American College Of Surgeons Research Articles

Ensuring goal-aligned care in a busy surgical practice

The US population is aging. Accordingly, older adults with multiple comorbidities and geriatric syndromes will present in increasing numbers for surgical consultation. Incorporating patient goals into surgical treatment decisions is recommended by a joint guideline from the American Geriatrics Society and American College of Surgeons, the American Society of Colon and Rectal Surgeons, the Geriatric Surgery Verification program, and the Institute for Healthcare Improvement as part of building Age-Friendly Health Systems, however uptake has been slow. The aim of this review is to provide a framework for how patient goals can be elicited and used in real-world surgical practices to achieve goal-aligned care.

SUSTAINED POSITIVE PRACTICE CHANGE AFTER TARGETED EDUCATION IN THE MANAGEMENT OF OVARIAN TORSION

BackgroundFailure to perform ovarian conservation surgery (OCS) for patients who present with adnexal torsion was recognized as a practice gap by the American Pediatric Surgical Association (APSA) in 2017. A targeted educational campaign was conducted to promote detorsion and ovarian conservation surgery as the standard of care for ovarian torsion. We evaluated the practice change that has occurred for pediatric and adolescent patients with ovarian torsion from 2012-2022. MethodsUsing the American College of Surgeons (ACS) National Safety and Quality Improvement Project-Pediatrics (NSQIP-P) Participant Use Files from 2012-2022, we compared surgical treatment types during the time periods that encompassed the targeted educational period. ResultsA total of 2,249 patients (70%) underwent OCS surgery and 980 (30%) patients underwent salpingectomy and/or oophorectomy (SO). Patients who were treated with OCS were older (12.4 [SD 3.6] vs. 11.4 [SD 4.8] years, p < 0.01). Patients were more likely to be treated with SO if they were Black (11 vs. 15%, p<0.01) or obese (28 vs. 33%, p<0.01). From 2012-2017, 42% of patients underwent OCS, compared to 76% in 2018-2022 (p <0.01). A mixed effect analysis comparing year-over-year rates of SO across pediatric surgeons and gynecologic surgeons showed a statistically significant difference in rates across specialty and time (p<0.01), with pediatric surgeons performing fewer SOs. ConclusionIn the treatment of ovarian torsion in pediatric and adolescent patients, we demonstrated successful adoption and sustained implementation of practice improvement coincident with the introduction of a comprehensive educational initiative. TYPE OF STUDYRetrospective cohort LEVEL OF EVIDENCEIV

A Century of Surgical Quality: Origins, Evolution, and Future Directions.

Over a century has passed since Ernst A. Codman's pioneering call for surgeons to take open responsibility for patient care, a concept integral to the emergence and leadership of the American College of Surgeons (ACS). Codman's End Result Idea, originating in the early 20th century, laid the groundwork for professional responsibility and accountability in surgical practice, catalyzing the formation of the ACS. His innovative use of the "end result" technique at Massachusetts General Hospital highlighted significant variability in surgical outcomes, spurring debates on specialization and accountability. The ACS, under John Bowman's leadership, aimed to ensure optimal care through defined standards and verification mechanisms. Codman's Bone Sarcoma Registry, initiated in 1920, marked an early attempt at quality assessment and improvement through data collection. Despite facing resistance, Codman's vision laid the foundation for modern quality initiatives in surgical care. ACS programs, spanning trauma care to cancer treatment and beyond, have significantly enhanced patient outcomes while reducing costs. Looking forward, advancing surgical quality requires measuring quality, leveraging trusted data, embracing change management, fostering collaboration, and empowering specialists. The future of surgical care depends on collective efforts to uphold standards that ensure optimal care for all.

