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Background <p>Non-adherence to pharmacotherapy in Parkinson’s disease (PD) is associated with worse clinical outcomes and poor quality of life (QoL). Early identification of non-adherent patients is crucial, as appropriate interventions can improve clinical conditions and QoL. Our study aimed to use cluster analysis to identify risk profiles of patients with lower adherence rates.</p> Methods <p>We included 124 cognitively intact patients with PD. Validated diagnostic instruments were used to measure adherence, QoL, non-motor symptoms (NMS), motor involvement, and complications. K-Means clustering was employed to create empirical subtypes based on these variables.</p> Results <p>Cluster analysis identified four distinct PD subtypes. Subtype 1 was characterized by worse motor state, frequent NMS, and poor QoL without complications. Subtype 2 had higher LEDD and complications but lower scores in other parameters. Subtype 3 showed low scores across all parameters, indicating a relatively good clinical condition. Subtype 4 showed higher scores in all observed parameters. Adherence levels significantly differed between subtypes (Cramer's V = 0.262, p = 0.009), with subtypes 1 and 4 showing lower adherence and subtype 3 showing higher adherence.</p> Conclusion <p>Patients with worsened motor state, NMS, and complications are more prone to lower adherence, which correlates with poorer QoL. Early identification and targeted interventions are essential to enhance adherence in these groups.</p>

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Clinical Clusters for Identification of Lower Adherence in Patients with Parkinson’s Disease: A Cross-Sectional Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月16日

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  • Igor Straka,
  • Michal Minár,
  • Veronika Boleková,
  • Matej Škorvánek,
  • Milan Grofik,
  • Katarína Danterová,
  • Ján Benetin,
  • Egon Kurča,
  • Kathryn A. Wyman-Chick,
  • Andrea Gažová,
  • Ján Kyselovič,
  • Peter Valkovič

摘要

Background

Non-adherence to pharmacotherapy in Parkinson’s disease (PD) is associated with worse clinical outcomes and poor quality of life (QoL). Early identification of non-adherent patients is crucial, as appropriate interventions can improve clinical conditions and QoL. Our study aimed to use cluster analysis to identify risk profiles of patients with lower adherence rates.

Methods

We included 124 cognitively intact patients with PD. Validated diagnostic instruments were used to measure adherence, QoL, non-motor symptoms (NMS), motor involvement, and complications. K-Means clustering was employed to create empirical subtypes based on these variables.

Results

Cluster analysis identified four distinct PD subtypes. Subtype 1 was characterized by worse motor state, frequent NMS, and poor QoL without complications. Subtype 2 had higher LEDD and complications but lower scores in other parameters. Subtype 3 showed low scores across all parameters, indicating a relatively good clinical condition. Subtype 4 showed higher scores in all observed parameters. Adherence levels significantly differed between subtypes (Cramer's V = 0.262, p = 0.009), with subtypes 1 and 4 showing lower adherence and subtype 3 showing higher adherence.

Conclusion

Patients with worsened motor state, NMS, and complications are more prone to lower adherence, which correlates with poorer QoL. Early identification and targeted interventions are essential to enhance adherence in these groups.