Quantified Kinematics to Evaluate Patient Chemotherapy Risks in Clinic

Abstract

© 2020 American Society of Clinical Oncology. All rights reserved. PURPOSE Performance status (PS) is a key factor in oncologic decision making, but conventional scales used to measure PS vary among observers. Consumer-grade biometric sensors have previously been identified as objective alternatives to the assessment of PS. Here, we investigate how one such biometric sensor can be used during a clinic visit to identify patients who are at risk for complications, particularly unexpected hospitalizations that may delay treatment or result in low physical activity. We aim to provide a novel and objective means of predicting tolerability to chemotherapy. METHODS Thirty-eight patients across three centers in the United States who were diagnosed with a solid tumor with plans for treatment with two cycles of highly emetogenic chemotherapy were included in this single-arm, observational prospective study. A noninvasive motion-capture system quantified patient movement from chair to table and during the get-up-and-walk test. Activity levels were recorded using a wearable sensor over a 2-month period. Changes in kinematics from two motion-capture data points pre- and post-treatment were tested for correlation with unexpected hospitalizations and physical activity levels as measured by a wearable activity sensor. RESULTS Among 38 patients (mean age, 48.3 years; 53% female), kinematic features from chair to table were the best predictors for unexpected health care encounters (area under the curve, 0.775 6 0.029) and physical activity (area under the curve, 0.830 6 0.080). Chair-to-table acceleration of the nonpivoting knee (t = 3.39; P = .002) was most correlated with unexpected health care encounters. Get-up-and-walk kinematics were most correlated with physical activity, particularly the right knee acceleration (t = −2.95; P = .006) and left arm angular velocity (t = −2.4; P = .025). CONCLUSION Chair-to-table kinematics are good predictors of unexpected hospitalizations, whereas the get-up- and-walk kinematics are good predictors of low physical activity.