Comparison of Emergency Department Disposition Times in Adult Level I and Level II Trauma Centers

Introduction: The efficient utilization of resources is a crucial aspect of healthcare, particularly in both Level I and Level II American College of Surgeons (ACS)-verified trauma centers. The effect of resource allocation on emergency department length of stay (ED-LOS) of trauma patients has remained under-investigated. As ED crowding has become more prevalent, especially at quaternary care centers, an evaluation of the potential disparities in ED-LOS between Level I and Level II trauma centers is warranted. We hypothesized a longer ED-LOS at Level I centers compared to Level II centers. Methods: We queried the 2017–2021 Trauma Quality Improvement Process (TQIP) database for trauma patients ≥18 years of age presenting to either a Level-I or -II center. The TQIP defines ED-LOS as the time from arrival until the time an ED disposition (admission or discharge) order is written. We excluded transferred patients and those with missing data regarding ACS trauma center verification level. We performed bivariate analyses, as well as subgroup analyses based on location of disposition. Results: Of 2,225,067 trauma patients, 59.3% (1,318,497) received treatment at Level I centers. No significant differences were found in Injury Severity Scores between patients admitted to the operating room or non-intensive care unit (ICU) locations, or discharged home from Level-I and -II centers (all P &amp;lt; 0.05). The ED-LOS for trauma patients was longer at Level-I centers for all patient categories: overall (198 vs 145 minutes [min], P &amp;lt; 0.001), discharged home (286 vs 160 min, P &amp;lt; 0.001), non-ICU admissions (234 vs 164 min, P &amp;lt; 0.001), and those requiring surgery (126 vs 101 min, P &amp;lt; 0.001). Conclusion: Even when treating patients with similar injury severity, trauma patients at Level I trauma centers had longer ED-LOS compared to Level II centers, irrespective of the patients’ final disposition (surgery, non-ICU admission, or discharge). To optimize resource utilization and alleviate ED saturation, further research must delve into the underlying causes of these discrepancies to identify best practices and solutions.

Survival and Treatment Patterns in Stage II to III Esophageal Cancer

Existing clinical trials favor neoadjuvant chemoradiation therapy (NCRT) followed by surgery alone for locally advanced esophageal cancer (EC) and perioperative chemotherapy as the preferred modality for esophageal adenocarcinoma (EAC). However, it is unclear whether these trial findings are reflected in the patterns of care and survival outcomes among patients in the clinical setting. To investigate survival outcomes in the clinical setting among patients with EC after various treatment modalities. This retrospective cohort study examined data from the National Cancer Database maintained by the American College of Surgeons and focused on patients with clinical stage II or III EC, excluding those with gastroesophageal junction cancer, who underwent trimodality therapy (NCRT followed by esophagectomy), definitive chemoradiation therapy (DCRT), radiotherapy (RT) alone, or perioperative chemotherapy from January 2006 to December 2020. Analyses were conducted from December 2023 to August 2024. Perioperative chemotherapy, trimodality therapy, DCRT, and single-modality RT. A Cox proportional hazards regression model was used to compare overall survival (OS) between treatment groups in the entire cohort, among patients with squamous cell carcinoma or adenocarcinoma, and among those older than 65 years. Landmark survival analysis at 6 months was performed to reduce survivorship bias. The study included 57 116 patients (median age, 64 [IQR, 57-72] years; 45 410 [79.5%] male); 21 619 patients (37.9%) received trimodality therapy, 32 493 (57.1%) received DCRT, 2692 (4.7%) received single-modality RT, and 312 (0.5%) received perioperative chemotherapy. In the overall study population, 37 698 patients (66.0%) had EAC, and of the 312 patients that received perioperative chemotherapy, 283 (90.7%) had EAC. In adjusted survival analysis, perioperative chemotherapy (adjusted hazard ratio [AHR], 0.33; 95% CI, 0.28-0.39; P <.001) and trimodality therapy (AHR, 0.45; 95% CI, 0.44-0.46; P < .001) were associated with improved OS compared with DCRT. In contrast, RT alone was associated with worse outcomes compared with DCRT (AHR, 1.37; 95% CI, 1.30-1.45; P < .001). The median OS for perioperative chemotherapy of 66.2 months (95% CI, 43.1-111.9 months; P < .001) was longer compared with that for DCRT alone (18.1 months; 95% CI, 17.8-18.4 months; P < .001). Trimodality therapy was associated with a median OS of 43.9 months (95% CI, 42.8-45.5 months; P < .001), which was shorter than that for perioperative chemotherapy but improved compared with DCRT and RT alone, which was associated with a median OS of 13.5 months (95% CI, 12.8-14.0 months; P < .001). In the subgroup of patients older than 65 years, those who received perioperative chemotherapy had longer median OS (56.7 months; 95% CI, 36.4-115.2 months; P < .001) compared with those receiving other treatment modalities (eg, trimodality therapy: 40.1 months; 95% CI, 38.1-42.0 months; P < .001). Patients who received RT alone had the worst median OS (13.6 months; 95% CI, 12.8-14.4 months; P < .001). In this cohort study of patients with stage II to III EC, trimodality therapy was associated with improved OS compared with DCRT or RT alone for locally advanced EC and perioperative chemotherapy was associated with improved OS for adenocarcinoma.

