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Elayna P. Kirsch, Alexander Suarez, Katherine E. McDaniel, Rajeev Dharmapurikar, Timothy Dunn, Shivanand P. Lad, and Michael M. Haglund

OBJECTIVE

There is no standard way in which physicians teach or evaluate surgical residents intraoperatively, and residents are proving to not be fully competent at core surgical procedures upon graduating. The Surgical Autonomy Program (SAP) is a novel educational model that combines a modified version of the Zwisch scale with Vygotsky’s social learning theory. The objective of this study was to establish preliminary validity evidence that SAP is a reliable measure of autonomy and a useful tool for tracking competency over time.

METHODS

The SAP breaks each surgical case into 4 parts, or zones of proximal development (ZPDs). Residents are evaluated on a 4-tier autonomy scale (TAGS scale) for each ZPD in every surgical case. Attendings were provided with a teaching session about SAP and identified appropriate ZPDs for surgical cases under their area of expertise. All neurosurgery residents at Duke University Hospital from July 2017 to July 2021 participated in this study. Chi-square tests and ordinal logistic regression were used for the analyses.

RESULTS

Between 2017 and 2021, there were 4885 cases logged by 27 residents. There were 30 attendings who evaluated residents using SAP. Faculty completed evaluations on 91% of cases. The ZPD of focus directly correlated with year of residency (postgraduate year) (χ2 = 1221.1, df = 15, p < 0.001). The autonomy level increased with year of residency (χ2 = 3553.5, df = 15, p < 0.001). An ordinal regression analysis showed that for every year increase in postgraduate year, the odds of operating at a higher level of independence was 2.16 times greater (95% CI 2.11–2.21, p < 0.001). The odds of residents performing with greater autonomy was lowest for the most complex portion of the case (ZPD3) (OR 0.18, 95% CI 0.17–0.20, p < 0.001). Residents have less autonomy with increased case complexity (χ2 = 160.28, df = 6, p < 0.001). Compared with average cases, residents were more likely to operate with greater autonomy on easy cases (OR 1.44, 95% CI 1.29–1.61, p < 0.001) and less likely to do so on difficult cases (OR 0.72, 95% CI 0.67–0.77, p < 0.001).

CONCLUSIONS

This study demonstrates preliminary evidence supporting the construct validity of the SAP. This tool successfully tracks resident autonomy and progress over time. The authors’ smartphone application was widely used among surgical faculty and residents, supporting integration into the perioperative workflow. Wide implementation of SAP across multiple surgical centers will aid in the movement toward a competency-based residency education system.

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Michael L. Martini, Sean N. Neifert, William H. Shuman, Emily K. Chapman, Alexander J. Schüpper, Eric K. Oermann, J Mocco, Michael Todd, James C. Torner, Andrew Molyneux, Stephan Mayer, Peter Le Roux, Mervyn D. I. Vergouwen, Gabriel J. E. Rinkel, George K. C. Wong, Peter Kirkpatrick, Audrey Quinn, Daniel Hänggi, Nima Etminan, Walter M. van den Bergh, Blessing N. R. Jaja, Michael Cusimano, Tom A. Schweizer, Jose I. Suarez, Hitoshi Fukuda, Sen Yamagata, Benjamin Lo, Airton Leonardo de Oliveira Manoel, Hieronymus D. Boogaarts, R. Loch Macdonald, and

OBJECTIVE

Rescue therapies have been recommended for patients with angiographic vasospasm (aVSP) and delayed cerebral ischemia (DCI) following subarachnoid hemorrhage (SAH). However, there is little evidence from randomized clinical trials that these therapies are safe and effective. The primary aim of this study was to apply game theory–based methods in explainable machine learning (ML) and propensity score matching to determine if rescue therapy was associated with better 3-month outcomes following post-SAH aVSP and DCI. The authors also sought to use these explainable ML methods to identify patient populations that were more likely to receive rescue therapy and factors associated with better outcomes after rescue therapy.

METHODS

Data for patients with aVSP or DCI after SAH were obtained from 8 clinical trials and 1 observational study in the Subarachnoid Hemorrhage International Trialists repository. Gradient boosting ML models were constructed for each patient to predict the probability of receiving rescue therapy and the 3-month Glasgow Outcome Scale (GOS) score. Favorable outcome was defined as a 3-month GOS score of 4 or 5. Shapley Additive Explanation (SHAP) values were calculated for each patient-derived model to quantify feature importance and interaction effects. Variables with high SHAP importance in predicting rescue therapy administration were used in a propensity score–matched analysis of rescue therapy and 3-month GOS scores.

RESULTS

The authors identified 1532 patients with aVSP or DCI. Predictive, explainable ML models revealed that aneurysm characteristics and neurological complications, but not admission neurological scores, carried the highest relative importance rankings in predicting whether rescue therapy was administered. Younger age and absence of cerebral ischemia/infarction were invariably linked to better rescue outcomes, whereas the other important predictors of outcome varied by rescue type (interventional or noninterventional). In a propensity score–matched analysis guided by SHAP-based variable selection, rescue therapy was associated with higher odds of 3-month GOS scores of 4–5 (OR 1.63, 95% CI 1.22–2.17).

CONCLUSIONS

Rescue therapy may increase the odds of good outcome in patients with aVSP or DCI after SAH. Given the strong association between cerebral ischemia/infarction and poor outcome, trials focusing on preventative or therapeutic interventions in these patients may be most able to demonstrate improvements in clinical outcomes. Insights developed from these models may be helpful for improving patient selection and trial design.