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Abstract Background Subarachnoid hemorrhage (SAH) patients with cerebral autoregulation (CA) impairment at an early post-SAH period are at high risk of unfavorable outcomes due to delayed cerebral ischemia (DCI) or other complications. Limited evidence exists for an association between early-stage CA impairments and SAH patient outcomes. The objective of this prospective study was to explore associations between CA impairments detected in early post-SAH snapshot examinations and patient outcomes. Methods The pilot observational study included 29 SAH patients whose CA status was estimated 2–3 days after spontaneous aneurysm rupture and a control group of 15 healthy volunteers for comparison. Inflatable leg recovery boots (reboots.com, Germany) were used for the safe controlled generation of arterial blood pressure (ABP) changes necessary for reliable CA examination. At least 5 inflation‒deflation cycles of leg recovery boots with a 2–3 min period were used during examinations. CA status was assessed according to the delay time (∆TCBFV) measured between ABP(t) and cerebral blood flow velocity (CBFV(t)) signals during artificially induced ABP changes at boot deflation cycle. CBFV was measured in middle cerebral artery by using transcranial Doppler device. Results Statistically significant differences in ∆TCBFV were found between SAH patients with unfavorable outcomes (∆TCBFV = 1.37 ± 1.23 s) and those with favorable outcomes (∆TCBFV = 2.86 ± 0.99 s) (p

Background and Objectives: Cardiac surgery is associated with various durations of cerebral autoregulation (CA) impairment and can significantly impact cognitive function. Cognitive functions such as memory, psychomotor speed, and attention are significantly impacted after cardiac surgery, necessitating prioritization of these areas in cognitive function tests. There is a lack of research connecting cerebral autoregulation impairment to specific cognitive function domains after cardiac surgery. This study aimed to determine if impaired cerebral autoregulation is associated with postoperative memory impairment and to test the hypothesis that the duration of this impairment affects the development of postoperative memory issues. Materials and Methods: A prospective study was conducted in 2021–2023. After approval of the Ethics Committee and with patient’s written consent, 83 adult patients undergoing elective on-pump coronary artery bypass graft (CABG) surgery were enrolled. All patients were assessed for cognitive function 1 day before surgery using the Mini-Mental state examination (MMSE-2) test as a screening tool and the Hopkins Verbal Learning Test-Revised (HVLT-R) to assess memory specifically. To diagnose possible memory impairment (IM), all patients underwent a repeat assessment of cognitive function on the 7th–10th postoperative day. Cerebral autoregulation monitoring using transcranial Doppler was performed. Cerebral autoregulation status index (Mx) was recorded using Intensive Care Brain Monitoring System software, 9.1.5.23 (Cambridge, UK). Results: According to our research, the incidence of postoperative memory impairment is 30.1%. Temporary cerebral autoregulation impairment occurs in all patients undergoing elective in-pump CABG surgery. The duration of the single longest CA impairment event in seconds (LCAI) and the LCAI dose were higher in patients with postoperative memory impairment, p = 0.006 and p < 0.007, respectively. Conclusions: Cerebral autoregulation impairment is important in developing memory loss after cardiac surgery. The duration and dose of the LCAI event are predictive of postoperative memory impairment.

Abstract Optimal cerebral perfusion pressure (CPPopt)-targeted treatment of traumatic brain injury (TBI) patients requires 2–8 h multi-modal monitoring data accumulation to identify CPPopt value for individual patient. Minimizing the time required for monitoring data accumulation is needed to improve the efficacy of CPPopt-targeted therapy. A retrospective analysis of multimodal physiological monitoring data from 87 severe TBI patients was performed by separately representing cerebrovascular autoregulation (CA) indices in relation to CPP, arterial blood pressure (ABP), and intracranial pressure (ICP) to improve the existing CPPopt identification algorithms. Machine learning (ML)-based algorithms were developed for automatic identification of informative data segments that were used for reliable CPPopt, ABPopt, ICPopt and the lower/upper limits of CA (LLCA/ULCA) identification. The reference datasets of the informative data segments and, artifact-distorted segments, and the datasets of different clinical situations were used for training the ML-based algorithms, allowing us to choose the appropriate individualized CPP-, ABP- or ICP-guided management for 79% of the full monitoring time for the studied population. The developed ML-based algorithms allow us to recognize informative physiological ABP/ICP variations within 24 min intervals with an accuracy up to 79% (compared to the initial accuracy of 74%) and use these segments for timely optimal value identification or CA limits determination in CPP, ABP or ICP data. Prospective clinical studies are needed to prove the efficiency of the developed algorithms.