System and method for noninvasively evaluating a limb suspected of compartment syndrome

• US 7,232,415 B2
• Filed: 06/14/2006
• Issued: 06/19/2007
• Est. Priority Date: 10/19/2001
• Status: Expired due to Term

• Current Assignee: NCSE LLC
• Original Assignee: NCSE LLC
• Inventors: Steinberg, Bruce
• Primary Examiner(s): Marmor, II; Charles A.
• Assistant Examiner(s): Hopkins; Christine D.
• Application Number:
  US11/453,292
• Publication Number:
  US 20070028929A1
• Time in Patent Office: 370 Days
• Field of Search: 600/488, 600/561, 600/587, 600/592, 600/595, 73/81, 735/73
• US Class Current: 600/587
• CPC Class Codes: A61B 5/0053 by applying pressure, e.g. compression, indentation, palpation, grasping, gauging
• CPC Class Codes: A61B 5/03 Detecting, measuring or recording fluid pressure within the body other than blood pressure, e.g. cerebral pressure; Measuring pressure in body tissues or organs A61B5/205 takes precedence
• CPC Class Codes: A61B 5/442 Evaluating skin mechanical properties, e.g. elasticity, hardness, texture, wrinkle assessment
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/4878 Evaluating oedema

Abstract

A system and method are disclosed for noninvasively diagnosing limb compartment syndrome by measuring a quantitative modulus of hardness. In the preferred embodiment, a nonmovable pressure probe mounted in the center of a movable spring loaded platform is applied against a limb compartment. Force is gradually applied to the probe and the platform, compressing a limb compartment. Pressure on the probe is measured as the probe pushes into the limb. The spring loaded platform displaces, and the distance of the probe tip to the platform is measured. This distance is the depth of compression into the limb by the probe. The relationship of incremental pressures in the probe and the corresponding distance of the probe tip to the platform for each pressure is plotted. A linear regression analysis is performed whose slope forms a quantitative modulus of hardness.

Claims

1. A method of employing an applicator instrument and force probe to noninvasively evaluate the muscle compartment of a limb suspected of compartment syndrome, the method comprising the following steps:

applying the applicator instrument to the limb suspected of compartment syndrome, the applicator instrument acquiring a number of pressure and displacement data points, wherein the acquired pressure data points correspond to the pressure encountered by the force probe and the acquired displacement data points correspond to the displacement of the force probe into the limb, the total displacement corresponding to the total travel of the force probe into the limb;

plotting the acquired data points and analyzing the plotted data using linear regression to calculate a regression curve, the regression curve constituting the best fit for the plotted data points and relating displacement to pressure, the regression curve having first, second and third segments;

computing a mean square error (MSE) of acquired pressure data points relative to pressure predicted by the regression curve over a given interval;

comparing the computed MSE to a predetermined value (Value-1) and re-computing the MSE over an increased interval if the MSE is less than Value-1;

designating the start of the second segment when MSE is equal to or exceeds Value-1;

comparing MSE to a predetermined value (Value-2) and re-computing the MSE over an increased interval if the MSE is less than Value-2;

designating the end of the second segment when MSE is equal to or exceeds Value-2;

calculating the slope of the second segment through a least squares linear regression analysis, the slope corresponding to pressure within the muscle compartment;

designating the third segment of the regression curve, the third segment starting at the end of the second segment and extending until the end of the regression curve;

calculating the linearity of the second and third segments via a coefficient of determination, an increased linearity of the second and third segments corresponding to increased hardness within the muscle compartment;

making a diagnosis on the basis of the slope and linearity.

2. A method of employing an applicator instrument and force probe to noninvasively evaluate the muscle compartment of a limb suspected of compartment syndrome, the method comprising the following steps:

applying the applicator instrument to the limb suspected of compartment syndrome, the applicator instrument acquiring a number of pressure and displacement data points;

plotting the acquired data points and analyzing the plotted data using linear regression to calculate a regression curve, the regression curve constituting the best fit for the plotted data points and relating displacement to pressure, the regression curve having first, second and third segments;

computing a mean square error (MSE) of acquired pressure data points relative to pressure predicted by the regression curve over a given interval;

comparing the computed MSE to a predetermined value (Value-1) and re-computing the MSE over an increased interval if the MSE is less than Value-1;

designating the start of the second segment when MSE is equal to or exceeds Value-1;

comparing MSE to a predetermined value (Value-2) and re-computing the MSE over an increased interval if the MSE is less than Value-2;

designating the end of the second segment when MSE is equal to or exceeds Value-2;

designating the third segment of the regression curve, the third segment- starting at the end of the second segment and extending until the end of the regression curve;

making a diagnosis on the basis of the slope and linearity of the second and third segments.