How Well Are Surgical Quality Improvement Projects Planned? Review of 242 Surgical Improvement Efforts Across Five American College of Surgeons Quality Programs.

Structured preparation is necessary to conduct quality improvement (QI) strategies that are relevant to the problem, feasible, appropriately resourced, and potentially effective. Recent work suggests improvement efforts are sub-optimally conducted. Our goal was to determine how well preparation for surgical QI is undertaken, including detailing the problem, setting project goals, and planning an intervention. This retrospective cross-sectional study included QI efforts submitted in 2019 to the American College of Surgeons (ACS) during review for accreditation across five ACS Quality Programs: Children's Surgery Verification (CSV), the Commission on Cancer (CoC), the Metabolic and Bariatric Surgical Accreditation and Quality Improvement Program (MBSAQIP), the National Accreditation Program for Breast Centers (NAPBC), and the Trauma Verification Program. Projects were scored for alignment with three components of the pre-conduct phase of the ACS Quality Framework: problem detailing, goal specification, and strategic planning. 242 projects satisfied inclusion criteria and were scored. Most projects in the final cohort were from MBSAQIP (36%), CoC (31%), or NAPBC (29%) programs. The average overall pre-conduct score was 52% (Standard Deviation (SD)=17). On average, projects performed best in the 'Goal Specification' component (65% SD=27), followed by 'Problem Detailing' (52% SD=16), and 'Strategic Planning' (44% SD=25). Within these components, identification of possible limitations (5%) and consideration of contextual issues (12%) were among the least frequently reported items. Thorough planning is a critical component of effective QI, and our study reflects significant opportunity for its improvement. The ACS Quality Framework may serve as a guide to improve QI planning, thereby promoting efficiency and effectiveness of these efforts.

American College of Surgeons: Owen H Wangensteen Scientific Forum Excellence in Research Awards 2024

American College of Surgeons: Owen H Wangensteen Scientific Forum Excellence in Research Awards 2024

The MOST (Mortality Score for TBI): A Novel Prediction Model Beyond CRASH-Basic and IMPACT-Core for Isolated Traumatic Brain Injury

BackgroundDue to significant injury heterogeneity, outcome prediction following traumatic brain injury (TBI) is challenging. This study aimed to develop a simple model for high-accuracy mortality risk prediction after TBI. Study DesignData from the American College of Surgeons (ACS) Trauma Quality Program (TQP) from 2019 to 2021 was used to develop a summary score based on age, the Glasgow Coma Scale (GCS) component subscores, and pupillary reactivity data. We then compared the predictive accuracy to that of the Corticosteroid Randomisation After Significant Head Injury Trial (CRASH)-Basic and International Mission for Prognosis and Analysis of Clinical Trial in TBI (IMPACT)-Core models. Two separate series of sensitivity analyses were conducted to further assess our model's generalizability. We evaluated predictive performance of the models with discrimination [the area under the receiver-operating characteristic curves (AUC), sensitivity, specificity] and calibration (Brier score). Discriminative ability was compared with DeLong tests. Results259,404 patients were included in the present study (mean age, 60 years; 93,495 (36%) female). The mortality score after TBI (MOST) model (AUC = 0.875) had better discrimination (DeLong test p values < 0.00001) than CRASH-Basic (AUC = 0.837) and IMPACT-Core (AUC = 0.821) models, and superior calibration (MOST = 0.02729, CRASH-Basic = 0.02962, IMPACT-Core = 0.02962) in predicting in-hospital mortality. The MOST model similarly outperformed in predicting 3-, 7-, 14-, and 30-day mortality. ConclusionThe MOST model can be rapidly calculated and outperforms two widely used models for predicting mortality in TBI patients. It utilizes a larger, contemporaneous dataset that reflects modern trauma care.

