PREDICTING THE DURATION OF SURGERIES TO IMPROVE PROCESS EFFICIENCY IN HOSPITALS

Predicting the duration of surgeries is an important task because of the many dependencies between surgery processes and the hospital processes within other departments. Thus, accurate predictions allow for better coordinating patient processes throughout the hospital. Prior data-driven research provides evidence for accurate predictions of surgery durations enhancing the efficiency of surgery schedules. However, the current prediction models require large sets of features, which make their adoption more intricate. Moreover, prediction models focus on the surgery department and neglect potential effects on other departments. We use a unique dataset of about 17,000 surgeries to study how particular features and machine learning algorithms affect the prediction accuracy of major surgery steps. The prediction models that we study require few features and are easy to apply. The empirical findings can be useful for the design of surgery scheduling systems

Challenges and pitfalls of experimental bariatric procedures in rats

Introduction: The impact of Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) on obesity and obesity-related diseases is unquestionable. Up to now, the technical descriptions of these techniques in animals/rats have not been very comprehensive. Methods: For SG and RYGB, operating time, learning curve, and intraoperative mortality in relation to weight of the rat and type of anesthesia were recorded. Furthermore, a review of the literature on experimental approaches towards SG and RYGB in rats was carried out, merging in a detailed technical description for both procedures. Results: The data presented here revealed that the mean operating time for SG (69.4 +/- 22.2 min (SD)) was shorter than for RYGB (123.0 +/- 20.7 min). There is a learning curve for both procedures, resulting in a reduced operating time of up to 60% in SG and 35% in RYGB (p < 0.05; t-test). However, with increased weight, operating time increases to about 80 min for SG and about 120 min for RYGB. Obese rats have an increased intraoperative mortality rate of up to 50%. After gaseous anesthesia the mortality can be even higher. The literature search revealed 40 papers dealing with SG and RYGB in rats. 18 articles (45%) contained neither photographs nor illustrations; 14 articles (35%) did not mention the applied type of anesthesia. The mortality rate was described in 15 papers (37.5%). Conclusion: Experimental obesity surgery in rats is challenging. Because of the high mortality in obese rats operated under gaseous anesthesia, exercises to establish the techniques should be performed in small rats using intraperitoneal anesthesia. Copyright (C) 2012 S. Karger GmbH, Freibur

Out of distribution detection for intra-operative functional imaging

Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.Comment: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32689-0_

Laparoscopic mesh-augmented hiatoplasty without fundoplication as a method to treat large hiatal hernias

PURPOSE: Laparoscopic hiatal hernia repair with additional fundoplication is a commonly recommended standard surgical treatment for symptomatic large hiatal hernias with paraesophageal involvement (PEH). However, due to the risk of persistent side effects, this method remains controversial. Laparoscopic mesh-augmented hiatoplasty without fundoplication (LMAH), which combines hiatal repair and mesh reinforcement, might therefore be an alternative. METHODS: In this retrospective study of 55 (25 male, 30 female) consecutive PEH patients, the perioperative course and symptomatic outcomes were analyzed after a mean follow-up of 72 months. RESULTS: The mean DeMeester symptom score decreased from 5.1 to 1.8 (P < 0.001) and the gas bloating value decreased from 1.2 to 0.5 (P = 0.001). The dysphagia value was 0.7 before surgery and 0.6 (P = 0.379) after surgery. The majority of the patients were able to belch and vomit (96 and 92 %, respectively). Acid-suppressive therapy on a regular basis was discontinued in 68 % of patients. In 4 % of patients, reoperation was necessary due to recurrent or persistent reflux. A mesh-related stenosis that required endoscopic dilatation occurred in 2 % of patients. CONCLUSIONS: LMAH is feasible, safe and provides an anti-reflux effect, even without fundoplication. As operation-related side effects seem to be rare, LMAH is a potential treatment option for large hiatal hernias with paraesophageal involvement

Does rating the operation videos with a checklist score improve the effect of E-learning for bariatric surgical training? Study protocol for a randomized controlled trial

Background: Laparoscopic training has become an important part of surgical education. Laparoscopic Roux-en-Y gastric bypass (RYGB) is the most common bariatric procedure performed. Surgeons must be well trained prior to operating on a patient. Multimodality training is vital for bariatric surgery. E-learning with videos is a standard approach for training. The present study investigates whether scoring the operation videos with performance checklists improves learning effects and transfer to a simulated operation. Methods/design: This is a monocentric, two-arm, randomized controlled trial. The trainees are medical students from the University of Heidelberg in their clinical years with no prior laparoscopic experience. After a laparoscopic basic virtual reality (VR) training, 80 students are randomized into one of two arms in a 1:1 ratio to the checklist group (group A) and control group without a checklist (group B). After all students are given an introduction of the training center, VR trainer and laparoscopic instruments, they start with E-learning while watching explanations and videos of RYGB. Only group A will perform ratings with a modified Bariatric Objective Structured Assessment of Technical Skill (BOSATS) scale checklist for all videos watched. Group B watches the same videos without rating. Both groups will then perform an RYGB in the VR trainer as a primary endpoint and small bowel suturing as an additional test in the box trainer for evaluation. Discussion: This study aims to assess if E-learning and rating bariatric surgical videos with a modified BOSATS checklist will improve the learning curve for medical students in an RYGB VR performance. This study may help in future laparoscopic and bariatric training courses. Trial registration: German Clinical Trials Register, DRKS00010493. Registered on 20 May 2016