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BackgroundPatients with trauma have a high predisposition for readmission after discharge. Unplanned solicitation of medical services is a validated quality of care indicator and is associated with considerable economic costs. While the existing literature emphasizes the severity of the injury, there is heterogeneity in defining preinjury health status. We evaluate the validity of the American Society of Anesthesiologists (ASA) Physical Status score as an independent predictor of readmission and compare it to the Charlson Comorbidity Index (CCI).MethodsThis is a single center, retrospective cohort study based on adult patients (>18 years of age) with trauma admitted to the Ottawa Hospital from January 1, 2004 to November 1, 2014. A multivariate logistic regression model is used to control for confounding and assess individual predictors. Outcome is readmission to hospital within 30 days, 3 months and 6 months.ResultsA total of 4732 adult patients were included in this analysis. Readmission rates were 6.5%, 9.6% and 11.8% for 30 days, 3 months and 6 months, respectively. Higher preinjury ASA scores demonstrated significantly increased risk of readmission across all levels in a dose-dependent manner for all time frames. The effect of preinjury ASA scores on readmission is most striking at 30 days, with patients demonstrating a 2.81 (1.88–4.22, P<0.0001), 3.59 (2.43–5.32, P<0.0001) and 7.52 (4.72–11.99, P<0.0001) fold odds of readmission for ASA class 2, 3 and 4, respectively, as compared with healthy ASA class 1 patients. The ASA scores outperformed the CCI at 30 days and 3 months.ConclusionsThe preinjury ASA score is a strong independent predictor of readmission after traumatic injury. In comparison to the CCI, the preinjury ASA score was a better predictor of readmission at 3 and 6 months after a major traumatic injury.Level of EvidencePrognostic and Epidemiological Study, Level III.

Within a matter of 48 hours, the promotion of the article entitled "Prevalence of unprofessional social media content among young vascular surgeons," aptly demonstrated the power of social media and the dangers of unconscious bias as it spread across Twitter with the #MedBikini tag. In response, vascular surgeons from around the world have come together in a call to action to address the article and highlight the misogynistic, racist, and oppressive issues facing young surgeons today. We, as female vascular surgery trainees, would like to make our own call to action. The publication of this article (now appropriately retracted) has encouraged important dialogue among female vascular surgeons, male colleagues who support #HeforShe initiatives, other disadvantaged and marginalized groups in surgery, and the future generation of surgeons who will pave the path forward. We have converged to discuss the current climate of our specialty and have determined that now is an opportunity for change.It is essential that we pursue ethics, as well as excellence, in surgical practice and research. The inherent conscious and unconscious biases, poor study design, and unethical data collection methods within the article have demonstrated a critical flaw within the editorial process of the Journal of Vascular Surgery (JVS). We are disappointed to find ourselves represented by the article. The publication was both tone deaf toward, and discriminatory against, us as professionals, trainees, and women. As vascular surgeons, we must hold ourselves to a higher standard. Our call to action for the JVS includes the following:1. Re-examine the review process for publication of ethical abstracts from regional and national meetings and manuscripts, and provide training in ethical research for all editors and reviewers.

Objective: The objective of this study was to determine the ability of current risk estimation models to predict operative mortality in patients undergoing elective aortic surgery. We hypothesize that perioperative events are more likely to predict 30-day mortality compared with these risk estimation models that incorporate only preoperative comorbidity variables.Methods: All patients who underwent elective abdominal aortic procedures (open and endovascular) between 2002 and 2016 were included in this single-center cohort study. Emergent and urgent procedures were excluded. Data were collected on patient demographics, comorbidities, intraoperative course, postoperative course, and 30-day mortality. Matched pairs survivor sampling with a 2:1 ratio was used (two survivors matched to one death by sex, age, and procedure type). Risk estimation model scores (Vascular Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity, British Aneurysm Repair [BAR], Glasgow Aneurysm Score, and Revised Cardiac Risk Index) were calculated and analyzed alongside perioperative factors and CLASSIC grade (severity classification for intraoperative adverse events). Multiple logistic regression with adjustments for covariates was used to assess the relationship between predictors and outcome.Results: A total of 2596 elective procedures were performed during the study period (open, 57.6%; endovascular, 38.9%; advanced endovascular, 1.7%; other, 1.8%). Overall 30-day mortality was 2.0% (n ¼ 53). There was a disproportionate number of deaths in female patients compared with the overall cohort (45.3% vs 21.5%; P < .0001). Intraoperative factors significantly predicted 30-day mortality, including operative time (P ¼ .036), proximal aortic clamp level (P ¼ .001), estimated blood loss (P ¼ .016), CLASSIC grade (P ¼ .0001), and postoperative reintervention rate (P < .00001). The BAR risk model had reasonable ability (C statistic, 0.76) to predict 30-day mortality risk. The other models (Vascular Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity: C statistic, 0.69; Revised Cardiac Risk Index: C statistic, 0.60; Glasgow Aneurysm Score: C statistic, 0.58) performed relatively poorly. A custom risk assessment model including preoperative factors (functional status and smoking history) demonstrated a C statistic of 0.85; however, with the inclusion of intraoperative factors (number of blood transfusions and proximal clamp time), the accuracy of the model greatly increased (C statistic, 0.98).Conclusions: With the exception of BAR, current risk prediction models do not predict 30-day mortality as well as reported in the literature. Although they are not available in preoperative decision-making, intraoperative and postoperative adverse events are significant in predicting 30-day mortality. Creating a risk prediction model that incorporates perioperative events may improve identification of at-risk patients during the postoperative course.