## 4 Discussion  
### Summary of Evidence  
The review identifies logistic regression, Gaussian process (GP) and long short-term memory (LSTM) models as the current leading machine learning techniques for predicting ICP crises in TBI patients. Notably, each of the three studies presented a different solution, with the most recent paper by [PERSON] et al. demonstrating superior performance using an LSTM model architecture, achieving an AUC-ROC of 0.95. This result outperforms the GP approach utilized by [PERSON] et al. by 8.56% and the logistic regression model described by [PERSON] et al. by 9.95%. These findings emphasize the potential of advanced neural network models, such as LSTM, for enhancing the accuracy of ICP crisis prediction. This systematic review suggests that the logistic regression, GP and LSTM models are the current leading machine learning techniques for predicting ICP crises in TBI patients. Each of the three studies presents a different solution. Of the different approaches, the most recent paper by [PERSON] et al. performed best, using an LSTM model architecture that returned an AUC-ROC of 0.95, which was 8.56% greater than the GP approach used by [PERSON] et al. and 9.95% greater than the logistic regression model described by [PERSON] et al. However, the review also draws attention to the lack of consistency in defining an ICP crisis across the three studies. Each study employed different thresholds of ICP, and varying durations above a defined threshold, to classify a crisis. This discrepancy raises concerns regarding the comparability of results and the establishment of standardized criteria for ICP crisis prediction. To address this issue, future research should prioritize consensus building efforts to define an ICP crisis uniformly, ensuring robust and reproducible findings. Furthermore, the review highlights the existence of some agreement regarding the clinically relevant prediction horizon. Two of the studies proposed a prediction horizon of 30 min, while the third suggested a two-hour horizon. Our review not only identifies partial alignment among the studies, regarding the clinically relevant prediction horizon for ICP prediction in TBI patients, but also emphasizes the need for further clarity in determining the optimal prediction horizon with clinical relevance. To establish consensus guidelines for the appropriate prediction period, various factors should be considered, such as the nature of the injury, patient characteristics, available healthcare professionals and treatment interventions. The nature of the injury and patient characteristics play a role in defining a suitable prediction horizon for predicting an ICP crisis. TBI encompasses a wide spectrum of severity and complexity, ranging from mild to severe cases. The extent of brain damage, the presence of associated injuries and individual patient factors can influence the progression and severity of the injury. In cases of severe TBI, where the risk of an ICP crisis is higher, a shorter prediction horizon, such as 30 min, may be more appropriate, to enable timely interventions and prevent adverse outcomes. The available healthcare resources and the context of a busy clinical setting are important considerations. In such settings, healthcare professionals often face resource constraints and time limitations. Allocating resources effectively, to address critical patient needs, is crucial for optimal patient care. A prediction horizon of two hours, as suggested by one of the studies, may potentially strain already stretched healthcare resources, as continuous monitoring and interventions over an extended period may not be feasible. Thus, it seems reasonable to suggest that a shorter prediction horizon, such as 30 min, could be more suitable in busy clinical settings with limited resources. This duration allows healthcare professionals to promptly respond to impending ICP crises while optimising resource utilization.