Effects of Volatile Anesthetic Choice on Hospital Length-of-stay

2013 ◽  
Vol 119 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Tatyana Kopyeva ◽  
Daniel I. Sessler ◽  
Stephanie Weiss ◽  
Jarrod E. Dalton ◽  
Edward J. Mascha ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Retrospective Analysis ◽  
Geometric Mean ◽  
Prospective Trial ◽  
Drug Acquisition ◽  
Volatile Anesthetic ◽  
Drug Acquisition Cost ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Duration Of Hospitalization

Abstract Background: Volatile anesthetic prices differ substantially. But differences in drug-acquisition cost would be inconsequential if hospitalization were prolonged by more soluble anesthetics. The authors tested the hypothesis that the duration of hospitalization is prolonged with isoflurane anesthesia. Methods: Initially, the authors queried their electronic records and used propensity matching to generate homogeneous sets of adults having inpatient noncardiac surgery who were given desflurane, sevoflurane, and isoflurane. The authors then conducted a prospective alternating intervention trial in which adults (mostly having colorectal surgery) were assigned to isoflurane or sevoflurane, based on protocol. Results: In the retrospective analysis, 2,898 matched triplets were identified among 43,352 adults, each containing one patient receiving isoflurane, desflurane, and sevoflurane, respectively. The adjusted geometric mean (95% CI) hospital length-of-stay for the isoflurane cases was 2.85 days (2.78–2.93); this was longer than that observed for both desflurane (2.64 [2.57–2.72]; P < 0.001) and sevoflurane (2.55 [2.48–2.62]; P < 0.001). In the prospective trial (N = 1,584 operations), no difference was found; the adjusted ratio of means (95% CI) of hospital length-of-stay in patients receiving isoflurane versus sevoflurane was 0.98 (0.88–1.10), P = 0.77, with adjusted geometric means (95% CI) estimated at 4.1 (3.8–4.4) and 4.2 days (3.8–4.5), respectively. Conclusions: Results of the propensity-matched retrospective analysis suggested that avoiding isoflurane significantly reduced the duration of hospitalization. In contrast, length-of-stay was comparable in our prospective trial. Volatile anesthetic choice should not be based on concerns about the duration of hospitalization. These studies illustrate the importance of following even the best retrospective analysis with a prospective trial.

Effect of Volatile Anesthetic Choice on Hospital Length-of-Stay

2015 ◽  
Vol 59 (6) ◽  
pp. 264
Author(s):  
Tatyana Kopyeva ◽  
Daniel I. Sessler ◽  
Stephanie Weiss ◽  
Jarrod E. Dalton ◽  
Edward J. Mascha ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Volatile Anesthetic ◽  
Hospital Length ◽  
Hospital Length Of Stay

scholarly journals Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

2018 ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Andrea McCoy ◽  
Carol Gu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Retrospective Analysis ◽  
Real World ◽  
Learning Algorithm ◽  
Hospital Length ◽  
Hospital Length Of Stay ◽  
Machine Learning Algorithm

AbstractObjectiveTo validate performance of a machine learning algorithm for severe sepsis determination up to 48 hours before onset, and to evaluate the effect of the algorithm on in-hospital mortality, hospital length of stay, and 30-day readmission.SettingThis cohort study includes a combined retrospective analysis and clinical outcomes evaluation: a dataset containing 510,497 patient encounters from 461 United States health centers for retrospective analysis, and a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals for clinical outcomes evaluation.ParticipantsFor retrospective analysis, 270,438 adult patients with at least one documented measurement of five out of six vital sign measurements were included. For clinical outcomes analysis, 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay were included.ResultsAt severe sepsis onset, the MLA demonstrated an AUROC of 0.91 (95% CI 0.90, 0.92), which exceeded those of MEWS (0.71, P<001), SOFA (0.74; P<.001), and SIRS (0.62; P<.001). For severe sepsis prediction 48 hours in advance of onset, the MLA achieved an AUROC of 0.77 (95% CI 0.73, 0.80). For the clinical outcomes study, when using the MLA, hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.ConclusionsThe MLA accurately predicts severe sepsis onset up to 48 hours in advance using only readily available vital signs in retrospective validation. Reductions of in-hospital mortality, hospital length of stay, and 30-day readmissions were observed in real-world clinical use of the MLA. Results suggest this system may improve severe sepsis detection and patient outcomes over the use of rules-based sepsis detection systems.KEY POINTSQuestionIs a machine learning algorithm capable of accurate severe sepsis prediction, and does its clinical implementation improve patient mortality rates, hospital length of stay, and 30-day readmission rates?FindingsIn a retrospective analysis that included datasets containing a total of 585,644 patient encounters from 461 hospitals, the machine learning algorithm demonstrated an AUROC of 0.93 at time of severe sepsis onset, which exceeded those of MEWS (0.71), SOFA (0.74), and SIRS (0.62); and an AUROC of 0.77 for severe sepsis prediction 48 hours in advance of onset. In an analysis of real-world data from nine hospitals across 75,147 patient encounters, use of the machine learning algorithm was associated with a 39.5% reduction in in-hospital mortality, a 32.3% reduction in hospital length of stay, and a 22.7% reduction in 30-day readmission rate.MeaningThe accurate and predictive nature of this algorithm may encourage early recognition of patients trending toward severe sepsis, and therefore improve sepsis related outcomes.STRENGTHS AND LIMITATIONS OF THIS STUDYA retrospective study of machine learning severe sepsis prediction from a dataset with 510,497 patient encounters demonstrates high accuracy up to 48 hours prior to onset.A multicenter clinical study of real-world data using this machine learning algorithm for severe sepsis alerts achieved reductions of in-hospital mortality, length of stay, and 30-day readmissions.The required presence of an ICD-9 code to classify a patient as severely septic in our retrospective analysis potentially limits our ability to accurately classify all patients.Only adults in US hospitals were included in this study.For the real-world section of the study, we cannot eliminate the possibility that implementation of a sepsis algorithm raised general awareness of sepsis within a hospital, which may lead to higher recognition of septic patients, independent of algorithm performance.

