Automating excellence: A breakthrough in emergency general surgery quality benchmarking.

Creation and Implementation of an Emergency General Surgery Registry Modeled after the National Trauma Data Bank

It has been theorized that a tiered, regionalized system of care for emergency general surgery (EGS) patients-akin to regional trauma systems-would translate into significant survival benefits. Yet data to support this supposition are lacking. The aim of this study was to determine the potential number of lives that could be saved by regionalizing EGS care to higher-volume, lower-mortality EGS institutions. Adult patients who underwent one of 10 common EGS operations were identified in the California Inpatient Database (2010-2011). An algorithm was constructed that "closed" lower-volume, higher-mortality hospitals and referred those patients to higher-volume, lower-mortality institutions ("closure" based on hospital EGS volume-threshold that optimized to 95% probability of survival). Primary outcome was the number of lives saved. Fifty thousand regionalization simulations were completed (5,000 for each operation) employing a bootstrap resampling method to proportionally redistribute patients. Estimates of expected deaths at the higher-volume hospitals were recalculated for every bootstrapped sample. Of the 165,123 patients who underwent EGS operations over the 2-year period, 17,655 (10.7%) were regionalized to a higher-volume hospital. On average, 128 (48.8%) of lower-volume hospitals were "closed," ranging from 68 (22.0%) hospital closures for appendectomy to 205 (73.2%) for small bowel resection. The simulations demonstrated that EGS regionalization would prevent 9.7% of risk-adjusted EGS deaths, significantly saving lives for every EGS operation: from 30.8 (6.5%) deaths prevented for appendectomy to 122.8 (7.9%) for colectomy. Regionalization prevented 4.6 deaths per 100 EGS patient-transfers, ranging from 1.3 for appendectomy to 8.0 for umbilical hernia repair. This simulation study provides important new insight into the concept of EGS regionalization, suggesting that 1 in 10 risk-adjusted deaths could be prevented by a structured system of EGS care. Future work should expand upon these findings using more complex discrete-event simulation models. Therapeutic/Care Management, level IV.

In acute care surgery, predicting mortality is important to determine appropriate patient transfer to a regional emergency general surgery (EGS) center. We hypothesized that distance to a referral center and severity of illness (SOI) would be predictors of death. We performed a retrospective analysis of a prospectively collected EGS registry from 2004 to 2008. The study population consisted of all patients discharged from the EGS service with an available home zip code in the registry. Study data included age, gender, length of stay (LOS), intensive care unit (ICU) LOS, distance between our facility and patient home zip code, and need for operative management. Systemic inflammatory response syndrome/sepsis/shock, peritonitis, perforation, and acute renal failure were used as SOI indicators. Mortality at discharge was the primary outcome. Patients were stratified by survival and compared using non-parametric statistical tests. Logistic regression assessed the simultaneous contribution of age, SOI, and distance to risk of death. A total of 3,439 patients met study criteria. Females slightly outnumbered males (1,813, 52.7%) with a median age of 47 years. The overall LOS was 6.4 days±9.3 days, and 2,331 (67.8%) of the patients underwent operation. Mean distance was 41.5 miles±51.2 miles (median, 22.2). Overall mortality was 2.7%. Increasing distance, age, and presence of SOI indicators were associated with mortality in univariable analyses. In multivariable logistic regression controlling for patient age and SOI, increasing distance in miles was related to increased mortality (odds ratio, 1.005; p<0.001). This odds ratio equates to a doubling in odds of death for each 132 miles between our center and the patient's home zip code. Age, SOI, and distance from a regional referral center explain much of the variation in mortality and can be used for triage to regional EGS centers.

