Big data anesthesia

OBJECTIVE: The management of urolithiasis in patients with a solitary kidney is challenging for urologists. This study aimed to evaluate the safety of retrograde intrarenal surgery (RIRS) in the treatment of stones in patients with solitary kidney and to reply to the question if there is any limit for this surgery. METHODS: Between January 2016 and December 2019, we enrolled 52 patients who had a solitary kidney and underwent RIRS. We collected data on preoperative patient characteristics, stone dimensions, and postoperative outcomes...

BACKGROUND: Previous research suggests that less emotionally stable, less conscientious, less extraverted, and less agreeable women tend to suffer from higher fear of childbirth and experience their delivery as worse. Moreover, there is evidence that birth characteristics and unexpected incidents during delivery may impact women's birth experiences. However, it remains unknown whether the role of personality in subjective birth experiences varies between women with different birth characteristics...

BACKGROUND: Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. METHODS: Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications...

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Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called "big data" to formulate an algorithm...

The digital transformation of healthcare is advancing, leading to an increasing availability of clinical data for research. Perioperative big data initiatives were established to monitor treatment quality and benchmark outcomes. However, big data analyses have long exceeded the status of pure quality surveillance instruments. Large retrospective studies nowadays often represent the first approach to new questions in clinical research and pave the way for more expensive and resource intensive prospective trials...

The objective and the goal of this study was to determine how ERAS guidelines affected on hospital stay days and other complications rates in case of elective colorectal surgery in our clinic, compared to traditional care methods. First of all, all team members including surgeons, anesthetists, nurses were being trained in ERAS guidelines principals during two months and we started active implementation process after this. 87 patients, who were needed to be done colorectal surgery treatment, were actively treated according to ERAS guidelines and these patients were gathered in experimental group...

Big data analytics research using heterogeneous electronic health record (EHR) data requires accurate identification of disease phenotype cases and controls. Overreliance on ground truth determination based on administrative data can lead to biased and inaccurate findings. Hospital-acquired venous thromboembolism (HA-VTE) is challenging to identify due to its temporal evolution and variable EHR documentation. To establish ground truth for machine learning modeling, we compared accuracy of HA-VTE diagnoses made by administrative coding to manual review of gold standard diagnostic test results...

OBJECTIVE: To evaluate continuous monitoring of maternal hemodynamics during labor and delivery utilizing an innovative, noninvasive, reflective photoplethysmography-based device. STUDY DESIGN: The Biobeat Monitoring Platform includes a wearable wristwatch monitor that automatically samples cardiac output (CO), blood pressure (BP), stroke volume (SV), systemic vascular resistance (SVR), heart rate (HR) every 5 s and uploads all data to a smartphone-based app and to a data cloud, enabling remote patient monitoring and analysis of data...

There are an increasing number of "big data" studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers' Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. When such a trial is infeasible, observational studies using robust regression techniques may be able to advance knowledge...

Physicians use perioperative decision-support tools to mitigate risks and maximize benefits to achieve the most successful outcome for patients. Contemporary risk-assessment practices augment surgeons' judgement and experience with decision-support algorithms driven by big data and machine learning. These algorithms accurately assess risk for a wide range of postoperative complications by parsing large datasets and performing complex calculations that would be cumbersome for busy clinicians. Even with these advancements, large gaps in perioperative risk assessment remain; decision-support algorithms often cannot account for risk-reduction therapies applied during a patient's perioperative course and do not quantify tradeoffs between competing goals of care (eg, balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with the risk for complications from pulmonary edema)...

Background: As an anesthesia induced during cesarean section, spinal anesthesia is preferred over general and epidural anesthesia. This study aimed to review the trend of anesthetic methods for cesarean section based on data obtained from the Korean Health Insurance Review and Assessment Service from 2013 to 2018. Methods: The anesthetic methods were analyzed in 753,285 parturients who underwent a cesarean section in Korea from 2013 to 2018. We determined the association between each anesthetic method and hospital type and maternal and fetal factors...

INTRODUCTION: Anesthetic management of parturients with COVID-19 is a big challenge to anesthesiologists. Limited data are available about COVID-19 during pregnancy; however, information on illnesses associated with SARS and MERS might provide insights into COVID-19's effects during pregnancy. EVIDENCE ACQUISITION: Evidence from previous reports from SARS and MERS, and from COVID-19 cases were reviewed. Concepts from guidelines from the government and academic societies were collected as well...

There are an increasing number of "big data" studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers' Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. When such a trial is infeasible, observational studies using robust regression techniques may be able to advance knowledge...

PURPOSE OF REVIEW: Acute care technologies, including novel monitoring devices, big data, increased computing capabilities, machine-learning algorithms and automation, are converging. This enables the application of augmented intelligence for improved outcome predictions, clinical decision-making, and offers unprecedented opportunities to improve patient outcomes, reduce costs, and improve clinician workflow. This article briefly explores recent work in the areas of automation, artificial intelligence and outcome prediction models in pediatric anesthesia and pediatric critical care...

Making good decisions in the era of Big Data requires a sophisticated approach to causality. We are acutely aware that association ≠ causation, yet untangling the two remains one of our greatest challenges. This realization has stimulated a Causal Revolution in epidemiology, and the lessons learned are highly relevant to anesthesia research. This article introduces readers to directed acyclic graphs; a cornerstone of modern causal inference techniques. These diagrams provide a robust framework to address sources of bias and discover causal effects...

With every hospital admission, a vast amount of data is collected from every patient. Big data can help in data mining and processing of this volume of data. The goal of this study is to investigate the potential of big data analyses by analyzing clinically relevant data from the immediate postoperative phase using big data mining techniques. A second aim is to understand the importance of different postoperative parameters. We analyzed all data generated during the admission of 739 women undergoing a free DIEAP flap breast reconstruction...

BACKGROUND: Although the effect of the recent Greek economic crisis and austerity on the population's health and the health system effectiveness have been discussed a lot recently, data on common surgical conditions affecting large part of the population are missing. Using inguinal hernia as a model we investigated possible changes of citizens' attitude regarding the time of referral, the perioperative details and the intraoperative findings of the emergency hernioplasties. METHODS: The present retrospective study was conducted by a Department of Surgery in a tertiary public hospital of the Greek capital...

PURPOSE OF REVIEW: This narrative review will discuss what value Big Data has to offer anesthesiology and aims to highlight recently published articles of large databases exploring factors influencing perioperative outcome. Additionally, the future perspectives of Big Data and its major pitfalls will be discussed. RECENT FINDINGS: The potential of Big Data has given an incentive to create nationwide and anesthesia-initiated registries like the MPOG and NACOR. These large databases have contributed in elucidating some of the rare perioperative complications, such as declined cognition after exposure to general anesthesia and epidural hematomas in parturients...

BACKGROUND: Automated systems are available in various application areas all over the world for the purpose of reducing workload and increasing safety. However, such support systems that would aid caregivers are still lacking in the medical sector. With respect to workload and safety, especially, the intensive care unit appears to be an important and challenging application field. Whereas many closed-loop subsystems for single applications already exist, no comprehensive system covering multiple therapeutic aspects and interactions is available yet...