ABSTRACT
BACKGROUND: Continuous subcutaneous insulin infusion (CSII) is a common treatment option for people with diabetes (PWD), but insulin infusion failures pose a significant challenge, leading to hyperglycemia, diabetes burnout, and increased hospitalizations. Current CSII pumps' occlusion alarm systems are limited in detecting infusion failures; therefore, a more effective detection method is needed. METHODS: We conducted five preclinical animal studies to collect data on infusion failures, utilizing both insulin and non-insulin boluses. Data were captured using in-line pressure and flow rate sensors, with additional force data from CSII pumps' onboard sensors in one study. A novel classifier model was developed using this dataset, aimed at detecting different types of infusion failures through direct utilization of force sensor data. Performance was compared against various occlusion alarm thresholds from commercially available CSII pumps. RESULTS: The testing dataset included 251 boluses. The Bagging classifier model showed the highest performance metrics among the models tested, exhibiting high accuracy (96%), sensitivity (94%), and specificity (98%), with lower false-positive and false-negative rate compared with traditional occlusion alarm pressure thresholds. CONCLUSIONS: Our study developed a novel non-threshold classifier that outperforms current occlusion alarm systems in CSII pumps in detecting infusion failures. This advancement has the potential to reduce the risk of hyperglycemia and hospitalizations due to undetected infusion failures, offering a more reliable and effective CSII therapy for PWD. Further studies involving human participants are recommended to validate these findings and assess the classifier's performance in a real-world setting.
ABSTRACT
Insulin infusion site (IIS) failures are a weakness in insulin pump therapy. We examined experience with IIS failures among U.S. individuals with diabetes on insulin pump through survey distributed to the T1D Exchange Online Community. Demographic factors, IIS characteristics, and diabetes-related perceptions were assessed by logistic regression to determine odds of higher (≥1 per month) or lower (<1 per month) reported IIS failure frequency. IIS failures were common; 41.4% reported ≥1 per month. IIS failure is usually detected through development of hyperglycemia rather than pump alarm. No assessed demographic factor or IIS characteristic was predictive; however, higher odds of ≥1 failure per month were associated with feelings of burnout (odds ratios [OR] 1.489 [1.024, 2.165]) and considering pump discontinuation (OR 2.233 [1.455, 3.427]). IIS failures are frequent and unpredictable, typically require hyperglycemia for detection, and are associated with negative perceptions. More should be done toward preventing IIS failures and/or detecting them sooner.