Emergency general surgery (EGS) diseases are time-sensitive conditions that require urgent surgical evaluation, yet the effect of geographic access to care on outcomes remains unclear. We examined the association of spatial access with outcomes for common EGS conditions. A retrospective analysis of twelve 2014 State Inpatient Databases, identifying adults admitted with eight EGS conditions, was performed. We assessed spatial access using the spatial access ratio (SPAR)-an advanced spatial model that accounts for travel distance, hospital capacity, and population demand, normalized against the national mean. Multivariable regression models adjusting for patient and hospital factors were used to evaluate the association between SPAR with (a) in-hospital mortality and (b) major morbidity. A total of 877,928 admissions, of which 104,332 (2.4%) were in the lowest-access category (SPAR, 0) and 578,947 (66%) were in the high-access category (SPAR, ≥1), were analyzed. Low-access patients were more likely to be White, male, and treated in nonteaching hospitals. Low-access patients also had higher incidence of complex EGS disease (low access, 31% vs. high access, 12%; p < 0.001) and in-hospital mortality (4.4% vs. 2.5%, p < 0.05). When adjusted for confounding factors, including presence of advanced hospital resources, increasing spatial access was protective against in-hospital mortality (adjusted odds ratio, 0.95; 95% confidence interval, 0.94-0.97; p < 0.001). Spatial access was not significantly associated with major morbidity. This is the first study to demonstrate that geospatial access to surgical care is associated with incidence of complex EGS disease and that increasing spatial access to care is independently associated with lower in-hospital mortality. These results support the consideration of spatial access in the development of regional health systems for EGS care. Prognostic and Epidemiologic; Level III.

Acute care surgery encompasses trauma, surgical critical care, and emergency general surgery (EGS). While the first two components are well defined, the scope of EGS practice remains unclear. This article describes the work of the American Association for the Surgery of Trauma to define EGS. A total of 621 unique International Classification of Diseases-9th Rev. (ICD-9) diagnosis codes were identified using billing data (calendar year 2011) from seven large academic medical centers that practice EGS. A modified Delphi methodology was used by the American Association for the Surgery of Trauma Committee on Severity Assessment and Patient Outcomes to review these codes and achieve consensus on the definition of primary EGS diagnosis codes. National Inpatient Sample data from 2009 were used to develop a national estimate of EGS burden of disease. Several unique ICD-9 codes were identified as primary EGS diagnoses. These encompass a wide spectrum of general surgery practice, including upper and lower gastrointestinal tract, hepatobiliary and pancreatic disease, soft tissue infections, and hernias. National Inpatient Sample estimates revealed over 4 million inpatient encounters nationally in 2009 for EGS diseases. This article provides the first list of ICD-9 diagnoses codes that define the scope of EGS based on current clinical practices. These findings have wide implications for EGS workforce training, access to care, and research.

Emergency general surgery (EGS) admissions account for a large proportion of surgical care and represent the majority of surgical patients who suffer in-hospital mortality. Health care systems continue to experience growing demand for emergency services: one way in which this is being increasingly addressed is dedicated subspecialty teams for emergency surgical admissions, most commonly termed "emergency general surgery" in the United Kingdom. This study aims to understand the impact of the emergency general surgery model of care on outcomes from emergency laparotomies. Data was obtained from the National Emergency Laparotomy Audit database. Patients were dichotomized into EGS hospital or non-EGS hospital. Emergency general surgery hospital is defined as a hospital where >50% of in-hours emergency laparotomy operating is performed by an emergency general surgeon. The primary outcome was in-hospital mortality. Secondary outcomes were intensive therapy unit (ITU) length of stay and duration of hospital stay. A propensity score weighting approach was used to reduce confounding and selection bias. There were 115,509 patients from 175 hospitals included in the final analysis. The EGS hospital care group included 5,789 patients versus 109,720 patients in the non-EGS group. Following propensity score weighting, mean standardized mean difference reduced from 0.055 to <0.001. In-hospital mortality was similar (10.8% vs. 11.1%, p = 0.094), with mean length of stay (16.7 days vs. 16.1 days, p < 0.001) and ITU stay (2.8 days vs. 2.6 days, p < 0.001) persistently longer in patients treated in EGS systems. No significant association between the emergency surgery hospital model of care and in-hospital mortality in emergency laparotomy patients was seen. There is a significant association between the emergency surgery hospital model of care and an increased length of ITU stay and overall hospital stay. Further studies are required to examine the impact of changing models of EGS delivery in the United Kingdom. Prognostic and Epidemiological; Level III.

