BACKGROUND: The growth of surgeon burnout is of significant concern. As we work to reimagine the practice of surgery, an accurate understanding of the extent of surgeon burnout is essential. Our goal was to define the current prevalence of burnout and quality of life (QOL) among SAGES surgeons.
METHODS: An electronic survey was administered to SAGES members to establish a current baseline for QOL, burnout, depression, and career satisfaction. To assess outcomes, we utilized the validated Maslach Burnout Inventory for Medical Personnel, the Medical Outcomes Study Short Form, and the Primary Care Evaluation of Mental Disorders. All scoring followed validated norm-based methods.
RESULTS: Of 4194 active members, 604 responded (14.40%). 69% met burnout threshold, with high levels of emotional exhaustion and depersonalization, and low personal accomplishment. 81% reported \"being at the end of their rope\", 74% felt emotionally drained, and 65% felt used up daily. Nearly all maintained caring about what happened to their patients (96%), easily understanding how their patients feel (84.3%) and being capable of dealing effectively with their patient\'s problems (87.6%). However, respondents never, rarely, or occasionally felt energetic (77.5%) or experienced a sense of professional accomplishment (57.8%). The overall QOL score was 69/100, with lower Mental than Physical scores (62.69 (SD 10.20) vs.77.27 (SD 22.24)). More than half of respondents met depression criteria. While 77% supported they would become a physician again, less than half would choose surgery again or recommend surgery to their children. Furthermore, less than a third felt work allowed sufficient time for their personal lives.
CONCLUSIONS: Participating SAGES surgeons reported alarmingly high rates of burnout and depression. Despite experiencing emotional exhaustion and depersonalization, they maintained a strong commitment to patient care. These findings likely reflect the broader state of surgeons, underscoring the urgent need for action to address this critical issue.

OBJECTIVE: Monitoring resident trainees\' patient outcomes is essential to improving surgical performance; however, resident-specific follow-up is rarely provided in the current surgical training environment. Whether there is a correlation between individual resident\'s surgical performance and patients\' clinical outcomes remains undefined. In this study, we aimed to use risk-adjusted patient outcomes as an educational tool to track individual surgical trainee performance.
METHODS: American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) appendectomy and partial colectomy operations (2013-2021) were examined. Residents performing ≥25 operations were included. The primary outcome was ACS NSQIP-defined morbidity adjusted using estimated probability of morbidity. Observed-to-expected ratios (O/E) of morbidity measured overall performance and risk-adjusted cumulative sum (RA-CUSUM) methodology represented surgical resident\'s performance over time.
METHODS: Academic quaternary care institution.
METHODS: Highest-ranking surgical resident participating in an operation and included in Quality In-Training Initiative.
RESULTS: A total of 449 operations were examined. 12 residents performed 343 appendectomy operations. 7 residents (29.3 ± 5.1 operations each) did not have any postoperative morbidity and demonstrated better-than-expected patient outcomes. Three residents did not have morbidity after their seventh/eleventh/fifteenth appendectomies. Two residents (case volume 29, 33) had an O/E ratio > 3. Partial colectomy (n = 106) performed by 4 residents had 2 residents (case volume 30, 26) with better-than-expected outcomes and 2 with worse-than-expected (case volume 25, 25).
CONCLUSIONS: Longitudinal monitoring of postoperative patient outcomes provides an opportunity for trainee self-reflection and system examination. RA-CUSUM methodology offers sequential monitoring allowing for early evaluation and intervention when RA-CUSUM results for a trainee demonstrate higher-than-expected morbidity.

BACKGROUND: Level-I and level-II trauma centers are required to offer equivalent resources since \"The Orange Book.\" This study evaluates differences between level-I and level-II management of solid organ injury (SOI) with traumatic brain injury (TBI).
METHODS: We conducted a retrospective review of the National Trauma Data Banks from 2013 to 2021 of adult (≥18 years), blunt trauma patients with both TBI and SOI treated at level-I or level-II trauma centers.
RESULTS: 48,479 TBI and SOI patients were identified, 32,611 (67.3%) at level-I centers. Unadjusted incidence of laparotomy was higher at level I (14.5% vs 11.7%, P < 0.001), and angiography rates were similar (3.3% vs 3.4%, P 0.717). Sub-group analysis of stable patients (SBP ≥100) showed an increase in nonoperative management at level II (87.3% vs 88.7%, P < 0.001) and decrease in laparotomy (9.9% vs 8.3%, P < 0.001). On logistic regression (LR), severe TBI, high-grade SOI, and level I trauma status were predictors of laparotomy. Logistic regression showed mild/moderate TBI with high-grade SOI and level II were associated with use of angiography. Unadjusted mortality rates were slightly different (14.8% vs 13.4%, P < 0.001), but there was no association with trauma level on LR.
CONCLUSIONS: Nonoperative management was seen more at level-II centers with laparotomy at level I. Subgroup analysis showed no difference in mortality in trauma levels. Matched patients for level I and II showed no statistical difference in management. Patients were treated similarly at both levels with similar outcomes and mortality.

