Improving Accuracy of Non-invasive Hemoglobin Monitors: A Functional Regression Model for Streaming SpHb Data.

OBJECTIVE: The propose of this article is to develop a method for improving the accuracy of SpHb monitors, which are non-invasive hemoglobin monitoring tools, leading to better critical care protocols in trauma care.

METHODS: The proposed method is based on fitting smooth spline functions to SpHb measurements collected over a time window and then using a functional regression model to predict the true HgB value for the end of the time window.

RESULTS: The accuracy of the proposed method is compared to traditional methods. The mean absolute error between the raw SpHb measurements and the gold standard hemoglobin measurements was 1.26 g/Dl. The proposed method reduced the mean absolute error to 1.08 g/Dl.

CONCLUSION: Fitting a smooth function to SpHb measurements improves the accuracy of Hgb predictions.

SIGNIFICANCE: Accurate prediction of current and future HgB levels can lead to sophisticated decision models that determine the optimal timing and amount of blood product transfusions.