Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during...

Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO₂), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasibility of continuous paO₂ prediction in an intraoperative setting among neurosurgical patients undergoing craniotomies with modern machine learning methods.

Data from routine clinical care of lung-healthy neurosurgical patients were extracted from databases of the respective clinical systems and normalized. We used recursive feature elimination to identify relevant features for the prediction of paO₂. Six machine learning regression algorithms (gradient boosting, k-nearest neighbors, random forest, support vector, neural network, linear model with stochastic gradient descent) and a multivariable linear regression were then tuned and fitted to the selected features. A performance matrix consisting of standard deviation of absolute errors (σ_ae), mean absolute percentage error (MAPE), adjusted R², root mean squared error (RMSE), mean absolute error (MAE) and Spearman's ρ was finally computed based on the test set, and used to compare and rank each algorithm.

We analyzed N = 4,581 patients with n = 17,821 observations. Between 5 and 22 features were selected from the analysis of the training dataset comprising 3,436 patients with 13,257 observations. The best algorithm, a regularized linear model with stochastic gradient descent, could predict paO₂ values with σ_ae = 86.4 mmHg, MAPE = 16 %, adjusted R² = 0.77, RMSE = 44 mmHg and Spearman's ρ = 0.83. Further improvement was possible by calibrating the algorithm with the first measured paO₂/FiO₂ (p/F) ratio during surgery.

PaO₂ can be predicted by perioperative routine data in neurosurgical patients even before blood gas analysis. The prediction improves further when including the first measured p/F ratio, realizing quasi-continuous paO₂ monitoring.