Are new pediatric trauma centers located close to the high-risk populations? A geolocation study.

Higher rates of injury occur in children who live in low socioeconomic areas. Since 2010, the number of verified Level I and Level II pediatric trauma centers (PTCs) has doubled. The purpose of this study is to look at the location of new verified PTC in relation to children living in high-risk areas. Historical and recent data about verified Level I and Level II PTCs were obtained from the American College of Surgeons (ACS), the American Trauma Society, and State Data where available. Census data were obtained from the US Census Bureau's American Community Survey for 2010 and 2020 for children younger than 18 years. The pediatric population was stratified by (1) poverty threshold and (2) living within 30 miles of PTC. The census data and trauma center locations were geocoded using the ArcGIS Pro program. Data analysis was performed at the census tract level. A total of 55 Level I or Level II ACS-verified PTCs were in the United States in 2010 and 116 in 2020. In 2010, there were 14.5 million children (19.6%) below the poverty threshold and 12.9 million (17.8%) in 2020. In 2010, 23 states did not have an ACS-verified PTC either in the state or in a neighboring state within 30 miles. By 2020, only nine states did not have an ACS-verified PTC or a center in a neighboring state within 30 miles. When state level data were included, this dropped to four. The 19 states who had new verified PTCs covered between 14% and 21% of children below the poverty level. The percentage of children below the poverty threshold and less than 30-mile access to a Level I or Level II PTC in 2010 was 74.56% compared with children above poverty and less than 30-mile access, which was 70.34% (p < 0.0001). In 2020, children below poverty without 30-mile access had been reduced to 52% and 45.54% for children above poverty level (p < 0.0001). Thus, there was a greater increase in 30-mile access for children above the poverty level than for those below the poverty level (24.8% vs. 22.6%, p < 0.001). On a national level, despite doubling the number of PTC, it has not improved 30-mile access for high-risk children. Alternatively, 19 states now have better access. Retrospective Cohort Study, Level III.

Scoping it Out: The Use of Laparoscopy After Penetrating Trauma in Stable Children

IntroductionIn stable children with penetrating abdominal trauma, literature regarding the use of laparoscopy (LAP) remains limited. Given increasing evidence in favor of LAP for selective adult trauma patients, we reviewed contemporary practices and outcomes in pediatric trauma patients. MethodsThe American College of Surgeons (ACS) Trauma Quality Programs data was utilized to identify children (<18 years) from 2016 to 2021 with a penetrating abdominal injury who underwent surgery within 24 h of admission. Patients with non-abdominal abbreviated injury score (AIS) ≥3, Glasgow Coma Scale (GCS) < 13, or instability using a shock index pediatric adjusted (SIPA) cutoff were excluded. Patients were compared based on whether they had LAP, open, or laparoscopic converted to open (LCO) exploration. Primary outcomes were length of stay (LOS) and complications, including missed injuries. ResultsAmong 1945 patients who underwent abdominal surgery for penetrating trauma, 32% were stabbed and 68% had gunshot wounds (GSW). LAP occurred in 235 (12%) and LCO in 145 (7.4%) patients. The proportion of LAP did not change over the study period. LAP was used more in patients presenting with stab wounds than GSW (24 vs. 7%, p < .01). There was higher use of LAP at pediatric centers (p < .01). Although there was no difference in mortality or complications by operative type, open patients were more severely injured, had a longer LOS (p < .01), and had more subsequent procedures (11% of open, 2 % LAP, 7% LCO, p < .01). ConclusionIn stable children with penetrating abdominal trauma, LAP is most often utilized after stabbing and at pediatric trauma centers, but its overall use remains uncommon. LAP patients had lower rates of subsequent procedures and no increase in unexpected operations suggesting low rates of missed injuries. Criteria are needed to identify stable patients presenting after penetrating trauma who can benefit from a LAP approach. Level of EvidenceIII.

