Predicting Clinical Outcomes 7-10 Years after Severe Traumatic Brain Injury: Exploring the Prognostic Utility of the IMPACT Lab Model and Cerebrospinal Fluid UCH-L1 and MAP-2.

BACKGROUND: Severe traumatic brain injury (TBI) is a major contributor to disability and mortality in the industrialized world. Outcomes of severe TBI are profoundly heterogeneous, complicating outcome prognostication. Several prognostic models have been validated for acute prediction of 6-month global outcomes following TBI (e.g., morbidity/mortality). In this preliminary observational prognostic study, we assess the utility of the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) Lab model in predicting longer term global and cognitive outcomes (7-10 years post injury) and the extent to which cerebrospinal fluid (CSF) biomarkers enhance outcome prediction. METHODS: Very long-term global outcome was assessed in a total of 59 participants (41 of whom did not survive their injuries) using the Glasgow Outcome Scale-Extended and Disability Rating Scale. More detailed outcome information regarding cognitive functioning in daily life was collected from 18 participants surviving to 7-10 years post injury using the Cognitive Subscale of the Functional Independence Measure. A subset (n = 10) of these participants also completed performance-based cognitive testing (Digit Span Test) by telephone. The IMPACT lab model was applied to determine its prognostic value in relation to very long-term outcomes as well as the additive effects of acute CSF ubiquitin C-terminal hydrolase-L1 (UCH-L1) and microtubule associated protein 2 (MAP-2) concentrations. RESULTS: The IMPACT lab model discriminated favorable versus unfavorable 7- to 10-year outcome with an area under the receiver operating characteristic curve of 0.80. Higher IMPACT lab model risk scores predicted greater extent of very long-term morbidity (ß = 0.488 p = 0.000) as well as reduced cognitive independence (ß = - 0.515, p = 0.034). Acute elevations in UCH-L1 levels were also predictive of lesser independence in cognitive activities in daily life at very long-term follow-up (ß = 0.286, p = 0.048). Addition of two CSF biomarkers significantly improved prediction of very long-term neuropsychological performance among survivors, with the overall model (including IMPACT lab score, UCH-L1, and MAP-2) explaining 89.6% of variance in cognitive performance 7-10 years post injury (p = 0.008). Higher acute UCH-L1 concentrations were predictive of poorer cognitive performance (ß = - 0.496, p = 0.029), whereas higher acute MAP-2 concentrations demonstrated a strong cognitive protective effect (ß = 0.679, p = 0.010). CONCLUSIONS: Although preliminary, results suggest that existing prognostic models, including models with incorporation of CSF markers, may be applied to predict outcome of severe TBI years after injury. Continued research is needed examining early predictors of longer-term outcomes following TBI to identify potential targets for clinical trials that could impact long-ranging functional and cognitive outcomes.

Clinical decision support recommending ventilator settings during noninvasive ventilation.

NIV therapy is used to provide positive pressure ventilation for patients. There are protocols describing what ventilator settings to use to initialize NIV; however, the guidelines for titrating ventilator settings are less specific. We developed an advisory system to recommend NIV ventilator setting titration and recorded respiratory therapist agreement rates at the bedside. We developed an algorithm (NIV advisor) to recommend when to change the non-invasive ventilator settings of IPAP, EPAP, and FiO2 based on patient respiratory parameters. The algorithm utilized a multi-target approach; oxygenation, ventilation, and patient effort. The NIV advisor recommended ventilator settings to move the patient's respiratory parameters in a preferred target range. We implemented a pilot study evaluating the usability of the NIV advisor on 10 patients receiving critical care with non-invasive ventilation (NIV). Respiratory therapists were asked their agreement on recommendations from the NIV advisor at the patient's bedside. Bedside respiratory therapists agreed with 91% of the ventilator setting recommendations from the NIV advisor. The POB and VT values were the respiratory parameters that were most often out of the preferred target range. The IPAP ventilator setting was the setting most often considered in need of changing by the NIV advisor. The respiratory therapists agreed with the majority of the recommendations from the NIV advisor. We consider the IPAP recommendations informative in providing the respiratory therapist assistance in targeting preferred POB and Vt values, as these values were frequently out of the target ranges. This pilot implementation was unable to produce the results required to determine the value of the EPAP recommendations. The FiO2 recommendations from the NIV advisor were treated as ancillary information behind the IPAP recommendations.

