A novel preoperative score to predict severe acute cholecystitis.

BACKGROUND: In a large multicenter trial, The Parkland Grading Scale (PGS) for acute cholecystitis outperformed other grading scales and has a positive correlation with complications but is limited in its inability to preoperatively predict high-grade cholecystitis. We sought to identify preoperative variables predictive of high-grade cholecystitis (PGS 4 or 5). METHODS: In a six-month period, patients undergoing cholecystectomy at a single institution with prospectively graded PGS were analyzed. Stepwise logistic regression models were constructed to predict high-grade cholecystitis. The relative weight of the variables was used to derive a novel score, the Severe Acute Cholecystitis Score (SACS). This score was compared with the Emergency Surgery Acuity Score(ESS), American Association for the Surgery of Trauma (AAST) preoperative score and Tokyo Guidelines (TG) for their ability to predict high-grade cholecystitis. Severe Acute Cholecystitis Score was then validated using the database from the AAST multicenter validation of the grading scale for acute cholecystitis. RESULTS: Of the 575 patients that underwent cholecystectomy, 172 (29.9%) were classified as high-grade. The stepwise logistic regression modeling identified seven independent predictors of high-grade cholecystitis. From these variables, the SACS was derived. Scores ranged from 0 to 9 points with a C statistic of 0.76, outperforming the ESS ( C statistic of 0.60), AAST (0.53), and TG (0.70) ( p < 0.001). Using a cutoff of 4 or more on the SACS correctly identifies 76.2% of cases with a specificity of 91.3% and a sensitivity of 40.7%. In the multicenter database, there were 464 patients with a prospectively collected PGS. The C statistic for SACS was 0.74. Using the same cutoff of 4, SACS correctly identifies 71.6% of cases with a specificity of 83.8% and a sensitivity of 52.2%. CONCLUSION: The Severe Acute Cholecystitis Score can preoperatively predict high-grade cholecystitis and may be useful for counseling patients and assisting in surgical decision making. LEVEL OF EVIDENCE: Diagnostic Test/Criteria; Level III.

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.

Physical injuries and burns among refugees in Lebanon: implications for programs and policies.

BACKGROUND: Refugees are prone to higher risks of injury due to often austere living conditions, social and economic disadvantages, and limited access to health care services in host countries. This study aims to systematically quantify the prevalence of physical injuries and burns among the refugee community in Western Lebanon and to examine injury characteristics, risk factors, and outcomes. METHODS: We conducted a cluster-based population survey across 21 camps in the Beqaa region of Lebanon from February to April 2019. A modified version of the 'Surgeons Overseas Assessment of Surgical Need (SOSAS)' tool (Version 3.0) was administered to the head of the refugee households and documented all injuries sustained by family members over the last 12 months. Descriptive and univariate regression analyses were performed to understand the association between variables. RESULTS: 750 heads of households were surveyed. 112 (14.9%) households sustained injuries in the past 12 months, 39 of which (34.9%) reported disabling injuries that affected their work and daily living. Injuries primarily occurred inside the tent (29.9%). Burns were sustained by at least one household member in 136 (18.1%) households in total. The majority (63.7%) of burns affected children under 5 years and were mainly due to boiling liquid (50%). Significantly more burns were reported in households where caregivers cannot lock children outside the kitchen while cooking (25.6% vs 14.9%, p-value = 0.001). Similarly, households with unemployed heads had significantly more reported burns (19.7% vs. 13.3%, p value = 0.05). Nearly 16.1% of the injured refugees were unable to seek health care due to the lack of health insurance coverage and financial liability. CONCLUSIONS: Refugees severely suffer from injuries and burns, causing substantial human and economic repercussions on the affected individuals, their families, and the host healthcare system. Resources should be allocated toward designing safe camps as well as implementing educational awareness campaigns specifically focusing on teaching about heating and cooking safety practices.

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.

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.

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.

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.

