Articles: cations.
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J Neurosurg Anesthesiol · Apr 2023
Meta AnalysisInhalational Versus Propofol-based Intravenous Maintenance of Anesthesia for Emergence Delirium in Adults: A Meta-analysis and Trial Sequential Analysis.
Emergence delirium (ED) is a severe postoperative complication that increases the risk for injury, self-extubation, and hemorrhage. Inhalational maintenance of anesthesia is a risk factor for ED in pediatric patients, but its impact in adults is undefined. This meta-analysis compares the incidence of ED between inhalational and propofol-based intravenous maintenance of anesthesia. ⋯ Compared with propofol-based intravenous maintenance of anesthesia, inhalational maintenance increased the incidence of ED in adults (risk ratio [RR], 2.02; 95% confidence interval [CI]: 1.30-3.14; P =0.002). This was confirmed by sensitivity analysis, trial sequential analysis, and subgroup analyses of studies that assessed ED via Aono's four-point scale (RR, 3.72; 95% CI: 1.48-9.31; P =0.005) and the Ricker Sedation Agitation Scale (RR, 3.48; 95% CI: 1.66-7.32; P =0.001), studies that included sevoflurane for maintenance of anesthesia (RR, 1.87; 95% CI: 1.13-3.09; P =0.02), studies that reported ED as the primary outcome (RR, 2.73; 95% CI: 1.53-4.86; P =0.0007), and studies that investigated ocular (RR, 2.98; 95% CI: 1.10-8.10; P =0.03), nasal (RR; 95% CI: 1.27-6.50; P =0.01), and abdominal (RR, 3.25; 95% CI: 1.12-9.40; P =0.03) surgeries, but not intracranial surgery (RR, 0.72; 95% CI: 0.34-1.54; P =0.40). In summary, inhalational maintenance of sevoflurane was a risk factor for ED compared with propofol-based intravenous maintenance in adults who underwent ocular, nasal, and abdominal surgeries but not intracranial surgery.
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Anesthesia and analgesia · Apr 2023
ReviewArtificial Intelligence for Perioperative Medicine: Perioperative Intelligence.
The anesthesiologist's role has expanded beyond the operating room, and anesthesiologist-led care teams can deliver coordinated care that spans the entire surgical experience, from preoperative optimization to long-term recovery of surgical patients. This expanded role can help reduce postoperative morbidity and mortality, which are regrettably common, unlike rare intraoperative mortality. Postoperative mortality, if considered a disease category, will be the third leading cause of death just after heart disease and cancer. ⋯ Using artificial intelligence technologies, we can critically examine every aspect of perioperative medicine and devise innovative value-based solutions that can potentially improve patient safety and care delivery, while optimizing cost of care. In this narrative review, we discuss specific applications of artificial intelligence that may help advance all aspects of perioperative medicine, including clinical care, education, quality improvement, and research. We also discuss potential limitations of technology and provide our recommendations for successful adoption.
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Randomized Controlled Trial
Estimating individualized treatment effects using a risk-modeling approach: an application to epidural steroid injections for lumbar spinal stenosis.
Conventional "1-variable-at-a-time" analyses to identify treatment effect modifiers are often underpowered and prone to false-positive results. This study used a "risk-modeling" approach guided by the Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement framework: (1) developing and validating a multivariable model to estimate predicted future back-related functional limitations as measured by the Roland-Morris Disability Questionnaire (RMDQ) and (2) stratifying patients from a randomized controlled trial (RCT) of lumbar epidural steroid injections (LESIs) for the treatment of lumbar spinal stenosis into subgroups with different individualized treatment effects on RMDQ scores at the 3-week follow-up. Model development and validation were conducted in a cohort (n = 3259) randomly split into training and testing sets in a 4:1 ratio. ⋯ R2 values in the training set, testing set, and RCT were 0.38, 0.32, and 0.34, respectively. There was statistically significant modification ( P = 0.03) of the LESI treatment effect according to predicted risk quartile, with clinically relevant LESI treatment effect point estimates in the 2 quartiles with greatest predicted risk (-3.7 and -3.3 RMDQ points) and no effect in the lowest 2 quartiles. A multivariable risk-modeling approach identified subgroups of patients with lumbar spinal stenosis with a clinically relevant treatment effect of LESI on back-related functional limitations.
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Purpose : Sepsis is the leading cause of death in patients with severe acute pancreatitis (SAP) in the intensive care unit (ICU). Early prediction of sepsis secondary to SAP developed in the late phase and of related mortality can enable appropriate treatment and improve outcomes. This study was conducted to evaluate the predictive value of presepsin in ICU patients with SAP at the early stage and compared it with established blood markers and scoring systems. ⋯ Among the analyzed biomarkers, presepsin was the only blood marker independently associated with sepsis secondary to SAP on days 3 and 7, and presepsin on day 3 was independently associated with mortality at ICU discharge and on days 28 and 90. It showed similar or even better predictive accuracy for both secondary sepsis and mortality than procalcitonin and Sequential Organ Failure Assessment score. Conclusion : Presepsin could be a valuable early predictor of secondary sepsis and mortality in patients admitted to the ICU with SAP and may serve as an indicator for early risk stratification.