Predicting hospitalization from real-world measures in patients with chronic kidney disease: A proof-of-principle study

Abstract

Objective To investigate if in-clinic measures of physical function and real-world measures of physical behavior and mobility effort are associated with one another and to determine if they predict future hospitalization in participants with chronic kidney disease (CKD). Methods In this secondary analysis, novel real-world measures of physical behavior and mobility effort, including the best 6-minute step count (B6SC), were derived from passively collected data from a thigh worn actigraphy sensor and compared to traditional in-clinic measures of physical function (e.g. 6-minute walk test (6MWT). Hospitalization status during 2 years of follow-up was determined from electronic health records. Correlation analyses were used to compare measures and Cox Regression analysis was used to compare measures with hospitalization. Results One hundred and six participants were studied (69 ± 13 years, 43% women). Mean ± SD baseline measures for 6MWT was 386 ± 66 m and B6SC was 524 ± 125 steps. Forty-four hospitalization events over 224 years of total follow-up occurred. Good separation was achieved for tertiles of 6MWT, B6SC and steps/day for hospitalization events. This pattern persisted in models adjusted for demographics (6MWT: HR = 0.63 95% CI 0.43–0.93, B6SC: HR = 0.75, 95% CI 0.56–1.02 and steps/day: HR = 0.75, 95% CI 0.50–1.13) and further adjusted for morbidities (6MWT: HR = 0.54, 95% CI 0.35–0.84, B6SC: HR = 0.70, 95% CI 0.49–1.00 and steps/day: HR = 0.69, 95% CI 0.43–1.09). Conclusion Digital health technologies can be deployed remotely, passively, and continuously to collect real-world measures of physical behavior and mobility effort that differentiate risk of hospitalization in patients with CKD.