A Systematic Review of Parkinson's Disease Cluster Analysis Research

Abstract

One way to understand the Parkinson's disease (PD) population is to investigate the similarities and differences among patients through cluster analysis, which may lead to defined, patient subgroups for diagnosis, progression tracking and treatment planning. This paper provides a systematic review of PD patient clustering research, evaluating the variables included in clustering, the cluster methods applied, the resulting patient subgroups, and evaluation metrics. A search was conducted from 1999 to 2021 on the PubMed database, using various search terms including: Parkinson's disease, cluster, and analysis. The majority of studies included a variety of clinical scale scores for clustering, of which many provide a numerical, but ordinal, categorical value. Even though the scale scores are ordinal, these were treated as numerical values with numerical and continuous values being the focus of the clustering, with limited attention to categorical variables, such as gender and family history, which may also provide useful insights into disease diagnosis, progression, and treatment. The results pointed to two to five patient clusters, with similarities among the age of onset and disease duration. The studies lacked the use of existing clustering evaluation metrics which points to a need for a thorough, analysis framework, and consensus on the appropriate variables to include in cluster analysis. Accurate cluster analysis may assist with determining if PD patients' symptoms can be treated based on a subgroup of features, if personalized care is required, or if a mix of individualized and group-based care is the best approach.

Keywords: Cluster Analysis; Parkinson’s Disease; Patient Subgroups.

Figure 1.

UPDRS Part I, Questions 1 and 2 [2].

Figure 2.

MDS-UPDRS Part I, Question 1.1 [8].

Figure 3.

Literature Search Strategy Process.

Figure 4.

Number of Parkinson’s Disease Cluster Analysis Publications Per Year.

Figure 5.

Example Dendrogram [37].

Figure 6.

Partitioning Clustering Illustration [39].