EndoTool SubQ

EndoTool SubQ is a software application designed to aid healthcare professionals by calculating patient-specific subcutaneous insulin and carbohydrate dose recommendations for managing Diabetes Mellitus in adult and pediatric patients. It utilizes proprietary feedback mathematics and a nonlinear dosing equation, dynamically adjusting to patient data and physician-defined parameters to optimize glycemic control.

EndoTool SubQ is a software application designed to assist trained healthcare professionals in managing blood glucose levels in patients with Diabetes Mellitus, including both adult and pediatric populations (age 2 years and older, weighing 12 kg or more). Its primary function is to calculate and recommend the next dose of insulin to be administered subcutaneously. The system also suggests carbohydrate doses when necessary to address hypoglycemia, based on parameters established by the prescribing healthcare provider, such as nutritional regimen, insulin regimen, target glucose range, and specific patient characteristics. It's emphasized that EndoTool SubQ serves as an aid and is not a replacement for clinical judgment; final treatment decisions must incorporate the patient's complete clinical status. The system operates using proprietary control technology and feedback mathematics, employing a nonlinear dosing equation that is individualized and continuously optimized. It calculates limited proportional incremental changes to the insulin dosing model—which includes the total daily dose and physician-set basal/bolus distribution—or carbohydrate recommendations by analyzing a patient's previous blood glucose readings in response to prior insulin doses. Healthcare staff input clinical data like food intake and blood glucose measurements, and the software then recommends the next insulin dose and the timing for the next glucose check. The system, installed on a hospital server and accessed via an internal website, aims to achieve patient-specific, sustained glycemic control, with performance demonstrated through requirements-based, integration, and algorithm testing.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the 'nonlinear dosing equation' adapt to rapid changes in a patient's insulin sensitivity, such as during acute illness or stress?; What specific 'patient specific characteristics' beyond age and weight are factored into the initial mathematical model and subsequent dose adjustments?; What are the typical ranges for physician-set parameters (e.g., target glucose range, basal/bolus distribution percentages) that the software is designed to accommodate?; How does the system differentiate its recommendations for various subcutaneous insulin types (e.g., rapid-acting, short-acting, intermediate-acting, long-acting) within the 'insulin regimen' set by the physician?; What mechanisms are in place within the software to alert clinicians if entered data (e.g., blood glucose readings, carbohydrate intake) falls outside plausible physiological ranges or significantly deviates from previous trends, prior to dose calculation?