SYSTEM FOR MONITORING AND DELIVERING MEDICATION TO A PATIENT AND METHOD OF USING THE SAME TO MINIMIZE THE RISKS ASSOCIATED WITH AUTOMATED THERAPY

Abstract

A system and method for monitoring and delivering medication to a patient includes a controller that has a control algorithm and a closed loop control that monitors the control algorithm. A sensor is in communication with the controller and monitors a medical condition. A rule base application in the controller receives data from the sensor and the closed loop control and compares the data to predetermined medical information to determine the risk of automation of therapy to the patient. The controller then provides a predetermined risk threshold where below the predetermined risk threshold automated closed loop medication therapy is provided. If the predetermined risk threshold is met or exceeded, automated therapy adjustments may not occur and user/clinician intervention is requested.

Claims

1-26. (canceled)

27. A system for determining whether it is safe to operate a closed loop delivery of insulin to a patient, the system comprising: a controller; and a sensor in communication with the controller, said sensor configured to measure an indication of glucose in the patient; wherein the controller is configured to: receive measurements indicative of glucose in the patient from the sensor; and determine whether it is safe to operate a closed loop delivery of insulin to the patient based on an application of rules on the received measurements.

28. The system of claim 27, wherein the controller is further configured to send a request for user intervention based on the determination of the safety using the application of rules.

29. The system of claim 28, wherein the user intervention requires authentication.

30. The system of claim 27, wherein the rules comprise an insulin-glucose model that predicts future glucose level and current insulin-on-board.

31. The system of claim 27, wherein the rules comprises a matrix indicating a risk level based on a function of the received measurements, the first trend, and the second trend.

32. The system of claim 27, wherein the rules comprise a Bayesian model of the received measurement, the first trend, and the second trend.

33. The system of claim 27, wherein the controller is configured to determine that user intervention is not needed based on a recommended change in insulin infusion from the closed loop delivery.

34. The system of claim 27, wherein the application of rules is further based on recommended change in insulin infusion from the closed loop delivery, the estimated insulin on board, and predicted glucose level in the future.

35. A method for determining whether it is safe to operate a closed loop delivery of insulin to a patient, the method comprising: measuring an indication of glucose in the patient with a sensor in communication with a controller; receiving, at the controller, measurements indicative of glucose in the patient from the sensor; determining whether it is safe to operate a closed loop delivery of insulin to the patient based on an application of rules; and preventing closed loop delivery of insulin based on the determination of safety using the application of rules.

36. The method of claim 35, further comprising sending a request for user intervention based on the determination of the safety using the application of rules.

37. The method of claim 36, wherein the user intervention requires authentication.

38. The method of claim 35, wherein the rules comprise an insulin-glucose model that predicts future glucose level and current insulin-on-board.

39. The method of claim 35, wherein the rules comprises a matrix indicating a risk level based on a function of the received measurements, the first trend, and the second trend.

40. The method of claim 35, wherein the rules comprise a Bayesian model of the received measurement, the first trend, and the second trend.

41. The method of claim 35, further comprising determining that user intervention is not needed based on a recommended change in insulin infusion from the closed-loop delivery.

42. The method of claim 35, wherein the application of rules is further based on recommended change in insulin infusion from the closed-loop delivery, the estimated insulin on board, and predicted glucose level in the future.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG. 1 is a schematic diagram of a closed loop control system augmented with the automation risk monitor of the present invention;

[0020] FIG. 2 is an example messaging diagram for the present invention;

[0021] FIG. 3 is a schematic diagram showing the architecture of a semi-automatic glucose management system; and

[0022] FIG. 4 is a schematic diagram of an automation risk monitor system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0023] FIG. 1 provides a system 10 for monitoring and delivering medication, such as insulin, to a patient 12. The system 10 includes a controller 14 that utilizes a control algorithm and an automation risk monitor 15 all presented in a closed loop. A sensor 16 is in communication with the controller 14 and monitors a medical condition of the patient 12. A rule based application 18 in the control receives data from the sensor 16 and compares the data to predetermined medical information to determine the risk to the patient 12 to automate the delivery of medication. The rule based application 18 in one embodiment includes physician or clinician entered conditions of when automation is acceptable. The system 10 is thus in communication with a clinician messaging system 20 that communicates to a clinician when the risk of automation is unacceptable. In a preferred embodiment the messaging system is remote from the system 10.

