Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05111925 |
| Other study ID # |
WRNMMC-2020-0283-001 |
| Secondary ID |
MO220177 |
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
October 26, 2022 |
| Est. completion date |
May 31, 2025 |
Study information
| Verified date |
December 2023 |
| Source |
Womack Army Medical Center |
| Contact |
Timothy C Mauntel, PhD |
| Phone |
910-849-7226 |
| Email |
timothy.c.mauntel.civ[@]mail.mil |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The purpose of this study is to develop comprehensive and efficient pre- and post-
musculoskeletal injury (MSKI) risk assessments for Service members, incorporating both
objective and subjective measures. This is a multi-site observational study to identify the
pre- and post-MSKI physical and psychosocial factors contributing to MSKI risks and undesired
patient outcomes following MSKI. The study hypothesis is that a set of field-expedient
clinical assessments can identify Service member specific MSKI risk factors and post-MSKI
deficits that contribute to undesired patient outcomes and provide data to guide
patient-specific risk mitigation and rehabilitation programs.
Description:
Non-combat related musculoskeletal injuries (MSKI) are the leading cause of morbidity and
disability in the United States Military, eroding combat readiness more than any other single
disease or health condition. The MSKI problem is challenging to address, in part because it
is difficult to comprehensively assess all of the factors that increase MSKI risk and those
factors that influence post-MSKI outcomes, value-based care must have the necessary
infrastructure to capture and process actionable information, and then implement relevant
changes (i.e., learning healthcare system). However, there are no comprehensive Military
Health System (MHS) recommendations regarding the collection and use of objective and
subjective patient-centric data to inform MSKI risk assessment and mitigation strategies.
PREPARE will develop efficient pre- and post-MSKI risk assessments that incorporate objective
and subjective measures. The overall study objective is to develop comprehensive clinical
assessments that identify the Service member specific factors that contribute to MSKI risks
and undesired post-MSKI outcomes.
These objectives will be achieved by the following Specific Aims (SA):
Specific Aim #1: Determine the intra-rater, inter-rater, and inter-day reliability of the
study related measurements within a (pilot) cohort of healthy active duty Service members.
Hypothesis SA#1: There will be good-to-excellent intra-rater, inter-rater, and inter-day
reliability for study related measurements.
Specific Aim #2: Determine the pre-MSKI physical and psychosocial traits of Service members
that differentiate between individuals who go on to sustain an MSKI and those who do not
within 1 year of study enrollment.
Hypothesis SA#2: A common set of field- expedient physical (e.g., movement assessments, gait,
joint range of motion) and psychosocial (e.g., National Institutes of Health Patient-Reported
Outcomes Measurement Information System [PROMIS], TSK-11) assessments can identify the
pre-MSKI factors that contribute to greater MSKI risks.
Specific Aim #3: Determine the post-MSKI physical and psychosocial traits of Service members
with non-surgically managed MSKIs that differentiate between individuals who go on to have
undesired outcomes and those who have expected outcomes.
Hypothesis SA#3: A common set of field- expedient physical (e.g., movement assessments, gait,
joint range of motion) and psychosocial (e.g., NIH PROMIS, TSK-11) assessments can identify
the post-MSKI factors that contribute to undesired patient outcomes (e.g., increased MSKI
risk, symptom/condition chronification, delayed return-to-duty/activity [RTD/A]).
Specific Aim #4: Create optimized (parsimonious) pre- and post-MSKI clinical assessments for
the identification of physical and psychosocial factors that provide the information needed
to improve pre- and post-MSKI risk mitigation and rehabilitation strategies.
Hypothesis SA#4: A common set of semi-automated field-expedient assessments can be structured
to correctly inform clinical decision-making and inform Service members and healthcare
providers about likely patient outcomes (e.g., MSKI risk, time to RTD/A).
Specific Aim #5: Validate the optimized versions of our pre- and post-MSKI assessments by
demonstrating their abilities to predict MSKI risks and outcomes in new Service Member
cohorts.
Hypothesis SA#5: The optimized pre and post-MSKI assessments will accurately identify Service
Members at the highest risk for sustaining MSKIs and undesired post-MSKI outcomes across
Service member populations.
This is a multi-site observational study to identify the pre- and post-MSKI physical and
psychosocial factors contributing to greater MSKI risk (Specific Aim #2) and undesired
post-MSKI outcomes (Specific Aim #3) and to identify the minimum set of clinical assessments
needed to provide healthcare providers with the data required to optimize MSKI care (Specific
Aim #4) and to validate the optimized pre- and post-MSKI assessments by demonstrating their
abilities to predict MSKI risks and outcomes in new Service Member cohorts. For Specific Aim
#1, 10 healthy active duty Service members will serve as a pilot cohort for participation in
the study. These individuals will complete all study related activities so that the study
team can establish the best time estimate possible for the completion of study related
activities. These individuals will repeat the same study related activities 7-10 days
following the initial visit; this will allow the study team to establish the intra-rater,
inter-rater, and inter-day reliability of the study related measures. For Specific Aim #2,
Service members (n=560) who are onboarding to a new military unit will be prospectively
enrolled. Participants will undergo a comprehensive pre-MSKI clinical assessment at the time
of study enrollment, including clinical movement and range of motion assessments, and patient
reported outcome (PRO) measures. MSKI data will be collected monthly for up to 1-year
following initial assessment. For Specific Aim #3, Service members (n=780) who are receiving
conservative treatment for a musculoskeletal injury of the low back or lower extremity will
be prospectively enrolled. Service members will receive treatment for MSKIs at the discretion
of the Service members' healthcare providers. Participants will undergo repeat (≤3 days of
starting physical therapy ["initial"]; 4 weeks post-initial assessment, or at RTD/A
clearance, if prior to 4 weeks; and 12 weeks post-initial assessment, or at RTD/A clearance,
if prior to 12 weeks) clinical movement and range of motion assessments. PRO and MSKI data
will be collected monthly for up to 1-year following the initial assessment. For Specific Aim
#5, Service Members who are in-processing to a unit at Fort Bragg, NC (n=560) and active duty
Service members receiving physical therapy for a MSKI within a Womack Army Medical Center
(WAMC; Ft. Bragg, NC) physical therapy clinic (n=780) will be enrolled. Participants will
complete our optimized pre- and post-MSKI assessments identified in Specific Aim #4. All
procedures and time points will be identical to those previously described for Specific Aims
#2 and #3.