To compare predictive accuracy of frailty instruments operationalizable in electronic data for prognosticating outcomes among older adults undergoing emergency general surgery (EGS). Older patients undergoing EGS are at higher risk of perioperative morbidity and mortality. Preoperative frailty is a common and strong perioperative risk factor in this population. Despite this, existing barriers preclude routine preoperative frailty assessment. We conducted a retrospective cohort study of adults above 65 undergoing EGS from 2012 to 2018 using Institute for Clinical Evaluative Sciences (ICES) provincial healthcare data in Ontario, Canada. We compared 4 frailty instruments: Frailty Index (FI), Hospital Frailty Risk Score (HFRS), Risk Analysis Index-Administrative (RAI), ACG Frailty-defining diagnoses indicator (ACG). We compared predictive accuracy beyond baseline risk models (age, sex, American Society of Anesthesiologists' score, procedural risk). Predictive performance was measured using discrimination, calibration, explained variance, net reclassification index and Brier score (binary outcomes); using explained variance, root mean squared error and mean absolute prediction error (continuous outcomes). Primary outcome was 30-day mortality. Secondary outcomes were 365-day mortality, nonhome discharge, days alive at home, length of stay, and 30-day and 365-day health systems cost. A total of 121,095 EGS patients met inclusion criteria. Of these, 11,422 (9.4%) experienced death 30 days postoperatively. Addition of FI, HFRS, and RAI to the baseline model led to improved discrimination, net reclassification index, and R2 ; RAI demonstrated the largest improvements. Adding 4 frailty instruments to typically assessed preoperative risk factors demonstrated strong predictive performance in accurately prognosticating perioperative outcomes. These findings can be considered in developing automated risk stratification systems among older EGS patients.

Hospital readmission rates following surgery are increasingly being used as a marker of quality of care and are used in pay-for-performance metrics. To our knowledge, comprehensive data on readmissions to the initial hospital or a different hospital after emergency general surgery (EGS) procedures do not exist. To define readmission rates and identify risk factors for readmission after common EGS procedures. Patients undergoing EGS, as defined by the American Association for the Surgery of Trauma, were identified in the California State Inpatient Database (2007-2011) on January 15, 2015. Patients were 18 years and older. We identified the 5 most commonly performed EGS procedures in each of 11 EGS diagnosis groups. Patient demographics (sex, age, race/ethnicity, and insurance type) as well as Charlson Comorbidity Index score, length of stay, complications, and discharge disposition were collected. Factors associated with readmission were determined using multivariate logistic regression models analysis. Thirty-day hospital readmission. Among 177,511 patients meeting inclusion criteria, 57.1% were white, 48.8% were privately insured, and most were 45 years and older (51.3%). Laparoscopic appendectomy (35.2%) and laparoscopic cholecystectomy (19.3%) were the most common procedures. The overall 30-day readmission rate was 5.91%. Readmission rates ranged from 4.1% (upper gastrointestinal) to 16.8% (cardiothoracic). Of readmitted patients, 16.8% were readmitted at a different hospital. Predictors of readmission included Charlson Comorbidity Index score of 2 or greater (adjusted odds ratio: 2.26 [95% CI, 2.14-2.39]), leaving against medical advice (adjusted odds ratio: 2.24 [95% CI, 1.89-2.66]), and public insurance (adjusted odds ratio: 1.55 [95% CI, 1.47-1.64]). The most common reasons for readmission were surgical site infections (16.9%), gastrointestinal complications (11.3%), and pulmonary complications (3.6%). Readmission after EGS procedures is common and varies widely depending on patient factors and diagnosis categories. One in 5 readmitted patients will go to a different hospital, causing fragmentation of care and potentially obscuring the utility of readmission as a quality metric. Assisting socially vulnerable patients and reducing postoperative complications, including infections, are targets to reduce readmissions.

Artificial Intelligence (AI) risk prediction algorithms such as the smartphone-available 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. A total of 150 patients who underwent EGS at a large quaternary care center between May 2018 and May 2019 were prospectively followed 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 2 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 vs. SURG, and 2) SURG vs. SURG-POTTER. 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). 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. AI algorithms such as POTTER could prove useful as a bedside adjunct to surgeons when preoperatively counselling patients. Level II, Prognostic/Epidemiological.

