TY - JOUR
T1 - Incidence and predictors of case cancellation within 24 h in patients scheduled for elective surgical procedures
AU - Wongtangman, Karuna
AU - Azimaraghi, Omid
AU - Freda, Jeffrey
AU - Ganz-Lord, Fran
AU - Shamamian, Peter
AU - Bastien, Alexandra
AU - Mirhaji, Parsa
AU - Himes, Carina P.
AU - Rupp, Samuel
AU - Green-Lorenzen, Susan
AU - Smith, Richard V.
AU - Medrano, Elilary Montilla
AU - Anand, Preeti
AU - Rego, Simon
AU - Velji, Salimah
AU - Eikermann, Matthias
N1 - Publisher Copyright:
© 2022
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Case cancellation
KW - Elective surgery cancellation
KW - Prediction model
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U2 - 10.1016/j.jclinane.2022.110987
DO - 10.1016/j.jclinane.2022.110987
M3 - Article
C2 - 36308990
AN - SCOPUS:85140458339
SN - 0952-8180
VL - 83
JO - Journal of Clinical Anesthesia
JF - Journal of Clinical Anesthesia
M1 - 110987
ER -