Breathing variability predicts the suggested need for corrective intervention due to the perceived severity of patient-ventilator asynchrony during NIV.

Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the perceived severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the perceived severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; "No to mild asynchrony-no intervention needed", "moderate asynchrony-non-emergent corrective intervention required", and "severe asynchrony-immediate intervention required". A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel's categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the perceived severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p < 0.001). Further analysis found a specificity of 84% and sensitivity of 99%. The tool appears to accurately match the suggested need for corrective intervention by a bedside practitioner. Application of the tool allows for continuous, real time, and non-invasive monitoring of patients receiving NIV, and may enable early corrective interventions to ameliorate potential patient-ventilator asynchrony.

Bi-stability of SUDR+K model of epidemics and test kits applied to COVID-19.

Motivated by the many diverse responses of different countries to the COVID-19 emergency, here we develop a toy model of the dependence of the epidemics spreading on the availability of tests for disease. Our model, that we call SUDR+K, grounds on the usual SIR model, with the difference of splitting the total fraction of infected individuals in two components: patients that are still undetected and patients that have been already detected through tests. Moreover, we assume that available tests increase at a constant rate from the beginning of epidemics but are consumed to detect infected individuals. Strikingly, we find a bi-stable behavior between a phase with a giant fraction of infected and a phase with a very small fraction. We show that the separation between these two regimes is governed by a match between the rate of testing and a rate of infection spread at given time. We also show that the existence of two phases does not depend on the mathematical choice of the form of the term describing the rate at which undetected individuals are tested and detected. Presented research implies that a vigorous early testing activity, before the epidemics enters its giant phase, can potentially keep epidemics under control, and that even a very small change of the testing rate around the bi-stable point can determine a fluctuation of the size of the whole epidemics of various orders of magnitude. For the real application of realistic model to ongoing epidemics, we would gladly collaborate with field epidemiologists in order to develop quantitative models of testing process.

Influence of Technological Innovations on Industrial Production: A Motif Analysis on the Multilayer Network.

In this work we aim at identifying combinations of technological advancements that reveal the presence of local capabilities for a given industrial production. To this end, we generated a multilayer network using country-level patent and trade data, and performed motif-based analysis on this network using a statistical-validation approach derived from maximum-entropy arguments. We show that in many cases the signal far exceeds the noise, providing robust evidence of synergies between different technologies that can lead to a competitive advantage in specific markets. Our results can be highly useful for policymakers to inform industrial and innovation policies.

Cardiac Arrest in the Operating Room: Part 2-Special Situations in the Perioperative Period.

As noted in part 1 of this series, periprocedural cardiac arrest (PPCA) can differ greatly in etiology and treatment from what is described by the American Heart Association advanced cardiac life support algorithms, which were largely developed for use in out-of-hospital cardiac arrest and in-hospital cardiac arrest outside of the perioperative space. Specifically, there are several life-threatening causes of PPCA of which the management should be within the skill set of all anesthesiologists. However, previous research has demonstrated that continued review and training in the management of these scenarios is greatly needed and is also associated with improved delivery of care and outcomes during PPCA. There is a growing body of literature describing the incidence, causes, treatment, and outcomes of common causes of PPCA (eg, malignant hyperthermia, massive trauma, and local anesthetic systemic toxicity) and the need for a better awareness of these topics within the anesthesiology community at large. As noted in part 1 of this series, these events are always witnessed by a member of the perioperative team, frequently anticipated, and involve rescuer-providers with knowledge of the patient and the procedure they are undergoing or have had. Formulation of an appropriate differential diagnosis and rapid application of targeted interventions are critical for good patient outcome. Resuscitation algorithms that include the evaluation and management of common causes leading to cardiac in the perioperative setting are presented. Practicing anesthesiologists need a working knowledge of these algorithms to maximize good outcomes.