In 2015, the United States moved from the International Classification of Diseases, Ninth Revision ( ICD-9 ), to the International Classification of Diseases, Tenth Revision ( ICD-10 ), coding system. The American Association for the Surgery of Trauma Committee on Severity Assessment and Patient Outcomes previously established a list of ICD-9 diagnoses to define the field of emergency general surgery (EGS). This study evaluates the general equivalence mapping (GEM) crosswalk to generate an equivalent list of ICD-10 -coded EGS diagnoses. The GEM was used to generate a list of ICD-10 codes corresponding to the American Association for the Surgery of Trauma ICD-9 EGS diagnosis codes. These individual ICD-9 and ICD-10 codes were aggregated by surgical area and diagnosis groups. The volume of patients admitted with these diagnoses in the National Inpatient Sample in the ICD-9 era (2013-2014) was compared with the ICD-10 volumes to generate observed to expected ratios. The crosswalk was manually reviewed to identify the causes of discrepancies between the ICD-9 and ICD-10 lists. There were 485 ICD-9 codes, across 89 diagnosis categories and 11 surgical areas, which mapped to 1,206 unique ICD-10 codes. A total of 196 (40%) ICD-9 codes have an exact one-to-one match with an ICD-10 code. The median observed to expected ratio among the diagnosis groups for a primary diagnosis was 0.98 (interquartile range, 0.82-1.12). There were five key issues identified with the ability of the GEM to crosswalk ICD-9 EGS diagnoses to ICD-10 : (1) changes in admission volumes, (2) loss of necessary modifiers, (3) lack of specific ICD-10 code, (4) mapping to a different condition, and (5) change in coding nomenclature. The GEM provides a reasonable crosswalk for researchers and others to use when attempting to identify EGS patients in with ICD-10 diagnosis codes. However, we identify key issues and deficiencies, which must be accounted for to create an accurate patient cohort. This is essential for ensuring the validity of policy, quality improvement, and clinical research work anchored in ICD-10 coded data. Diagnostic Test/Criteria; Level III.

Many emergency general surgery (EGS) conditions can be managed both operatively or nonoperatively; however, it is unknown whether the decision to operate affects Black and White patients differentially. We identified a nationwide cohort of Black and White Medicare beneficiaries, hospitalized for common EGS conditions from July 2015 to June 2018. Using near-far matching to adjust for measurable confounding and an instrumental variable analysis to control for selection bias associated with treatment assignment, we compare outcomes of operative and nonoperative management in a stratified population of Black and White patients. Outcomes included in-hospital mortality, 30-day mortality, nonroutine discharge, and 30-day readmissions. An interaction test based on a t test was used to determine the conditional effects of operative versus nonoperative management between Black and White patients. A total of 556,087 patients met inclusion criteria, of which 59,519 (10.7%) were Black and 496,568 (89.3%) were White. Overall, 165,932 (29.8%) patients had an operation and 390,155 (70.2%) were managed nonoperatively. Significant outcome differences were seen between operative and nonoperative management for some conditions; however, no significant differences were seen for the conditional effect of race on outcomes. The decision to manage an EGS patient operatively versus nonoperatively has varying effects on surgical outcomes. These effects vary by EGS condition. There were no significant conditional effects of race on the outcomes of operative versus nonoperative management among universally insured older adults hospitalized with EGS conditions.

Fragmentation of care (FOC) is defined as readmission to a nonindex hospital and can be associated with worse outcomes among emergency general surgery (EGS) patients. This study assessed hospital characteristics associated with patterns of FOC in EGS patients and the effect of interfacility transfer after FOC on mortality. Using the Nationwide Readmissions Database 2019, we included patients 18 years or older who were admitted to an index hospital with an EGS diagnosis managed operatively and had an emergency readmission within 90 days. Patients were classified into 4 patterns of fragmentation: no FOC, FOC, defragmented care (readmitted to a nonindex hospital, then transferred back), and hyperfragmented care (readmitted to a nonindex hospital, then transferred to another nonindex hospital). Patient severity of illness was defined using severity of illness categories of All Patient-Refined Diagnosis Related Groups (APR-DRG). Multivariable logistic regression analyses were used to identify hospital characteristics associated with FOC and the effect of FOC on 90-day mortality. Of 26,100 patients included, 82.5% had no FOC, 16.4% had FOC, 0.3% had defragmented care, and 0.9% had hyperfragmented care. Index hospital characteristics associated with FOC were small size (by number of beds), private investor or government owned, and low EGS volumes. Controlling for hospital and patient characteristics, FOC (odds ratio 1.22; 95% CI 1.01 to 1.47; p = 0.045) was significantly associated with increased 90-day mortality, driven by those with major or extreme severity of illness of APR-DRGs (odds ratio 1.36; 95% CI 1.13 to 1.64; p = 0.001). FOC is associated with worse outcomes in EGS patients. This study highlights the impact of FOC and the hospital characteristics associated with FOC, highlighting the need for further investigation into EGS systems of care.

