Endocrine Abstracts

Background: Effective management of diabetic ketoacidosis (DKA) improves clinical outcomes. Regular auditing and performance feedback are key to achieving sustained and significant improvement in the management of DKA. One of the major limitations for maximal impact of an audit is the delay from initiation to results as the latter may not be applicable to the then current practice. In order to overcome this, we created an automated auditing system called Digital Evaluation of Ketosis and Other Diabetes Emergencies (DEKODE). This system identifies DKA episodes based on prescriptions for fixed rate intravenous insulin infusion (FRIII). Here, we retrospectively validated DEKODE system for monitoring DKA management.

Methods: To retrospectively validate DEKODE model, all episodes identified by DEKODE from September 2018 to August 2019 was manually verified for confirmation of diagnosis. DKA duration was defined as the difference in time between FRIII prescription time and end time for DEKODE. For manually collected data, the difference in the time from diagnosisto resolution as per standard criteria was considered as DKA duration. Further, appropriateness of glucose and ketone measurements during entire DKA duration and fluids prescribed in the first 12 hours of diagnosis were compared between the two datasets. The difference between manual and automated data for DKA duration, FRIII appropriateness, hourly glucose and ketone measurements were analysed using Prism v6.0 (Graphpad Inc) and results are presented as mean and standard error of mean (s.e.m.). Difference in frequencies of hypokalemia and hyperkalemia between manual and automated data was analysed by chi-square test.

Results: 150 episodes were identified by DEKODE during the study period. Of these, 147 had manually confirmed DKA. There was no significant difference in DKA duration between DEKODE and manual data (mean ± s.e.m., 16.0 ± 1.0 hours; 17.5 ± 0.9 hours; P = ns) respectively. Similarly, there was no difference in FRIII appropriateness (98.3% ± 1.2%; 97.9% ± 1.1%; P = ns), hourly glucose (98.5% ± 2.6%; 105.6% ± 2.5%; P = ns) and ketone measurements (43.3% ± 2.1%; 47.1% ± 2.2%; P = ns) between the two systems. DEKODE also accurately predicted the frequency of kalaemic complications with no significant difference in the number of patients recorded with hyperkaelema (7/147; 6/150; P = ns) and hypokalaemia (9/147; 9/147; P = ns). However, DEKODE over-predicted proportion of fluids prescribed (96.9% ± 3.2%; 84.4% ± 3.1%; P = 0.0047).

Conclusion: DEKODE system could reliably predict DKA duration and management. This can help in monitoring DKA management cutting time from collecting data to analysis, thus providing real-time performance results. Further prospective validation is currently underway.