Cardiac Arrest in the Operating Room: Resuscitation and Management for the Anesthesiologist: Part 1.

Cardiac arrest in the operating room and procedural areas has a different spectrum of causes (ie, hypovolemia, gas embolism, and hyperkalemia), and rapid and appropriate evaluation and management of these causes require modification of traditional cardiac arrest algorithms. There is a small but growing body of literature describing the incidence, causes, treatments, and outcomes of circulatory crisis and perioperative cardiac arrest. These events are almost always witnessed, frequently known, and involve rescuer providers with knowledge of the patient and their procedure. In this setting, there can be formulation of a differential diagnosis and a directed intervention that treats the likely underlying cause(s) of the crisis while concurrently managing the crisis itself. Management of cardiac arrest of the perioperative patient is predicated on expert opinion, physiologic rationale, and an understanding of the context in which these events occur. Resuscitation algorithms should consider the evaluation and management of these causes of crisis in the perioperative setting.

Real time noninvasive estimation of work of breathing using facemask leak-corrected tidal volume during noninvasive pressure support: validation study.

We describe a real time, noninvasive method of estimating work of breathing (esophageal balloon not required) during noninvasive pressure support (PS) that uses an artificial neural network (ANN) combined with a leak correction (LC) algorithm, programmed to ignore asynchronous breaths, that corrects for differences in inhaled and exhaled tidal volume (VT) from facemask leaks (WOBANN,LC/min). Validation studies of WOBANN,LC/min were performed. Using a dedicated and popular noninvasive ventilation ventilator (V60, Philips), in vitro studies using PS (5 and 10 cm H2O) at various inspiratory flow rate demands were simulated with a lung model. WOBANN,LC/min was compared with the actual work of breathing, determined under conditions of no facemask leaks and estimated using an ANN (WOBANN/min). Using the same ventilator, an in vivo study of healthy adults (n = 8) receiving combinations of PS (3-10 cm H2O) and expiratory positive airway pressure was done. WOBANN,LC/min was compared with physiologic work of breathing/min (WOBPHYS/min), determined from changes in esophageal pressure and VT applied to a Campbell diagram. For the in vitro studies, WOBANN,LC/min and WOBANN/min ranged from 2.4 to 11.9 J/min and there was an excellent relationship between WOBANN,LC/breath and WOBANN/breath, r = 0.99, r(2) = 0.98 (p < 0.01). There were essentially no differences between WOBANN,LC/min and WOBANN/min. For the in vivo study, WOBANN,LC/min and WOBPHYS/min ranged from 3 to 12 J/min and there was an excellent relationship between WOBANN,LC/breath and WOBPHYS/breath, r = 0.93, r(2) = 0.86 (p < 0.01). An ANN combined with a facemask LC algorithm provides noninvasive and valid estimates of work of breathing during noninvasive PS. WOBANN,LC/min, automatically and continuously estimated, may be useful for assessing inspiratory muscle loads and guiding noninvasive PS settings as in a decision support system to appropriately unload inspiratory muscles.

Biomarkers improve clinical outcome predictors of mortality following non-penetrating severe traumatic brain injury.