3. The method of claim 2 comprising the following additional steps:

calculating the slope of the second segment through a least squares linear regression analysis, the slope corresponding to pressure within the muscle compartment;

calculating the linearity of the second and third segments via a coefficient of determination, an increased linearity of the second and third segments corresponding to increased hardness within the muscle compartment;

making a diagnosis on the basis of the slope and linearity.

4. The method of claim 2 wherein Value-1 is equal to 10.

5. The method of claim 2 wherein Value-2 is equal to 1300.

6. The method of claim 3 wherein a slope that is 1.5 times greater then the slope for an uninjured limb indicates compartment syndrome.

7. The method of claim 3 wherein a coefficient of determination that is greater than or equal to 0.975 indicates a limb with compartment syndrome.

8. A data processing system for evaluating a muscle compartment of a limb suspected of compartment syndrome, the system comprising:

a computer processing unit for processing data;

a storage medium storing a number of acquired pressure and displacement data points relative to the muscle compartment being evaluated;

an arithmetic logic circuit configured to calculate a regression curve relating displacement to pressure from the stored data points, the regression curve constituting the best fit for the stored data points and having first, second and third segments and;

an arithmetic logic circuit configured to calculate a mean square error (MSE) for acquired pressure data points relative to the regression curve over a given distance, the arithmetic logic circuit comparing the computed MSE to a predetermined value (Value-1) and re-computing the MSE over an increased distance if MSE is less than Value-1, the arithmetic logic circuit storing a value that represents the beginning of the second segment of the regression curve when MSE is equal to or exceeds Value-1, the arithmetic logic circuit further comparing MSE to a predetermined value (Value-2) and re-computing the MSE over an increased distance if the MSE is less than Value-2, the arithmetic logic circuit storing a value that represents the end of the second segment of the regression curve when MSE is equal to or exceeds Value-2;

an arithmetic logic circuit configured to calculate the slope of the second segment through a least squares linear regression analysis, the slope corresponding to the hardness of a muscle within the muscle compartment when it is compressed;

an arithmetic logic circuit configured to calculate the linearity of the second and third segments of the regression curve via a coefficient of determination, the linearity corresponding to the hardness of a muscle within the muscle compartment as it becomes progressively compacted.

9. A data processing system for evaluating a muscle compartment of a limb suspected of compartment syndrome, the system comprising:

a computer processing unit for processing data;

a storage medium storing a number of acquired pressure and displacement data points relative to the muscle compartment being evaluated;

an arithmetic logic circuit configured to calculate a regression curve relating displacement to pressure from the stored data points, the regression curve constituting the best fit for the stored data points and having first, second and third segments and;

an arithmetic logic circuit configured to calculate a mean square error (MSE) for acquired pressure data points relative to the regression curve over a given distance, the arithmetic logic circuit comparing the computed MSE to a predetermined value (Value-1) and re-computing the MSE over an increased distance if MSE is less than Value-1, the arithmetic logic circuit storing a value that represents the beginning of the second segment of the regression curve when MSE is equal to or exceeds Value-1, the arithmetic logic circuit further comparing MSE to a predetermined value (Value-2) and re-computing the MSE over an increased distance if the MSE is less than Value-2, the arithmetic logic circuit storing a value that represents the end of the second segment of the regression curve when MSE is equal to or exceeds Value-2;

an arithmetic logic circuit configured to calculate the slope of the second segment through a least squares linear regression analysis, the slope corresponding to the hardness of a muscle within the muscle compartment when it is compressed.

10. The data processing system as described in claim 9 further comprising:

an arithmetic logic circuit configured to calculate the linearity of the second and third segments of the regression curve via a coefficient of determination, the linearity corresponding to the hardness of a muscle within the muscle compartment as it becomes progressively compacted.