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Uncontrolled movement of instruments in laparoscopic surgery can lead to inadvertent tissue damage, particularly when the dissecting or electrosurgical instrument is located outside the field of view of the laparoscopic camera. The incidence and relevance of such events are currently unknown. The present work aims to identify and quantify potentially dangerous situations using the example of laparoscopic cholecystectomy (LC). Twenty-four final year medical students were prompted to each perform four consecutive LC attempts on a well-established box trainer in a surgical training environment following a standardized protocol in a porcine model. The following situation was defined as a critical event (CE): the dissecting instrument was inadvertently located outside the laparoscopic camera’s field of view. Simultaneous activation of the electrosurgical unit was defined as a highly critical event (hCE). Primary endpoint was the incidence of CEs. While performing 96 LCs, 2895 CEs were observed. Of these, 1059 (36.6%) were hCEs. The median number of CEs per LC was 20.5 (range: 1–125; IQR: 33) and the median number of hCEs per LC was 8.0 (range: 0–54, IQR: 10). Mean total operation time was 34.7 min (range: 15.6–62.5 min, IQR: 14.3 min). Our study demonstrates the significance of CEs as a potential risk factor for collateral damage during LC. Further studies are needed to investigate the occurrence of CE in clinical practice, not just for laparoscopic cholecystectomy but also for other procedures. Systematic training of future surgeons as well as technical solutions address this safety issue.
Background
Personalized medicine requires the integration and analysis of vast amounts of patient data to realize individualized care. With Surgomics, we aim to facilitate personalized therapy recommendations in surgery by integration of intraoperative surgical data and their analysis with machine learning methods to leverage the potential of this data in analogy to Radiomics and Genomics.
Methods
We defined Surgomics as the entirety of surgomic features that are process characteristics of a surgical procedure automatically derived from multimodal intraoperative data to quantify processes in the operating room. In a multidisciplinary team we discussed potential data sources like endoscopic videos, vital sign monitoring, medical devices and instruments and respective surgomic features. Subsequently, an online questionnaire was sent to experts from surgery and (computer) science at multiple centers for rating the features’ clinical relevance and technical feasibility.
Results
In total, 52 surgomic features were identified and assigned to eight feature categories. Based on the expert survey (n = 66 participants) the feature category with the highest clinical relevance as rated by surgeons was “surgical skill and quality of performance” for morbidity and mortality (9.0 ± 1.3 on a numerical rating scale from 1 to 10) as well as for long-term (oncological) outcome (8.2 ± 1.8). The feature category with the highest feasibility to be automatically extracted as rated by (computer) scientists was “Instrument” (8.5 ± 1.7). Among the surgomic features ranked as most relevant in their respective category were “intraoperative adverse events”, “action performed with instruments”, “vital sign monitoring”, and “difficulty of surgery”.
Conclusion
Surgomics is a promising concept for the analysis of intraoperative data. Surgomics may be used together with preoperative features from clinical data and Radiomics to predict postoperative morbidity, mortality and long-term outcome, as well as to provide tailored feedback for surgeons.