The Canary Tibial Extension (CTE)/canturio Smart Extension (CSE) with Canary Health
Implanted Reporting Processor (CHIRP) System are intended to provide objective kinematic
data from the implanted medical device during a patient's total knee arthroplasty (TKA)
post-surgical care. The kinematic data are an adjunct to other physiological parameter
measurement tools applied or utilized by the physician during the course of patient
monitoring and treatment post-surgery.
The device is indicated for use in patients undergoing a cemented TKA procedure that are
normally indicated for at least a 30 mm sized tibial stem extension and/or normally
indicated for at least a 58mm sized tibial stem extension. The objective kinematic data
generated by the CTE/CSE with CHIRP System are not intended to support clinical
decision-making and have not been shown to provide any clinical benefit. The CTE/CSE with
CHIRP System is compatible with Zimmer Persona® Personalized Knee System.The CTE/CSE with CHIRP System uses external OR and Home Base Station units to query the
CTE/CSE implant (which has an internal radio and antenna) and upload the data collected
by the CTE/CSE implant to the Canary Cloud data management platform (CDMP). Information
from the implant is processed by the system's Canary Medical Gait Parameter (CMGP)
software, located in the Cloud, into clinically relevant metrics.
Follow-up Procedures:
Follow-up evaluations will be conducted at 1 month (± 14 days), 3 months (± 30 days), and
1 year (± 90 days) after surgery. The 1-year visit will be optional. At every follow-up
visit, pain and function of the study knee will be evaluated, and subject's quality of
life will be assessed.
RTM assessments will be collected pre-operatively and post-operatively through the
mymobility® care plan pathway as defined within the application for each specified
assessment. The interval window will open and close automatically within the software for
each specified target and captured clinically up-to 1-year post-operatively and
indefinitely if the patient continues to utilize the mymobility App. Data will also be
captured via the CTE/CSE sensors at variable intervals post-operatively and transmit data
indefinitely if the home base station is connected and has power.
Data collected through mymobility® will include, but is not limited to the following:
- - Pain & Opioid Tracking.
Data collected passively through CTE/CSE Sensors will include, but is not limited to the
following:
- - Distance Traveled In-Person clinical assessments will be collected at 1-month,
3-months, and 1-year (optional).
Clinical Data Collection: The Sponsor will collect all data within multiple databases
(e.g. including but not limited to: EDC, mymobility®, HealthKit, Google Fit, PIQ, ROSA).
The management of all study data received by the Sponsor will be the responsibility of
the Sponsor or its designee. The use or disclosure of all protected health information
will comply with all relevant data privacy and data security laws and requirements. All
information will be treated with strict adherence to professional standards of
confidentiality and will be held by the Sponsor under adequate security and restricted
accessibility.
Sample Size Calculation:
The study objective is to assess the seven kinematic metrics (step count, functional knee
ROM, tibial ROM, distance walked, walking speed, cadence and stride length) to be
captured via the CTE/CSE sensor in combination with the mymobility® platform and develop
correlative insights to assist surgeons in understanding and managing recovery in their
patient populations through post-operative gait metrics. All the clinical outcomes and
device safety data to be collected will be summarized by the applicable univariate
statistics and analyzed by the appropriate statistical models. In general, the logistic
regressions for the categorical data point, especially binary data for rare events such
as revision, hospital readmission etc., tend to require relatively large sample sizes to
have enough power to perform conclusive analyses. According to Bujang et al. [5], 100 +
50i patients are needed to effectively use logistics regression in predicting outcomes,
where i refers to the number of independent variables in the final model. It is expected
between 15 and 20 independent variables will be available for the model. Assuming 18
independent variables, the sample size needs to be n = 1,000.
General Statistical Methods:
Data collected in this study will be summarized descriptively. Categorical data (such as
biological sex, complications, satisfaction status, etc.) will be summarized using counts
and percentages over the time periods of interest. Continuous data (such as age, ROM,
etc.) will be summarized by using means, medians, standard deviation, minimum, maximum,
and 95% CI over the time periods of interest.
Appropriate linear regression and generalized linear regression models including logistic
regression, cox regression, mixed model for repeated measurements will be applied for the
primary, secondary and exploratory endpoints analysis per the data type and analysis
goals. Subgroup analysis on a variety of subsets of the collected data will also be
performed. The applied statistical models will be used to evaluate and quantify the
relationship among the differences in the episode of care data, patient pre-op physical
condition and medical history, gait parameters, range of motion, patient satisfaction,
nine most common post TKR complication incident rates, PROs, etc. Implant survival will
be evaluated using Kaplan-Meier product limit estimate and raw survival rates. In
addition to inference models, the predictive models will also be built to predict the
patient satisfaction and PROs using patient baseline status and early PIQ data.
Data Analysis:
Primary Endpoints: The primary endpoints based on the clinical outcomes of the Canary
Smart Tibial Stem when used in conjunction with the Persona IQ and mymobility® Platform
for primary total knee arthroplasty will be analyzed by the appropriate linear and
generalized linear regression models per the data type prospectively. The descriptive
statistics will be provided as needed to individually describe the data points. The
incidence of the most common post TKR medical complications will be analyzed by binary
logistic regression model and time to event model such as cox regression. The
relationship between pre-op, episode of care and post-operative gait recoveries will be
evaluated by both binary logistic regression model and mixed model. The correlation
between continuous 14 to 30-days Persona IQ data and 90-days post-operative sagittal ROM
data will be evaluated by Pearson correlation, Intraclass Correlation Coefficient (ICC)
with and without patient key baseline features adjusted. The relationship between
categorical 14 to 30-days Persona IQ data and 90-days post-operative sagittal ROM data
will be evaluated by either ANOVA or ANCOVA model per the analysis goal. The ordinary
logistic regression will be applied to build predicting model for patient satisfaction at
42 and 90-days.
Secondary Endpoints: The evaluation of value related to the use of Remote Therapeutic
Monitoring and healthcare utilization in the form of non-standard of care surgeon office
visits, post-operative physical therapy, emergency department visits, and hospital
readmissions associated with the index procedure will be evaluated using descriptive
statistics as described in Section 16.2 along with appropriate statistical models.
Exploratory Endpoints: Implant survival and safety will be analyzed by Kaplan-Meier
product limit estimate and raw survival rates with 95% confidence interval. Patient
functional performance, clinical benefit measurements and healthcare utilization with
index procedure will be analyzed for the whole cohort and for varied subgroups stratified
by important surgical or clinical or patient physical features via both appropriate
statistical models and descriptive statistics. The continuous clinical outcome
differences between the subjects utilizing ROSA and conventional implantation will be
evaluated by ANCOVA model with baseline function score, patient key baseline features and
visit time adjusted. Other statistical methods will be utilized as appropriate to address
the exploratory hypotheses.
