## 3 Results
### Results of Individual Studies  
[PERSON] et al. [23] used a _long short-term memory_ (LSTM) network architecture [26] to predict the onset of critical ICP \"_phases\"_. The authors defined critical ICP \"phases\" as durations above an ICP threshold. An hour was defined as a critical phase if at least one ICP measurement was \(\geq\)22 mmHg. A duration of two sequential critical hours was defined as a short critical phase. A duration of more than two sequential critical hours was defined as a long critical phase. A primary dataset of 1346 patients with invasive ICP monitoring was used for the training and testing of the predictive model. The median patient age in this dataset was 54.8. The dataset comprised 59.9% and 40.1% male and female patients, respectively. The main diagnosis was TBI. The LSTM model was trained using 80% of the data, and was tested on the additional 20%. In addition to this primary dataset, the authors used the eICU and MIMIC datasets [27; 28] for external validation of their model. [PERSON] et al. focused their analysis on the two-hour prediction horizon, as it was deemed the most clinically relevant. The model performed with an _area under the receiver operating characteristics curve_ (AUC-ROC) of 0.95 (\(\pm\)0.0009) on the testing dataset when predicting long (\(>\)2 h) and short (\(\leq\)2 h) critical phases. The model returned an _area under the precision-recall curve_ (AUC-PR) of 0.71 (\(\pm\)0.0067) for the prediction of long critical phases. Similar results were reported in the external validation datasets, with an AUC-ROC of 0.948 (\(\pm\)0.0025) in the MIMIC dataset, and an AUC-ROC of 0.903 (\(\pm\)0.0033) in the eICU dataset. The authors suggested that the most important features for predicting long critical phases were ICP, mean arterial pressure (MAP) and _cerebral perfusion pressure_ (CPP). CPP was calculated using ICP and MAP, using the equation CPP = MAP - ICP. Consequently, there may have been a collinearity issue in the interpretation of the three most significant features presented by the authors; however, given that the core aim of the developed model was prediction rather than interpretation, having correlated prediction variables did not inhibit prediction performance. [PERSON] et al. [24] developed a _Gaussian processes_ (GP) model to predict ICP crises. The authors defined an ICP crisis as an event where ICP \(>\) 30 mmHg lasted for \(\geq\)10 min. The BrainIT dataset [29] was used in the development of this predictive model, which contained 264 TBI patients admitted to 22 neuro-ICUs in 11 European countries. The median age of the patients was 31. The dataset comprised 80% male patients and 20% female patients. The median total GCS score of patients within the dataset was 7. A brain injury was defined as severe if the GCS score was within the range 3-8. Minute-by-minute ICP and MAP of 239 patients was recorded for a minimum of four consecutive hours. The training dataset comprised data from 178 patients, and the remaining 61 patients made up the testing dataset. [PERSON] et al. judged a 30 min prediction horizon to be clinically appropriate, suggesting that 30 min offers sufficient time to allow for therapeutic intervention to reduce ICP. The authors reported a GP with an AUC-ROC of 0.872 on the testing dataset, with an accuracy of 77.4%, sensitivity of 81.6% and a specificity of 75.2% as their optimal prediction model. The study used 4 h of data to make predictions. Features used as input to the prediction model were derived from the analysis of the 4 h data window of ICP and MAP data along with ICU LOS at the time of prediction. [PERSON] et al. reported that the features with the most predictive power were within the ICP signal, with the most recent measurements being more relevant. [PERSON] et al. [25] developed and studied three models for the prediction of ICP crises: (i) GP; (ii) logistic regression and (iii) _autoregressive ordinal regression_ (AR-OR). The authors defined ICP crises as an event where ICP was \(\geq\)20 mmHg for \(\geq\)15 min.