Development of a neural network model for predicting glucose levels in a surgical critical care setting

Development of neural network models for the prediction of glucose levels in critically ill patients through the application of continuous glucose monitoring may provide enhanced patient outcomes. Here we demonstrate the utilization of a predictive model in real-time bedside monitoring. Such modeling may provide intelligent/directed therapy recommendations, guidance, and ultimately automation, in the near future as a means of providing optimal patient safety and care in the provision of insulin drips to prevent hyperglycemia and hypoglycemia

Continuous glucose monitoring identifies relationship between optimized glycemic control and post-discharge acute care facility needs

Abstract Objective Hyperglycemia is an independent risk factor in hospitalized patients for adverse outcomes, even if patients are not diabetic. We used continuous glucose monitoring to evaluate whether glycemic control (hyperglycemia) in the first 72 h after an intensive care admission was associated with the need for admission to a post discharge long-term medical facility. Results We enrolled 59 coronary artery bypass grafting patients. Poor glycemic control was defined as greater than 33% of continuous glucose monitoring values  180 mg/dL (group 1); and then these patients were reevaluated with a less strict definition of poor glycemic control with greater than 25% of continuous glucose values  180 mg/dL (group 2). In group 1 4/10 (40.0%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 6/49 (12.2%) that were well controlled. In reevaluation as group 2, 5/14 (35.7%) whose glucose was not well controlled went to an extended care post discharge facility as opposed to 5/45 (11.1%) who were well controlled. Admission to a post discharge facility was increased in patients with poor glycemic control p = 0.045 and p = 0.042 for group 1 and group 2, and with odds ratios of 4.8 (95% CI 1.0–22.5) and 4.4 (95% CI 1.0–19.4), respectively

Comorbidity polypharmacy score and its clinical utility: A pragmatic practitioner′s perspective

Modern medical management of comorbid conditions has resulted in escalating use of multiple medications and the emergence of the twin phenomena of multimorbidity and polypharmacy. Current understanding of how the polypharmacy in conjunction with multimorbidity influences trauma outcomes is limited, although it is known that trauma patients are at increased risk for medication-related adverse events. The comorbidity-polypharmacy score (CPS) is a simple clinical tool that quantifies the overall severity of comorbidities using the polypharmacy as a surrogate for the "intensity" of treatment necessary to adequately control chronic medical conditions. Easy to calculate, CPS is derived by counting all known pre-injury comorbid conditions and medications. CPS has been independently associated with mortality, increased risk for complications, lower functional outcomes, readmissions, and longer hospital stays. In addition, CPS may help identify older trauma patients at risk of post-emergency department undertriage. The goal of this article was to review and refine the rationale for CPS and to provide an evidence-based outline of its potential clinical applications

Evaluation of a model for glycemic prediction in critically ill surgical patients.

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

Patient Demographics.

<p>This table includes the demographics of the patients enrolled in the study and used for model development. The patients are divided into two groups based on ICU admission type (trauma or cardiothoracic surgical intervention). Key demographics include: percentage of male patients, age, and BMI.</p>*<p>Values presented as Mean ± SD.</p

Clarke Error Grid Predictions demonstrating 97.5% clinically acceptable predictive values.

<p>Clarke Error Grid Predictions demonstrating 97.5% clinically acceptable predictive values.</p

Neural network model design.

<p>The feed-forward neural network design implemented for real-time prediction of glucose. Error (mean squared error) is calculated between neural network output and desired response (actual continuous glucose monitoring values). This error is back propagated to each layer in the neural network architecture and a gradient descent with momentum algorithm is implemented to determine optimal weight values to minimize model error.</p

The main menu of the developed Electronic Clinical Intensive Data-Logger (eCIDL).

<p>This main menu contains buttons that link the user to various interfaces which contain text fields and drop-down menus to log all medical records present in the comprehensive intensive care unit medical record. This software application was utilized to convert paper-based medical records into electronic records suitable for direct neural network model utilization.</p

Glycemic predictions generated by neural network model.

<p>Glycemic predictions generated by neural network model.</p