Use of Historical Surgical Times to Predict Duration of Primary Aortic Valve Replacement.

Autor: Wu A; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA., Rinewalt DE; Division of Cardiac Surgery, Brigham and Women's Hospital, Boston, MA., Lekowski RW Jr; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA., Urman RD; Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, MA; Center for Perioperative Research, Brigham and Women's Hospital, Boston, MA. Electronic address: rurman@partners.org.
Jazyk: angličtina
Zdroj: Journal of cardiothoracic and vascular anesthesia [J Cardiothorac Vasc Anesth] 2017 Jun; Vol. 31 (3), pp. 810-815. Date of Electronic Publication: 2016 Nov 17.
DOI: 10.1053/j.jvca.2016.11.023
Abstrakt: Objectives: To test whether a model using a historical average of a surgeon's surgical times for primary aortic valve replacements is a more accurate predictor of actual surgical times than solely relying on a surgeon's estimate.
Design: Retrospective review.
Setting: Single university hospital that serves as a tertiary referral center.
Participants: All patients undergoing primary aortic valve replacement between October 2008 and September 2014.
Interventions: None.
Measurements and Main Results: Estimation biases, calculated as the difference between actual and predicted surgical time, were compared between the surgeon and the model, which included between 2 and 20 cases in the historical average. Kruskal-Wallis analysis of variance was used to compare all values. Pairwise comparisons were made using the Steel-Dwass test to determine whether using more cases in the model resulted in smaller estimation biases. Using the historical model reduced mean overestimation bias from 55.30 minutes to 0.90-to-4.67 minutes. No significant difference was seen based on the number of cases used.
Conclusions: An uncomplicated model can assist in providing comparatively unbiased estimations of surgical time for aortic valve replacements. The model can rely on a fewer number of cases (eg, 5) and does not benefit from including more cases (eg, 20).
(Copyright © 2017 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE