Key result
Random forest model accurately forecasts ICP episodes ~30 minutes early using dynamic clinical features.
Why the study?
Does a regression model using dynamic characteristics of vitals and medications accurately forecast intracranial pressure episodes 30 minutes in advance in children?
Does a regression model using dynamic characteristics of vitals and medications accurately forecast intracranial pressure episodes 30 minutes in advance in children?
A random forest regression model incorporating dynamic features from vitals and medications can accurately forecast intracranial pressure episodes 30 minutes in advance in children.
Answers are grounded in this paper and cite their sources.
May enable preemptive ICP management in children; hypothesis-generating and requires prospective validation before clinical use.
Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values.
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Farhadi et al. (2018) studied Increased Intracranial Pressure (ICP). Random forest regression model for ICP forecasting vs. Models without medication variables was evaluated on Accuracy of ICP forecast (RMSE and correlation) (Correlation 0.99). A random forest regression model using dynamic features from vitals, medications, and continuous ICP accurately forecasted ICP episodes 30 minutes in advance with a correlation of 0.99 and an RMSE of 0.89%.
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