American Black Authorship Has Decreased Across All Clinical Specialties Despite an Increasing Number of Black Physicians Between 1990 and 2020 in the USA.

PURPOSE: Many diversity, equity, and inclusion initiatives assume that attainment of a racially diverse healthcare workforce will translate to increased diversity elsewhere in the healthcare system (e.g., leadership roles or academic authorship). We sought to investigate these trends over time by examining the evolution of physician demographics in the USA, in concert with demographic changes in US authorship in US medical journals from 1990 to 2020 across 25 specialties. METHODS: We reviewed all articles indexed in PubMed, with a primary author affiliation located in the USA and limited to journals based in the USA, relative to the proportion of medical professionals in the CMS National Provider Registry. We employed a previously peer-reviewed/validated algorithm called "averaging-of-proportions" that probabilistically predicts racial identity from surname using the US Census to assess the relationship between diversity among medical professionals and diversity in medical journal authorship. RESULTS: Data reveals a sharp disconnect between the demographic breakdown of physicians and authors. Despite an increase in the number of Black physicians (from 8.5% in 2005 to 9.1% in 2020), there has been a decrease in Black early-career authorship from 7.2% in 1990 to 5.8% in 2020. The percentage of Black early-career authors across all specialties in 2020 is lower than the average per specialty in 1990. Similar trends were noted for Black senior authorship, decreasing from 7.6% in 1990 to 6.2% in 2020, as well as a plateau in Hispanic authorship over the same time interval despite an increasing number of Hispanic physicians. CONCLUSION: Modest advances in physician diversity have not translated to increased diversity in academic authorship. Increasing diversity requires initiatives focused beyond recruitment of underrepresented minorities to medical schools or residencies.

Use of artificial intelligence for nonlinear benchmarking of surgical care.

BACKGROUND: Existent methodologies for benchmarking the quality of surgical care are linear and fail to capture the complex interactions of preoperative variables. We sought to leverage novel nonlinear artificial intelligence methodologies to benchmark emergency surgical care. METHODS: Using a nonlinear but interpretable artificial intelligence methodology called optimal classification trees, first, the overall observed mortality rate at the index hospital's emergency surgery population (index cohort) was compared to the risk-adjusted expected mortality rate calculated by the optimal classification trees from the American College of Surgeons National Surgical Quality Improvement Program database (benchmark cohort). Second, the artificial intelligence optimal classification trees created different "nodes" of care representing specific patient phenotypes defined by the artificial intelligence optimal classification trees without human interference to optimize prediction. These nodes capture multiple iterative risk-adjusted comparisons, permitting the identification of specific areas of excellence and areas for improvement. RESULTS: The index and benchmark cohorts included 1,600 and 637,086 patients, respectively. The observed and risk-adjusted expected mortality rates of the index cohort calculated by optimal classification trees were similar (8.06% [95% confidence interval: 6.8-9.5] vs 7.53%, respectively, P = .42). Two areas of excellence and 4 for improvement were identified. For example, the index cohort had lower-than-expected mortality when patients were older than 75 and in respiratory failure and septic shock preoperatively but higher-than-expected mortality when patients had respiratory failure preoperatively and were thrombocytopenic, with an international normalized ratio ≤1.7. CONCLUSION: We used artificial intelligence methodology to benchmark the quality of emergency surgical care. Such nonlinear and interpretable methods promise a more comprehensive evaluation and a deeper dive into areas of excellence versus suboptimal care.

Development of a Machine Learning-Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma.

