Background: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. Methods: We introduce explainable machine learning to make predictions of hypoglycemia (<70 mg/dL) and hyperglycemia (>270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. Results: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. Conclusions: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.
Background: The occurrences of acute complications arising from hypoglycaemia and hyperglycaemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time blood glucose readings enabling users to manage their control pro-actively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual's longer-term control.
Methods: We introduce explainable machine learning to make predictions of hypoglycaemia (< 70 mg/dL) and hyperglycaemia (> 270 mg/dL) 60 minutes ahead-of-time. We train our models using CGM data from 153 people living with T1D in the CITY survey totalling over 28000 days of usage, which we summarise into (short-term, medium-term, and long-term) blood glucose features along with demographic information. We use machine learning explanations (SHAP) to identify which features have been most important in predicting risk per user.
Results: Machine learning models (XGBoost) show excellent performance at predicting hypoglycaemia (AUROC: 0.998) and hyperglycaemia (AUROC: 0.989) in comparison to a baseline heuristic and logistic regression model.
Conclusions: Maximising model performance for blood glucose risk prediction and management is crucial to reduce the burden of alarm-fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison to baseline models. SHAP helps identify what about a CGM user's blood glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.
Objectives
Hypothalamic hamartoma (HH) typically presents with gonadotrophin-dependent precocious puberty and/or seizures. Other endocrine disturbances are rare. We describe an infant with syndrome of inappropriate secretion of anti-diuretic hormone (SIADH) and a HH.
Case presentation
A 6-week-old infant presented with seizures and life-threatening hyponatremia. A HH was identified on magnetic resonance imaging. Clinical examination and biochemistry were consistent with SIADH, and serum copeptin was high during hyponatremia, further supporting this diagnosis. Tolvaptan was effective in normalizing plasma sodium and enabling liberalization of fluids to ensure sufficient nutritional intake and weight gain and manage hunger.
Conclusions
Hyponatremia due to SIADH is novel at presentation of a HH, and can be challenging to diagnose and manage. Successful management of hyponatremia in this case was achieved using tolvaptan.
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