Acute Compartment Syndrome Clinical Trial
Official title:
Predicting Acute Compartment Syndrome (PACS) Using Optimized Clinical Assessment, Continuous Pressure Monitoring, and Continuous Tissue Oximetry
The long-term objective is to develop a tool to aid in making a timely and accurate diagnosis
of acute compartment syndrome (ACS).
The immediate objective is to develop a model to accurately predict the likelihood of ACS
based on data available to the clinician within the first 48 hours of injury (specific
clinical findings supplemented by muscle oxygenation measured by near-infrared spectroscopy
(NIRS), and continuous intramuscular pressure (IMP) and perfusion pressure (PP) monitoring).
Our primary outcome is the retrospective assessment of the likelihood of compartment syndrome
made by a panel of clinicians using the following data:
- A physiologic "fingerprint" composed of continuous pressure versus time curve,
continuous oximetry values, response of muscle to fasciotomy when performed, and serum
biomarkers of muscle injury (CPK levels).
- Clinical and functional outcomes at 6 months post-injury including: sensory exam, muscle
function, presence/absence of myoneural deficit, and patient reported function using the
Short Musculoskeletal Function Assessment (SMFA).
Specific Aim 1: Prospectively enroll and follow for 6 months a sample of 200 patients.
Patients will receive continuous tissue perfusion monitoring using NIRS in all 4 leg
compartments and intramuscular pressure (IMP) via indwelling catheters placed in the anterior
and deep posterior compartments. These measures will be blinded and not provided in real time
to treating physicians. All clinical care, including diagnosis of ACS, will be according to
current standard-of-care practiced at each institution.
Specific Aim 2: Convene expert panels of 5 orthopaedic surgeons experienced in the diagnosis
and treatment of ACS to retrospectively assess the likelihood that each patient had ACS. This
retrospective assessment will be based on a 'patient profile' summarizing data collected as
part of this study.
Specific Aim 3: Determine the extent to which clinicians agree in retrospective assessments
of the likelihood of ACS.
Hypothesis: On the basis of known clinical and functional outcome at 6 months and monitoring
information, clinicians will agree on the likelihood of ACS in < 90% of cases.
Specific Aim 4: Model the panel's assessment of the likelihood of ACS as a function of data
available to the clinician within the first 48 hours of injury using a training set of the
data. This model can then be used to compute a point estimate of the risk of ACS (and
associated 95% confidence interval) for any given patient.
Specific Aim 5: Assess, for patients in a test/validation data set, the performance of the
model in predicting the panel's assessment of the likelihood of ACS.
Hypothesis: In < 95% of the cases, the panel's assessment of the likelihood of ACS will fall
within the 95% interval of uncertainty predicted by the model.
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