Department of Bioengineering, University of Toledo, Toledo, Ohio, United States of America.
PLoS One. 2013 Jul 19;8(7):e69475. doi: 10.1371/journal.pone.0069475. Print 2013.
We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to "train" the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.
我们评估了一种用于预测危重症创伤和心胸外科手术后患者血糖的神经网络模型。进行了一项前瞻性、可行性试验，以评估连续血糖监测设备。在机构审查委员会批准后，使用新型软件将所有同意参与外科重症监护病房患者的临床数据转换为电子格式。利用这些数据开发并训练了一个神经网络模型，用于实时预测血清葡萄糖浓度，预测时间为 75 分钟。从 19 名患者的血糖数据中“训练”神经网络模型。随后在 5 名对神经网络模型不熟悉的患者中进行实时模拟测试。通过计算平均绝对差百分比 (MAD%)、Clarke 误差网格分析以及计算模型准确预测的低血糖 (≤70mg/dL)、正常血糖 (>70 且 <150mg/dL) 和高血糖 (≥150mg/dL) 值的百分比来评估模型的性能；共分析了 9405 个数据点。该模型成功预测了 5 名测试患者的血糖趋势。Clarke 误差网格分析表明，100.0%的预测结果具有临床可接受性，其中 87.3%和 12.7%的预测值分别落在误差网格的 A 和 B 区。整体模型误差 (MAD%)相对于实际连续血糖模型数据为 9.0%。该模型成功预测了 96.7%和 53.6%的正常和高血糖值，这些患者均未发生低血糖事件。在外科重症监护病房环境中使用神经网络模型实时预测血糖为医疗保健提供者提供了潜在有用的信息，有助于优化血糖控制、患者安全和改善护理。类似的模型可以在更广泛的生物医学变量范围内实施，以提供实时优化、培训和适应，从而提高治疗的预测准确性和性能。
