Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92866, USA.
Fowler School of Engineering, Chapman University, Orange, CA 92866, USA.
Sensors (Basel). 2021 Mar 2;21(5):1704. doi: 10.3390/s21051704.
Gait speed assessment increases the predictive value of mortality and morbidity following older adults' cardiac surgery. The purpose of this study was to improve clinical assessment and prediction of mortality and morbidity among older patients undergoing cardiac surgery through the identification of the relationships between preoperative gait and postural stability characteristics utilizing a noninvasive-wearable mobile phone device and postoperative cardiac surgical outcomes. This research was a prospective study of ambulatory patients aged over 70 years undergoing non-emergent cardiac surgery. Sixteen older adults with cardiovascular disease (Age 76.1 ± 3.6 years) scheduled for cardiac surgery within the next 24 h were recruited for this study. As per the Society of Thoracic Surgeons (STS) recommendation guidelines, eight of the cardiovascular disease (CVD) patients were classified as frail (prone to adverse outcomes with gait speed ≤0.833 m/s) and the remaining eight patients as non-frail (gait speed >0.833 m/s). Treating physicians and patients were blinded to gait and posture assessment results not to influence the decision to proceed with surgery or postoperative management. Follow-ups regarding patient outcomes were continued until patients were discharged or transferred from the hospital, at which time data regarding outcomes were extracted from the records. In the preoperative setting, patients performed the 5-m walk and stand still for 30 s in the clinic while wearing a mobile phone with a customized app "Lockhart Monitor" available at iOS App Store. Systematic evaluations of different gait and posture measures identified a subset of smartphone measures most sensitive to differences in two groups (frail versus non-frail) with adverse postoperative outcomes (morbidity/mortality). A regression model based on these smartphone measures tested positive on five CVD patients. Thus, clinical settings can readily utilize mobile technology, and the proposed regression model can predict adverse postoperative outcomes such as morbidity or mortality events.
步态速度评估提高了老年人心血管手术后死亡率和发病率的预测价值。本研究的目的是通过利用非侵入性可穿戴移动电话设备识别术前步态与术后心脏手术结果之间的关系，改善老年患者心脏手术后的临床评估和预测死亡率和发病率。这是一项针对 70 岁以上接受非紧急心脏手术的门诊患者的前瞻性研究。本研究招募了 16 名患有心血管疾病的老年人（年龄 76.1±3.6 岁），他们计划在接下来的 24 小时内接受心脏手术。根据胸外科医师学会（STS）的推荐指南，8 名心血管疾病（CVD）患者被归类为虚弱（步态速度≤0.833 m/s 易发生不良后果），其余 8 名患者为非虚弱（步态速度>0.833 m/s）。治疗医生和患者对步态和姿势评估结果均不知情，以免影响决定进行手术或术后管理。继续对患者的结果进行随访，直到患者出院或从医院转走，此时从记录中提取有关结果的数据。在术前，患者在佩戴有可在 iOS 应用商店中使用的定制应用程序“Lockhart Monitor”的移动电话的情况下在诊所中进行 5 米行走和 30 秒站立静止。对不同步态和姿势测量值的系统评估确定了智能手机测量值的子集，该子集对具有不良术后结果（发病率/死亡率）的两组（虚弱与非虚弱）之间的差异最敏感。基于这些智能手机测量值的回归模型在 5 名 CVD 患者中呈阳性。因此，临床环境可以方便地利用移动技术，并且所提出的回归模型可以预测不良的术后结果，例如发病率或死亡率事件。