scholarly journals The Impact of Volatile Anesthetic Choice on Postoperative Outcomes of Cardiac Surgery: A Meta-Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Andres Zorrilla-Vaca ◽  
Rafael A. Núñez-Patiño ◽  
Valentina Torres ◽  
Yudy Salazar-Gomez
Keyword(s):  
Cardiac Surgery ◽  
Length Of Stay ◽  
Meta Analysis ◽  
Unit Length ◽  
Volatile Anesthetic ◽  
Hospital Length ◽  
Postoperative Outcomes ◽  
Hospital Length Of Stay ◽  
Randomized Controlled ◽  
The Impact

Objectives. To evaluate the impact of volatile anesthetic choice on clinically relevant outcomes of patients undergoing cardiac surgery. Methods. Major databases were systematically searched for randomized controlled trials (RCTs) comparing volatile anesthetics (isoflurane versus sevoflurane) in cardiac surgery. Study-level characteristics, intraoperative events, and postoperative outcomes were extracted from the articles. Results. Sixteen RCTs involving 961 patients were included in this meta-analysis. There were no significant differences between both anesthetics in terms of intensive care unit length of stay (SMD −0.07, 95% CI −0.38 to 0.24, P=0.66), hospital length of stay (SMD 0.06, 95% CI −0.33 to 0.45, P=0.76), time to extubation (SMD 0.29, 95% CI −0.08 to 0.65, P=0.12), S100β (at the end of surgery: SMD 0.08, 95% CI −0.33 to 0.49, P=0.71; 24 hours after surgery: SMD 0.21, 95% CI −0.23 to 0.65, P=0.34), or troponin (at the end of surgery: SMD −1.13, 95% CI −2.39 to 0.13, P=0.08; 24 hours after surgery: SMD 0.74, 95% CI −0.15 to 1.62, P=0.10). CK-MB was shown to be significantly increased when using isoflurane instead of sevoflurane (SMD 2.16, 95% CI 0.57 to 3.74, P=0.008). Conclusions. The volatile anesthetic choice has no significant impact on postoperative outcomes of patients undergoing cardiac surgery.

THE EFFECTS OF PERIOPERATIVE DEXAMETHASONE ON GLYCEMIC CONTROL AND POSTOPERATIVE OUTCOMES

2020 ◽  
Vol 26 (2) ◽  
pp. 218-225 ◽  
Author(s):  
Rebecca A. Herbst ◽  
Onala T. Telford ◽  
John Hunting ◽  
W. Michael Bullock ◽  
Erin Manning ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Length Of Stay ◽  
Confidence Interval ◽  
Orthopedic Surgery ◽  
Geometric Mean ◽  
Hospital Length ◽  
Hazard Ratio ◽  
Hospital Length Of Stay ◽  
Geometric Mean Ratio ◽  
Readmission Rates

Objective: Perioperative glucocorticoids are commonly given to reduce pain and nausea in patients undergoing surgery. However, the glycemic effects of steroids and the potential effects on morbidity and mortality have not been systematically evaluated. This study investigated the association between perioperative dexamethasone and postoperative blood glucose, hospital length of stay (LOS), readmission rates, and 90-day survival. Methods: Data from 4,800 consecutive orthopedic surgery patients who underwent surgery between 2000 and 2016 within a single health system were analyzed retrospectively. Results: Patients with and without diabetes mellitus (DM) who were given a single dose of dexamethasone had higher rates of hyperglycemia during the first 24 hours after surgery as compared to those who did not receive dexamethasone (hazard ratio [HR] was 1.81, and 95% confidence interval [CI] was [1.46, 2.24] for the DM cohort; HR 2.34, 95% CI [1.66, 3.29] for the nonDM cohort). LOS was nearly 1 day shorter in patients who received dexamethasone (geometric mean ratio [GMR] 0.79, 95% CI [0.75, 0.83] for patients with DM; GMR 0.75, 95% CI [0.72, 0.79] for patients without DM), and there was no difference in 90-day readmission rates. In patients without DM, dexamethasone was associated with a higher 90-day overall survival (99.07% versus 96.90%; P = .004). Conclusion: In patients with and without DM who undergo orthopedic surgery, perioperative dexamethasone was associated with a transiently higher risk of hyperglycemia. However, dexamethasone treatment was associated with a shorter LOS in patients with and without DM, and a higher overall 90-day survival rate in patients without DM, compared to patients who did not receive dexamethasone. Abbreviations: BMI = body mass index; CAD = coronary artery disease; CI = confidence interval; DM = diabetes mellitus; GMR = geometric mean ratio; HR = hazard ratio; IV = intravenous; LOS = length of stay; POD = postoperative day

Sign in / Sign up

Export Citation Format

Share Document