinical and translational neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Tyler D AlexanderDepartment of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, 19107, USA.ORCID 0000-0002-6886-9189
Chandrasekhar NatarajVillanova Center for Analytics of Dynamic Systems (VCADS), Villanova University, Villanova, Pennsylvania, 19085, USA.
Chengyuan WuDepartment of Neurological Surgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania, 19107, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveParkinson disease (PD) is a progressive neurodegenerative disorder with an annual incidence of approximately 0.1%. While primarily considered a motor disorder, increasing emphasis is being placed on its non-motor features. Both manifestations of the disease affect quality of life (QoL), which is captured in part II of the Unified Parkinson's Disease Rating Scale (UPDRS-II). While useful in the management of patients, it remains challenging to predict how QoL will change over time in PD. The goal of this work is to explore the feasibility of a machine learning algorithm to predict QoL changes in PD patients.

methodsIn this retrospective cohort study, patients with at least 12 months of follow-up were identified from the Parkinson's Progression Markers Initiative database (N = 630) and divided into two groups: those with and without clinically significant worsening in UPDRS-II (n = 404 and n = 226, respectively). We developed an artificial neural network using only UPDRS-II scores, to predict whether a patient would clinically worsen or not at 12 months from follow-up.

resultsUsing UPDRS-II at baseline, at 2 months, and at 4 months, the algorithm achieved 90% specificity and 56% sensitivity.

interpretationA learning model has the potential to rule in patients who may exhibit clinically significant worsening in QoL at 12 months. These patients may require further testing and increased focus.

Indexed as

Parkinson DiseaseHumansMental Status and Dementia TestsNeural Networks, ComputerQuality of LifeRetrospective Studies

Identifiers

PMID36751867
PMCPMC10014008

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