Predicting Acute Compartment Syndrome (PACS)
This trial has been completed.
|Condition||acute compartment syndrome|
|Treatment||continuous tissue perfusion monitoring by near-infrared spectroscopy (nirs) and intramuscular pressure (imp)|
|Sponsor||Major Extremity Trauma Research Consortium|
|Start date||October 2012|
|End date||December 2015|
|Trial size||194 participants|
|Trial identifier||NCT01561261, 00004105|
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).
|United States||No locations recruiting|
|Other countries||No locations recruiting|
|Denver, CO||Denver Health and Hospital Authority||completed|
|Baltimore, MD||University of Maryland/R Adams Cowley Shock Trauma Medical Center||completed|
|Minneapolis, MN||Hennepin County Medical Center / Regions Hospita||completed|
|Charlotte, NC||Carolinas Medical Center||completed|
|Winston-Salem, NC||Wake Forest University Baptist Medical Center||completed|
|Nashville, TN||Vanderbilt Medical Center||completed|
|Fort Sam Houston, TX||San Antonio Military Medical Center||completed|
|Intervention model||single group assignment|
Retrospective assessment of the likelihood of compartment syndrome
time frame: 6 months post index injury
Clinician agreement in retrospective assessments of the likelihood of ACS.
time frame: 6 months post index injury
All participants from 18 years up to 60 years old.
Inclusion Criteria: 1. Patient between the ages of 18 and 60 2. Weight of > 88 lb/40 kg 3. Patient presents with one of the following injuries: - Closed tibial shaft fracture with displacement, comminution, or segmental pattern - Closed bicondylar tibial plateau fracture or medial tibial plateau-knee dislocation - Open tibial shaft fracture (Gustilo Type I, II or IIIA) - Open bicondylar tibial plateau fracture or medial tibial plateau-knee dislocation (Gustilo Type I, II or IIIA) - Severe soft tissue crush injury to lower leg - Gun shot injury to leg - Proximal fibula fracture 4. Injury resulted from a high-energy mechanism (e.g. pedestrian struck; fall > 10 ft; MVA/MCA at speed > 30 mph; injury due to shotgun, rifle, or projectile) 5. The injury occurs no more than 12 hours prior to initiation of monitoring 6. If bilateral leg injuries are present, only the limb that is most severely injured in the judgment of the investigator will be studied 7. At least one extremity must be uninjured to serve as a control for muscle oximetry 8. Patients may have other injuries except as noted below under exclusion criteria 9. Patient may have impending compartment syndrome at time of evaluation; however, the surgeon must be able to initiate monitoring and take at least one set of muscle pressures and obtain one set of tissue oxygenation measurements prior to performing fasciotomy Exclusion Criteria: 1. Soft tissue wounds that will interfere with monitoring (i.e. the insertion of indwelling pressure catheters and/or application of NIRS pads to the anterior and deep posterior compartments of the leg) 2. Patients with known peripheral vascular disease 3. Informed consent from the patient or from a legally authorized representative (LAR) is not obtained early enough to begin monitoring within 12 hours post-injury 4. Non-ambulatory due to an associated complete spinal cord injury 5. Non-ambulatory before the injury due to a pre-existing condition 6. Patient speaks neither English nor Spanish 7. Severe problems with maintaining follow-up (e.g. patients who are homeless at the time of injury or those how are intellectually challenged without adequate family support). 8. Prior extensive traumatic injury requiring surgery to either lower extremity.
|Official title||Predicting Acute Compartment Syndrome (PACS) Using Optimized Clinical Assessment, Continuous Pressure Monitoring, and Continuous Tissue Oximetry|
|Principal investigator||Andrew Schmidt, MD|
|Description||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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