Cerebral autoregulation (CA) refers to the control of cerebral tissue blood flow (CBF) in response to changes in perfusion pressure. Due to the challenges of measuring intracranial pressure, CA is often described as the relationship between mean arterial pressure (MAP) and CBF. Dynamic CA (dCA) can be assessed using multiple techniques, with transfer function analysis (TFA) being the most common. A 2016 white paper by members of an international Cerebrovascular Research Network (CARNet) that is focused on CA strove to improve TFA standardization by way of introducing data acquisition, analysis, and reporting guidelines. Since then, additional evidence has allowed for the improvement and refinement of the original recommendations, as well as for the inclusion of new guidelines to reflect recent advances in the field. This second edition of the white paper contains more robust, evidence-based recommendations, which have been expanded to address current streams of inquiry, including optimizing MAP variability, acquiring CBF estimates from alternative methods, estimating alternative dCA metrics, and incorporating dCA quantification into clinical trials. Implementation of these new and revised recommendations is important to improve the reliability and reproducibility of dCA studies, and to facilitate inter-institutional collaboration and the comparison of results between studies.

Delirium is a neuropsychiatric syndrome characterized by acute and fluctuating impairment in attention, memory, perception, and consciousness. Postoperative delirium (POD) affects up to 50% of hospitalized surgical patients and is associated with increased morbidity and mortality,^1,2 postoperative cognitive decline, poor functional recovery, prolonged hospitalization, higher rates of hospital readmission, and increased health care resource expenditures.^3-5 Prevention and treatment of POD may be achievable through pharmacological, psychological, and nonpharmacological interventions.⁶

Background: The Montreal Cognitive Assessment (MoCA) has good sensitivity for mild cognitive impairment, but specificity is low when the original cut-off (25/26) is used. We aim to revise the cut-off on the German MoCA for its use in clinical routine. Methods: Data were analyzed from 496 Memory Clinic outpatients (447 individuals with a neurocognitive disorder; 49 with cognitive normal findings) and from 283 normal controls. Cut-offs were identified based on (a) Youden’s index and (b) the 10th percentile of the control group. Results: A cut-off of 23/24 on the MoCA had better correct classification rates than the MMSE and the original MoCA cut-off. Compared to the original MoCA cut-off, the cut-off of 23/24 points had higher specificity (92% vs 63%), but lower sensitivity (65% vs 86%). Introducing two separate cut-offs increased diagnostic accuracies with 92% specificity (23/24 points) and 91% sensitivity (26/27 points). Scores between these two cut-offs require further examinations. Conclusions: Using two separate cut-offs for the MoCA combined with scores in an indecisive area enhances the accuracy of cognitive screening.

Awake craniotomy (AC) is performed for the resection of brain tumours in close proximity to areas of eloquent brain function to maximize reduction of tumour mass and minimize neurological injury. This study compares the efficacy and safety of dexmedetomidine vs propofol-remifentanil-based conscious sedation, during AC for supratentorial tumour resection. Prospective, randomized, controlled trial including 50 adult patients undergoing AC who were randomly assigned to a dexmedetomidine (DEX group, n =25) or propofol-remifentanil group (P-R group, n =25). The primary outcome was the ability to perform intraoperative brain mapping assessed on a numeric rating scale (NRS). Secondary outcome was the efficacy of sedation measured by the modified Observer's Assessment of Alertness/Sedation (OAA/S) scale. Other outcome measures including haemodynamic and respiratory variables, pain, sedation and anxiety scores, adverse events, and patient satisfaction were also compared. There were no differences between DEX and P-R groups regarding the ability to perform intraoperative brain mapping [mean NRS score (95% CI): 10.0 (9.9–10.0) vs 9.7 (9.5–10.0), P =0.13] and level of sedation during mapping [mean OAA/S score (95% CI): 4.1 (3.5–4.7) vs 4.3 (3.9–4.7), P =0.51], respectively. Respiratory adverse events were more frequent in the P-R group (20 vs 0%, P =0.021). Heart rate was significantly lower in the DEX group across time ( P <0.001); however, the need for treatment of bradycardia was not different between groups. Quality of intraoperative brain mapping and efficacy of sedation with dexmedetomidine were similar to propofol-remifentanil during AC for supratentorial tumour resection. Dexmedetomidine was associated with fewer respiratory adverse events. NCT01545297.

Background: The Montreal Cognitive Assessment (MoCA) is used to evaluate multiple cognitive domains in elderly individuals. However, it is influenced by demographic characteristics that have yet to be adequately considered. Objective: The aim of our

Background: Early and accurate detection of cognitive changes using simple tools is essential for an appropriate referral to a more detailed neurocognitive assessment and for the implementation of therapeutic strategies. The Mini‐Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are two commonly used psychometric tests for cognitive screening. Both tests have different strengths and weaknesses. Preferences regarding test selection may therefore differ among clinicians. The aim of this retrospective observational cohort study was to define corresponding scores for the MMSE and the MoCA. Methods: We examined the relationship between the cognitive screening tests in 803 German‐speaking Memory Clinic outpatients, encompassing a wide range of neurocognitive disorders. We produced a conversion table using the equipercentile equating method with log‐linear smoothing. In addition, we conducted a systematic review of existing MMSE‐MoCA conversions to create a table allowing for the conversion of MoCA scores into MMSE scores and vice versa using the weighted mean method. Results: The Memory Clinic sample showed that the prediction of MMSE to MoCA was overall less accurate compared to the conversion from MoCA to MMSE. The 19 studies included after thorough literature search showed that MoCA scores were consistently lower than MMSE scores. Eleven of 19 conversion studies had addressed the conversion of the MoCA to the MMSE, while two studies converted MMSE to MoCA scores. Another six studies applied bi‐directional conversions. We provide an easy‐to‐use table covering the entire range of scores and taking into account all currently existing conversion formulas. Conclusion: The comprehensive MMSE‐MoCA conversion table enables a direct comparison of cognitive test scores at screening examinations and over the course of disease in patients with neurocognitive disorders.