Importance: The use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to postinjury rehabilitation services. Objective: To leverage a novel, interpretable AI-based technology to uncover racial disparities in access to postinjury rehabilitation care and create an AI-based prescriptive tool to address these disparities. Design, Setting, and Participants: This cohort study used data from the 2010-2016 American College of Surgeons Trauma Quality Improvement Program database for Black and White patients with a penetrating mechanism of injury. An interpretable AI methodology called optimal classification trees (OCTs) was applied in an 80:20 derivation/validation split to predict discharge disposition (home vs postacute care [PAC]). The interpretable nature of OCTs allowed for examination of the AI logic to identify racial disparities. A prescriptive mixed-integer optimization model using age, injury, and gender data was allowed to "fairness-flip" the recommended discharge destination for a subset of patients while minimizing the ratio of imbalance between Black and White patients. Three OCTs were developed to predict discharge disposition: the first 2 trees used unadjusted data (one without and one with the race variable), and the third tree used fairness-adjusted data. Main Outcomes and Measures: Disparities and the discriminative performance (C statistic) were compared among fairness-adjusted and unadjusted OCTs. Results: A total of 52 468 patients were included; the median (IQR) age was 29 (22-40) years, 46 189 patients (88.0%) were male, 31 470 (60.0%) were Black, and 20 998 (40.0%) were White. A total of 3800 Black patients (12.1%) were discharged to PAC, compared with 4504 White patients (21.5%; P < .001). Examining the AI logic uncovered significant disparities in PAC discharge destination access, with race playing the second most important role. The prescriptive fairness adjustment recommended flipping the discharge destination of 4.5% of the patients, with the performance of the adjusted model increasing from a C statistic of 0.79 to 0.87. After fairness adjustment, disparities disappeared, and a similar percentage of Black and White patients (15.8% vs 15.8%; P = .87) had a recommended discharge to PAC. Conclusions and Relevance: In this study, we developed an accurate, machine learning-based, fairness-adjusted model that can identify barriers to discharge to postacute care. Instead of accidentally encoding bias, interpretable AI methodologies are powerful tools to diagnose and remedy system-related bias in care, such as disparities in access to postinjury rehabilitation care.

Artificial intelligence versus surgeon gestalt in predicting risk of emergency general surgery.

BACKGROUND: Artificial intelligence (AI) risk prediction algorithms such as the smartphone-available Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) for emergency general surgery (EGS) are superior to traditional risk calculators because they account for complex nonlinear interactions between variables, but how they compare to surgeons' gestalt remains unknown. Herein, we sought to: (1) compare POTTER to surgeons' surgical risk estimation and (2) assess how POTTER influences surgeons' risk estimation. STUDY DESIGN: A total of 150 patients who underwent EGS at a large quaternary care center between May 2018 and May 2019 were prospectively followed up for 30-day postoperative outcomes (mortality, septic shock, ventilator dependence, bleeding requiring transfusion, pneumonia), and clinical cases were systematically created representing their initial presentation. POTTER's outcome predictions for each case were also recorded. Thirty acute care surgeons with diverse practice settings and levels of experience were then randomized into two groups: 15 surgeons (SURG) were asked to predict the outcomes without access to POTTER's predictions while the remaining 15 (SURG-POTTER) were asked to predict the same outcomes after interacting with POTTER. Comparing to actual patient outcomes, the area under the curve (AUC) methodology was used to assess the predictive performance of (1) POTTER versus SURG, and (2) SURG versus SURG-POTTER. RESULTS: POTTER outperformed SURG in predicting all outcomes (mortality-AUC: 0.880 vs. 0.841; ventilator dependence-AUC: 0.928 vs. 0.833; bleeding-AUC: 0.832 vs. 0.735; pneumonia-AUC: 0.837 vs. 0.753) except septic shock (AUC: 0.816 vs. 0.820). SURG-POTTER outperformed SURG in predicting mortality (AUC: 0.870 vs. 0.841), bleeding (AUC: 0.811 vs. 0.735), pneumonia (AUC: 0.803 vs. 0.753) but not septic shock (AUC: 0.712 vs. 0.820) or ventilator dependence (AUC: 0.834 vs. 0.833). CONCLUSION: The AI risk calculator POTTER outperformed surgeons' gestalt in predicting the postoperative mortality and outcomes of EGS patients, and when used, improved the individual surgeons' risk prediction. Artificial intelligence algorithms, such as POTTER, could prove useful as a bedside adjunct to surgeons when preoperatively counseling patients. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level II.

Trauma patients with limited English proficiency: Outcomes from two level one trauma centers.

BACKGROUND: Outcomes for surgical patients with limited English proficiency (LEP) may be worse compared to patients with English proficiency. We sought to evaluate the association of LEP with outcomes for trauma patients. METHODS: Admitted adult patients on trauma service at two Level One trauma centers from 2015 to 2019 were identified. RESULTS: 12,562 patients were included in total; 7.3% had LEP. On multivariable analyses, patients with LEP had lower odds of discharge to post-acute care versus home compared to patients with English proficiency (OR 0.69; 95% CI 0.58-0.83; p < 0.001) but had similar length of stay (Beta coefficient 1.16; 95% CI 0.00-2.32; p = 0.05), and 30-day readmission (OR 1.08; 95% CI 0.87-1.35; p = 0.46). CONCLUSIONS: Trauma patients with LEP had comparable short-term outcomes to English proficient patients but were less likely to be discharged to post-acute care facilities. The role of structural barriers, family preferences, and other factors merit future investigation.