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator. Data from 365,240 patients who underwent a primary TKA between 2013 and 2020 were extracted from the ACS-National Surgical Quality Improvement Program database and used to develop an artificial neural network (ANN) to predict discharge disposition following primary TKA. The ANN and ACS calculator were assessed and compared using discrimination, calibration and decision curve analysis. Age (>68 years), BMI (>35.5 kg/m2) and ASA Class (≥2) were found to be the most important variables in predicting NHD following TKA. When compared to the ACS calculator, the ANN model demonstrated a significantly superior ability to distinguish the area under the receiver operating characteristic curve (AUC) among NHD patients and provided probability predictions well aligned with the true outcomes (AUCANN = 0.69, AUCACS = 0.50, p = 0.002, slopeANN = 0.85, slopeACS = 4.46, interceptANN = 0.04, and interceptACS = 0.06). Our findings support the hypothesis that machine learning models outperform the ACS Risk Calculator in predicting non-home discharge after TKA, even when constrained to the same clinical variables. Our findings underscore the potential benefits of integrating machine learning models into clinical practice for improving preoperative patient risk identification, optimisation, counselling and clinical decision-making. III.

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma.

Prehospital trauma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines proposed by the American College of Surgeons have proven to be relatively insensitive when identifying severe traumas. This study aimed to build a prehospital triage model to predict severe trauma and enhance the performance of the national field triage guidelines. This was a multisite prediction study, and the data were extracted from the National Trauma Data Bank between 2017 and 2019. All patients with injury, aged 16 years of age or older, and transported by ambulance from the injury scene to any trauma center were potentially eligible. The data were divided into training, internal, and external validation sets of 672,309; 288,134; and 508,703 patients, respectively. As the national field triage guidelines recommended, age, 7 vital signs, and 8 injury patterns at the prehospital stage were included as candidate variables for model development. Outcomes were severe trauma with an Injured Severity Score ≥16 (primary) and critical resource use within 24 hours of emergency department arrival (secondary). The triage model was developed using an extreme gradient boosting model and Shapley additive explanation analysis. The model's accuracy regarding discrimination, calibration, and clinical benefit was assessed. At a fixed specificity of 0.5, the model showed a sensitivity of 0.799 (95% CI 0.797-0.801), an undertriage rate of 0.080 (95% CI 0.079-0.081), and an overtriage rate of 0.743 (95% CI 0.742-0.743) for predicting severe trauma. The model showed a sensitivity of 0.774 (95% CI 0.772-0.776), an undertriage rate of 0.158 (95% CI 0.157-0.159), and an overtriage rate of 0.609 (95% CI 0.608-0.609) when predicting critical resource use, fixed at 0.5 specificity. The triage model's areas under the curve were 0.755 (95% CI 0.753-0.757) for severe trauma prediction and 0.736 (95% CI 0.734-0.737) for critical resource use prediction. The triage model's performance was better than those of the Glasgow Coma Score, Prehospital Index, revised trauma score, and the 2011 national field triage guidelines RED criteria. The model's performance was consistent in the 2 validation sets. The prehospital triage model is promising for predicting severe trauma and achieving an undertriage rate of <10%. Moreover, machine learning enhances the performance of field triage guidelines.

Survey of pediatric massive transfusion protocol practice at United States level I trauma centers: An AABB Pediatric Transfusion Medicine Subsection study.

Trauma remains the leading cause of pediatric mortality in the United States. Although use of massive transfusion protocols (MTPs) in this population is widespread, optimal pediatric resuscitation is not well established. We sought to assess contemporary pediatric MTP practice in the United States. A web-based survey was designed by the Association for the Advancement of Blood & Biotherapies (AABB) Pediatric Transfusion Medicine Subsection and distributed to select American College of Surgeons (ACS) Level I Verified pediatric trauma centers. The survey assessed current MTP policy, implementation, and recent changes in practice. Response rate was 55% (22/40). Almost half of the respondents were from the South. The median RBC:plasma ratio was 1 (interquartile range 1-1.5). Protocolized fibrinogen supplementation was common while integration of antifibrinolytic therapy into MTPs was infrequent. Viscoelastic testing (VET) was available at most sites, 71% (15/21, one site did not respond), and was generally utilized on an ad-hoc basis. Roughly, a third of sites had changed their MTP in the past 3 years due to blood supply issues, and about a third reported having group O Whole Blood on-site. MTP practice is similar throughout the United States. Though fibrinogen supplementation is common-other emerging interventions such as antifibrinolytic therapy or utilization of routine viscoelastic testing-are not widespread. Pediatric transfusion medicine experts must continue to follow practice change, as contemporary large trials begin to characterize new supportive modalities to optimize resuscitation in pediatric trauma patients.

