1 INTRODUCTION Nearly 25% of diabetes patients use insulin Many practitioners: –Are uncomfortable with insulin dosing –Base dosing decisions on empiric algorithms Computer assisted insulin dosing may: –Allow more accurate titration –Better attainment of goals –Improved practitioner comfort with drug

2 THE INTELLIGENT DOSING SYSTEM (IDS TM ) Dosing software suite Utilizes individual patient dose-response Employs following parameters: –Previous value of a dose and a marker –Current value of a dose and a marker –Desired target value at follow-up visit Data incorporated into equation that calculates new dose and provides next expected target value

3 OBJECTIVE Evaluate applicability of the Intelligent Dosing System TM for insulin management

4 METHODS Prospective observational study –Urban outpatient diabetes clinic setting –Insulin IDS placed on hand held platform –Platform provided to diabetes educators for use at point of care to adjust total daily insulin doses when indicated Cases selected –At least two consecutive visits over 6 months –Insulin was increased –Could be on oral agents if doses not changed

5 METHODS Data analysis –Fasting glucose, random glucose, or A1c used as target markers against which insulin adjusted –Same marker used between at least two visits. –Cases on insulin monotherapy analyzed separately from insulin+oral agents –Insulin doses prescribed compared IDS suggested –Observed follow-up marker values compared to the expected value predicted by the IDS

6 Basic operation of IDS on PDA platform

7 RESULTS

8 Correlation between prescribed and IDS recommended insulin doses Prescribed units R= visits All markers Practitioners in general agreeing with IDS recommended doses Recommended units

9 Changes in glycemic markers during IDS use All previous and follow-up differences significant p<0.0001

10 Comparison of observed and expected fasting glucose levels P=0.51 Glucose (mg/dl) Observed and expected are comparable

11 Correlation of observed vs. expected fasting glucose levels using IDS Observed (mg/dl) Expected (mg/dl) Insulin onlyInsulin + oral agents R= visits R= visits Good correlation between observed and expected fasting glucose using the IDS

12 Comparison of observed and expected random glucose levels P=0.86 Glucose (mg/dl) Observed and expected are comparable

13 Correlation of observed vs. expected random glucose levels using IDS Observed (mg/dl) Expected (mg/dl) Insulin onlyInsulin + oral agents R= visits R= visits Good correlation between observed and expected random glucose using the IDS

14 Average observed and expected A1c levels P=< A1c (%) Observed not as low as expected Average time between observed values = 114 days

Correlation of expected and observed follow-up A1c levels Observed A1c Expected A1c R= visits R= visits Insulin onlyInsulin + oral agents Correlation fair with insulin monotherapy Correlation improved with combination oral agents

16 CONCLUSIONS The IDS can be used at the point of care Agreement between prescribed and IDS recommended doses suggests acceptance Expected glucose values are being attained Expected A1c values not attained, but may require greater of passage of time between measurements System behaves comparably in presence of stable oral agents, and may actually improve performance of A1c as marker

17 SUMMARY Need trial in primary care site Need trials to determine if IDS: –Increases practitioner insulin use –Leads to faster goal attainment Need to develop flexibility to: –Divide doses –Incorporate CHO and exercise Trial of Multiagent Insulin Dosing System (MAIDS TM ) needed to test simultaneous adjustment of insulin and oral agents

18 New Dose = Dose - Change in level function Degree of non-linearity over the dosing range function x Dose + some individualized amount based on individuals response to dosing (0.2 * CD) * ((CPL - CL)/ CL)/ 1.3 ^ (CD/Range) Description of IDS Equation

19 New Dose = Dose - ((CL – DL)/CL) (1 + (Dose /60)) CL = Current Level DL = Desired Level )( x Dose The Insulin IDS Parameter Equation of Fit