Statistical transformation and the interpretation of inpatient glucose control data from the intensive care unit

Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations

Aim:

Evaluate forecasting models applied to smaller geographic locations within the hospital.

Materials & methods:

Damped trend models were applied to blood glucose measurements of progressively smaller inpatient geographic subpopulations. Mean absolute percentage error (MAPE) and 95% prediction intervals (PIs) assessed validity of the models to forecasts 48 weeks into the future.

Results:

MAPE values increased, and 95% PIs widened, when data from progressively smaller geographic areas were analyzed. MAPE values were highest and 95% PIs were broadest with the smallest geographic areas. In contrast, observations missed at larger geographical locations were more evident with smaller subpopulations.

Conclusion:

The utility of damped trend models to forecast inpatient glucose control diminished when applied to smaller geographic areas within the hospital.

Hyperglycemia (high blood sugar) is associated with worse outcomes in hospitalized patients. Predicting future glucose control could identify problems sooner and lead to earlier quality improvement changes. This study examined whether a forecasting tool used across an entire hospital might be useful in smaller geographic areas of the facility. The models became less reliable when tested with progressively smaller geographic subpopulations.

How is the weather? Forecasting inpatient glycemic control

Aim

Apply methods of damped trend analysis to forecast inpatient glycemic control.

Method

Observed and calculated point-of-care blood glucose data trends were determined over 62 weeks. Mean absolute percent error was used to calculate differences between observed and forecasted values. Comparisons were drawn between model results and linear regression forecasting.

Results

The forecasted mean glucose trends observed during the first 24 and 48 weeks of projections compared favorably to the results provided by linear regression forecasting. However, in some scenarios, the damped trend method changed inferences compared with linear regression. In all scenarios, mean absolute percent error values remained below the 10% accepted by demand industries.

Conclusion

Results indicate that forecasting methods historically applied within demand industries can project future inpatient glycemic control. Additional study is needed to determine if forecasting is useful in the analyses of other glucometric parameters and, if so, how to apply the techniques to quality improvement.