Introduction Clinical coding is a process that converts healthcare information into a machine-readable format. Accurate coding is clinically essential as an integral part of the electronic patient record (EPR) as well as underpinning the data used for healthcare research, service planning and revenue. The nature of emergency general surgery (EGS), however, introduces coding challenges due to significant individual variation in clinical presentation and management. This audit evaluates the accuracy of clinical coding in EGS at a large UK acute tertiary centre. Methods A retrospective audit was designed to evaluate the accuracy of clinical coding of unselected EGS patients. A ‘gold standard’ coding for past medical (PMH) and surgical history (PSH) was defined for each patient by an experienced surgeon from any information available within the EPR. This standard was compared to the EPR coding, and clerking entries from both Emergency Department (ED) doctors and the admitting surgeon. Results Fifty patients admitted in January 2024 were included. On average, EPR coding contained 48% of PMH and PSH elements when compared to the gold standard. Clerking documents from ED doctors and admitting surgeons averaged 49% and 58% respectively. Drug history recorded in the EPR was concordant in 71% of patients compared to ED (47%) and surgical (53%) clerkings. Conclusion This audit highlights the need for quality improvement in clinical coding accuracy. The data transformation benefits of EPRs are well established but are dependent on good quality data input. Inaccurate coding can have wide ranging implications inside and outside the direct care setting.

Aim To establish the outcomes of Emergency General Surgery patients admitted with acute pathology that did not progress to Emergency Laparotomy Method Retrospective single site review of all Emergency General Surgery patients admitted over three calendar months. Review of patient demographics, diagnostics, diagnosis and management. Identification of and further collection of data from electronic patient records for patients that did not proceed to Emergency Laparotomy despite indicative pathology. Results A total of 619 emergency general surgical patients were admitted from 1st January to 31st March 2023. 39 (6.3%) patients had an Emergency Laparotomy. 17 (2.7%) patients did not proceed to laparotomy despite pathology that could indicate the need for emergency laparotomy. 100% of these patients has CT imaging to diagnose the pathology. Of the 17 patients without laparotomy 12 were female patients. Median age of 86 years (range 42 – 91 years). At 30 days post admission 6 patients who did not proceed to laparotomy were alive. 7 patients survived to discharge, 5 were discharged home and 2 were discharged to a Nursing Home for full nursing care requirements. Conclusion Surgeons are starting to explore the issues around futility in emergency general surgery. This snapshot retrospective review shows alignment with the very limited published literature. We recognise our numbers are extremely limited. Further data collection and analysis is needed to explore futility in emergency general surgery.

Aim To support trainees in completing specific and achievable quality improvement projects (QIP) that are beneficial to patient care and aligned with clinical priority areas in emergency general surgery. Method A fishbone analysis of factors contributing to poor outcomes in patients undergoing emergency laparotomy was created. From this, key priorities for improvement were identified by a multidisciplinary team, enabling multiple QIP with clear aims to be developed. QIP included National Emergency Laparotomy Audit (NELA) data capture, risk scoring and documentation of mortality, identification and management of sepsis, pre-operative antibiotics, infection prevention and early radiological imaging. QIP were allocated to groups of trainees comprising of at least one foundation doctor, one core trainee and one specialty registrar. Mortality and length of stay data was collected over 6 months using the run charts for the hospital site available at data.nela.org. Results In 6 months, 9 QIP were started, with the first cycle of data collected in all projects and 3 projects completed by trainees. Initial QIP results have been presented locally for 5 projects and 2 QIP have been presented nationally, thus supporting completion of ARCP requirements and specialty job applications, as well as the sharing of outcomes. Although not statistically significant a downwards trend in length of stay and mortality has also been observed. Conclusion Having a focused and co-ordinated approach to quality improvement with clear aims and objectives can support training and improve patient outcomes. Further study is required to assess the sustainability and longevity of these results.