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.

Intraoperative Deaths: Who, Why, and Can We Prevent Them?

INTRODUCTION: Intraoperative deaths (IODs) are rare but catastrophic. We systematically analyzed IODs to identify clinical and patient safety patterns. METHODS: IODs in a large academic center between 2015 and 2019 were included. Perioperative details were systematically reviewed, focusing on (1) identifying phenotypes of IOD, (2) describing emerging themes immediately preceding cardiac arrest, and (3) suggesting interventions to mitigate IOD in each phenotype. RESULTS: Forty-one patients were included. Three IOD phenotypes were identified: trauma (T), nontrauma emergency (NT), and elective (EL) surgery patients, each with 2 sub-phenotypes (e.g., ELm and ELv for elective surgery with medical arrests or vascular injury and bleeding, respectively). In phenotype T, cardiopulmonary resuscitation was initiated before incision in 42%, resuscitative thoracotomy was performed in 33%, and transient return of spontaneous circulation was achieved in 30% of patients. In phenotype NT, ruptured aortic aneurysms accounted for half the cases, and median blood product utilization was 2,694 mL. In phenotype ELm, preoperative evaluation did not include electrocardiogram in 12%, cardiac consultation in 62%, stress test in 87%, and chest x-ray in 37% of patients. In phenotype ELv, 83% had a single peripheral intravenous line, and vascular injury was almost always followed by escalation in monitoring (e.g., central/arterial line), alert to the blood bank, and call for surgical backup. CONCLUSIONS: We have created a framework for IOD that can help with intraoperative safety and quality analysis. Focusing on interventions that address appropriateness versus futility in care in phenotypes T and NT, and on prevention and mitigation of intraoperative vessel injury (e.g., intraoperative rescue team) or preoperative optimization in phenotype EL may help prevent IODs.

The Beirut Port Explosion Injuries and Lessons Learned: Results of the Beirut Blast Assessment for Surgical Services (BASS) Multicenter Study.

OBJECTIVE: This multicenter study aims to describe the injury patterns, emergency management and outcomes of the blast victims, recognize the gaps in hospital disaster preparedness, and identify lessons to be learned. SUMMARY BACKGROUND DATA: On August 4th, 2020, the city of Beirut, Lebanon suffered the largest urban explosion since Hiroshima and Nagasaki, resulting in hundreds of deaths and thousands of injuries. METHODS: All injured patients admitted to four of the largest Beirut hospitals within 72 hours of the blast, including those who died on arrival or in the emergency department (ED), were included. Medical records were systematically reviewed for: patient demographics and comorbidities; injury severity and characteristics; prehospital, ED, operative, and inpatient interventions; and outcomes at hospital discharge. Lessons learned are also shared. RESULTS: An estimated total of 1818 patients were included, of which 30 died on arrival or in the ED and 315 were admitted to the hospital. Among admitted patients, the mean age was 44.7 years (range: 1 week-93 years), 44.4% were female, and the median injury severity score (ISS) was 10 (5, 17). ISS was inversely related to the distance from the blast epicenter (r = --0.18, P = 0.035). Most injuries involved the upper extremities (53.7%), face (42.2%), and head (40.3%). Mildly injured (ISS <9) patients overwhelmed the ED in the first 2 hours; from hour 2 to hour 8 post-injury, the number of moderately, severely, and profoundly injured patients increased by 127%, 25% and 17%, respectively. A total of 475 operative procedures were performed in 239 patients, most commonly soft tissue debridement or repair (119 patients, 49.8%), limb fracture fixation (107, 44.8%), and tendon repair (56, 23.4%). A total of 11 patients (3.5%) died during the hospitalization, 56 (17.8%) developed at least 1 complication, and 51 (16.2%) were discharged with documented long-term disability. Main lessons learned included: the importance of having key hospital functions (eg, laboratory, operating room) underground; the nonadaptability of electronic medical records to disasters; the ED overwhelming with mild injuries, delay in arrival of the severely injured; and the need for realistic disaster drills. CONCLUSIONS: We, therefore, describe the injury patterns, emergency flow and trauma outcome of patients injured in the Beirut port explosion. The clinical and system-level lessons learned can help prepare for the next disaster.

Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older.

BACKGROUND: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient. METHODS: All patients aged 65 years and older in the American College of Surgeons-Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 260,505 patients were included. Median age was 77 (71-84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively). CONCLUSION: The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

Factors affecting road safety compliance in a low- and middle-income country: An observational study from Lebanon.

Road traffic injury is a major public health problem in Lebanon. This study aims to assess compliance with safety measures including seatbelt and helmet use in adults and children, and the prevalence of distracted driving among road users across Lebanon different governorates. It further aims to investigate predictors of compliance with seatbelt and helmet use. A cross-sectional observational field study was conducted at multiple governorates in Lebanon. Data collected included information on vehicles, road users and passengers. Univariate and multivariable logistic regression analyses were performed to identify trends in compliance with safety measures and distracted driving, and predictors of compliance. A total of 13,790 road users were observed. The rate of seatbelt and helmet use were 37.4% and 38.9%, respectively, among adults. Distracted behavior was present in 23.7% of car drivers and 22.8% of motorcyles adult riders. Compliance with seatbelt use was lower outside the capital city Beirut [OR = 5.236 (4.566-6.004), P <0.001], in males [OR = 1.688 (1.52-1.874), P <0.001], in drivers of taxi/vans [OR = 1.929 (1.71-2.175), P <0.001] or trucks [OR = 3.014 (2.434-3.732), P <0.001], and vehicles of lower price [OR = 3.291 (2.836-3.819), P <0.001]. Children vehicle passengers were 87.9% while motorcycles pillion riders were 12.1%. The rates for child car restraint and helmet use were 25.8% and 20.1%, respectively. Predictors of failure to use a child restraint system in vehicles were the youngest age group (0-5 years) [OR = 2.06, CI (1.40-3.02), P<0.001], sitting in the back seat [OR = 1.56, CI (1.09-2.23), P<0.001], ridding in the afternoon [OR = 1.43, CI (1.05-1.94), P = 0.02], and being outside Beirut [OR = 2.12, CI (1.41-3.17), P<0.00]. Public awareness efforts and better enforcement of road safety legislations are needed to increase the alarmingly low rates of compliance with safety measures and safeguard lives on the road.

The Role of Transaminases in Predicting Choledocholithiasis. A Novel Predictive Composite Score Development in a Cohort of 1089 Patients Undergoing Laparoscopic Cholecystectomy.

BACKGROUND: Optimal use of interventional procedures and diagnostic tests for patients with suspected choledocholithiasis depends on accurate pretest risk estimation. We sought to define sensitivity/specificity of transaminases in identifying choledocholithiasis and to incorporate them into a biochemical marker composite score that could accurately predict choledocholithiasis. METHODS: All adult patients who underwent laparoscopic cholecystectomy by our Emergency Surgery Service between 2010 and 2018 were reviewed. Admission total bilirubin (TB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) was captured. Choledocholithiasis was confirmed via intraoperative cholangiogram, endoscopic retrograde cholangiopancreatography, or magnetic resonance cholangiopancreatography. Area under receiver operating characteristic curve (AUC) or C-statistic for AST, ALT, ALP, and TB as a measure of detecting choledocholithiasis was calculated. For score development, our database was randomly dichotomized to derivation and validation cohort and a score was derived. The score was validated by calculating its C-statistic. RESULTS: 1089 patients were included; 210 (20.3%) had confirmed choledocholithiasis. The AUC was .78 for TB, .77 for ALP and AST, and .76 for ALT. 545 and 544 patients were included in the derivation and the validation cohort, respectively. The elements of the derived score were TB, AST, and ALP. The score ranged from 0 to 4. The AUC was .82 in the derivation and .77 in the validation cohort. The probability of choledocholithiasis increased from 8% to 89% at scores 0 to 4, respectively. CONCLUSIONS: Aspartate aminotransferase predicted choledocholithiasis adequately and should be featured in choledocholithiasis screening algorithms. We developed a biochemical composite score, shown to be accurate in preoperative choledocholithiasis risk assessment in an emergency surgery setting.