Background: Unplanned readmission to intensive care units (UR-ICU) in trauma is associated with increased hospital length of stay and significant morbidity and mortality. We identify independent predictors of UR-ICU and construct a nomogram to estimate readmission probability. Materials and Methods: We performed an IRB-approved retrospective case-control study at a Level I trauma center between January 2019 and December 2021. Patients with UR-ICU (n = 175) were matched with patients who were not readmitted (NR-ICU) (n = 175). Univariate and multivariable binary linear regressionanalyses were performed (SPSS Version 28, IBM Corp), and a nomogram was created (Stata 18.0, StataCorp LLC). Results: Demographics, comorbidities, and injury- and hospital course-related factors were examined as potential prognostic indicators of UR-ICU. The mortality rate of UR-ICU was 22.29% vs 6.29% for NR-ICU (P < .001). Binary linear regression identified seven independent predictors that contributed to UR-ICU: shock (P < .001) or intracranial surgery (P = .015) during ICU admission, low hematocrit (P = .001) or sedation administration in the 24 hours before ICU discharge (P < .001), active infection treatment (P = .192) or leukocytosis on ICU discharge (P = .01), and chronic obstructive pulmonary disease (COPD) (P = .002). A nomogram was generated to estimate the probability of UR-ICU and guide decisions on ICU discharge appropriateness. Discussion: In trauma, UR-ICU is often accompanied by poor outcomes and death. Shock, intracranial surgery, anemia, sedative administration, ongoing infection treatment, leukocytosis, and COPD are significant risk factors for UR-ICU. A predictive nomogram may help better assess readiness for ICU discharge.

BACKGROUND: The Geriatric Surgery Verification Program (GSV) was developed to address perioperative care for patients ≥75 years, with a goal of improving outcomes and functional abilities after surgery. We sought to evaluate preoperative factors that place patients at risk for inability to return home (ie, discharge to a facility).
METHODS: Retrospective review of patients ≥75 years old who underwent inpatient surgery from January 2018 to December 2022 at a referral Veterans Administration Medical Center enrolled in the GSV program. Preoperative factors included fall history, mobility aids, housing status, function, cognition, and nutritional status. Postoperative outcomes were discharge designations as home and home with services compared to a facility (skilled nursing facility and acute rehab). Exclusion criteria included preoperative facility residence, cardiac surgery, hospital transfer, postoperative complications, hospice discharge, or in-hospital mortality.
RESULTS: 605 patients met inclusion criteria and 173 (29%) excluded as above. Of the remaining 432 patients, mean age was 79 ± 5 and the majority were male, 426 (99%). The majority of patients were discharged home, 388 (90%), compared to a facility, 44 (10%). Patients with a fall history (OR: 2.95, 95% CI: 1.56, 5.57), utilizing a mobility aid (OR: 6.0, 95% CI: 2.8, 12.83), were partial or totally dependent (OR: 4.83, 95% CI: 2.29, 10.17), or who lived alone (OR: 2.57, 95% CI: 1.08, 6.07) had higher rates of discharge to a facility.
CONCLUSIONS: Preoperative mobility compromise and functional dependence are associated with higher rates of discharge to a facility. These preoperative factors are possibly modifiable with multidisciplinary care teams to decrease risks of facility placement.

Our careers as surgeons are some of the busiest and perhaps most sought after in existence. We have all put in countless years of tenacious effort, at times blood, frequent sweat, and occasional tears, to have the privilege to care for others and correct their ailments. Many of us are like freight trains rolling down the tracks indefinitely. But all too often we finish our training and head down those tracks without considering what stops we should make along the way, which forks in the tracks we should consider taking, and perhaps most often, we do not consider how we are going to eventually stop the train. Most of us have been witness to colleagues who keep working beyond their prime, be it for lack of alternative opportunities, lack of hobbies to retire to, or for lack of insight into their own decline. From these observations was born this presidential panel. As you can see, it is a collection of past presidents of So Cal ACS, with the exception for Dr Freischlag (who we all know would have served as president at some point had she never relocated away from Southern California). Each of these speakers has unique experience from their own careers that they will share with us so we can take pause and consider their insights and wisdom for how to navigate a successful and satisfying career.

At Cleveland clinic, an incorrect surgical count triggers Code Rust; a protocol that mandates an intraoperative patient X-ray, staff radiology read, and discussion with the surgeon before the incision is closed. Code Rust calls from November 2014 to December 2022 were retrospectively reviewed. Realtime workflow and operative details of Code Rust cases were analyzed.1277 Code Rusts were identified. Average time from ordering the X-ray to final radiology report was 50 minutes, totalling $2,362,450.00 spent on operating room time. Code Rust was called twice as frequently during urgent or emergent cases, compared to elective. There were more staff in Code Rust rooms compared to non-Code Rust rooms. A foreign body on X-ray was identified in 42/1277 (3.3%) cases. Code Rust is a resource intensive process that is more common in emergent cases that involve multiple staff. While retained foreign bodies are identified in a small percentage of cases, the current system should be revisited to reduce operating time and expense.

