Image-based laparoscopic camera steering versus conventional steering: a comparison study.

In the last 2 decades, multiple robotic camera holders have been developed to improve camera steering during laparoscopic surgery. A new image-based steering method has been developed for more intuitive camera control. In this article, the efficiency and user experience of image-based steering were compared to conventional steering methods. Four participants (two senior surgical registrars, one junior surgical registrar and a technical medicine student) were enrolled in this study. All participants performed multiple camera steering exercises with three different steering modalities in randomized order: image-based, joystick and manual camera steering. Steering of the laparoscope was evaluated by execution time and with the SMEQ and NASA-TLX questionnaires to analyze user experience. A total of 267 camera steering exercises were performed. The analyzed data showed a significantly shorter execution time for manual camera steering compared to image-based robotic steering (p = 0.001) and joystick robotic steering (p = 0.001). The participants reported the lowest user experience with joystick camera steering. The results of the questionnaires showed no significant difference in all subscales of user experience for image-based and manual camera steering. Manual camera steering resulted in significantly higher perceived physiological workload scores (M = 30.0, IQR = 27.5) compared to image-based (M = 10, IQR = 5.0) and joystick camera steering (M = 15.0, IQR = 10.0). Manual control of the laparoscope remains the fastest steering method at the expense of a high physical workload. Using image-based camera steering is a viable alternative to the current joystick control of robotic camera holders, as it improves speed and user experience. The study results suggest that optimisation of robotic camera steering with algorithms based on image analysis is a promising technology.

Robotic-assisted minimally invasive esophagectomy: past, present and future.

Esophagectomy for cancer of the esophagus is increasingly performed using minimally invasive techniques. After the introduction of minimally invasive esophagectomy (MIE) in the early 1990's, robotic-assisted techniques followed after the turn of the millennium. The advent of robotic platforms has allowed the development of robotic-assisted minimally invasive esophagectomy (RAMIE) over the past 15 years. Although recent trials have shown superior peri-operative morbidity and quality of life compared to open esophagectomy, no randomized trials have compared RAMIE to conventional MIE. This paper summarizes the current literature on RAMIE and provides an overview of expected future developments in robotic surgery.

Development and Validation of a Prediction Model for Prehospital Triage of Trauma Patients.

Importance: Prehospital trauma triage protocols are used worldwide to get the right patient to the right hospital and thereby improve the chance of survival and avert lifelong disabilities. The American College of Surgeons Committee on Trauma set target levels for undertriage rates of less than 5%. None of the existing triage protocols has been able to achieve this target in isolation. Objective: To develop and validate a new prehospital trauma triage protocol to improve current triage rates. Design, Setting, and Participants: In this multicenter cohort study, all patients with trauma who were 16 years and older and transported to a trauma center in 2 different regions of the Netherlands were included in the analysis. Data were collected from January 1, 2012, through June 30, 2014, in the Central Netherlands region for the design data cohort and from January 1 through December 31, 2015, in the Brabant region for the validation cohort. Data were analyzed from May 3, 2017, through July 19, 2018. Main Outcomes and Measures: A new prediction model was developed in the Central Netherlands region based on prehospital predictors associated with severe injury. Severe injury was defined as an Injury Severity Score greater than 15. A full-model strategy with penalized maximum likelihood estimation was used to construct a model with 8 predictors that were chosen based on clinical reasoning. Accuracy of the developed prediction model was assessed in terms of discrimination and calibration. The model was externally validated in the Brabant region. Results: Using data from 4950 patients with trauma from the Central Netherlands region for the design data set (58.3% male; mean [SD] age, 47 [21] years) and 6859 patients for the validation Brabant region (52.2% male; mean [SD] age, 51 [22] years), the following 8 significant predictors were selected for the prediction model: age; systolic blood pressure; Glasgow Coma Scale score; mechanism criteria; penetrating injury to the head, thorax, or abdomen; signs and/or symptoms of head or neck injury; expected injury in the Abbreviated Injury Scale thorax region; and expected injury in 2 or more Abbreviated Injury Scale regions. The prediction model showed a C statistic of 0.823 (95% CI, 0.813-0.832) and good calibration. The cutoff point with a minimum specificity of 50.0% (95% CI, 49.3%-50.7%) led to a sensitivity of 88.8% (95% CI, 87.5%-90.0%). External validation showed a C statistic of 0.831 (95% CI, 0.814-0.848) and adequate calibration. Conclusions and Relevance: The new prehospital trauma triage prediction model may lower undertriage rates to approximately 10% with an overtriage rate of 50%. The next step should be to implement this prediction model with the use of a mobile app for emergency medical services professionals.

Accuracy of Prehospital Triage in Selecting Severely Injured Trauma Patients.

Importance: A major component of trauma care is adequate prehospital triage. To optimize the prehospital triage system, it is essential to gain insight in the quality of prehospital triage of the entire trauma system. Objective: To prospectively evaluate the quality of the field triage system to identify severely injured adult trauma patients. Design, Setting, and Participants: Prehospital and hospital data of all adult trauma patients during 2012 to 2014 transported with the highest priority by emergency medical services professionals to 10 hospitals in Central Netherlands were prospectively collected. Prehospital data collected by the emergency medical services professionals were matched to hospital data collected in the trauma registry. An Injury Severity Score of 16 or more was used to determine severe injury. Main Outcomes and Measures: The quality and diagnostic accuracy of the field triage protocol and compliance of emergency medical services professionals to the protocol. Results: A total of 4950 trauma patients were evaluated of which 436 (8.8%) patients were severely injured. The undertriage rate based on actual destination facility was 21.6% (95% CI, 18.0-25.7) with an overtriage rate of 30.6% (95% CI, 29.3-32.0). Analysis of the protocol itself, regardless of destination facility, resulted in an undertriage of 63.8% (95% CI, 59.2-68.1) and overtriage of 7.4% (95% CI, 6.7-8.2). The compliance to the field triage trauma protocol was 73% for patients with a level 1 indication. Conclusions and Relevance: More than 20% of the patients with severe injuries were not transported to a level I trauma center. These patients are at risk for preventable morbidity and mortality. This finding indicates the need for improvement of the prehospital triage protocol.