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

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.

Smartphone-Based Prediction Model for Postoperative Cardiac Surgery Outcomes Using Preoperative Gait and Posture Measures

“…Stopwatch was started and stopped as per first footfall when crossing 0 m and 5 m mark. The participants were instructed to repeat for three trials of sit-to-stand postural transition followed by postural stability and walking trials with appropriate rest in between each trial [ 9 , 10 ]. The smartphone app had two data collection modules: (1) 5 m walk; (2) STS and postural stability.…”

“…Primary Outcome Measures [ 8 ]: postoperative mortality and morbidity were the dependent variables. Morbidity among CVD patients was stroke, prolonged ventilation, deep sternal wound infection, renal failure, and reoperation [ 10 ]. Dependent variables are several traditional gait and posture measures, which have been reported in a multitude of studies.…”

“…The resultant acceleration signals were filtered using zero-lag 4th order low-pass Butterworth filter (cut-off frequency = 6 Hz). A half-second moving window variance was computed and the threshold was set using initial stand-still data as described in our previous studies [ 9 , 10 ]. Once the time to start and stop is detected, the average velocity can be computed over the 5 m walk.…”

“…The pattern length (m) was chosen as 2, the similarity coefficient (d) was set as 0.2% of the standard deviation of ACC data (collected from start to stop during 5 m walk at sampling frequency of 50 Hz) which has been shown two produce reasonable statistical validity of ApEn [ 10 , 17 ]. C i m (d) is the mean of the fraction of patterns of length m that resemble the pattern of same length that begins at index i.…”

StraightenUp+: Monitoring of Posture during Daily Activities for Older Persons Using Wearable Sensors

“…Observation-based methods, such as video-recording, have the benefits of direct observation, but are difficult to implement for large groups [ 6 ] or people who are not in a fixed location. Another approach is to use sensors—either smartphone sensors [ 7 ], smartwatch sensors [ 8 ], or fixed sensors attached to the body, which have been used to measure physical and physiological parameters; e.g., measuring metabolic energy consumption [ 9 ] and bodily posture stability [ 10 , 11 , 12 ]; predicting falls [ 13 , 14 , 15 ]; and detecting regular daily activities [ 16 , 17 ]. Sensor-based physical activity classification systems can be described in terms of several factors: dataset (where the activities were collected and which activities were chosen as relevant), number of sensors, placement of sensors, features set, window size and classifier [ 18 ].…”