Influence of Practitioner Dashboard Feedback on Anesthetic Greenhouse Gas Emissions: A Prospective Performance Improvement Investigation.

Anesthetic gases contribute to global warming. We described a two-year performance improvement project to examine the association of individualized provider dashboard feedback of anesthetic gas carbon dioxide equivalent (CDE) production and median perioperative fresh gas flows (FGF) during general anesthetics during perioperative management. Using a custom structured query language (SQL) query, hourly CDE for each anesthetic gas and median FGF were determined.

Prevalence and determinants of metabolic syndrome in 2338 childhood cancer survivors: A Dutch Childhood Cancer Survivor LATER 2 study.

Background: Because the occurrence of metabolic syndrome (MetS) might contribute to childhood cancer survivor's excess risk of cardiovascular disease, the authors assessed the prevalence and determinants of MetS in the Dutch Childhood Cancer Survivor Study (DCCSS-LATER2) cohort.

Methods: In total, 2338 adult childhood cancer survivors (CCS) were cross-sectionally assessed for the prevalence of MetS, using the Lifelines cohort (N = 132,226 adults without a history of cancer) as references. The prevalence of MetS was clinically assessed using existing classifications, as well as an alternative method using dual-energy x-ray absorptiometry fat% instead of waist circumference to define abdominal adiposity.

A retrospective analysis using comorbidity detecting algorithmic software to determine the incidence of International Classification of Diseases (ICD) code omissions and appropriateness of Diagnosis-Related Group (DRG) code modifiers.

Background: The mechanism for recording International Classification of Diseases (ICD) and diagnosis related groups (DRG) codes in a patient's chart is through a certified medical coder who manually reviews the medical record at the completion of an admission. High-acuity ICD codes justify DRG modifiers, indicating the need for escalated hospital resources. In this manuscript, we demonstrate that value of rules-based computer algorithms that audit for omission of administrative codes and quantifying the downstream effects with regard to financial impacts and demographic findings did not indicate significant disparities.

Economic Impact of Postoperative Urinary Retention in the US Hospital Setting.

Postoperative urinary retention (POUR) is a common and distressing surgical complication that may be associated with the pharmacological reversal technique of neuromuscular blockade (NMB). This study aimed to investigate the impact that POUR has on medical charges. This was a retrospective observational study of adult patients undergoing select surgeries who were administered neuromuscular blockade agent (NMBA), which was pharmacologically reversed between February 2017 and November 2021 using data from the PINC-AI™ Healthcare Database.

Factors Influencing Neuromuscular Blockade Reversal Choice in the United States Before and During the COVID-19 Pandemic: Retrospective Longitudinal Analysis.

Background: Neuromuscular blockade (NMB) agents are a critical component of balanced anesthesia. NMB reversal methods can include spontaneous reversal, sugammadex, or neostigmine and the choice of reversal strategy can depend on various factors. Unanticipated changes to clinical practice emerged due to the COVID-19 pandemic, and a better understanding of how NMB reversal trends were affected by the pandemic may help provide insight into how providers view the tradeoffs in the choice of NMB reversal agents.

Postoperative Acute Kidney Injury is Associated with Persistent Renal Dysfunction: A Multicenter Propensity Matched Cohort Study.

Background: The risk of developing a persistent reduction in renal function after postoperative acute kidney injury (pAKI) is not well-established.

Objective: Perform a multi-center retrospective propensity matched study evaluating whether patients that develop pAKI have a greater decline in long-term renal function than patients that did not develop postoperative AKI.

Design: Multi-center retrospective propensity matched study.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

Background: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.

Methods: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database.

Corrigendum to "Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study" [ 8 (2023) 100236].

[This corrects the article DOI: 10.1016/j.bjao.

Remote Monitoring and Artificial Intelligence: Outlook for 2050.

Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions.

Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study.

Background: International guidelines recommend quantitative neuromuscular monitoring when administering neuromuscular blocking agents. The train-of-four count is important for determining the depth of block and appropriate reversal agents and doses. However, identifying valid compound motor action potentials (cMAPs) during surgery can be challenging because of low-amplitude signals and an inability to observe motor responses.

Post-operative urinary retention is impacted by neuromuscular block reversal agent choice: A retrospective cohort study in US hospital setting.

Study Objective: Perioperative neuromuscular blocking agents are pharmacologically reversed to minimize complications associated with residual neuromuscular block. Neuromuscular block reversal with anticholinesterases (e.g.

External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study.

Study Objective: Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation.

Design: We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed.

Setting: Three academic medical centers in the United States.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

Background: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models.

Deep learning on electronic medical records identifies distinct subphenotypes of diabetic kidney disease driven by genetic variations in the pathway.

Kidney disease affects 50% of all diabetic patients; however, prediction of disease progression has been challenging due to inherent disease heterogeneity. We use deep learning to identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering of electronic health record data on 1,372 diabetic kidney disease patients, we establish two clusters with differential prevalence of end-stage kidney disease.