Predicting Complications in Head and Neck Surgery: Comparing Calculators to Surgeons.

Objectives: Surgical outcomes determine national ranking, reputation, and funding, and are often assessed with objective surgical risk calculators (SRCs). Surgeons' assessments are not considered. This study aims to determine if surgeons or SRCs are more accurate in predicting outcomes. Methods: This prospective cohort study identified a surgeon's assessment on a patient's risk preoperatively. The patient's risk was also calculated using the SRC. Predictions were compared to patient outcomes and to each other to assess whether surgeons or the SRC were more accurate. Results: Of the 101 patients included, 37 (36.6%) experienced a complication of any kind and 18 (17.8%) experienced a serious complication. Smoking resulted in a 2.49 times higher overall complication rate (P = .04). Laryngectomy patients experienced the highest rate of complications (P = .02) compared to those undergoing free flap reconstruction [odds ratio (OR) 0.9] or any other surgery (OR 0.26). Both surgeons and the American College of Surgeons (ACS) tool performed poorly on the prediction of the outcome of any complication, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.51 [95% confidence interval (CI): 0.39-0.62] and 0.58 (95% CI: 0.47-0.70), respectively, which was not statistically significant (P = .34). For the prediction of the outcome of serious complication, the AUC for surgeons and the ACS tool were 0.55 (95% CI: 0.41-0.69) and 0.60 (95% CI: 0.46-0.74), respectively, which was not statistically significant (P = .58). Conclusions: Neither validated risk calculators nor surgeons are accurate in predicting perioperative risk. The only risk factor that contributes to improving predictions for complications is preoperative smoking, although age and type of surgery are also significant predictors. Risk calculators may therefore not be appropriate metrics for assessing hospital performance. These findings can help guide preoperative counseling and may help in the development of more accurate predictive tools as the healthcare field continues to incorporate artificial intelligence into surgical planning.

Within-hospital Temporal Clustering of Postoperative Complications and Implications for Safety Monitoring and Benchmarking Using ACS-NSQIP Data.

To determine the extent to which within-hospital temporal clustering of postoperative complications is observed in the American College of Surgeons, National Surgical Quality Improvement Program (ACS-NSQIP). ACS-NSQIP relies on periodic and on-demand reports for quality benchmarking. However, if rapid increases in postoperative complication rates (clusters) are common, other reporting methods might be valuable additions to the program. This article focuses on estimating the incidence of within-hospital temporal clusters. ACS-NSQIP data from 1,547,440 patients, in 425 hospitals, over a 2-year period was examined. Hospital-specific Cox proportional hazards regression was used to estimate the incidence of mortality, morbidity, and surgical site infection (SSI) over a 30-day postoperative period, with risk adjustment for patient and procedure and with additional adjustments for linear trend, day-of-week, and season. Clusters were identified using scan statistics, and cluster counts were compared, using unpaired and paired t tests, for different levels of adjustment and when randomization of cases across time eliminated all temporal influences. Temporal clusters were rarely observed. When clustering was adjusted only for patient and procedure risk, an annual average of 0.31, 0.85, and 0.51 clusters were observed per hospital for mortality, morbidity, and SSI, respectively. The number of clusters dropped after adjustment for linear trend, day-of-week, and season (0.31-0.24; P = 0.012; 0.85-0.80; P = 0.034; and 0.51-0.36; P < 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. There was 1 significant difference in the number of clusters when comparing data with all adjustments and after data were randomized (0.24 and 0.25 for mortality; P = 0.853; 0.80 and 0.82 for morbidity; P = 0.529; and 0.36 and 0.46 [randomized data had more clusters] for SSI; P = 0.001; using paired t tests) for mortality, morbidity, and SSI, respectively. Temporal clusters of postoperative complications were rarely observed in ACS-NSQIP data. The described methodology may be useful in assessing clustering in other surgical arenas.

