[Personalized glycemic management for patients with diabetic ketoacidosis based on machine learning].

Abstract

Objective: To explore the optimal blood glucose-lowering strategies for patients with diabetic ketoacidosis (DKA) to enhance personalized treatment effects using machine learning techniques based on the United States Critical Care Medical Information Mart for Intensive Care- IV (MIMIC- IV).

Methods: Utilizing the MIMIC- IV database, the case data of 2 096 patients with DKA admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center from 2008 to 2019 were analyzed. Machine learning models were developed, and receiver operator characteristic curve (ROC curve) and precision-recall curve (PR curve) were plotted to evaluate the model's effectiveness in predicting four common adverse outcomes: hypoglycemia, hypokalemia, reductions in Glasgow coma scale (GCS), and extended hospital stays. The risk of adverse outcomes was analyzed in relation to the rate of blood glucose decrease. Univariate and multivariate Logistic regression analyses were conducted to examine the relationship between relevant factors and the risk of hypokalemia. Personalized risk interpretation methods and predictive technologies were applied to individualize the analysis of optimal glucose control ranges for patients.

Results: The machine learning models demonstrated excellent performance in predicting adverse outcomes in patients with DKA, with areas under the ROC curve (AUROC) and 95% confidence interval (95%CI) for predicting hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stays being 0.826 (0.803-0.849), 0.850 (0.828-0.870), 0.925 (0.903-0.946), and 0.901 (0.883-0.920), respectively. Analysis of the relationship between the rate of blood glucose reduction and the risk of four adverse outcomes showed that a maximum glucose reduction rate > 6.26 mmol×L^-1×h^-1 significantly increased the risk of hypoglycemia (P < 0.001); a rate > 2.72 mmol×L^-1×h^-1 significantly elevated the risk of hypokalemia (P < 0.001); a rate > 5.53 mmol×L^-1×h^-1 significantly reduced the risk of GCS score reduction (P < 0.001); and a rate > 8.03 mmol×L^-1×h^-1 significantly shortened the length of hospital stay (P < 0.001). Multivariate Logistic regression analysis indicated significant correlations between maximum bicarbonate levels, blood urea nitrogen levels, and total insulin doses with the risk of hypokalemia (all P < 0.01). In terms of establishing personalized optimal treatment thresholds, assuming optimal glucose reduction thresholds for hypoglycemia, hypokalemia, GCS score reduction, and extended hospital stay were x₁, x₂, x₃, x₄, respectively, the recommended glucose reduction rates to minimize the risks of hypokalemia and hypoglycemia should be ≤min{x₁, x₂}, while those to reduce GCS score decline and extended hospital stay should be ≥ max{x₃, x₄}. When these ranges overlap, i.e., max{x₃, x₄} ≤ min{x₁, x₂}, this interval was the recommended optimal glucose reduction range. If there was no overlap between these ranges, i.e., max{x₃, x₄} > min{x₁, x₂}, the treatment strategy should be dynamically adjusted considering individual differences in the risk of various adverse outcomes.

Conclusions: The machine learning models shows good performance in predicting adverse outcomes in patients with DKA, assisting in personalized blood glucose management and holding important clinical application prospects.