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. 2022 Dec:83:110987.
doi: 10.1016/j.jclinane.2022.110987. Epub 2022 Oct 26.

Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures

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Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures

Karuna Wongtangman et al. J Clin Anesth. 2022 Dec.

Abstract

Objective: Avoidable case cancellations within 24 h reduce operating room (OR) efficiency, add unnecessary costs, and may have physical and emotional consequences for patients and their families. We developed and validated a prediction tool that can be used to guide same day case cancellation reduction initiatives.

Design: Retrospective hospital registry study.

Setting: University-affiliated hospitals network (NY, USA).

Patients: 246,612 (1/2016-6/2021) and 58,662 (7/2021-6/2022) scheduled elective procedures were included in the development and validation cohort.

Measurements: Case cancellation within 24 h was defined as cancelling a surgical procedure within 24 h of the scheduled date and time. Our candidate predictors were defined a priori and included patient-, procedural-, and appointment-related factors. We created a prediction tool using backward stepwise logistic regression to predict case cancellation within 24 h. The model was subsequently recalibrated and validated in a cohort of patients who were recently scheduled for surgery.

Main results: 8.6% and 8.7% scheduled procedures were cancelled within 24 h of the intended procedure in the development and validation cohort, respectively. The final weighted score contains 29 predictors. A cutoff value of 15 score points predicted a 10.3% case cancellation rate with a negative predictive value of 0.96, and a positive predictive value of 0.21. The prediction model showed good discrimination in the development and validation cohort with an area under the receiver operating characteristic curve (AUC) of 0.79 (95% confidence interval 0.79-0. 80) and an AUC of 0.73 (95% confidence interval 0.72-0.73), respectively.

Conclusions: We present a validated preoperative prediction tool for case cancellation within 24 h of surgery. We utilize the instrument in our institution to identify patients with high risk of case cancellation. We describe a process for recalibration such that other institutions can also use the score to guide same day case cancellation reduction initiatives.

Keywords: Case cancellation; Elective surgery cancellation; Prediction model.

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Conflict of interest statement

Declaration of Competing Interest None.

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