Dataset Information

Effect of Remote Glucose Monitoring Utilizing Computerized Insulin Dose Adjustment Algorithms: A Pilot Project.

ABSTRACT:

Introduction

Primary care physicians are often challenged to adjust insulin doses. To facilitate this process, we evaluated in a safety net clinic the work flow and glycemic effects of remote glucose monitoring utilizing Federal Drug Administration (FDA) cleared, Conformité Européenne (CE) registered software that contained computerized algorithms for insulin dose adjustments to help clinicians make dosing decisions for insulin-requiring patients.

Methods

Patients taking insulin for at least 6 months with HbA1_c levels of at least 8.0% measured glucose levels with a meter attached to their smartphones. Readings were automatically transmitted to a secure, Health Insurance Portability and Accountability Act (HIPAA)-approved server. Values were analyzed every 2-3 weeks and reports, including recommendations for insulin dose changes, were sent to a clinic nurse practitioner (NP) who modified or accepted the recommendations. A staff person contacted patients with the new doses determined by the NP.

Results

Insulin regimens included basal alone (N = 11), basal/bolus (N = 14), and self-mixed/split (N = 3). Baseline HbA1 levels of 10.0% fell to 8.1% at 3 months (N = 28) and 7.6% at 6 months (N = 17) without any clinic visits for dose adjustments. There were 268 reports which allowed providers to see 268 other patients during these avoided clinic visits. The NP agreed with 82% of the recommendations. The total doses of insulin increased by 24%. No patient experienced severe hypoglycemia or visited an emergency department for hypoglycemia.

Conclusion

Remote glucose monitoring utilizing computerized insulin dose adjustment algorithms saved time for both providers and patients while effectively improving glycemia.

Funding

The Leonard M. Lipman Charitable Trust and Mellitus Health.

SUBMITTER: Davidson MB

PROVIDER: S-EPMC6437238 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:DisclaimerIn an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.PurposeInpatient diabetes management involves frequent assessment of glucose levels for treatment decisions. Here we describe a program for inpatient real-time continuous glucose monitoring (rtCGM) at a community hospital and the accuracy of rtCGM-based glucose estimates.MethodsAdult inpatients with preexisting diabetes managed with intensive insulin therapy and a diagnosis of coronavirus disease 2019 (COVID-19) were monitored via rtCGM for safety. An rtCGM system transmitted glucose concentration and trending information at 5-minute intervals to nearby smartphones, which relayed the data to a centralized monitoring station. Hypoglycemia alerts were triggered by rtCGM values of ≤85 mg/dL, but rtCGM data were otherwise not used in management decisions; insulin dosing adjustments were based on blood glucose values measured via blood sampling. Accuracy was evaluated retrospectively by comparing rtCGM values to contemporaneous point-of-care (POC) blood glucose values.ResultsA total of 238 pairs of rtCGM and POC data points from 10 patients showed an overall mean absolute relative difference (MARD) of 10.3%. Clarke error grid analysis showed 99.2% of points in the clinically acceptable range, and surveillance error grid analysis showed 89.1% of points in the lowest risk category. It was determined that for 25% of the rtCGM values, discordances in rtCGM and POC values would likely have resulted in different insulin doses. Insulin dose recommendations based on rtCGM values differed by 1 to 3 units from POC-based recommendations.ConclusionrtCGM for inpatient diabetes monitoring is feasible. Evaluation of individual rtCGM-POC paired values suggested that using rtCGM data for management decisions poses minimal risks to patients. Further studies to establish the safety and cost implications of using rtCGM data for inpatient diabetes management decisions are warranted.