NCT06903637

Identification of Priority Clinical Variables in Rehabilitation of Lower Extremity Amputation

Personalized Rehabilitation for Lower Limb Amputation: the Role of Hierarchical Cluster Analysis in Identifying Priority Clinical Variables

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 70 (actual)
• Sponsor: Cigdem Cinar · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Background: Identifying which variables influence the personalized rehabilitation of patients with lower limb amputation and understanding their interrelationships optimizes resource allocation. This study aims to identify priority variables that influence clinical follow-up using hierarchical cluster analysis (HCA). Methods: Data on 26 variables were collected from 70 patients diagnosed with lower limb amputation; (age, gender, body mass index(BMI), marital status, education, occupation, smoking and alcohol use, amputation - level/duration/lateral/etiology, prosthesis type, type of additional prosthesis, Kellgren Lawrence Classification(KLC) - right/left knee, Houghton Scale(HS), Timed-Up\&Go(TUG) Test, Trinity Amputation and Prosthesis Experience Scales(TAPES)-prosthesis satisfaction/psychosocial adjustment/activity limitation, Using the 12-item Short Form Health Questionnaire(SF-12)-physical score(PS)/mental score(MS), Locomotor Capabilities Index-5(LCI-5), Medicare Functional Classification Level(MFCL) and Falls Efficacy Scale(FES)). From the collected data, dendrograms were formed by using HCA with Ward linkage method.

Conditions

• Lower Limb Amputation

Interventions

Type	Name	Description
OTHER	Hierarchical Cluster Analysis	The complexity of clinical follow-up stems from the wide range of variables involved, such as physical performance, mobility, pain, psychological well-being, and prosthetic functionality. Therefore, developing methods to identify the most relevant and interrelated clinical variables is critical for enhancing patient management. To address this need, Hierarchical Cluster Analysis (HCA) offers a powerful multivariate statistical approach for detecting relationships among clinical variables.

Timeline

• Start date: 2023-12-23
• Primary completion: 2024-12-23
• Completion: 2025-01-01
• First posted: 2025-03-31
• Last updated: 2025-03-31

Locations

1 site across 1 country: Turkey (Türkiye)