Does an aptitude for surgery exist and can we predict it? An experimental study

Abstract

Background: The selection and allocation of surgeons with a greater potential for high surgical performance are essential aspects of improving the quality, safety, and effectiveness of surgical procedures. Objective of this trial was to determine the existence of basic skills and traits that would predict better performance in surgery, and those predictive factors that constitute a driving force in different stages of training.

Materials and methods: The randomized crossover training trial took place from January 2021 to December 2021 and was conducted at an educational training center for minimally invasive surgery. A total of n =87 physicians (residents and experts) from surgical disciplines and n =239 fifth-year medical students were studied. The participants underwent extensive neuropsychological testing and surgical training, which was performed with conventional as well as robot-assisted laparoscopy by way of identical brief tasks conducted six times in a randomized crossover setting. Main Outcome was the latent factor structure of 'psychomotor skills', 'personality', and 'motivation' based on structural equation modeling.

Results: The training performance of both students and physicians was significantly explained by the interaction of the three factors (explained variance: 8.2% for students, 23.8% for physicians). In students, motivation (explained variance 8.4%) and personality (explained variance 4.5%) revealed the highest contribution to surgical training performance (explained variance through psychomotor skills 1.1%). In physicians, psychomotor skills (explained variance 27.4%) made the greatest contribution to surgical training performance (explained variance through motivation 2.3%; explained variance through personality 10.5%).

Conclusion: The study showed that surgical performance is sensitive to, and fragile in regard of nonsurgery-related general individual traits. This aligns with the notion that early selection of surgeons with prospects of high surgical performance is possible, and perhaps even necessary in order to keep up with future demands on the medical system.

Figure 1

Study Protocol. CANTAB, Cambridge Neuropsychological Test Automated Battery; NEO-FFI, NEO Five Factor Inventory; QCM, questionnaire on current motivation.

Figure 2

Surgical tasks.

Figure 3

Learning curve.

Figure 4

Structural equation model (SEM) in Students. Note: The training performance of students was tested for its prediction by the latent constructs psychomotor skills, personality, and motivation, and the covariates of sex and age, using an SEM approach. Psychomotor skills were modeled as being constituted by the variables visual information processing (RVPA), learning-based memory (PALFAMS), spatial working memory (SWMS), reaction time capabilities (RTIFMDRT), and motor response capabilities (MOTML). Personality was modeled as being constituted by neuroticism, extraversion, openness, agreeableness, and conscientiousness. Motivation was modeled as being constituted by fear of failure, challenge, probability of success, and interest [P<0.05].

Figure 5

Structural Equation Model (SEM) in Physicians. Note: The training performance of physicians was tested for its prediction by the latent constructs psychomotor skills, personality, and motivation, and the covariates of gender and age, using an SEM approach. Psychomotor skills were modeled as being constituted by the variables visual information processing (RVPA), learning-based memory (PALFAMS), spatial working memory (SWMS), reaction time capabilities (RTIFMDRT), and motor response capabilities (MOTML). Personality was modeled as being constituted by neuroticism, extraversion, openness, agreeableness, and conscientiousness. Motivation was modeled as being constituted by fear of failure, challenge, probability of success, and interest [P<0.05].

Figure 6

Summary plot of the distribution of SHAP values for each variable across the entire dataset to identify the most important single variables for the prediction of training performance. (A) SHapley Additive exPlanations (SHAPs) in the student sample. (B) SHAPs in the physician sample. SHAP values constitute the contribution of each of the single variables related to psychomotor skills, personality, and motivation to the prediction of training performance. A positive SHAP value (in pink) indicates that the variable increases training performance, while a negative value indicates that the variable reduces training performance. The magnitude of the SHAP values reflects the strength of the respective variable’s influence on training performance, reflected also by the order of the variables, indicating their impact on prediction – a higher order signifies a greater contribution. [MOTML, motor-screening task - mean latency; PALFAMS, paired associates learning – first attempt memory].