Inertial Sensor-Based Variables Are Indicators of Frailty and Adverse Post-Operative Outcomes in Cardiovascular

Rahul Soangra1, Thurmon E Lockhart2

  • 1Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92866, USA. soangra@chapman.edu.

Insights

Gait speed and movement variability measured by smartphone inertial sensors can identify frail cardiovascular disease (CVD) patients at risk for adverse post-operative outcomes. This objective assessment aids in predicting surgical risks for elderly patients.

Area of Science:

  • Biomedical Engineering
  • Geriatrics
  • Cardiology

Background:

  • Cardiovascular disease (CVD) patients are at increased risk of frailty, morbidity, and mortality post-surgery.
  • The Society of Thoracic Surgeons (STS) suggests gait speed as a predictor of post-operative outcomes in elderly CVD patients, though this requires further validation.
  • Frailty in CVD patients is linked to an increased risk of falls and adverse surgical outcomes.

Purpose of the Study:

  • To investigate the utility of inertial sensor-based movement variability characteristics in identifying CVD patients prone to adverse post-operative outcomes.
  • To establish relationships between gait, postural predictors, and post-operative adverse outcomes in elderly CVD patients.
  • To explore the potential of smartphone-embedded inertial sensors for objective patient assessment in clinical settings.

Main Methods:

  • Recruited sixteen elderly CVD patients scheduled for cardiac surgery.
  • Classified patients into frail (gait speed ≤ 0.833 m/s) and non-frail (gait speed > 0.833 m/s) groups based on STS guidelines.
  • Utilized smartphone inertial sensors to measure walking velocity, Center of Pressure (COP) parameters, and nonlinear variability measures (sample entropy).

Main Results:

  • Frail patients exhibited significantly lower walking velocity compared to non-frail patients (p < 0.01).
  • Frail patients showed significantly higher COP radius (p < 0.01), COP Area (p < 0.01), COP path length (p < 0.05), and mean COP velocity (p < 0.05).
  • Nonlinear variability measures, such as sample entropy, were significantly lower in frail participants in anterior-posterior and resultant sway directions (p < 0.01).

Conclusions:

  • Inertial sensor-based gait and postural variability parameters can effectively identify frail CVD patients at risk for adverse post-operative outcomes.
  • Smartphone-based assessments offer a quick, objective method for evaluating elderly CVD patients in perioperative care.
  • These findings support the development of smartphone-based clinical decision support systems for perioperative risk assessment.

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