OBJECTIVE: This study assessed whether early levels of biomarkers measured in CSF within 24-h of severe TBI would improve the clinical prediction of 6-months mortality. METHODS: This prospective study conducted at two Level 1 Trauma Centers enrolled adults with severe TBI (GCS ≤8) requiring a ventriculostomy as well as control subjects. Ventricular CSF was sampled within 24-h of injury and analyzed for seven candidate biomarkers (UCH-L1, MAP-2, SBDP150, SBDP145, SBDP120, MBP, and S100B). The International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) scores (Core, Extended, and Lab) were calculated for each patient to determine risk of 6-months mortality. The IMPACT models and biomarkers were assessed alone and in combination. RESULTS: There were 152 patients enrolled, 131 TBI patients and 21 control patients. Thirty six (27 %) patients did not survive to 6 months. Biomarkers were all significantly elevated in TBI versus controls (p < 0.001). Peak levels of UCH-L1, SBDP145, MAP-2, and MBP were significantly higher in non-survivors (p < 0.05). Of the seven biomarkers measured at 12-h post-injury MAP-2 (p = 0.004), UCH-L1 (p = 0.024), and MBP (p = 0.037) had significant unadjusted hazard ratios. Of the seven biomarkers measured at the earliest time within 24-h, MAP-2 (p = 0.002), UCH-L1 (p = 0.016), MBP (p = 0.021), and SBDP145 (0.029) had the most significant elevations. When the IMPACT Extended Model was combined with the biomarkers, MAP-2 contributed most significantly to the survival models with sensitivities of 97-100 %. CONCLUSIONS: These data suggest that early levels of MAP-2 in combination with clinical data provide enhanced prognostic capabilities for mortality at 6 months.

Oxygenation advisor recommends appropriate positive end expiratory pressure and FIO2 settings: retrospective validation study.

A decision support, rule-based oxygenation advisor that provides guidance for setting positive end expiratory pressure (PEEP) and fractional inhaled oxygen concentration (FIO2) for patients with respiratory failure is described. The target oxygenation goal is to achieve and maintain pulse oximeter oxygen saturation (SpO2) ≥ 88 and ≤ 95%, as posited by the Acute Respiratory Distress Syndrome Network, by recommending appropriate combinations of PEEP and FIO2. For patient safety, the oxygenation advisor monitors mean arterial blood pressure (MAP) to ensure it is ≥ 65 mmHg for hemodynamic stability and inspiratory plateau pressure (Pplt) so it is ≤ 30 cm H2O for lung protection. The purpose of this validation study was to compare attending physicians' recommendations to those recommendations of the oxygenation advisor for setting PEEP and FIO2. Adults with respiratory failure (n = 117) receiving ventilatory support were studied. PEEP, FIO2, SpO2, MAP, and Pplt are input variables into the advisor. Recommendations to increase, maintain, or decrease PEEP and FIO2 are the oxygenation advisor's output variables. Physicians' recommendations for setting PEEP and FIO2 were recorded; the oxygenation advisor's recommendations were also recorded for comparison. At all times, ventilator settings were based on recommendations from attending physicians. PEEP ranged from 2 to 22 cm H2O and FIO2 ranged from 0.30 to 0.65. A total of 326 recommendations by the oxygenation advisor and attending physicians were made to increase, maintain, or decrease PEEP and FIO2. There was a very significant relationship (p < 0.0001) between recommendations of the oxygenation advisor and attending physicians for setting PEEP and FIO2. The agreement rate for recommendations by the oxygenation advisor and attending physicians was 92%. The K statistic, a test of the strength of agreement of recommendations between the oxygenation advisor and attending physicians, was 0.82 (p < 0.0001), indicating "almost perfect agreement". Relationships for recommendations made by the oxygenation advisor and attending physicians for setting PEEP and FIO2 were excellent, PEEP: r = 0.98 (p < 0.01), r(2) = 0.96; FIO2: r = 0.91 (p < 0.01), r(2) = 0.83, bias and precision values were negligible. A novel oxygenation advisor provided continuous and automatic recommendations for setting PEEP and FIO2 that were shown to be as good as the clinical judgment of experienced attending physicians. For all patients, the target oxygenation goal was achieved. Concerning patient safety, the oxygenation advisor detected those occasions when MAP and Pplt were in potentially unsafe ranges.

Evidence of scale-free clusters of vegetation in tropical rainforests.

Tropical rainforests exhibit a rich repertoire of spatial patterns emerging from the intricate relationship between the microscopic interaction between species. In particular, the distribution of vegetation clusters can shed much light on the underlying process that regulates the ecosystem. Analyzing the distribution of vegetation clusters at different resolution scales, we show the first robust evidence of scale-invariant clusters of vegetation, suggesting the coexistence of multiple intertwined scales in the collective dynamics of tropical rainforests. We use field data and computational simulations to confirm our hypothesis, proposing a predictor that could be particularly interesting to monitor the ecological resilience of the world's "green lungs."

