UNASSIGNED: Blood pressure management is extremely important to prevent cerebral hypoxia and influence the outcome of critically ill patients. In medicine, precise instruments are essential to increase patient safety in the intensive care unit (ICU), including intracranial compliance (ICC) monitoring. A new technology developed by Brain4care, makes it possible to analyze the waveform of intracranial pressure (ICP) non-invasively associated with ICC, and this instrument was used in the patient for monitoring.
UNASSIGNED: A 40-year-old male underwent aortic endocarditis surgery involving 182-min extracorporeal circulation and 9-min aortic clamping. Post-surgery, he exhibited a seizure bilateral mydriasis, followed by isochoric pupils and rapid foot movements. Neuroprotection measures were applied in the ICU, with noninvasive ICC monitoring initiated to assess intervention effectiveness.
UNASSIGNED: The non-invasive measurement of ICP can help clinical decision-making regarding the optimization of adapted protocols for neuroprotection in the ICU.

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BACKGROUND: Mechanical thrombectomy (MT) is the standard of care for the treatment of acute ischemic stroke (AIS) with large vessel occlusion (LVO), but unfavorable outcomes remain common. Procedural arterial hypotension is associated with poor patient outcome. This study aimed to assess the impact of arterial hypotension \"magnitude\" (a combination of the depth, defined as the percentage relative to baseline arterial blood pressure, and the duration of hypotensive episodes)\" during MT on neurological outcome.
METHODS: This is a monocentric retrospective study. Charts were reviewed between January 2018 and June 2021. \"Patients were eligible if they were 18 years or older, anterior LVO was diagnosed on cerebral imaging\" and MT performed under general anesthesia. Mean arterial pressure (MAP) was recorded every 5 min throughout the procedure, and the arterial hypotension \"magnitude\" was estimated by the area under the curve (AUC) for different thresholds of MAP drops.
METHODS: The modified Rankin Scale (mRS) at 90 days.
RESULTS: Among the 117 patients analyzed, 46% had poor neurological outcome. Our study showed correlations between poor outcome and a greater procedural AUC of arterial hypotension for the different thresholds: 5% (k 0.18; 95% CI 0.06-0.30; P = 0.007), 10% (k 0.18; 95% CI 0.05-0.30; P = 0.008), 15% (k 0.18; 95% CI 0.04-0.30; P = 0.011), 20% (k 0.18; 95% CI 0.05-0.30; P = 0.010) and 30% (k 0.19; 95% CI 0.05-0.31; P = 0.010). This association persisted after controlling for age, baseline NIHSS score, and ASPECT score.
CONCLUSIONS: The magnitude of hypotension during MT under general anesthesia for AIS is an independent factor of poor outcome at 90 days. These associations have been observed in patients with mild and severe hypotensive episodes.

BACKGROUND: Synchronous acquisition of haemodynamic signals is crucial for their multimodal analysis, such as dynamic cerebral autoregulation (DCA) analysis of arterial blood pressure (ABP) and transcranial Doppler (TCD)-derived cerebral blood velocity (CBv). Several technical problems can, however, lead to (varying) time-shifts between the different signals. These can be difficult to recognise and can strongly influence the multimodal analysis results.
METHODS: We have developed a multistep, cross-correlation-based time-shift detection and synchronisation algorithm for multimodal pulsatile haemodynamic signals. We have developed the algorithm using ABP and CBv measurements from a dataset that contained combinations of several time-shifts. We validated the algorithm on an external dataset with time-shifts. We additionally quantitatively validated the algorithm\'s performance on a dataset with artificially added time-shifts, consisting of sample clock differences ranging from -0.2 to 0.2 s/min and sudden time-shifts between -4 and 4 s. The influence of superimposed noise and variation in waveform morphology on the time-shift estimation was quantified, and their influence on DCA-indices was determined.
RESULTS: The instantaneous median absolute error (MedAE) between the artificially added time-shifts and the estimated time-shifts was 12 ms (median, IQR 12-12, range 11-14 ms) for drifts between -0.1 and 0.1 s/min and sudden time-shifts between -4 and 4 s. For drifts above 0.1 s/min, MedAE was higher (median 753, IQR 19 - 766, range 13 - 772 ms). When a certainty threshold was included (peak cross-correlation > 0.9), MedAE for all drifts-shift combinations decreased to 12 ms, with smaller variability (IQR 12 - 13, range 8 - 22 ms, p < 0.001). The time-shift estimation is robust to noise, as the MedAE was similar for superimposed white noise with variance equal to the signal variance. After time-shift correction, DCA-indices were similar to the original, non-time-shifted signals. Phase shift differed by 0.17° (median, IQR 0.13-0.2°, range 0.0038-1.1°) and 0.54° (median, IQR 0.23-1.7°, range 0.0088-5.6°) for the very low frequency and low frequency ranges, respectively.
CONCLUSIONS: This algorithm allows visually interpretable detection and accurate correction of time-shifts between pulsatile haemodynamic signals (ABP and CBv).