Contemporary Surgical Management of Pancreatic Necrosis.

Importance: For decades, infected or symptomatic pancreatic necrosis was managed by open surgical necrosectomy, an approach that has now been largely supplanted by an array of techniques referred to as the step-up approach. Observations: This review describes the evidence base behind the step-up approach, when to use the different techniques, and their technical basics. The most common treatment strategies are included: percutaneous drainage, video-assisted retroperitoneal debridement, sinus tract endoscopy, endoscopic transgastric necrosectomy, and surgical transgastric necrosectomy. Also included is the evidence base around management of common complications that can occur during step-up management, such as hemorrhage, intestinal fistula, and thrombosis, in addition to associated issues that can arise during step-up management, such as the need for cholecystectomy and disconnected pancreatic duct syndrome. Conclusions and Relevance: The treatment strategies highlighted in this review are those most commonly used during step-up management, and this review is designed as a guide to the evidence base underlying these strategies, as surgeons tailor their therapeutic approach to individual patients.

Enhanced Recovery After Surgery Patients Are Prescribed Fewer Opioids at Discharge: A Propensity-score Matched Analysis.

OBJECTIVE: We aimed to compare discharge opioid prescriptions pre- and post-ERAS implementation. SUMMARY OF BACKGROUND DATA: ERAS programs decrease inpatient opioid use, but their relationship with postdischarge opioids remains unclear. METHODS: All patients undergoing hysterectomy between October 2016 and November 2020 and pancreatectomy or hepatectomy between April 2017 and November 2020 at 1 tertiary care center were included. For each procedure, ERAS was implemented during the study period. PSM was performed to compare pre - versus post-ERAS patients on discharge opioids (number of pills and oral morphine equivalents). Patients were matched on age, sex, race, payor, American Society of Anesthesiologists score, prior opioid use, and procedure. Sensitivity analyses in open versus minimally invasive surgery cohorts were performed. RESULTS: A total of 3983 patients were included (1929 pre-ERAS; 2054 post-ERAS). Post-ERAS patients were younger (56.0 vs 58.4 years; P < 0.001), more often female (95.8% vs 78.1%; P < 0.001), less often white (77.2% vs 82.0%; P < 0.001), less often had prior opioid use (20.1% vs 28.1%; P < 0.001), and more often underwent hysterectomy (91.1% vs 55.7%; P < 0.001). After PSM, there were no significant differences between cohorts in baseline characteristics. Matched post-ERAS patients were prescribed fewer opioid pills (17.4 pills vs 22.0 pills; P < 0.001) and lower oral morphine equivalents (129.4 mg vs 167.6 mg; P < 0.001) than pre-ERAS patients. Sensitivity analyses confirmed these findings [open (18.8 pills vs 25.4 pills; P < 0.001 \ 138.9 mg vs 198.7 mg; P < 0.001); minimally invasive surgery (17.2 pills vs 21.1 pills; P < 0.001 \ 127.1 mg vs 160.1 mg; P < 0.001). CONCLUSIONS: Post-ERAS patients were prescribed significantly fewer opioids at discharge compared to matched pre-ERAS patients.

Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients. SUMMARY BACKGROUND DATA: The POTTER tool was derived using a novel Artificial Intelligence (AI)-methodology called optimal classification trees and validated for prediction of ES outcomes. POTTER outperforms all existent risk-prediction models and is available as an interactive smartphone application. Predicting outcomes in elderly patients has been historically challenging and POTTER has not yet been tested in this population. METHODS: All patients ≥65 years who underwent ES in the ACS-NSQIP 2017 database were included. POTTER's performance for 30-day mortality and 18 postoperative complications (eg, respiratory or renal failure) was assessed using c-statistic methodology, with planned sub-analyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 29,366 patients were included, with mean age 77, 55.8% females, and 62% who underwent emergency general surgery. POTTER predicted mortality accurately in all patients over 65 (c-statistic 0.80). Its best performance was in patients 65 to 74 years (c-statistic 0.84), and its worst in patients ≥85 years (c-statistic 0.71). POTTER had the best discrimination for predicting septic shock (c-statistic 0.90), respiratory failure requiring mechanical ventilation for ≥48 hours (c-statistic 0.86), and acute renal failure (c-statistic 0.85). CONCLUSIONS: POTTER is a novel, interpretable, and highly accurate predictor of in-hospital mortality in elderly ES patients up to age 85 years. POTTER could prove useful for bedside counseling and for benchmarking of ES care.

