Abstract
Cardiovascular disease (CVD) patients with intrinsic cardiac cause for falling have been found to be frail and submissive to morbidity and mortality as post-operative outcomes. In these older CVD patients, gait speed is conjectured by the Society of Thoracic Surgeons (STS) as an independent predictor of post-operative morbidity and mortality. However, this guideline by STS has not been studied adequately with a large sample size; rather it is based largely on expert opinions of cardiac surgeons and researchers. Although one’s gait speed is not completely associated with one’s risk of falls, gait speed is a quick robust measure to classify frail/non-frail CVD patients and undoubtedly frail individuals are more prone to falls. Thus, this study examines the effects of inertial sensor-based quick movement variability characteristics in identifying CVD patients likely to have an adverse post-operative outcome. This study establishes a relationship with gait and postural predictor variables with patient’s post-operative adverse outcomes. Accordingly, inertial sensors embedded inside smartphones are indispensable for the assessment of elderly patients in clinical environments and may be necessary for quick objective assessment. Sixteen elderly CVD patients (Age 76.1 ± 3.6 years) who were scheduled for cardiac surgery the next day were recruited for this study. Based on STS recommendation guidelines, eight of the CVD patients were classified as frail (prone to adverse outcomes with gait speed ≤ 0.833 m/s) and the other eight patients as non-frail (gait speed > 0.833 m/s). Smartphone-derived walking velocity was found to be significantly lower in frail patients than that in non-frail patients (p < 0.01). Mean Center of Pressure (COP) radius (p < 0.01), COP Area (p < 0.01), COP path length (p < 0.05) and mean COP velocity (p < 0.05) were found to be significantly higher in frail patients than that in the non-frail patient group. Nonlinear variability measures such as sample entropy were significantly lower in frail participants in anterior-posterior (p < 0.01) and resultant sway direction (p < 0.01) than in the non-frail group. This study identified numerous postural and movement variability parameters that offer insights into predictive inertial sensor-based variables and post-operative adverse outcomes among CVD patients. In future, smartphone-based clinical measurement systems could serve as a clinical decision support system for assessing patients quickly in the perioperative period.
Highlights
Falls [1] and frailty [2,3] in elderly patients are multifactorial [4] and are attributed to a complex interaction of intrinsic and extrinsic risk factors superimposed on normal aging process [5,6,7,8].Patients with intrinsic cardiac cause for falling have been found to have higher mortality rateSensors 2018, 18, 1792; doi:10.3390/s18061792 www.mdpi.com/journal/sensorsSensors 2018, 18, 1792 than those with non-cardiovascular or unknown causes of falls [9]
Walking velocities computed using stopwatch time and smartphone time were found to be correlated with Pearson correlation coefficient = 0.8154 and spearman’s rho = 0.8834 (Figure 7)
This study was conducted in a clinical environment using smartphone-based inertial sensors and found that variability of postural and gait movements in cardiovascular disease (CVD) patients was associated with frailty and adverse post-operative outcomes
Summary
Falls [1] and frailty [2,3] in elderly patients are multifactorial [4] and are attributed to a complex interaction of intrinsic and extrinsic risk factors superimposed on normal aging process [5,6,7,8].Patients with intrinsic cardiac cause for falling have been found to have higher mortality rateSensors 2018, 18, 1792; doi:10.3390/s18061792 www.mdpi.com/journal/sensorsSensors 2018, 18, 1792 than those with non-cardiovascular or unknown causes of falls [9]. Falls in cardiovascular disease (CVD) patients are reported to be caused by underlying cardiovascular disorders or are linked to aging [10] It remains unclear which factors are responsible for high fall risk in CVD patients, but some experts speculate that certain environments, medications, age-related changes, and diseases make a particular genotype of people vulnerable to frailty and falls in CVD patients [11,12]. This frailty phenotype is independently predictive of falls [13]. Some researchers have linked functional limitation [14,15,16,17], poor nutritional status [14,15,18], cognitive impairment [15,19,20], depression [20,21]
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