Predicting the Unpredictable A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon's Estimate

Rene Eijkemans, M van Houdenhoven, T Nguyen, Eric Boersma, Ewout Steyerberg, G Kazemier

Research output: Contribution to journalArticleAcademicpeer-review

160 Citations (Scopus)


Background: Routine predictions made by surgeons or historical mean durations have only limited capacity to predict operating room (OR) time. The authors aimed to devise a prediction model using the surgeon's estimate and characteristics of the surgical team, the operation, and the patient. Methods: Seventeen thousand four hundred twelve consecutive, elective operations from the general surgical department in an academic hospital were analyzed. The outcome was OR time, and the potential predictive factors were surgeon's estimate, number of planned procedures, number and experience of surgeons and anesthesiologists, patient's age and sex, number of previous hospital admissions, body mass index, and eight cardiovascular risk factors, Linear mixed modeling on the logarithm of the total OR time was performed. Results: Characteristics of the operation and the team had the largest predictive performance, whereas patient characteristics had a modest but distinct effect on OR time: operations were shorter for patients older than 60 yr, and higher body mass index was associated with longer OR times. The surgeon's estimate had an independent and substantial contribution to the prediction, and the final model explained 27% of the residual variation in log (OR time). Using the prediction model instead of the surgeon's prediction based on historical averages would reduce shorter-than-predicted and longer-than-predicted OR time by 2.8 and 6.6 min per case (a relative reduction of 12 and 25%, respectively), assessed on independent validation data. Conclusions: Detailed information on the operative session, the team, and the patient substantially improves the prediction of OR times, but the surgeon's estimate remains important. The prediction model may be used in OR scheduling.
Original languageUndefined/Unknown
Pages (from-to)41-49
Number of pages9
Issue number1
Publication statusPublished - 2010

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