[0024] The rule based application 18 in one embodiment can include a risk profile wherein a clinician implements a risk profile according to a metric that may be qualitative (low, medium or high) or quantitative (1-10 where 10 is the highest risk) and a threshold defining when intervention is required. In either case, a quantitative metric is internally calculated and compared to a quantitative threshold. For example, in the case of low, medium or high each qualitative measurement is assigned a quantitative value such as 2, 5 and 7 respectively. Consequently, a risk scale is specified and a threshold is defined at or above which intervention is requested. The rule based application 18 can also include a risk matrix that is developed to enable a determination of risk. Although the matrix is ultimately stored internally, it can be parameteritized by the user. One example of the risk matrix is shown below:

TABLE-US-00001 Glucose Range (mg/dL) Glucose Change (derivative) Calculated Change in Insulin Risk Level 0-70 Increasing Increasing High 0-70 Increasing Decreasing Low 0-70 Decreasing Increasing High 0-70 Decreasing Decreasing Low 70-90 Increasing Increasing Medium 70-90 Increasing Decreasing Low 70-90 Decreasing Increasing High 70-90 Decreasing Decreasing Low 90-120 Increasing Increasing Medium 90-120 Increasing Decreasing Low 90-120 Decreasing Increasing High 90-120 Decreasing Decreasing Low 120-180 Increasing Increasing Low 120-180 Increasing Decreasing Low 120-180 Decreasing Increasing Medium 120-180 Decreasing Decreasing Low 180-250 Increasing Increasing Low 180-250 Increasing Decreasing High 180-250 Decreasing Increasing Medium 180-250 Decreasing Decreasing Low Above 250 Increasing Increasing High Above 250 Increasing Decreasing Low Above 250 Decreasing Increasing Low Above 250 Decreasing Decreasing Medium

[0025] Specifically, the second column is the calculated or requested insulin level from the closed loop controller. The table is an example of how the treatment condition is mapped to a risk level. There are numerous other methods for implementing this information which may include continuous mapping functions, fuzzy logic, probabilistic models (e.g., Bayesian networks), probability calculations and the like.

[0026] A second way to provide this type of system is to employ an insulin/glucose pharmacokinetic/pharmacodynamic model as shown below which predicts the future glucose level and current insulin-on-board. The clinician can then use a predicted value and/or the anticipated insulin effect rather than or in addition to glucose level and a derivative.

[00001]$\dot{G}\left(t\right)=-p_{G\cdot }G\left(t\right)-S_I\left(t\right).G.\frac{Q\left(t\right)}{1+{\alpha }_{G}Q\left(t\right)}+\frac{P\left(t\right)+EGP-CNS}{V_g}$

[00002]$\dot{I}\left(t\right)=-n\frac{I\left(t\right)}{1+{\alpha }_{I}I\left(t\right)}+\frac{u_{ex}\left(t\right)}{V_I}+\frac{u_{en}\left(t\right)}{V_I}$

[00003]${\dot{P}}_1\left(t\right)=-d_1{P}_1\left(t\right)+P_e\left(t\right)$

[00004]${\dot{P}}_2\left(t\right)=-\min \left(d_2{P}_2\left(t\right),P_{\max }\right)+d_1{P}_1\left(t\right)$

[00005]$P\left(t\right)=\min \left(d_2{P}_2\left(t\right),P_{\max }\right)+P_N\left(t\right)$

[00006]$\dot{G}\left(t\right)=-p_G\left(t\right)G\left(t\right)-S_1\left(t\right)G\left(t\right)\frac{Q\left(t\right)}{1+{\alpha }_{G}Q\left(t\right)}+\frac{P\left(t\right)}{V_G}$

[00007]$\dot{Q}\left(t\right)=-kQ\left(t\right)+kI\left(t\right)$

[00008]$\dot{I}\left(t\right)=-n\frac{I\left(t\right)}{1+{\alpha }_I\left(t\right)}+\frac{u_{ex}\left(t\right)}{V_I}$

[0027] In Equations (1)-(3), G(t) [mmol/L] denotes the total plasma glucose concentration, and I(t) [mU/L] is the plasma insulin concentration. The effect of previously infused insulin being utilized over time is represented by Q(t) [mU/L], with k [1/min] accounting for the effective life of insulin in the system. Exogenous insulin infusion rate is represented by Uex (t) [mU/min], whereas P(t) [mmol/L min] is the exogenous glucose infusion rate. Patient’s endogenous glucose removal and insulin sensitivity through time are described by P.sub.G(t) [1/min] and S.sub.I(t) [L/mU min], respectively. The parameters V.sub.I [L] and V.sub.G [L] stand for insulin and glucose distribution volumes. n [1/min] is the first order decay rate of insulin from plasma. Two Michaelis-Menten constants are used to describe saturation, with α.sub.I [L/mU] used for the saturation of plasma insulin disappearance, and α.sub.I [L/mU] for the saturation of insulin-dependent glucose clearance.

