Utilizing the Timed Up and Go Test to Predict Five-Year Mortalities Among Older Cardiovascular Inpatients: A

Wenzheng Li1,2, Min Zeng1,2, Yuhao Wan1,2

  • 1Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, 100730 Beijing, China.

PubMed

Insights

The Timed Up and Go test (TUGT) can predict mortality in older cardiovascular disease (CVD) patients. A TUGT result over 15 seconds indicates a higher risk of death within five years.

Area of Science:

  • Gerontology
  • Cardiology
  • Clinical Epidemiology

Background:

  • Cardiovascular disease (CVD) is a leading cause of mortality in older adults.
  • Predictive markers for long-term outcomes in CVD patients are crucial for risk stratification.
  • The Timed Up and Go test (TUGT) assesses functional mobility and may reflect overall health status.

Purpose of the Study:

  • To evaluate the predictive capability of the TUGT for five-year all-cause mortality in elderly individuals diagnosed with CVD.
  • To determine if a TUGT duration exceeding 15 seconds is associated with increased mortality risk.

Main Methods:

  • A prospective cohort study involving 491 older patients with CVD was conducted.
  • Patients performed the TUGT at baseline and were grouped based on results (TUGT >15 s vs. TUGT ≤15 s).
  • All-cause mortality was tracked over a five-year follow-up period.

Main Results:

  • A total of 69 deaths (14.05%) occurred during the five-year follow-up.
  • Patients with TUGT >15 s were older and had higher rates of heart failure and stroke/TIA.
  • Multivariate analysis showed TUGT >15 s was independently associated with a 2.03-fold increased hazard of five-year mortality (HR: 2.029; 95% CI: 1.198-3.437).

Conclusions:

  • The TUGT is a valuable, independent predictor of five-year all-cause mortality in older patients with CVD.
  • A TUGT duration exceeding 15 seconds signifies a poorer prognosis and warrants closer monitoring.
  • This simple functional test can aid in identifying high-risk CVD patients for targeted interventions.
Abstract

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