Risk modeling for pressure ulcer formation

• US 9,320,665 B2
• Filed: 01/27/2011
• Issued: 04/26/2016
• Est. Priority Date: 01/27/2010
• Status: Active Grant

• Current Assignee: Xsensor Technology Corp
• Original Assignee: Xsensor Technology Corp
• Inventors: Main, Ian, Miller, Robert, Russell, Terry, Ousdahl, Mitch
• Primary Examiner(s): Dougherty, Sean
• Assistant Examiner(s): CERIONI, DANIEL LEE
• Application Number:
  US13/575,094
• Publication Number:
  US 20130006151A1
• Time in Patent Office: 1,916 Days
• Field of Search: 600/587, 600/595
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 5/103 Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes A61B5/08 takes precedence; measuring aids for tailors A41H1/00; measuring instruments specially adapted for dentistry A61C19/04
• CPC Class Codes: A61B 5/447 specially adapted for aiding the prevention of ulcer or pressure sore development, i.e. before the ulcer or sore has developed
• CPC Class Codes: A61B 5/6891 Furniture
• CPC Class Codes: A61G 2203/34 for pressure
• CPC Class Codes: A61G 2203/46 for temperature
• CPC Class Codes: A61G 7/057 Arrangements for preventing bed-sores or for supporting patients with burns, e.g. mattresses specially adapted therefor ventilation openings in mattresses A47C21/042; pneumatic or hydraulic mattresses A47C27/08

Abstract

A method for modelling pressure exposure and/or the risk of pressure ulcer formation includes steps of using pressure sensors to derive pressure exposure or risk values and displaying the pressure exposure or risk values in a graphical manner to a user. Computer-implemented systems includes a pressure-sensing interface mat and components for implementing the steps of the methods.

Claims

1. A method of assessing the risk of a patient developing a pressure ulcer, wherein the patient is supported on a support surface, a pressure sensing interface is placed between the patient and the support surface, and the pressure sensing interface has a plurality of sensels, the method comprising the steps of:

periodically receiving, by a processor, pressure signals indicative of pressure values from the plurality of sensels associated with the pressure sensing interface, the plurality of sensels arranged in a two-dimensional grid;

processing, by the processor, the pressure signals to obtain the pressure values;

using a machine vision technique, by the processor, to process the obtained pressure values in order to identify a plurality of body zones of the patient and to assign a set of sensels from the plurality of sensels to each body zone in the identified plurality of body zones of the patient, the machine vision technique tracking the plurality of body zones over time, the tracking comprising adjusting which of the plurality of sensels is included in the set of sensels assigned to each body zone in the identified plurality of body zones of the patient;

separately for each body zone of the identified plurality of body zones, based on the set of sensels assigned to the body zone;

deriving, by the processor, a plurality of time-to-high risk (THR) values for the set of sensels from the pressure values, the THR values for the set of sensels being a time to high risk of pressure ulcer formation in the body zone according to the pressure values measured for the set of sensels;

adjusting, by the processor, the THR values by considering at least one risk modifier;

converting, by the processor, the risk-adjusted THR values into risk deltas representing changes in risk values over an interval of time, the risk values indicative of a current risk of pressure ulcer formation at the sensel locations correlated with the body zone;

updating, by the processor, the risk values by the risk deltas; and

based on the updated risk values, indicating, by the processor, to a user a risk of pressure ulcer formation in the body zone.

2. The method of claim 1, wherein the T_HR values are determined by comparing the obtained pressure values to stored pressure vs. time data which comprises a T_HR value for a given obtained pressure value.

3. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of a risk assessment model.

4. The method of claim 1 wherein the risk deltas are determined by converting the T_HR values into risk coefficients.

5. The method of claim 1 wherein the updated risk values for the plurality of sensels are graphically displayed to the user as a risk map image.

6. The method of claim 5 wherein the risk map image is either a two dimensional or a three dimensional image.

7. The method of claim 5 wherein the risk map image is divided into the plurality of body zones which correspond to the patient'"'"'s anatomical features.

8. The method of claim 7 wherein a risk modifier level is independently determined for each of the body zones.

9. The method of claim 7 wherein a size and location of each body zone is automatically derived from an interface pressure map by automatic identification of the patient'"'"'s anatomical features from the interface pressure map.

10. The method of claim 1, wherein the T_HR values are determined by applying a pre-determined mathematical formula to the pressure values.

11. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of shear.

12. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of moisture.

13. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of temperature.

14. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of tissue health.

15. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of tissue deflection.

16. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative of a clinical observation.

17. The method of claim 1 wherein the at least one risk modifier comprises at least one data input representative for at least one combination of a risk assessment model, shear, moisture, temperature, tissue health, tissue deflection, or a clinical observation.

18. The method of claim 1 further comprising reducing the risk values in response to the pressure values being lower than a minimum pressure threshold.

19. The method of claim 1 further comprising resetting the risk values in response to an automatic identification of a patient repositioning.

20. The method of claim 1 further comprising detecting the patient'"'"'s body position using a machine vision technique and adjusting the risk values in response to the patient'"'"'s body position.

21. The method of claim 1 further comprising detecting the patient'"'"'s turns using a machine vision technique and adjusting the risk values in response to the patient'"'"'s turns.

22. The method of claim 1 further comprising initiating a patient turn or repositioning in response to the risk values exceeding a pre-determined threshold.

23. The method of claim 1 wherein a separate risk value is provided for each sensel.

24. The method of claim 1 wherein the T_HR values are displayed to the user.

25. The method of claim 1 wherein each T_HR value is an estimate of a duration of time the patient can be exposed to the obtained pressure values before a pressure ulcer begins to form.

26. A method of assessing the risk of a patient developing a pressure ulcer, comprising:

periodically receiving, by a processor, pressure signals indicative of pressure values from a plurality of sensels associated with a pressure sensing interface, the pressure sensing interface placed between a patient and a support surface supporting the patient and the plurality of sensels arranged in a two-dimensional grid;

processing, by the processor, the pressure signals to obtain the pressure values;

processing, by the processor, the obtained pressure values to identify a body zone of the patient and to assign a set of sensels from the plurality of sensels to the body zone using a machine vision technique, the machine vision technique tracking the body zone over time and the tracking comprising adjusting which of the plurality of sensels is included in the set of sensels assigned to the body zone;

based on the set of sensels assigned to the body zone;

deriving, by the processor, a plurality of time-to-high risk (THR) values for the set of sensels from the pressure values, the THR values for the set of sensels being a time to high risk of pressure ulcer formation in the body zone according to the pressure values measured for the set of sensels;

adjusting, by the processor, the THR values by considering at least one risk modifier;

converting, by the processor, the risk-adjusted THR values into risk deltas representing changes in risk values over an interval of time, the risk values indicative of a current risk of pressure ulcer formation at the sensel locations correlated with the body zone;

updating, by the processor, the risk values by the risk deltas; and

based on the updated risk values, indicating, by the processor, to a user a risk of pressure ulcer formation in the body zone.