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

Karuna Wongtangman, Omid Azimaraghi, Jeffrey Freda, Fran Ganz-Lord, Peter Shamamian, Alexandra Bastien, Parsa Mirhaji, Carina P. Himes, Samuel Rupp, Susan Green-Lorenzen, Richard V. Smith, Elilary Montilla Medrano, Preeti Anand, Simon Rego, Salimah Velji, Matthias Eikermann

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number110987
JournalJournal of Clinical Anesthesia
Volume83
DOIs
StatePublished - Dec 2022

Keywords

  • Case cancellation
  • Elective surgery cancellation
  • Prediction model

ASJC Scopus subject areas

  • Anesthesiology and Pain Medicine

Fingerprint

Dive into the research topics of 'Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures'. Together they form a unique fingerprint.

Cite this