For Specific Aims #2 and #3, participants will complete: 1) Movement assessments utilizing
semi-automated kinematic (markerless motion capture system) and kinetic (instrumented
walkway) measurements for jump-landing, triple hop, single leg squat, double leg squat, and
gait; 2) Range of motion measurements including hip extension, hip abduction, knee flexion,
knee extension, and dorsiflexion; 3) PRO measures including NIH PROMIS and Tampa Scale of
Kinesiophobia (TSK-11),and 4) MSKI Tracking.
To address Specific Aim #2 and #4, the study team will use a statistical approach to select
and evaluate candidate assessments for the prediction of MSKIs. Optimal assessments will be
determined based on lowest rates of misclassification of MSKI prediction. This analysis will
use a machine learning algorithm (hierarchical cluster analysis) to define groups of highly
correlated response variables among the comprehensive set of clinical assessments. The study
team will describe and compare variables obtained from individual assessments by the presence
versus absence of MSKI for up to one year follow-up, using t-tests or a nonparametric
alternative. Hierarchical cluster analysis (Ward's method) will be used to next identify
clusters of correlated variables that minimize within-cluster variance in the dataset. Tree
diagrams and cluster means will be used to interpret clusters in relation to individual
variables and functional domains described in the literature, including performance and
general health measures and self- assessment of function. Sensitivity analyses will evaluate
the robustness of assigned groupings to the choice of clustering method, using alternative
algorithms to calculate similarity between clusters (e.g., complete linkage or group average
methods).
Cluster analysis results will be used to derive reduced, candidate assessments from potential
combinations of the individual variables found characterized by the identified clusters. The
candidate assessment variables will be evaluated as predictors in binary classification
models of MSKI through up to one year follow-up. Misclassification rates will be reported as
the percent of participants where the outcome was incorrectly predicted after thresholding
using clinically relevant sensitivities and specificities. Cross-validation will be used to
minimize bias in estimated probabilities. The study team will select assessments that combine
to have the lowest misclassification rates and shortest completion times to create the
optimal MSKI risk assessment (Specific Aim #4). Completion times will be estimated based on
observed times to administer each individual item. Analyses will be repeated restricting
outcomes to the most common individual MSKI types (versus no MSKI), to evaluate for
qualitative differences in optimal assessments by injury type.
To address Specific Aims #3 and #4, the study team will use a statistical approach to select
and evaluate candidate assessments for the prediction of MSKI outcomes (Specific Aim #3).
Optimal assessments will be determined based on lowest rates of misclassification of outcomes
and shortest assessment times. This analysis will use a machine learning algorithm
(hierarchical cluster analysis) to define groups of highly correlated response variables
among the comprehensive set of clinical assessments. The study team will use hierarchical
cluster analysis (Ward's method) to identify clusters of observations that minimize
within-cluster variance at the baseline visit. Tree diagrams and cluster means will be used
to interpret clusters in relation to individual variables and functional domains described in
the literature. Sensitivity analyses will evaluate the robustness of assigned groupings to
the choice of clustering method, using alternative algorithms to calculate similarity between
clusters (e.g., complete linkage or group average methods). Analyses will be repeated within
subgroups of MSKI type to evaluate for differences in identified clusters across injury
types.
Cluster analysis results will be used to derive reduced, candidate assessments from potential
combinations of the individual variables characterized by the identified clusters. The
candidate assessment variables will be evaluated as predictors in binary classification
models (e.g., logistic or Lasso regression) of undesired patient outcomes. Misclassification
rates will be reported as the percent of patients where the outcome was incorrectly predicted
after thresholding using clinically relevant sensitivities and specificities.
Cross-validation will be used to minimize bias in estimated probabilities. The study team
will select assessments that combine to have the lowest misclassification rates and shortest
completion times to create the optimal post-MSKI assessment (Specific Aim #4). Completion
times will be estimated based on observed times to administer each individual item. Analyses
will be repeated for back pain versus less frequent MSKI types, and for the 12-week follow-up
visit, to evaluate for differences in optimal assessments by MSKI type and time post-MSKI.
Because primary analyses will be based on baseline assessments and outcomes ascertained by
internet-based questionnaires to minimize attrition, no imputation of missing data is
planned.
To address Specific Aim #5, the study team will evaluate the ability of our optimal
assessments (Specific Aim #4) to predict MSKIs and undesired outcomes in validation cohorts
over six month follow-up. Misclassification rates for unadjusted and full models will be
described using receiver operating characteristic curves (ROC) and 95% confidence intervals.
Analyses will be repeated separately for back pain and lower extremity MSKIs using the
optimal assessments identified for these injury types.