Fibration symmetry-breaking supports functional transitions in a brain network engaged in language.

In his book 'A Beautiful Question', physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.

Fibration symmetry-breaking supports functional transitions in a brain network engaged in language.

In his book 'A Beautiful Question' 1, physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures 1-4. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems 5, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations 6. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken 7 in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles.

Comprehensive Airway Quality Improvement Program: Reducing Life-Threatening Airway Complications.

OBJECTIVE: There is often unfamiliarity with the care of artificial airway devices (ie, endotracheal tubes, tracheostomies, and laryngectomies). The objective of this study was to design an Airway Quality Improvement Program (AQIP) to improve airway care. METHODS: This was a retrospective chart review of a prospectively-initiated AQIP. The AQIP has 3 parts: 1) Mandatory "airway signs" 2) In-service teaching with a corresponding order set and 3) an overhead "Surgical Airway Emergency" page involving automatic pages to Anesthesia, Otolaryngology, and Respiratory Therapy. Pre- and post-intervention survey data was collected. The incidence of airway emergency was the primary patient outcome and was hypothesized to decrease after AQIP intervention. RESULTS: Airway emergencies decreased 8.4% after AQIP (P = .45). Length of stay decreased after AQIP, 47.0 ± 76.5 days compared to 23.5 ± 23.6 (P = .004). Two hundred eight-one nurses and 76 respiratory therapists were educated. Pre-AQIP comfortability scores improved for the routine care of endotracheal tubes, tracheostomies, and laryngectomies, 4.3 ± 0.9, 4.3 ± 0.8, and 3.5 ± 1.2, compared to 4.7 ± 0.6, 4.5 ± 0.7, and 4.4 ± 0.7 post-AQIP (P = .0006, P = .02, P = .0001). The same improvement was noted for emergency airway care. Tracheostomy vs. laryngectomy recognition increased from 66.5 to 97.0% (P = .0001). Quiz questions regarding emergency airway management for laryngectomies improved from 76.2 to 93.8% (P = .0001). CONCLUSION: The AQIP was associated with decreased length of hospital stay and improved competency in airway care among hospital staff. Further application of AQIP is needed for replication in other institutions and long-term application.

Expiratory time constant for determinations of plateau pressure, respiratory system compliance, and total resistance.

INTRODUCTION: We hypothesized the expiratory time constant (ƬE) may be used to provide real time determinations of inspiratory plateau pressure (Pplt), respiratory system compliance (Crs), and total resistance (respiratory system resistance plus series resistance of endotracheal tube) (Rtot) of patients with respiratory failure using various modes of ventilatory support. METHODS: Adults (n = 92) with acute respiratory failure were categorized into four groups depending on the mode of ventilatory support ordered by attending physicians, i.e., volume controlled-continuous mandatory ventilation (VC-CMV), volume controlled-synchronized intermittent mandatory ventilation (VC-SIMV), volume control plus (VC+), and pressure support ventilation (PSV). Positive end expiratory pressure as ordered was combined with all aforementioned modes. Pplt, determined by the traditional end inspiratory pause (EIP) method, was combined in equations to determine Crs and Rtot. Following that, the ƬE method was employed, ƬE was estimated from point-by-point measurements of exhaled tidal volume and flow rate, it was then combined in equations to determine Pplt, Crs, and Rtot. Both methods were compared using regression analysis. RESULTS: ƬE, ranging from mean values of 0.54 sec to 0.66 sec, was not significantly different among ventilatory modes. The ƬE method was an excellent predictor of Pplt, Crs, and Rtot for various ventilatory modes; r2 values for the relationships of ƬE and EIP methods ranged from 0.94 to 0.99 for Pplt, 0.90 to 0.99 for Crs, and 0.88 to 0.94 for Rtot (P <0.001). Bias and precision values were negligible. CONCLUSIONS: We found the ƬE method was just as good as the EIP method for determining Pplt, Crs, and Rtot for various modes of ventilatory support for patients with acute respiratory failure. It is unclear if the ƬE method can be generalized to patients with chronic obstructive lung disease. ƬE is determined during passive deflation of the lungs without the need for changing the ventilatory mode and disrupting a patient's breathing. The ƬE method obviates the need to apply an EIP, allows for continuous and automatic surveillance of inspiratory Pplt so it can be maintained ≤ 30 cm H2O for lung protection and patient safety, and permits real time assessments of pulmonary mechanics.