Underuse and Variability in Substance Use Screening Among Adult Trauma Patients in the U.S.: A Nationwide Retrospective Cohort Study.

OBJECTIVE: To characterize the rates and variability in substance screening among adult trauma patients in the U.S. SUMMARY BACKGROUND DATA: Emergency Department trauma visits provide a unique opportunity to identify patients with substance use disorders. Despite the existence of screening guidelines, underscreening and variability in screening practices remain. METHODS: Retrospective cohort study including adult trauma patients (18- 64-year-old) from the ACS-TQIP 2017-18 database. Multivariable logistic regressions were performed to adjust for demographics, clinical, and facility factors, and marginal probabilities were calculated using these multivariable models. The primary outcomes were substance screening and positivity, which were defined relative to the observation-weighted grand mean (mean). RESULTS: 2,048,176 patients were contained in the TQIP dataset, 809,878 (39.5%) were screened for alcohol (20.8% positive), and 617,129 (30.1%) were screened for drugs (37.3% positive). After all exclusion criteria were applied, 765,897 patients were included in the analysis, 394,391 (52.9%) were screened for alcohol (22.1% tested positive), and 279,531 (36.5%) were screened for drugs (44.3% tested positive). Among the patients included in our study, significant variability in screening rates existed with respect to demo-graphic, trauma mechanism, injury severity, and facility factors. Furthermore, in several cases, patient subpopulations who were less likely to be screened were in fact more likely to screen positive or vice versa. CONCLUSIONS: Effective substance-screening guidelines should be predicated on achieving universal screening. Current lapses in screening, along with the observed variability, likely affect different patient populations in disparate manners and lead to both under-detection as well as waste of valuable resources.

The variation of opioid prescription after injury and its association with long-term chronic pain: A multicenter cohort study.

BACKGROUND: Opioid overprescription in trauma contributes to the opioid epidemic through diversion of unused pills. Through our study, we sought to do the following: (1) understand the variation in opioid prescription after injury and its relationship to patient and/or clinical variables, and (2) study the relationship between opioid prescribing and long-term pain and analgesic use. METHOD: Trauma patients with an injury severity score ≥9 admitted to 3 level 1 trauma centers were screened for chronic pain and analgesic use 6 to 12 months postinjury. First, multivariable linear regression models were constructed with "oral morphine equivalents" and "number of opioid pills prescribed" at discharge as dependent variables. The coefficients of determination were calculated to determine how much of the variation in opioid prescription was explained by patient and clinical variables. Second, a multivariable logistic regression analysis was created to study the association between opioid prescription at discharge and chronic pain/analgesic use at 6 to 12 months. Analyses were adjusted for patient demographics, socioeconomics, comorbidities, injury parameters, and hospital course. RESULTS: Of the 2,702 patients included (mean [standard deviation] age: 61.0 [21.5]; 55% males), 74% were prescribed opioids at discharge (mean number of pills [standard deviation]: 24.0 [26.5]; mean oral morphine equivalent [standard deviation]: 204.8 [348.1]). The adjusted coefficients of determination for oral morphine equivalents and number of pills was 0.12 and 0.21, respectively, suggesting that the measured patient and clinical factors explain <21% of the variation in opioid prescribing in trauma. Patients prescribed opioids were more likely to have chronic pain (odds ratio [95%] confidence interval: 1.34 [1.05-1.71]) and use analgesics daily (odds ratio [95%] confidence interval: 1.86 [1.25-2.77]) 6 to 12 months postinjury. CONCLUSION: The variation in opioid prescription after traumatic injury is more affected by system and provider level rather than clinical or patient-related factors, and opioid prescribing correlates independently with long-term chronic pain and continued analgesic use postinjury. Efforts to decrease opioid use should prioritize standardizing prescription practices after traumatic injury.

Preperitoneal Pelvic Packing is Associated With Increased Risk of Venous Thromboembolism.

