Introducing the American Journal of Surgery Virtual Research Mentor: A primer for aspiring surgeon-scientists

Translating the Value of the Academic Surgeon Into Salary, Time, and Resources

Academic surgeons provide tremendous value to institutions including notoriety, publicity, cutting-edge clinical advances, extramural funding, and academic growth and development. In turn, these attributes may result in improved reputation scores and hospital or medical center rankings. While many hospital systems, schools of medicine, and departments of surgery claim to have a major commitment to academic surgery and research, academic surgeons are often undercompensated compared to clinically focused counterparts. Existing salary benchmarks (e.g., the Medical Group Management Association (MGMA) or the Association of American Medical Colleges (AAMC)) are often used but are imperfect. Thus, the value proposition for academic surgeons goes beyond compensation and often includes protected time for academic pursuit, nonsalary financial support, and other intangible benefits to being associated with a major academic center (e.g., abundance of scientific collaborators, infrastructure for grant management). As a result, institution-specific practices have developed and academic surgeons are left to negotiate salary support including bonus structures, protected time, and recruitment packages on a case-by-case basis without a clear roadmap. A diverse panel representing a range of academic surgical experiences was convened at the 2022 Academic Surgical Congress to illuminate this complex, often stress-inducing, aspect of an academic surgeon's professional career.

Training prediction models for individual risk assessment of postoperative complications after surgery for colorectal cancer

BACKGROUND

The occurrence of postoperative complications and anastomotic leakage are major drivers of mortality in the immediate phase after colorectal cancer surgery. We trained prediction models for calculating patients' individual risk of complications based only on preoperatively available data in a multidisciplinary team setting. Knowing prior to surgery the probability of developing a complication could aid in improving informed decision-making by surgeon and patient and individualize surgical treatment trajectories.

METHODS

All patients over 18 years of age undergoing any resection for colorectal cancer between January 1, 2014 and December 31, 2019 from the nationwide Danish Colorectal Cancer Group database were included. Data from the database were converted into Observational Medical Outcomes Partnership Common Data Model maintained by the Observation Health Data Science and Informatics initiative. Multiple machine learning models were trained to predict postoperative complications of Clavien-Dindo grade ≥ 3B and anastomotic leakage within 30 days after surgery.

RESULTS

Between 2014 and 2019, 23,907 patients underwent resection for colorectal cancer in Denmark. A Clavien-Dindo complication grade ≥ 3B occurred in 2,958 patients (12.4%). Of 17,190 patients that received an anastomosis, 929 experienced anastomotic leakage (5.4%). Among the compared machine learning models, Lasso Logistic Regression performed best. The predictive model for complications had an area under the receiver operating characteristic curve (AUROC) of 0.704 (95%CI 0.683-0.724) and an AUROC of 0.690 (95%CI 0.655-0.724) for anastomotic leakage.

CONCLUSIONS

The prediction of postoperative complications based only on preoperative variables using a national quality assurance colorectal cancer database shows promise for calculating patient's individual risk. Future work will focus on assessing the value of adding laboratory parameters and drug exposure as candidate predictors. Furthermore, we plan to assess the external validity of our proposed model.