Health care performance metrics are offered predominantly in terms of outcomes, processes, or structural components of health care delivery. However, measurement is limited by variability in data sources, definitions, and workarounds. The American College of Surgeons has recently developed a new type of performance metric known as a \"programmatic measure\". These metrics align structures, processes, and outcomes to better coordinate quality measurement with support of frontline care teams. In this multifaceted way, these measures differ from current \"single\" measures such as targeting surgical site infection. The thematic focus of these measures and alignment of structure-resource components to support processes and outcomes also sets these measures apart from contemporary composite measures. Importantly, structural elements of these measures reflect minimum resources required for patient care, addressing staffing and resource barriers felt by local institutions in addressing numerous existing quality metrics. These metrics will streamline quality reporting to improve care navigation for patients. Clinicians will find more appropriately aligned goals and responsibilities, resulting in increased teamwork and communication. These measures are designed to address the current burdens of overabundant metrics, priority misalignment, and low resources in a patient-centric fashion to better align health care quality and measurement.

Artificial intelligence (AI) will power many of the tools in the armamentarium of digital surgeons. AI methods and surgical proof-of-concept flourish, but we have yet to witness clinical translation and value. Here we exemplify the potential of AI in the care pathway of colorectal cancer patients and discuss clinical, technical, and governance considerations of major importance for the safe translation of surgical AI for the benefit of our patients and practices.

OBJECTIVE: Cumulative, probability-based metrics are regularly used to measure quality in professional sports, but these methods have not been applied to health care delivery. These techniques have the potential to be particularly useful in describing surgical quality, where case volume is variable and outcomes tend to be dominated by statistical \"noise.\" The established statistical technique used to adjust for differences in case volume is reliability-adjustment, which emphasizes statistical \"signal\" but has several limitations. We sought to validate a novel measure of surgical quality based on earned outcomes methods (deaths above average [DAA]) against reliability-adjusted mortality rates, using abdominal aortic aneurysm (AAA) repair outcomes to illustrate the measure\'s performance.
METHODS: Earned outcomes methods were used to calculate the outcome of interest for each patient: DAA. Hospital-level DAA was calculated for non-ruptured open AAA repair and endovascular aortic repair (EVAR) in the Vascular Quality Initiative database from 2016 to 2019. DAA for each center is the sum of observed - predicted risk of death for each patient; predicted risk of death was calculated using established multivariable logistic regression modeling. Correlations of DAA with reliability-adjusted mortality rates and procedure volume were determined. Because an accurate quality metric should correlate with future results, outcomes from 2016 to 2017 were used to categorize hospital quality based on: (1) risk-adjusted mortality; (2) risk- and reliability-adjusted mortality; and (3) DAA. The best performing quality metric was determined by comparing the ability of these categories to predict 2018 to 2019 risk-adjusted outcomes.
RESULTS: During the study period, 3734 patients underwent open repair (106 hospitals), and 20,680 patients underwent EVAR (183 hospitals). DAA was closely correlated with reliability-adjusted mortality rates for open repair (r = 0.94; P < .001) and EVAR (r = 0.99; P < .001). DAA also correlated with hospital case volume for open repair (r = -.54; P < .001), but not EVAR (r = 0.07; P = .3). In 2016 to 2017, most hospitals had 0% mortality (55% open repair, 57% EVAR), making it impossible to evaluate these hospitals using traditional risk-adjusted mortality rates alone. Further, zero mortality hospitals in 2016 to 2017 did not demonstrate improved outcomes in 2018 to 2019 for open repair (3.8% vs 4.6%; P = .5) or EVAR (0.8% vs 1.0%; P = .2) compared with all other hospitals. In contrast to traditional risk-adjustment, 2016 to 2017 DAA evenly divided centers into quality quartiles that predicted 2018 to 2019 performance with increased mortality rate associated with each decrement in quality quartile (Q1, 3.2%; Q2, 4.0%; Q3, 5.1%; Q4, 6.0%). There was a significantly higher risk of mortality at worst quartile open repair hospitals compared with best quartile hospitals (odds ratio, 2.01; 95% confidence interval, 1.07-3.76; P = .03). Using 2016 to 2019 DAA to define quality, highest quality quartile open repair hospitals had lower median DAA compared with lowest quality quartile hospitals (-1.18 DAA vs +1.32 DAA; P < .001), correlating with lower median reliability-adjusted mortality rates (3.6% vs 5.1%; P < .001).
CONCLUSIONS: Adjustment for differences in hospital volume is essential when measuring hospital-level outcomes. Earned outcomes accurately categorize hospital quality and correlate with reliability-adjustment but are easier to calculate and interpret. From 2016 to 2019, highest quality open AAA repair hospitals prevented >40 perioperative deaths compared with the average hospital, and >80 perioperative deaths compared with lowest quality hospitals.