INTRODUCTION: Preperitoneal pelvic packing (PPP) is an important intervention for control of severe pelvic hemorrhage in blunt trauma patients. We hypothesized that PPP is associated with an increased incidence of deep vein thrombosis (DVT) and pulmonary embolism (PE). METHODS: A retrospective cohort analysis of blunt trauma patients with severe pelvic fractures (AIS ≥4) using the 2015-2017 American College of Surgeons-Trauma Quality Improvement Program database was performed. Patients who underwent PPP within four hours of admission were matched to patients who did not using propensity score matching. Matching was performed based on demographics, comorbidities, injury- and resuscitation-related parameters, vital signs at presentation, and initiation and type of prophylactic anticoagulation. The rates of DVT and PE were compared between the matched groups. RESULTS: Out of 5129 patients with severe pelvic fractures, 157 (3.1%) underwent PPP within four h of presentation and were matched with 157 who did not. No significant differences were detected between the two matched groups in any of the examined baseline variables. Similarly, mortality and end-organ failure rates were not different. However, PPP patients were significantly more likely to develop DVT (12.7% versus 5.1%, P = 0.028) and PE (5.7% versus 0.0%, P = 0.003). CONCLUSIONS: PPP in severe pelvic fractures secondary to blunt trauma is associated with an increased risk of DVT and PE. A high index of suspicion and a low threshold for screening for these conditions should be maintained in patients who undergo PPP.

Role of Transfusion Volume and Transfusion Rate as Markers of Futility During Ultramassive Blood Transfusion in Trauma.

BACKGROUND: Using a large national database, we evaluated the relationship between RBC transfusion volume, RBC transfusion rate, and in-hospital mortality to explore the presence of a futility threshold in trauma patients receiving ultramassive blood transfusion. STUDY DESIGN: The ACS-TQIP 2013 to 2018 database was analyzed. Adult patients who received ultramassive blood transfusion (≥20 units of RBC/24 hours) were included. RBC transfusion volume and rate were captured at the only 2 time points available in TQIP (4 hours and 24 hours), or time of death, whichever came first. RESULTS: Among 5,135 patients analyzed, in-hospital mortality rate was 62.1% (n = 3,190), and 4-hour and 24-hour mortality rates were 17.53% (n = 900) and 42.41% (n = 2,178), respectively. RBC transfusion volumes at 4 hours (area under the receiver operating characteristic curve [AUROC] 0.59 [95% CI 0.57 to 0.60]) and 24 hours (AUROC 0.59 [95% CI 0.57 to 0.60]) had low discriminatory ability for mortality and were inconclusive for futility. Mean RBC transfusion rates calculated within 4 hours (AUROC 0.65 [95% CI 0.63 to 0.66]) and 24 hours (AUROC 0.85 [95% CI 0.84 to 0.86]) had higher discriminatory ability than RBC transfusion volume. A futility threshold was not found for the mean RBC transfusion rate calculated within 4 hours. All patients with a final mean RBC transfusion rate of ≥7 U/h calculated within 24 hours of arrival experienced in-hospital death (n = 1,326); the observed maximum length of survival for these patients during the first 24 hours ranged from 24 hours for a rate of 7 U/h to 4.5 hours for rates ≥21 U/h. CONCLUSION: RBC transfusion volume within 4 or 24 hours and mean RBC transfusion rate within 4 hours were not markers of futility. The observed maximum length of survival per mean RBC transfusion rate could inform resuscitation efforts in trauma patients receiving ongoing transfusion between 4 and 24 hours.

Treatment and Prevention of Intraabdominal Bleeding in Necrotizing Pancreatitis Patients Treated With a Step-Up Approach.

OBJECTIVES: A minimally invasive step-up (MIS) approach for management of necrotizing pancreatitis (NP) has been associated with reduced morbidity and mortality compared with open surgical techniques. We sought to evaluate bleeding complications in NP patients treated with a MIS approach and to describe the management and outcomes of these events. METHODS: An observational, cohort study was performed using a prospectively maintained NP database at a tertiary referral center from 2013 to 2019. RESULTS: Of 119 NP patients, 13% suffering bleeding events, and 18% underwent an intervention. There was a 6-fold higher mortality rate in patients with bleeding events (n = 3; 18.8%) compared with those without (n = 3; 2.9%) ( P = 0.031). The most common intervention for hemorrhage control was endovascular coil embolization (75%), which was successful 88% of the time. Seven patients underwent prophylactic vascular intervention, which was 100% successful in preventing bleeding events from the embolized vessel. CONCLUSIONS: Bleeding events in NP patients treated with a MIS approach are associated with a 6-fold increase in mortality. Endovascular intervention is an effective strategy for the management of bleeding events. Prophylactic embolization may be an effective technique for reducing bleeding complications.

