Family Medicine & Primary Care Review

Abstract

1/2026 vol. 28
Original paper

Analysis of risk prediction models for neurological and musculoskeletal disorders in relation to quality-of-life indicators in post-stroke patients

  1. Department of Neurology, I. Horbachevsky Ternopil National Medical University (TNMU), Ternopil, Ukraine
  2. Department of Higher Nursing Education, I. Horbachevsky Ternopil National Medical University (TNMU), Ternopil, Ukraine
  3. Department of Medical Informatics, I. Horbachevsky Ternopil National Medical University (TNMU), Ternopil, Ukraine
  4. Department of Pathologic Anatomy, I. Horbachevsky Ternopil National Medical University (TNMU), Ternopil, Ukraine
  5. Department of Medical Biochemistry, I. Horbachevsky Ternopil National Medical University (TNMU), Ternopil, Ukraine
  6. Department of Medical Rehabilitation, I. Horbachevsky Ternopil National Medical University (TNMU), Ternopil, Ukraine
Family Medicine & Primary Care Review 2026; 28(1): 70–74
Online publish date: 2026/03/30
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Background

This study examined factors associated with quality of life in stroke patients as an important outcome measure after stroke that can contribute to a broader description of the disease and its consequences.

Objectives

To provide a comparative analysis of risk prediction models for neurological and musculoskeletal disorders in relation to quality-of-life indicators in post-stroke patients.

Material and methods

The study included 105 post-stroke patients with various symptoms of neurological and musculoskeletal disorders. The paper proposes risk criteria for neurological and musculoskeletal disorders, as well as quality-of-lifeindicators.

Results

The prognostic model with quality-of-life indicators included such factors with a significance level of < 0.05 as social functioning, physical functioning, localization of lesion in the occipital region, symptoms of musculoskeletal disorders, dizziness, limb numbness, paresis, hemihypesthesia, and motor disorders.

Conclusions

The proposed prognostic models are effective for the timely determination of RPN&MSD and RPN&MSDQoL while monitoring post-stroke patients and preventing complications. However, the RPN&MSDQoL model is more complete, as it includes the quality-of-life indicators PF and SF as important parameters for ensuring the effectiveness of comprehensive rehabilitation measures for post-stroke patients and has a higher determination coefficient R2 of 0.85 compared to 0.84 for the RPN&MSD model.

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