The Technology of Processed Electroencephalogram Monitoring Devices for Assessment of Depth of Anesthesia.

Commercial brain function monitors for depth of anesthesia have been available for more than 2 decades; there are currently more than 10 devices on the market. Advances in this field are evidenced by updated versions of existing monitors, development of new monitors, and increasing research unveiling the mechanisms of anesthesia on the brain. Electroencephalography signal processing forms an integral part of the technology supporting the brain function monitors for derivation of a depth-of-anesthesia index. This article aims to provide a better understanding of the technology and functionality behind these monitors. This review will highlight the general design principles of these devices and the crucial stages in electroencephalography signal processing and classification, with a focus on the key mathematical techniques used in algorithm development for final derivation of the index representing anesthetic state. We will briefly discuss the advantages and limitations of this technology in the clinical setting as a tool in our repertoire used for optimizing individualized patient care. Also included is a table describing 10 available commercial depth-of-anesthesia monitors. (C) 2017 International Anesthesia Research Society

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Atlas of Peripheral Regional Anesthesia Anatomy and Techniques, 3rd edition.

No abstract available

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Perioperative Drill-Based Crisis Management.

No abstract available

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Reduction in Operating Room Plasma Waste After Evidence-Based Quality Improvement Initiative.

Anesthesiologists request units of plasma in anticipation of transfusion. The amount of plasma transfused intraoperatively is less than that issued (requested, thawed, and sent). We presented institutional-specific data on plasma usage including anesthesiologist-specific ratios of plasma issued-to-transfused. In month-to-month comparisons from the year before the presentation (June-December 2015) to 7 months after (June-December 2016), plasma issued to the operating room was reduced from 434.9 +/- 81 to 327.3 +/- 65 units, a change of 107.6 units per month (95% confidence interval [CI], 22-193); plasma discarded by the blood bank was reduced from 109.7 +/- 48 units to 69.1 +/- 9 units, a change of 40.6 units per month (95% CI, 0.2-81); and plasma transfused went from 188.4 +/- 42 units to 160.7 +/- 52 units, a nonsignificant change of 27.7 units per month (95% CI, -27 to 83). (C) 2017 International Anesthesia Research Society

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An Appraisal of the Carlisle-Stouffer-Fisher Method for Assessing Study Data Integrity and Fraud.

Data fabrication and scientific misconduct have been recently uncovered in the anesthesia literature, partly via the work of John Carlisle. In a recent article in Anaesthesia, Carlisle analyzed 5087 randomized clinical trials from anesthesia and general medicine journals from 2000 to 2015. He concluded that in about 6% of studies, data comparing randomized groups on baseline variables, before the given intervention, were either too similar or dissimilar compared to that expected by usual sampling variability under the null hypothesis. Carlisle used the Stouffer-Fisher method of combining P values in Table 1 (the conventional table reporting baseline patient characteristics) for each study, then calculated trial P values and assessed whether they followed a uniform distribution across studies. Extreme P values targeted studies as likely to contain data fabrication or errors. In this Statistical Grand Rounds article, we explain Carlisle's methods, highlight perceived limitations of the proposed approach, and offer recommendations. Our main findings are (1) independence was assumed between variables in a study, which is often false and would lead to "false positive" findings; (2) an "unusual" result from a trial cannot easily be concluded to represent fraud; (3) utilized cutoff values for determining extreme P values were arbitrary; (4) trials were analyzed as if simple randomization was used, introducing bias; (5) not all P values can be accurately generated from summary statistics in a Table 1, sometimes giving incorrect conclusions; (6) small numbers of P values to assess outlier status within studies is not reliable; (7) utilized method to assess deviations from expected distributions may stack the deck; (8) P values across trials assumed to be independent; (9) P value variability not accounted for; and (10) more detailed methods needed to understand exactly what was done. It is not yet known to what extent these concerns affect the accuracy of Carlisle's results. We recommend that Carlisle's methods be improved before widespread use (applying them to every manuscript submitted for publication). Furthermore, lack of data integrity and fraud should ideally be assessed using multiple simultaneous statistical methods to yield more confident results. More sophisticated methods are needed for nonrandomized trials, randomized trial data reported beyond Table 1, and combating growing fraudster sophistication. We encourage all authors to more carefully scrutinize their own reporting. Finally, we believe that reporting of suspected data fraud and integrity issues should be done more discretely and directly by the involved journal to protect honest authors from the stigma of being associated with potential fraud. (C) 2017 International Anesthesia Research Society

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Mortality, Geriatric, and Nongeriatric Surgical Risk Factors Among the Eldest Old: A Prospective Observational Study.

BACKGROUND: Preoperative risk and postoperative outcomes among the elderly are the subject of extensive debate. However, the eldest old, that is, the fastest-growing and most vulnerable group, are insufficiently studied; even their mortality rate is unclear. This prospective observational study was performed with the aim of determining the mortality rate of this population and establishing which preoperative conditions were predictors of which postoperative outcomes. The study was undertaken between 2011 and 2015 in a major tertiary care university hospital. METHODS: All patients aged >=85 years undergoing any elective procedure during the study period were included. Patients were followed up for 30 days postoperatively. The preoperative conditions studied were demographic data, grade of surgical complexity (1-3), preoperative comorbidities, and some characteristically geriatric conditions (functional reserve, nutrition, cognitive status, polypharmacy, dependency, and frailty). The outcome measures were 30-day all-cause mortality (primary end point), morbidity, prolonged length of stay, and escalation of care in living conditions. RESULTS: Of 139 eligible patients, 127 completed follow-up. The 30-day mortality was 7.9%; 95% confidence interval (CI), 3.2-12.6. It had 3 predictors: malnutrition (odds ratio [OR], 15; 95% CI, 3-89), complexity 3 (OR, 9.1; CI, 2-52), and osteoporosis/osteoporotic fractures (OR, 14.7; CI, 2-126). Significant predictors for morbidity (40%) were ischemic heart disease (OR, 3.9; CI, 1-11) and complexity 3 (OR, 3.6; CI, 2-9), while a nonfrail phenotype (OR, 0.3; CI, 0.1-0.8) was found to be protective. Only 2 factors were found to be predictive of longer admissions, namely complexity 3 (OR, 4.4; CI, 2-10) and frailty (OR, 2.7; CI, 2-7). Finally, risk factors for escalation of care in living conditions were slow gait (a surrogate for frailty, OR, 2.5; CI, 1-6), complexity 3 (OR, 3.2; CI, 1-7), and hypertension (OR, 2.9; CI, 1-9). CONCLUSIONS: The eldest old is a distinct group with a considerable mortality rate and their own particular risk factors. Surgical complexity and certain geriatric variables (malnutrition and frailty), which are overlooked in American Society of Anesthesiologists and most other usual scores, are particularly relevant in this population. Inclusion of these factors along with appropriate comorbidities for risk stratification should guide better decision making for families and doctors alike and encourage preoperative optimization of patients. (C) 2017 International Anesthesia Research Society

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Perioperative Considerations for the Use of Sodium-Glucose Cotransporter-2 Inhibitors in Patients With Type 2 Diabetes.

No abstract available

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