Aman B Ali, MD1, Nabil Tariq, MD1, Cendrine Mony, PhD2, Guillaume Joerger, PhD3, Vishwanath Chegireddy, MD1, Joseph Nguyen-Lee, MD1, Marc Garbey, PhD3. 1Houston Methodist Hospital, 2University of Rennes, 3Houston Methodist Research Institute
Background: Prediction of operative time even for a large volume procedure such as bariatric surgery is a difficult task. Surgical time can depend on multiple factors such as patient’s comorbidities, preinduction anesthesia preparation, experience and efficiency of the surgeon and his/her assistant amongst other factors. Confidence in predicting surgical time may come with large samples of observation, but its utility in scheduling for a hospital system that has a volume of the order of three to four hundred patients per year with sometimes complex history is doubtful. We conducted our study to assess if there are factors that can help predict time disruption in the OR.
Method: We collected data on 70 bariatric surgeries performed by three surgeons from April 2015 to December 2016. Surgeries were performed in the Smart OR, where automated noninvasive tracking methods were used with multiple noninvasive sensors. Our data includes time in OR prior to laparoscopy, laparoscopy, abdominal closure and exiting the OR. We have analyzed factors such as surgeon/assistant, BMI, age, smoking history, cardiopulmonary conditions, previous surgery, etc.
We used multivariate statistical analysis to study our population sample and classified the impact of each factor or their combination with the use of principal component analysis.
We used systematic clustering to identify subpopulations that have significant differences in statistical distribution.
Result: The main determinant of total operative time was the surgeon and the level of his assistant. Prior surgeries, BMI and smoking history had a statistically significant impact on the laparoscopic time (p value < 0.05). Removing the impact of various surgeons, we detected four clusters of patients based on more than 15 patient characteristics. We noticed total OR time had two different clusters: one with a standard-deviation of 17 to 21 min while the other had over 50 min.
Conclusion: This study may have practical implications on improving scheduling. The different comorbidities of these bariatric patients helped to stratify patients into these 2 main cluster groups. Better predictability on length of operative procedure can lead to more efficient use of OR time and staff, thus ultimately leading to savings for the hospital. In addition, we used automated noninvasive tracking methods to identify phases of bariatric procedures that will allow more accurate estimated OR time to efficiently schedule cases. The Smart OR, which is equipped with multiple noninvasive sensors, allows for error free tracking and monitoring without human interference.
Presented at the SAGES 2017 Annual Meeting in Houston, TX.
Abstract ID: 88531
Program Number: P572
Presentation Session: iPoster Session (Non CME)
Presentation Type: Poster