Planning for the Next Pandemic: Trauma Injuries Require Pre-COVID-19 Levels of High-Intensity Resources.

As hospital systems plan for health care utilization surges and stress, understanding the necessary resources of a trauma system is essential for planning capacity. We aimed to describe trends in high-intensity resource utilization (operating room [OR] usage and intensive care unit [ICU] admissions) for trauma care during the initial months of the COVID-19 pandemic. Trauma registry data (2019 pre-COVID-19 and 2020 COVID-19) were collected retrospectively from 4 level I trauma centers. Direct emergency department (ED) disposition to the OR or ICU was used as a proxy for high-intensity resource utilization. No change in the incidence of direct ED to ICU or ED to OR utilization was observed (2019: 24%, 2020 23%; P = .62 and 2019: 11%, 2020 10%; P = .71, respectively). These results suggest the need for continued access to ICU space and OR theaters for traumatic injury during national health emergencies, even when levels of trauma appear to be decreasing.

Hospital Academic Status and the Volume-Outcome Association in Postoperative Patients Requiring Intensive Care: Results of a Nationwide Analysis of Intensive Care Units in the United States.

Objective: To determine whether the outcomes of postoperative patients admitted directly to an intensive care unit (ICU) differ based on the academic status of the institution and the total operative volume of the unit. Methods: This was a retrospective analysis using the eICU Collaborative Research Database v2.0, a national database from participating ICUs in the United States. All patients admitted directly to the ICU from the operating room were included. Transfer patients and patients readmitted to the ICU were excluded. Patients were stratified based on admission to an ICU in an academic medical center (AMC) versus non-AMC, and to ICUs with different operative volume experience, after stratification in quartiles (high, medium-high, medium-low, and low volume). Primary outcomes were ICU and hospital mortality. Secondary outcomes included the need for continuous renal replacement therapy (CRRT) during ICU stay, ICU length of stay (LOS), and 30-day ventilator free days. Results: Our analysis included 22,180 unique patients; the majority of which (15,085[68%]) were admitted to ICUs in non-AMCs. Cardiac and vascular procedures were the most common types of procedures performed. Patients admitted to AMCs were more likely to be younger and less likely to be Hispanic or Asian. Multivariable logistic regression indicated no meaningful association between academic status and ICU mortality, hospital mortality, initiation of CRRT, duration of ICU LOS, or 30-day ventilator-free-days. Contrarily, medium-high operative volume units had higher ICU mortality (OR = 1.45, 95%CI = 1.10-1.91, p-value = 0.040), higher hospital mortality (OR = 1.33, 95%CI = 1.07-1.66, p-value = 0.033), longer ICU LOS (Coefficient = 0.23, 95%CI = 0.07-0.39, p-value = 0.038), and fewer 30-day ventilator-free-days (Coefficient = -0.30, 95%CI = -0.48 - -0.13, p-value = 0.015) compared to their high operative volume counterparts. Conclusions: This study found that a volume-outcome association in the management of postoperative patients requiring ICU level of care immediately after a surgical procedure may exist. The academic status of the institution did not affect the outcomes of these patients.

POTTER-ICU: An artificial intelligence smartphone-accessible tool to predict the need for intensive care after emergency surgery.

