Browsing by Subject "perioperative"
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Item Open Access A model to predict risk of blood transfusion after gynecologic surgery.(Am J Obstet Gynecol, 2017-05) Stanhiser, Jamie; Chagin, Kevin; Jelovsek, J EricBACKGROUND: A model that predicts a patient's risk of receiving a blood transfusion may facilitate selective preoperative testing and more efficient perioperative blood management utilization. OBJECTIVE: We sought to construct and validate a model that predicts a patient's risk of receiving a blood transfusion after gynecologic surgery. STUDY DESIGN: In all, 18,319 women who underwent gynecologic surgery at 10 institutions in a single health system by 116 surgeons from January 2010 through June 2014 were analyzed. The data set was split into a model training cohort of 12,219 surgeries performed from January 2010 through December 2012 and a separate validation cohort of 6100 surgeries performed from January 2013 through June 2014. In all, 47 candidate risk factors for transfusion were collected. Multiple logistic models were fit onto the training cohort to predict transfusion within 30 days of surgery. Variables were removed using stepwise backward reduction to find the best parsimonious model. Model discrimination was measured using the concordance index. The model was internally validated using 1000 bootstrapped samples and temporally validated by testing the model's performance in the validation cohort. Calibration and decision curves were plotted to inform clinicians about the accuracy of predicted probabilities and whether the model adds clinical benefit when making decisions. RESULTS: The transfusion rate in the training cohort was 2% (95% confidence interval, 1.72-2.22). The model had excellent discrimination and calibration during internal validation (bias-corrected concordance index, 0.906; 95% confidence interval, 0.890-0.928) and maintained accuracy during temporal validation using the separate validation cohort (concordance index, 0.915; 95% confidence interval, 0.872-0.954). Calibration curves demonstrated the model was accurate up to 40% then it began to overpredict risk. The model provides superior net benefit when clinical decision thresholds are between 0-50% predicted risk. CONCLUSION: This model accurately predicts a patient's risk of transfusion after gynecologic surgery facilitating selective preoperative testing and more efficient perioperative blood management utilization.Item Open Access Demonstration and Performance Evaluation of Two Novel Algorithms for Removing Artifacts From Automated Intraoperative Temperature Data Sets: Multicenter, Observational, Retrospective Study.(JMIR perioperative medicine, 2022-10) Bardia, Amit; Deshpande, Ranjit; Michel, George; Yanez, David; Dai, Feng; Pace, Nathan L; Schuster, Kevin; Mathis, Michael R; Kheterpal, Sachin; Schonberger, Robert BBackground
The automated acquisition of intraoperative patient temperature data via temperature probes leads to the possibility of producing a number of artifacts related to probe positioning that may impact these probes' utility for observational research.Objective
We sought to compare the performance of two de novo algorithms for filtering such artifacts.Methods
In this observational retrospective study, the intraoperative temperature data of adults who received general anesthesia for noncardiac surgery were extracted from the Multicenter Perioperative Outcomes Group registry. Two algorithms were developed and then compared to the reference standard-anesthesiologists' manual artifact detection process. Algorithm 1 (a slope-based algorithm) was based on the linear curve fit of 3 adjacent temperature data points. Algorithm 2 (an interval-based algorithm) assessed for time gaps between contiguous temperature recordings. Sensitivity and specificity values for artifact detection were calculated for each algorithm, as were mean temperatures and areas under the curve for hypothermia (temperatures below 36 C) for each patient, after artifact removal via each methodology.Results
A total of 27,683 temperature readings from 200 anesthetic records were analyzed. The overall agreement among the anesthesiologists was 92.1%. Both algorithms had high specificity but moderate sensitivity (specificity: 99.02% for algorithm 1 vs 99.54% for algorithm 2; sensitivity: 49.13% for algorithm 1 vs 37.72% for algorithm 2; F-score: 0.65 for algorithm 1 vs 0.55 for algorithm 2). The areas under the curve for time × hypothermic temperature and the mean temperatures recorded for each case after artifact removal were similar between the algorithms and the anesthesiologists.Conclusions
The tested algorithms provide an automated way to filter intraoperative temperature artifacts that closely approximates manual sorting by anesthesiologists. Our study provides evidence demonstrating the efficacy of highly generalizable artifact reduction algorithms that can be readily used by observational studies that rely on automated intraoperative data acquisition.Item Open Access Implementation Analysis of a Patient Safety Program in a Pediatric Perioperative Unit in Guatemala(2019) Sico, Isabelle Rae PapillaBackground: Patient safety is critical to prevent medical errors and to improve clinical outcomes. The need to implement programs in patient safety is increasingly recognized as a prime component of healthcare delivery in low- and middle- income countries (LMICs). The goal for our study is to assess the implementation of a patient safety program in Guatemala.
Methods: We used a mixed-methods approach to assess implementation of a patient safety program in the pediatric perioperative unit in Hospital Roosevelt, Guatemala. We collected data from unit staff respondents (n=16) using a qualitative de novo survey, the Evidence-Based Practice Attitude Scale-36 (EBPAS-36) survey, and a semi-structured interview. Interviews and surveys were conducted in Spanish, translated, and analyzed in English using NVivo v12. Quantitative data were analyzed to compare group means across survey domains. Data were triangulated, with final analysis guided by the Consolidated Framework for Implementation Research (CFIR). Data were collected over a 10-day period in July 2018.
Results: Responses underscored several emergent thematic determinants representing the Inner Setting and Characteristics of Individuals CFIR domains, indicating a gap in knowledge of patient safety programs and attitude towards the use of evidence-based patient safety programs. Though respondents expressed an openness and willingness to adopt patient safety practices, few existing practices are in place to prevent medical errors.
Conclusions: The main determinants which affect the implementation of an evidence-based patient safety program in the pediatric perioperative unit in Guatemala are related to the internal structure and culture of the unit, and not to external factors or the intervention itself. Positive attitudes and knowledge of patient safety practices are insufficient to overcome the challenges towards implementation. A framework for future implementation should include education and communication programs, adaptation of existing practices to increase leadership engagement, and use of tools to create a strong culture of safety.
Item Open Access Perioperative goal-directed therapy.(J Cardiothorac Vasc Anesth, 2014-12) Waldron, Nathan H; Miller, Timothy E; Gan, Tong J