Outcomes of Patients With Traumatic Brain Injury Transferred to Trauma Centers

Wide variations exist in traumatic brain injury (TBI) management strategies and transfer guidelines across the country. To assess the outcomes of patients with TBI transferred to the American College of Surgeons (ACS) level I (LI) or level II (LII) trauma centers (TCs) on a nationwide scale. In this secondary analysis of the ACS Trauma Quality Improvement Program database (2017 to 2020), adult patients with isolated TBI (nonhead abbreviated injury scale = 0) with intracranial hemorrhage (ICH) who were transferred to LI/LII TCs we re included. Data were analyzed from January 1, 2017, through December 31, 2020. Outcomes were rates of head computed tomography scans, neurosurgical interventions (cerebral monitors, craniotomy/craniectomy), hospital length of stay, and mortality. Descriptive statistics and hierarchical mixed-model regression analyses were performed. Of 117 651 patients with TBI with ICH managed at LI/LII TCs 53 108; (45.1%; 95% CI, 44.8%-45.4%) transferred from other centers were identified. The mean (SD) age was 61 (22) years and 30 692 were male (58%). The median (IQR) Glasgow Coma Scale score on arrival was 15 (14-15); 5272 patients had a Glasgow Coma Scale score of 8 or less on arrival at the receiving trauma center (10%). A total of 30 973 patients underwent head CT scans (58%) and 2144 underwent repeat head CT scans at the receiving TC (4%). There were 2124 patients who received cerebral monitors (4%), 6862 underwent craniotomy/craniectomy (13%), and 7487 received mechanical ventilation (14%). The median (IQR) hospital length of stay was 2 (1-5) days and the mortality rate was 6.5%. There were 9005 patients (17%) who were discharged within 24 hours and 19 421 (37%) who were discharged within 48 hours of admission without undergoing any neurosurgical intervention. Wide variations between and within trauma centers in terms of outcomes were observed in mixed-model analysis. In this study, nearly half of the patients with TBI managed at LI/LII TCs were transferred from lower-level hospitals. Over one-third of these transferred patients were discharged within 48 hours without any interventions. These findings indicate the need for systemwide guidelines to improve health care resource use and guide triage of patients with TBI.

Beyond American College of Surgeons Verification: Quality Metrics Associated with High Performance at Level I and II Trauma Centers.

The American College of Surgeons (ACS) Committee on Trauma has established a framework for trauma center quality improvement. Despite efforts, recent studies show persistent variation in patient outcomes across national trauma centers. We aimed to investigate whether risk-adjusted mortality varies at the hospital level and if high-performing centers demonstrate better adherence to ACS Verification, Review, and Consultation (VRC) program quality measures. We analyzed data from the 2018-2021 ACS TQIP Participant Use Files, focusing on adult admissions at ACS-verified Level I or II trauma centers for blunt, penetrating, or isolated traumatic brain injury. We used mixed-effects models to assess center-specific risk-adjusted mortality and identified high-performing centers (HPTC), defined as those with the lowest decile of overall risk-adjusted mortality. We compared patient and hospital characteristics, outcomes, and adherence to ACS-VRC quality measures between HPTC and non-HPTC. Over the study period, 1,498,602 patients across 442 Level I and II trauma centers met inclusion criteria: 65.3% presenting with blunt injury, 9.3% with penetrating injury, and 25.4% with isolated TBI. Management at HPTC was associated with lower odds of major complications, failure-to-rescue and takeback. Furthermore, HPTC status was associated with increased odds of adherence to several ACS-VRC quality measures, including balanced resuscitation (Odds Ratio [OR] 1.40, 95%Confidence Interval [CI] 1.29-1.51), appropriate pediatric admissions (OR 1.88, 95%CI 1.07-3.68), and substance abuse screening (AOR 1.14, 95%CI 1.12-1.16). Significant variation in risk-adjusted mortality persists across trauma centers. Given the association between adherence to quality measures and high-performance, multidisciplinary efforts to refine and implement guidelines are warranted.

