System and method for dynamic determination of disease prognosis

• US 20060289020A1
• Filed: 06/08/2006
• Published: 12/28/2006
• Est. Priority Date: 06/08/2005
• Status: Abandoned Application

• Current Assignee: Carefusion 303 Incorporated (Becton, Dickinson & Co)
• Original Assignee: Carefusion 303 Incorporated (Becton, Dickinson & Co)
• Inventors: Johannes, Richard S., Tabak, Ying P., Kurtz, Stephen G., Yamaga, Cynthia
• Application Number:
  US11/449,450
• Publication Number:
  US 20060289020A1
• Time in Patent Office: Days
• US Class Current: 128/898
• CPC Class Codes: A61B 5/4833 Assessment of subject's compliance to treatment
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method of obtaining and processing patient data and patient treatment data to provide a prognosis parameter related to a patient'"'"'s disease state is provided. The method identifies and calculates coefficients related to appropriate predictor variables which are then used by the prediction model to calculate the prognosis parameter. The prediction model may be a logistic regression model. The method may also be used to assess the level of care being provided to patients, as well as providing a way of assessing the outcome of the patient'"'"'s condition as a function of treatment. A method of calculating a harm index reflective of the risk of treatment is also provided.

Claims

1. A method for determining a value for a prognosis parameter in real time, comprising:

obtaining current condition related information about a patient;

identifying appropriate predictor variables;

inputting the condition related information associated with the appropriate predictor variables into a prediction model;

calculating a value for a prognosis parameter.

2. The method of claim 1, wherein calculating a value for a prognosis parameter includes using calculated coefficients related to the predictor variables.

3. The method of claim 2, wherein the calculated coefficients are determined by analyzing a database of information containing condition related information obtained from a plurality of patients.

4. The method of claim 1, wherein the prediction model is a logistic regression model.

5. The method of claim 4, wherein the logistic regression determines a probability of outcome is equal to

1/[1+e⁻

(β

<sup>0+β

1χ

1+β

2χ

2+ . . . +β

nχ

n)</sup>]

where β

₀ is the constant, X_i'"'"'s are predictor variables and β

_i'"'"'s are regression coefficients.

6. The method of claim 2, wherein the coefficients related to the predictor values are continuously updated using individual patient information acquired during treatment of the patient.

7. The method of claim 6, further comprising monitoring a change in the value of the prognosis parameter.

8. The method of claim 7, further comprising adjusting the patient'"'"'s treatment as a function of the monitored change in the value of the prognosis parameter.

9. The method of claim 7, further comprising adjusting a level of care provided to the patient as a function of the monitored change in the value of the prognosis parameter.

10. The method of claim 1, further comprising monitoring a change in the value of the prognosis parameter over time to determine a trend in outcome of treatment delivered to patients in the institution having common diagnoses.

11. The method of claim 10, further comprising analyzing the trend to determine if a change in best practices for treating a condition is necessary.

12. The method of claim 10, further comprising analyzing the trend to determine if a change in level of care for treating a condition is necessary.

13. A method for determining a value for a harm index in real time, comprising:

obtaining current treatment related information about a patient;

identifying appropriate predictor variables;

inputting the treatment related information and the predictor variables into a prediction model;

calculating a value for a harm index.

14. The method of claim 13, wherein the prediction model is a logistic regression model.

15. The method of claim 13, wherein calculating a value for a harm index includes using calculated coefficients related to the predictor variables.

16. The method of claim 15, wherein the calculated coefficients are determined by analyzing a database of information containing treatment related information obtained from a plurality of patients.