[0028] Thus, the rule base application 18 determines the risk of therapy automation to a patient 12 by referencing or comparing the monitored, measured, or determined present or future condition to a predetermined risk threshold. Below the predetermined risk threshold, because a low risk condition is detected, the system 10 can move forward in an automated fashion and provide medication as required. If the risk is determined to meet or exceed (i.e., be at or above) the predetermined risk threshold, the controller triggers a request for user intervention by contacting the clinician, for example via a clinician messaging system 20, instead of moving forward with automation.

[0029] In operation, the system 10 monitors a control algorithm of a controller 14 to receive data. The controller 14 additionally receives continuous data from a sensor 16 regarding a medical condition such as a glucose level. The controller 14 then compares the data from the control algorithm and the sensor 16 to predetermined medical information so that the controller 18 can determine whether a predetermined risk threshold of automating the delivery of medication has been met or exceeded. Then, based on the data, if a risk of automated therapy is below a predetermined threshold, automation is permitted and a command or request for medication or insulin is provided to the electronic insulin pump and the insulin delivery rate is automatically updated. Therefore the insulin delivery rate is automatically updated according to the algorithm model or closed loop controller used. Alternatively, if the risk is above a predetermined threshold, a request for user intervention is triggered sending a message to the clinician, for example via a clinician messaging system 20, so that a user may intervene to make a determination regarding whether the medication should be provided. The request for intervention is generated and sent directly to the user through a messaging system that is bi-directional. The message system 20 provides information and requests a user response. When the response is related to a change in therapy an authentication step is included.

[0030] The response to a request is provided by the user directly through the user interface of the system. Alternatively, the response can be returned through an authenticated messaging system involving a unique identifier specific to a positive or negative response.

[0031] During the course of normal operation glucose measurements may be received that generate a change in the recommended insulin. However, the change may not be significant enough to provide a therapeutic advantage to the patient versus the burden of requesting confirmation from the nurse. Consequently, a rule based system is provided which evaluates therapy changes to trigger a request for an automatic update or nursing intervention. The input to the rule based system includes the blood glucose level, the change in glucose, the insulin infusion, the recommended change in insulin infusion, the estimated insulin on board, and the predicted glucose in the future. Rules involving comparisons to thresholds, regression equations, and calculations are created which trigger a therapy update based on the inputs.

[0032] Thus, the present system can be used to make determinations of treatment decisions requiring user intervention based upon a diagnostic value, the change in diagnostic value, the current drug infusion rate, the updated drug infusion rate, and the treatment target range. In addition, the system notifies a clinician that intervention is required and receives the implementing clinician instruction in response to the notification.

[0033] An additional advantage is presented because the system 10 determines when clinician intervention is necessary and unnecessary. Specifically, system 10 is independent of an adaptive control algorithm or a computerized protocol. The system 10 functions as a safety supervisor that watches the performance of the closed loop system. Consequently, data from the closed loop system and diagnostic sensor 16 are provided to a rules database that uses a matrix to produce a quantitative level of risk of automation. The risk is compared to a particular risk threshold to either generate and/or provide an “okay” to proceed with automated therapy or to trigger a request for user intervention. The risk threshold can be selected or customized based on the desires of the user or the healthcare facility or organization.

[0034] This operation differs from current systems that do not determine risk of automation. Instead prior art systems allow automation to occur regardless of potential risk and then when sensors indicate a patient is experiencing an unacceptable medical condition a clinician is alerted. Therefore the system 10 provides an advantage of preventing the unacceptable medical condition from occurring in the first place as a result of monitoring the automation process, predetermining risks of automation, and comparing the risk of automation to a predetermined risk of automation threshold. The user can customize or select what factors are used to determine the risk of automation, as well as the predetermined threshold of automation risk that they are willing to accept without triggering a request for user intervention and preventing automated therapy. Thus, at the very least all of the stated objectives have been met.