Revision of the AAST grading scale for acute cholecystitis with comparison to physiologic measures of severity.

BACKGROUND: Grading systems for acute cholecystitis are essential to compare outcomes, improve quality, and advance research. The American Association for the Surgery of Trauma (AAST) grading system for acute cholecystitis was only moderately discriminant when predicting multiple outcomes and underperformed the Tokyo guidelines and Parkland grade. We hypothesized that through additional expert consensus, the predictive capacity of the AAST anatomic grading system could be improved. METHODS: A modified Delphi approach was used to revise the AAST grading system. Changes were made to improve distribution of patients across grades, and additional key clinical variables were introduced. The revised version was assessed using prospectively collected data from an AAST multicenter study. Patient distribution across grades was assessed, and the revised grading system was evaluated based on predictive capacity using area under receiver operating characteristic curves for conversion from laparoscopic to an open procedure, use of a surgical "bail-out" procedure, bile leak, major complications, and discharge home. A preoperative AAST grade was defined based on preoperative, clinical, and radiologic data, and the Parkland grade was also substituted for the operative component of the AAST grade. RESULTS: Using prospectively collected data on 861 patients with acute cholecystitis the revised version of the AAST grade has an improved distribution across all grades, both the overall grade and across each subscale. A higher AAST grade predicted each of the outcomes assessed (all p ≤ 0.01). The revised AAST grade outperformed the original AAST grade for predicting operative outcomes and discharge disposition. Despite this improvement, the AAST grade did not outperform the Parkland grade or the Emergency Surgery Score. CONCLUSION: The revised AAST grade and the preoperative AAST grade demonstrated improved discrimination; however, a purely anatomic grade based on chart review is unlikely to predict outcomes without addition of physiologic variables. Follow-up validation will be necessary. LEVEL OF EVIDENCE: Diagnostic Test or Criteria, Level IV.

The Burden of Surgical Disease and Access to Care in a Vulnerable Syrian Refugee Population in Lebanon.

BACKGROUND: The Syrian conflict has produced one of the largest refugee crises in modern times. Lebanon has taken in more Syrian refugees per capita than any other nation. We aimed to study the burden of surgical disease and access to surgical care among Syrian refugees in Lebanon. METHODS: This study was designed as a convenient cross-sectional cluster-based population survey of all refugee camps throughout the Bekaa region of Lebanon. We used a modified version of the Surgeons OverSeas Assessment of Surgical Need to identify surgical conditions and barriers to care access. The head of household of each informal tented settlement provided demographic information after which two household members were randomly chosen and administered the survey. RESULTS: A total of 1,500 individuals from 750 households representing 21 camps were surveyed. Respondents had a mean age of 36.6 (15.0) years, 54.6% were female, and 59% were illiterate. Nearly 25% of respondents reported at least one surgical condition within the past year, most commonly involving the face, head, and neck region (32%) and extremities (22%). Less than 20% of patients with a surgical condition reported seeing any healthcare provider, > 75% due to financial hardship. CONCLUSIONS: The prevalence of surgical disease among Syrian refugees is very high with a fourth of refugees suffering from one or more surgical conditions over the past year. The surgical needs of this vulnerable population are largely unmet as financial reasons prevent patients from seeking care. Local and humanitarian efforts need to include increased access to surgical care.

Abdominal Wall Thickness Predicts Surgical Site Infection in Emergency Colon Operations.

