Intraparenchymal near-infrared spectroscopy for detection of delayed cerebral ischemia in poor-grade aneurysmal subarachnoid hemorrhage.

OBJECTIVE: Detection of delayed cerebral ischemia (DCI) is challenging in comatose patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH). Brain tissue oxygen pressure (PbtO2) monitoring may allow early detection of its occurrence. Recently, a probe for combined measurement of intracranial pressure (ICP) and intraparenchymal near-infrared spectroscopy (NIRS) has become available. In this pilot study, the parameters PbtO2, Hboxy, Hbdeoxy, Hbtotal and rSO2 were measured in parallel and evaluated for their potential to detect perfusion deficits or cerebral infarction. METHODS: In patients undergoing multimodal neuromonitoring due to poor neurological condition after aSAH, Clark oxygen probes, microdialysis and NIRS-ICP probes were applied. DCI was suspected when the measured parameters in neuromonitoring deteriorated. Thus, perfusion CT scan was performed as follow up, and DCI was confirmed as perfusion deficit. Median values for PbtO2, Hboxy, Hbdeoxy, Hbtotal and rSO2 in patients with perfusion deficit (Tmax > 6 s in at least 1 vascular territory) and/or already demarked infarcts were compared in 24- and 48-hour time frames before imaging. RESULTS: Data from 19 patients (14 University Hospital Zurich, 5 Charité Universitätsmedizin Berlin) were prospectively collected and analyzed. In patients with perfusion deficits, the median values for Hbtotal and Hboxy in both time frames were significantly lower. With perfusion deficits, the median values for Hboxy and Hbtotal in the 24 h time frame were 46,3 [39.6, 51.8] µmol/l (no perfusion deficits 53 [45.9, 55.4] µmol/l, p = 0.019) and 69,3 [61.9, 73.6] µmol/l (no perfusion deficits 74,6 [70.1, 79.6] µmol/l, p = 0.010), in the 48 h time frame 45,9 [39.4, 51.5] µmol/l (no perfusion deficits 52,9 [48.1, 55.1] µmol/l, p = 0.011) and 69,5 [62.4, 74.3] µmol/l (no perfusion deficits 75 [70,80] µmol/l, p = 0.008), respectively. In patients with perfusion deficits, PbtO2 showed no differences in both time frames. PbtO2 was significantly lower in patients with infarctions in both time frames. The median PbtO2 was 17,3 [8,25] mmHg (with no infarctions 29 [22.5, 36] mmHg, p = 0.006) in the 24 h time frame and 21,6 [11.1, 26.4] mmHg (with no infarctions 31 [22,35] mmHg, p = 0.042) in the 48 h time frame. In patients with infarctions, the median values of parameters measured by NIRS showed no significant differences. CONCLUSIONS: The combined NIRS-ICP probe may be useful for early detection of cerebral perfusion deficits and impending DCI. Validation in larger patient collectives is needed.

A supervised, externally validated machine learning model for artifact and drainage detection in high-resolution intracranial pressure monitoring data.

OBJECTIVE: In neurocritical care, data from multiple biosensors are continuously measured, but only sporadically acknowledged by the attending physicians. In contrast, machine learning (ML) tools can analyze large amounts of data continuously, taking advantage of underlying information. However, the performance of such ML-based solutions is limited by different factors, for example, by patient motion, manipulation, or, as in the case of external ventricular drains (EVDs), the drainage of CSF to control intracranial pressure (ICP). The authors aimed to develop an ML-based algorithm that automatically classifies normal signals, artifacts, and drainages in high-resolution ICP monitoring data from EVDs, making the data suitable for real-time artifact removal and for future ML applications. METHODS: In their 2-center retrospective cohort study, the authors used labeled ICP data from 40 patients in the first neurocritical care unit (University Hospital Zurich) for model development. The authors created 94 descriptive features that were used to train the model. They compared histogram-based gradient boosting with extremely randomized trees after building pipelines with principal component analysis, hyperparameter optimization via grid search, and sequential feature selection. Performance was measured with nested 5-fold cross-validation and multiclass area under the receiver operating characteristic curve (AUROC). Data from 20 patients in a second, independent neurocritical care unit (Charité - Universitätsmedizin Berlin) were used for external validation with bootstrapping technique and AUROC. RESULTS: In cross-validation, the best-performing model achieved a mean AUROC of 0.945 (95% CI 0.92-0.969) on the development dataset. On the external validation dataset, the model performed with a mean AUROC of 0.928 (95% CI 0.908-0.946) in 100 bootstrapping validation cycles to classify normal signals, artifacts, and drainages. CONCLUSIONS: Here, the authors developed a well-performing supervised model with external validation that can detect normal signals, artifacts, and drainages in ICP signals from patients in neurocritical care units. For future analyses, this is a powerful tool to discard artifacts or to detect drainage events in ICP monitoring signals.