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Intracranial pressure (ICP) is commonly monitored to guide treatment in patients with serious brain disorders such as traumatic brain injury and stroke. Established methods to assess ICP are resource intensive and highly invasive. We hypothesized that ICP waveforms can be computed noninvasively from three extracranial physiological waveforms routinely acquired in the Intensive Care Unit (ICU): arterial blood pressure (ABP), photoplethysmography (PPG), and electrocardiography (ECG). We evaluated over 600 h of high-frequency (125 Hz) simultaneously acquired ICP, ABP, ECG, and PPG waveform data in 10 patients admitted to the ICU with critical brain disorders. The data were segmented in non-overlapping 10-s windows, and ABP, ECG, and PPG waveforms were used to train deep learning (DL) models to re-create concurrent ICP. The predictive performance of six different DL models was evaluated in single- and multi-patient iterations. The mean average error (MAE) ± SD of the best-performing models was 1.34 ± 0.59 mmHg in the single-patient and 5.10 ± 0.11 mmHg in the multi-patient analysis. Ablation analysis was conducted to compare contributions from single physiologic sources and demonstrated statistically indistinguishable performances across the top DL models for each waveform (MAE±SD 6.33 ± 0.73, 6.65 ± 0.96, and 7.30 ± 1.28 mmHg, respectively, for ECG, PPG, and ABP; p = 0.42). Results support the preliminary feasibility and accuracy of DL-enabled continuous noninvasive ICP waveform computation using extracranial physiological waveforms. With refinement and further validation, this method could represent a safer and more accessible alternative to invasive ICP, enabling assessment and treatment in low-resource settings.

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Background: Autonomic function and baroreflex control might influence the survival rate of coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) compared to respiratory failure patients without COVID-19 (non-COVID-19). This study describes physiological control mechanisms in critically ill COVID-19 patients admitted to the ICU in comparison to non-COVID-19 individuals with the aim of improving stratification of mortality risk. Methods: We evaluated autonomic and baroreflex control markers extracted from heart period (HP) and systolic arterial pressure (SAP) variability acquired at rest in the supine position (REST) and during a modified head-up tilt (MHUT) in 17 COVID-19 patients (age: 63 ± 10 years, 14 men) and 33 non-COVID-19 patients (age: 60 ± 12 years, 23 men) during their ICU stays. Patients were categorized as survivors (SURVs) or non-survivors (non-SURVs). Results: We found that COVID-19 and non-COVID-19 populations exhibited similar vagal and sympathetic control markers; however, non-COVID-19 individuals featured a smaller baroreflex sensitivity and an unexpected reduction in the HP-SAP association during the MHUT compared to the COVID-19 group. Nevertheless, none of the markers of the autonomic and baroreflex functions could distinguish SURVs from non-SURVs in either population. Conclusions: We concluded that COVID-19 patients exhibited a more preserved baroreflex control compared to non-COVID-19 individuals, even though this information is ineffective in stratifying mortality risk.

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