BACKGROUND: Body mass index (BMI) does not reliably predict Surgical site infections (SSI). We hypothesize that abdominal wall thickness (AWT) would serve as a better predictor of SSI for patients undergoing emergency colon operations. METHODS: We retrospectively evaluated our Emergency Surgery Database (2007-2018). Emergency colon operations for any indication were included. AWT was measured by pre-operative CT scans at 5 locations. Only superficial and deep SSIs were considered as SSI in the analysis. Univariate then multivariable analyses were used to determine predictors of SSI. RESULTS: 236 patients met inclusion criteria. The incidence of post-operative SSI was 25.8% and the median BMI was 25.8kg/m2 [22.5-30.1]. The median AWT between patients with and without SSI was significantly different (2.1cm [1.4, 2.8] and 1.8cm [1.2, 2.5], respectively). A higher BMI trended toward increased rates of SSI, but this was not statistically significant. In overweight (BMI 25-29.9kg/m2) and obese (BMI ≥30kg/m2) patients, SSI versus no SSI rates were (50.0% versus 41.9% and 47.4% versus 36.4%, P = 0.365 and 0.230) respectively. The incidence of SSI in patients with an average AWT < 1.8cm was 20% and 30% for patients with average AWT ≥1.8cm. On multivariable analysis, AWT ≥1.8cm at 2cm inferior to umbilicus was an independent predictor of SSI (OR 2.98, 95%CI 1.34-6.63, P = 0.007). CONCLUSIONS: AWT is a better predictor of SSI than BMI. Preoperative imaging of AWT may direct intraoperative decisions regarding wound management. Future clinical outcomes research in emergency surgery should include abdominal wall thickness as an important patient variable.

The Obesity Paradox in Elderly Patients Undergoing Emergency Surgery: A Nationwide Analysis.

BACKGROUND: Obesity has long been considered a risk factor for postoperative adverse events in surgery. We sought to study the impact of body mass index (BMI) on the clinical outcomes of the high-risk emergency general surgery (EGS) elderly patients. METHODS: All EGS ≥65 years old patients in the 2007-2016 ACS-NSQIP database, identified using the variables 'emergency' and 'surgspec,' were included. Patients were classified into five groups: normal weight: BMI <25 kg/m2, overweight: BMI ≥25 kg/m2 and <30 kg/m2, Class I: BMI ≥30 kg/m2 and <35 kg/m2, Class II: BMI ≥35 kg/m2 and <40 kg/m2, and Class III: BMI ≥40 kg/m2. Patients with BMI<18.5 kg/m2 were excluded. Multivariable logistic regression models were built to assess the relationship between obesity and 30-day postoperative mortality, overall morbidity, and individual postoperative complications after adjusting for demographics (e.g., age, gender), comorbidities (e.g., diabetes mellitus, heart failure), laboratory tests (e.g., white blood cell count, albumin), and operative complexity (e.g., ASA classification). RESULTS: A total of 78,704 patients were included, of which 26,011 were overweight (33.1%), 13,897 (17.6%) had Class I obesity, 5904 (7.5%) had Class II obesity, and 4490 (5.7%) had Class III obesity. On multivariable analyses, compared to the nonobese, patients who are overweight or with Class I-III obesity paradoxically had a lower risk of mortality, bleeding requiring transfusion, pneumonia, stroke and myocardial infarction (MI). Additionally, the incidence of MI and stroke decreased in a stepwise fashion as BMI progressed from overweight to severely obese (MI: OR: 0.84 [0.73-0.95], OR: 0.73 [0.62-0.86], OR: 0.66 [0.52-0.83], OR: 0.51 [0.38-0.68]; stroke: OR: 0.80 [0.65-0.99], OR: 0.79 [0.62-1.02], OR: 0.71 [0.50-1.00], OR: 0.43 [0.28-0.68]). CONCLUSION: In our study of elderly EGS patients, overweight and obese patients had a lower risk of mortality, bleeding requiring transfusion, pneumonia, reintubation, stroke, and MI. Further studies are needed to confirm and investigate the obesity paradox in this patient population.

Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients.

BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linear. We aimed to use artificial intelligence (AI) technology to design and validate a nonlinear risk calculator for trauma patients. METHODS: A novel, interpretable AI technology called Optimal Classification Trees (OCTs) was used in an 80:20 derivation/validation split of the 2010 to 2016 American College of Surgeons Trauma Quality Improvement Program database. Demographics, emergency department vital signs, comorbidities, and injury characteristics (e.g., severity, mechanism) of all blunt and penetrating trauma patients 18 years or older were used to develop, train then validate OCT algorithms to predict in-hospital mortality and complications (e.g., acute kidney injury, acute respiratory distress syndrome, deep vein thrombosis, pulmonary embolism, sepsis). A smartphone application was created as the algorithm's interactive and user-friendly interface. Performance was measured using the c-statistic methodology. RESULTS: A total of 934,053 patients were included (747,249 derivation; 186,804 validation). The median age was 51 years, 37% were women, 90.5% had blunt trauma, and the median Injury Severity Score was 11. Comprehensive OCT algorithms were developed for blunt and penetrating trauma, and the interactive smartphone application, Trauma Outcome Predictor (TOP) was created, where the answer to one question unfolds the subsequent one. Trauma Outcome Predictor accurately predicted mortality in penetrating injury (c-statistics: 0.95 derivation, 0.94 validation) and blunt injury (c-statistics: 0.89 derivation, 0.88 validation). The validation c-statistics for predicting complications ranged between 0.69 and 0.84. CONCLUSION: We suggest TOP as an AI-based, interpretable, accurate, and nonlinear risk calculator for predicting outcome in trauma patients. Trauma Outcome Predictor can prove useful for bedside counseling of critically injured trauma patients and their families, and for benchmarking the quality of trauma care.

Deep immune profiling of whole blood to identify early immune signatures that correlate to patient outcome after major trauma.

BACKGROUND: Major injury results in an early cascade of immunologic responses that increase susceptibility to infection and multiorgan dysfunction. Detailed immune profiling by mass cytometry has the potential to identify immune signatures that correspond to patient outcomes. Our objective was to determine the prognostic value of immune signatures early after major trauma injury. METHODS: Trauma patients (n = 17) were prospectively enrolled between September 2018 and December 2019. Serial whole blood samples were obtained from trauma patients (mean Injury Severity Score, 26.2; standard error of the mean, 3.7) at Days 1 and 3 after injury, and from age- and sex-matched uninjured controls using a standardized protocol for fixation, storage, and labeling. Computational analyses including K-nearest neighbor automated clustering of immune cells and Spearman's correlation analysis were used to identify correlations between cell populations, clinical measures, and patient outcomes. RESULTS: Analysis revealed nine immune cell clusters that correlated with one or more clinical outcomes. On Days 1 and 3 postinjury, the abundance of immature neutrophil and classical monocytes exhibited a strong positive correlation with increased intensive care unit and hospital length of stay. Conversely, the abundance of CD4 T-cell subsets, namely Th17 cells, is associated with improved patient outcomes including decreased ventilator days (r = -0.76), hospital-acquired pneumonia (r = -0.69), and acute kidney injury (r = -0.73). CONCLUSION: Here, we provide a comprehensive multitime point immunophenotyping analysis of whole blood from patients soon after traumatic injury to determine immune correlates of adverse outcomes. Our findings indicate that alterations in myeloid-origin cell types may contribute to immune dysfunction after injury. Conversely, the presence of effector T cell populations corresponds with decreased hospital length of stay and organ dysfunction. Overall, these data identify novel immune signatures following traumatic injury that support the view that monitoring of immune (sub)-populations may provide clinical decision-making support for at-risk patients early in their hospital course. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, Level IV.