Capability accumulation patterns across economic, innovation, and knowledge-production activities.

The evolution of economic and innovation systems at the national scale is shaped by a complex dynamics related to the multi-layer network connecting countries to the activities in which they are proficient. Each layer represents a different domain, related to the production of knowledge and goods: scientific research, technology innovation, industrial production and trade. Nestedness, a footprint of a complex dynamics, emerges as a persistent feature across these multiple kinds of activities (i.e. network layers). We observe that, in the layers of innovation and trade, the competitiveness of countries correlates unambiguously with their diversification, while the science layer shows some peculiar features. The evolution of the scientific domain leads to an increasingly modular structure, in which the most developed countries become relatively less active in the less advanced scientific fields, where emerging countries acquire prominence. This observation is in line with a capability-based view of the evolution of economic systems, but with a slight twist. Indeed, while the accumulation of specific know-how and skills is a fundamental step towards development, resource constraints force countries to acquire competitiveness in the more complex research fields at the expense of more basic, albeit less visible (or more crowded) ones. This tendency towards a relatively specialized basket of capabilities leads to a trade-off between the need to diversify in order to evolve and the need to allocate resources efficiently. Collaborative patterns among developed countries reduce the necessity to be competitive in the less sophisticated research fields, freeing resources for the more complex ones.

Noise cleaning the precision matrix of short time series.

We present a comparison between various algorithms of inference of covariance and precision matrices in small data sets of real vectors of the typical length and dimension of human brain activity time series retrieved by functional magnetic resonance imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists of denoising the empirically observed matrices to obtain a better estimator of the (unknown) true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of data sets. The first type consists of synthetic time series sampled from a generative Gaussian model of which we can vary the fraction of dimensions per sample q and the strength of off-diagonal correlations. The second type consists of time series of fMRI brain activity of human subjects at rest. The reliability of each algorithm is assessed in terms of test-set likelihood and, in the case of synthetic data, of the distance from the true precision matrix. We observe that the so-called optimal rotationally invariant estimator, based on random matrix theory, leads to a significantly lower distance from the true precision matrix in synthetic data and higher test likelihood in natural fMRI data. We propose a variant of the optimal rotationally invariant estimator in which one of its parameters is optimzed by cross-validation. In the severe undersampling regime (large q) typical of fMRI series, it outperforms all the other estimators. We furthermore propose a simple algorithm based on an iterative likelihood gradient ascent, leading to very accurate estimations in weakly correlated synthetic data sets.

Anesthesia advanced circulatory life support.

PURPOSE: The constellation of advanced cardiac life support (ACLS) events, such as gas embolism, local anesthetic overdose, and spinal bradycardia, in the perioperative setting differs from events in the pre-hospital arena. As a result, modification of traditional ACLS protocols allows for more specific etiology-based resuscitation. PRINCIPAL FINDINGS: Perioperative arrests are both uncommon and heterogeneous and have not been described or studied to the same extent as cardiac arrest in the community. These crises are usually witnessed, frequently anticipated, and involve a rescuer physician with knowledge of the patient's comorbidities and coexisting anesthetic or surgically related pathophysiology. When the health care provider identifies the probable cause of arrest, the practitioner has the ability to initiate medical management rapidly. CONCLUSIONS: Recommendations for management must be predicated on expert opinion and physiological understanding rather than on the standards currently being used in the generation of ACLS protocols in the community. Adapting ACLS algorithms and considering the differential diagnoses of these perioperative events may prevent cardiac arrest.