Cerebral Autoregulation Assessment Using the Near Infrared Spectroscopy 'NIRS-Only' High Frequency Methodology in Critically Ill Patients:A Prospective Cross-Sectional Study

Impairments in cerebral autoregulation (CA) are related to poor clinical outcome. Near infrared spectroscopy (NIRS) is a non-invasive technique applied to estimate CA. Our general purpose was to study the clinical feasibility of a previously published 'NIRS-only' CA methodology in a critically ill intensive care unit (ICU) population and determine its relationship with clinical outcome. Bilateral NIRS measurements were performed for 1-2 h. Data segments of ten-minutes were used to calculate transfer function analyses (TFA) CA estimates between high frequency oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) signals. The phase shift was corrected for serial time shifts. Criteria were defined to select TFA phase plot segments (segments) with 'high-pass filter' characteristics. In 54 patients, 490 out of 729 segments were automatically selected (67%). In 34 primary neurology patients the median (q1-q3) low frequency (LF) phase shift was higher in 19 survivors compared to 15 non-survivors (13° (6.3-35) versus 0.83° (-2.8-13), p = 0.0167). CA estimation using the NIRS-only methodology seems feasible in an ICU population using segment selection for more robust and consistent CA estimations. The 'NIRS-only' methodology needs further validation, but has the advantage of being non-invasive without the need for arterial blood pressure monitoring

Measuring cerebrovascular autoregulation in preterm infants using near-infrared spectroscopy:An overview of the literature

Introduction: The preterm born infant's ability to regulate its cerebral blood flow (CBF) is crucial in preventing secondary ischemic and hemorrhagic damage in the developing brain. The relationship between arterial blood pressure (ABP) and CBF estimates, such as regional cerebral oxygenation as measured by near-infrared spectroscopy (NIRS), is an attractive option for continuous non-invasive assessment of cerebrovascular autoregulation.Areas covered: The authors performed a literature search to provide an overview of the current literature on various current clinical practices and methods to measure cerebrovascular autoregulation in the preterm infant by NIRS. The authors focused on various aspects: Characteristics of patient cohorts, surrogate measures for cerebral perfusion pressure, NIRS devices and their accompanying parameters, definitions for impaired cerebrovascular autoregulation, methods of measurements and clinical implications.Expert commentary: Autoregulation research in preterm infants has reported many methods for measuring autoregulation using different mathematical models, signal processing and data requirements. At present, it remains unclear which NIRS signals and algorithms should be used that result in the most accurate and clinically relevant assessment of cerebrovascular autoregulation. Future studies should focus on optimizing strategies for cerebrovascular autoregulation assessment in preterm infants in order to develop autoregulation-based cerebral perfusion treatment strategies

Near-Infrared Spectroscopy-Derived Dynamic Cerebral Autoregulation in Experimental Human Endotoxemia-An Exploratory Study

Cerebral perfusion may be altered in sepsis patients. However, there are conflicting findings on cerebral autoregulation (CA) in healthy participants undergoing the experimental endotoxemia protocol, a proxy for systemic inflammation in sepsis. In the current study, a newly developed near-infrared spectroscopy (NIRS)-based CA index is investigated in an endotoxemia study population, together with an index of focal cerebral oxygenation. Methods: Continuous-wave NIRS data were obtained from 11 healthy participants receiving a continuous infusion of bacterial endotoxin for 3 h (ClinicalTrials.gov NCT02922673) under extensive physiological monitoring. Oxygenated–deoxygenated hemoglobin phase differences in the (very)low frequency (VLF/LF) bands and the Tissue Saturation Index (TSI) were calculated at baseline, during systemic inflammation, and at the end of the experiment 7 h after the initiation of endotoxin administration. Results: The median (inter-quartile range) LF phase difference was 16.2° (3.0–52.6°) at baseline and decreased to 3.9° (2.0–8.8°) at systemic inflammation (p = 0.03). The LF phase difference increased from systemic inflammation to 27.6° (12.7–67.5°) at the end of the experiment (p = 0.005). No significant changes in VLF phase difference were observed. The TSI (mean ± SD) increased from 63.7 ± 3.4% at baseline to 66.5 ± 2.8% during systemic inflammation (p = 0.03) and remained higher at the end of the experiment (67.1 ± 4.2%, p = 0.04). Further analysis did not reveal a major influence of changes in several covariates such as blood pressure, heart rate, PaCO(2), and temperature, although some degree of interaction could not be excluded. Discussion: A reversible decrease in NIRS-derived cerebral autoregulation phase difference was seen after endotoxin infusion, with a small, sustained increase in TSI. These findings suggest that endotoxin administration in healthy participants reversibly impairs CA, accompanied by sustained microvascular vasodilation

Dynamic cerebral autoregulation reproducibility is affected by physiological variability

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of > 0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p less than 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ? 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters. Copyright © 2019 Sanders, Elting, Panerai, Aries, Bor-Seng-Shu, Caicedo, Chacon, Gommer, Van Huffel, Jara, Kostoglou, Mahdi, Marmarelis, Mitsis, Müller, Nikolic, Nogueira, Payne, Puppo, Shin, Simpson, Tarumi, Yelicich, Zhang and Claassen

Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.

peer reviewedOBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features

Development of a quality indicator set to measure and improve quality of ICU care for patients with traumatic brain injury.

BACKGROUND: We aimed to develop a set of quality indicators for patients with traumatic brain injury (TBI) in intensive care units (ICUs) across Europe and to explore barriers and facilitators for implementation of these quality indicators. METHODS: A preliminary list of 66 quality indicators was developed, based on current guidelines, existing practice variation, and clinical expertise in TBI management at the ICU. Eight TBI experts of the Advisory Committee preselected the quality indicators during a first Delphi round. A larger Europe-wide expert panel was recruited for the next two Delphi rounds. Quality indicator definitions were evaluated on four criteria: validity (better performance on the indicator reflects better processes of care and leads to better patient outcome), feasibility (data are available or easy to obtain), discriminability (variability in clinical practice), and actionability (professionals can act based on the indicator). Experts scored indicators on a 5-point Likert scale delivered by an electronic survey tool. RESULTS: The expert panel consisted of 50 experts from 18 countries across Europe, mostly intensivists (N = 24, 48%) and neurosurgeons (N = 7, 14%). Experts agreed on a final set of 42 indicators to assess quality of ICU care: 17 structure indicators, 16 process indicators, and 9 outcome indicators. Experts are motivated to implement this finally proposed set (N = 49, 98%) and indicated routine measurement in registries (N = 41, 82%), benchmarking (N = 42, 84%), and quality improvement programs (N = 41, 82%) as future steps. Administrative burden was indicated as the most important barrier for implementation of the indicator set (N = 48, 98%). CONCLUSIONS: This Delphi consensus study gives insight in which quality indicators have the potential to improve quality of TBI care at European ICUs. The proposed quality indicator set is recommended to be used across Europe for registry purposes to gain insight in current ICU practices and outcomes of patients with TBI. This indicator set may become an important tool to support benchmarking and quality improvement programs for patients with TBI in the future