BACKGROUND: Delays in admitting high-risk emergency surgery patients to the intensive care unit result in worse outcomes and increased health care costs. We aimed to use interpretable artificial intelligence technology to create a preoperative predictor for postoperative intensive care unit need in emergency surgery patients. METHODS: A novel, interpretable artificial intelligence technology called optimal classification trees was leveraged in an 80:20 train:test split of adult emergency surgery patients in the 2007-2017 American College of Surgeons National Surgical Quality Improvement Program database. Demographics, comorbidities, and laboratory values were used to develop, train, and then validate optimal classification tree algorithms to predict the need for postoperative intensive care unit admission. The latter was defined as postoperative death or the development of 1 or more postoperative complications warranting critical care (eg, unplanned intubation, ventilator requirement ≥48 hours, cardiac arrest requiring cardiopulmonary resuscitation, and septic shock). An interactive and user-friendly application was created. C statistics were used to measure performance. RESULTS: A total of 464,861 patients were included. The mean age was 55 years, 48% were male, and 11% developed severe postoperative complications warranting critical care. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application was created as the user-friendly interface of the complex optimal classification tree algorithms. The number of questions (ie, tree depths) needed to predict intensive care unit admission ranged from 2 to 11. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application had excellent discrimination for predicting the need for intensive care unit admission (C statistics: 0.89 train, 0.88 test). CONCLUSION: We recommend the Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application as an accurate, artificial intelligence-based tool for predicting severe complications warranting intensive care unit admission after emergency surgery. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application can prove useful to triage patients to the intensive care unit and to potentially decrease failure to rescue in emergency surgery patients.

Adding to the story, did penetrating trauma really increase? changes in trauma patterns during the COVID-19 pandemic: A multi-institutional, multi-region investigation.

BACKGROUND: Results from single-region studies suggest that stay at home orders (SAHOs) had unforeseen consequences on the volume and patterns of traumatic injury during the initial months of the Coronavirus disease 2019 (COVID-19). The aim of this study was to describe, using a multi-regional approach, the effects of COVID-19 SAHOs on trauma volume and patterns of traumatic injury in the US. METHODS: A retrospective cohort study was performed at four verified Level I trauma centers spanning three geographical regions across the United States (US). The study period spanned from April 1, 2020 - July 31, 2020 including a month-matched 2019 cohort. Patients were categorized into pre-COVID-19 (PCOV19) and first COVID-19 surge (FCOV19S) cohorts. Patient demographic, injury, and outcome data were collected via Trauma Registry queries. Univariate and multivariate analyses were performed. RESULTS: A total 5,616 patients presented to participating study centers during the PCOV19 (2,916) and FCOV19S (2,700) study periods.  Blunt injury volume decreased (p = 0.006) due to a significant reduction in the number of motor vehicle collisions (MVCs) (p = 0.003). Penetrating trauma experienced a significant increase, 8% (246/2916) in 2019 to 11% (285/2,700) in 2020 (p = 0.007), which was associated with study site (p = 0.002), not SAHOs. Finally, study site was significantly associated with changes in nearly all injury mechanisms, whereas SAHOs accounted for observed decreases in calculated weekly averages of blunt injuries (p < 0.02) and MVCs (p = 0.003). CONCLUSION: Results of this study suggest that COVID-19 and initial SAHOs had variable consequences on patterns of traumatic injury, and that region-specific shifts in traumatic injury ensued during initial SAHOs. These results suggest that other factors, potentially socioeconomic or cultural, confound trauma volumes and types arising from SAHOs. Future analyses must consider how regional changes may be obscured with pooled cohorts, and focus on characterizing community-level changes to aid municipal preparation for future similar events.

Increasing Frequency of Interpreting Services is Associated With Shorter Peri-operative Length of Stay.

BACKGROUND: Patients with limited English proficiency have barriers to accessing care. Rather than a binary use or no use, this study uses granular data on frequency of interpreting services to determine if this frequency is associated with differences in peri-operative length of stay for patients with limited English proficiency. MATERIALS AND METHODS: This is a cross sectional study on length of stay for peri-operative admissions of at least one night during 2018, for patients who used medical interpreting services in an academic medical center in Boston, Massachusetts. The participants are split into quartiles of ascending number of interpreting events per day. The exposure for the primary outcome is the frequency of interpreting events per day during peri-operative admission. The primary study outcome measurement is peri-operative length of stay in days. RESULTS: There was a statistically significant decrease in length of stay for patients in the highest two quartiles of interpreting service frequency, compared to the lowest quartile: quartile 2 trended shorter by 1.4 d (95% CI -4.5 to 1.7, P = 0.37), quartile 3 was 4.2 d shorter (95% CI -7.6 to -0.7, P = 0.02), and quartile 4 was 4.6 d shorter (95% CI -8.1 to -1.1, P = 0.01). CONCLUSIONS: More frequent interpreting services per day during peri-operative admission are associated with shorter length of stay in adjusted analysis. The findings merit further study in an intervention to increase use of interpreting services for surgical patients with limited English proficiency to study the impact of increased frequency of culturally competent care.