Profile regression for subgrouping patients with early stage Parkinson's disease

TL;DR

This study used profile regression and clustering to identify distinct fall risk groups among early Parkinson's Disease patients, highlighting key clinical and functional factors associated with fall frequency.

Contribution

It applies finite mixture modeling to classify early PD patients into fall risk groups and identifies specific measures related to fall frequency, offering insights for targeted interventions.

Findings

Three patient clusters: non/single-fallers, low, and high fallers.

UPDRS subscales and PIGD are important for clustering.

Functional tests like Tinetti, TUG, and BBS differentiate clusters.

Abstract

Falls are detrimental to people with Parkinson's Disease (PD) because of the potentially severe consequences to the patients' quality of life. While many studies have attempted to predict falls/non-falls, this study aimed to determine factors related to falls frequency in people with early PD. Ninety nine participants with early stage PD were assessed based on two types of tests. The first type of tests is disease-specific tests, comprised of the Unified Parkinson's Disease Rating Scale (UPDRS) and the Schwab and England activities of daily living scale (SEADL). A measure of postural instability and gait disorder (PIGD) and subtotal scores for subscales I, II, and III were derived from the UPDRS. The second type of tests is functional tests, including Tinetti gait and balance, Berg Balance Scale (BBS), Timed-Up and Go (TUG), Functional Reach (FR), Freezing of Gait (FOG), Mini Mental State Examination (MMSE), and Melbourne Edge Test (MET). Falls were recorded each month for 6 months. Clustering of patients via Finite Mixture Model (FMM) was conducted. Three clusters of patients were found: non-or single-fallers, low frequency fallers, and high frequency fallers. Several factors that are important to clustering PD patients were identified: UPDRS subscales II and III subtotals, PIGD and SE ADL. However these factors could not differentiate PD patients with low frequency fallers from high frequency fallers. While Tinetti,TUG, and BBS turned to be important factors in clustering PD patients, and could differentiate the three clusters. FMM is able to cluster people with PD into three groups. We obtain several factors important to explaining the clusters and also found different role of disease specific measures and functional tests to clustering PD patients. Upon examining these measures, it might be possible to develop new disease treatment to prevent, or to delay, the occurrence of falls.