Derivation and validation of a risk calculator for the prediction of incidence of complications following repair of Achilles Tendon Rupture

BackgroundThe Achilles tendon is the body’s strongest and largest tendon. It is commonly injured, particularly among athletes, accounting for a significant portion of serious tendon injuries. Several factors play a precipitating role in increasing the risk of these injuries.ObjectiveOur objective is to derive and validate a risk calculator for the prediction of incidence of any complication following Achilles tendon repair.MethodsWe used de-identified data from the American College of Surgeons’ National Surgical Quality Improvement Project (NSQIP) database from 2005 to 2021. It comprises 7010 individuals who had undergone Achilles tendon rupture repair. Demographic and risk factors information was collected. To develop the calculator, the sample was divided into a derivation cohort (40%) and a validation cohort (60%). Multivariate logistic regression was used for statistical analysis, and a risk calculator for incidence of any complication was derived from the derivation cohort and validated on the remaining 60% of the sample. Patients with missing data were excluded, and the significance level was set at p < 0.05.ResultsWe analyzed the derivation cohort of 2245 individuals who underwent Achilles tendon repair surgery between 2005 and 2021, with a 5.5% overall complication. Multivariate logistic regression identified anesthesia type, ASA classification, certain co-morbidities (pre-operative dialysis and medication-requiring hypertension), and wound classification as significant predictors of complications. The developed risk calculator model had an area under the curve (AUC) of 0.685 in the derivation cohort and 0.655 in the validation cohort, surpassing the widely used and validated modified frailty index. A cut-off score threshold of 0.06 was established using Youden’s index to dichotomize individuals into low and high risk for developing any postoperative complications.ConclusionOur risk calculator includes factors that most significantly affect the incidence of any complication following Achilles tendon repair.

Bowel Resection Outcomes in Ovarian Cancer Cytoreductive Surgery by Surgeon Specialty

Extensive bowel surgery is often necessary to achieve complete cytoreduction in patients with epithelial ovarian cancer. Regardless of who performs the surgery, it has been well documented that bowel resections are a high-risk procedure and an anastomotic leak is a severe complication that can occur. There are few studies addressing whether surgeon type impacts surgical outcomes in this patient population. To compare surgical outcomes between gynecologic oncologist, general surgeons, and a 2-surgeon team approach for patients with advanced epithelial ovarian cancer who underwent bowel surgery during cytoreductive debulking. This retrospective cohort study used the American College of Surgeons' National Surgical Quality Improvement Program datasets from 2012 through 2020. The aforementioned years of the dataset were analyzed from March 2022 to March 2023 and reanalyzed in May 2024 for quality assurance. Analysis of cytoreductive surgeries performed by a gynecologic oncologist, a general surgeon, or a 2-surgeon team approach for patients with ovarian cancer recorded in National Surgical Quality Improvement Program datasets was included. The 2-surgeon team approach included any combination of the aforementioned surgical specialties. The primary outcome of interest was anastomotic leak after bowel surgery during ovarian cancer debulking. A total of 1810 patients were included in the study; in the general surgery cohort, mean (SD) patient age was 65.1 (11.1) years and mean (SD) body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) was 26.9 (7.4); in the gynecologic oncology cohort, mean (SD) patient age was 63.5 (11.7) years and mean BMI (SD) was 27.7 (6.5); and in the 2-surgeon team cohort, mean (SD) patient age 62.4 (12.1) years and mean (SD) BMI was 28.1 (7.0). Gynecologic oncologists performed 1217 cases (67.2%), general surgery performed 97 cases (5.4%), and 496 cases had 2-surgeon teams involved (27.4%). Bivariate analysis revealed an anastomotic leak rate of 3.6% for gynecologic oncologists, 5.2% for general surgeons, and 0.4% for cases that had 2 surgical teams involved (P < .001). By multivariable analysis, the adjusted odds ratio for anastomotic leak was 1.53 (95% CI, 0.59-3.96) for the general surgeon group (P = .38) vs an adjusted odds ratio of 0.11 (95% CI, 0.03-0.47) for the 2-surgeon team approach (P = .003) with the referent being gynecologic oncology. In this study, the anastomotic leak rate was found to be lower when 2 surgeons participated in the case, regardless of the surgical specialty. These results suggest that team-based care improves surgical outcomes.

A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.

A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.