Can the Emergency Surgery Score (ESS) predict outcomes in emergency general surgery patients with missing data elements? A nationwide analysis

Prognostic accuracy of emergency surgery score: a systematic review

PURPOSE

This systematic review aimed to summarize the literature regarding the prognostic accuracy of the emergency surgery score (ESS).

METHOD

PubMed, Embase, Web of Science, and Scopus were comprehensively searched by May 30, 2023. Two independent researchers performed the initial screening by reviewing the titles and abstracts of the non-duplicate records and selecting the full text of articles meeting our inclusion criteria. Finally, original studies that reported the prognostic accuracy of ESS in any emergency surgeries were included. Data from the included studies were extracted into a checklist designed based on the PRISMA guidelines. The area under the curve (AUC) was used to compare the prognostic accuracy of ESS in different settings.

RESULTS

Twenty-six studies met the inclusion criteria. ESS performed excellently in 30-day post-op mortality (AUC 0.84-0.89) and incidence of cardiac arrest (AUC 0.86-0.88) in emergency general surgeries. The AUC of ESS in overall 30-day morbidities varied from 0.72 to 0.82 in five cohort studies. In predicting the need for ICU admission, the study with the largest sample size reported the best sensitivity of ESS at 80% and the specificity at 85%. Moreover, an outstanding accuracy was observed for the prediction of 30-day sepsis/septic shock in emergency general surgeries (AUC 0.75-0.92).

CONCLUSION

Despite the acceptable prognostic accuracy of ESS in 30-day mortality, morbidities, and in-hospital ICU admission in different emergency surgeries, the high number of required variables and the high probability of missing data highlight the need for modifications to this scoring system.

Validation of the emergency surgery score (ESS) in a UK patient population and comparison with NELA scoring: a retrospective multicentre cohort study

INTRODUCTION

Accurate risk scoring in emergency general surgery (EGS) is vital for consent and resource allocation. The emergency surgery score (ESS) has been validated as a reliable preoperative predictor of postoperative outcomes in EGS but has been studied only in the US population. Our primary aim was to perform an external validation study of the ESS in a UK population. Our secondary aim was to compare the accuracy of ESS and National Emergency Laparotomy Audit (NELA) scores.

METHODS

We conducted an observational cohort study of adult patients undergoing emergency laparotomy over three years in two UK centres. ESS was calculated retrospectively. NELA scores and all other variables were obtained from the prospectively collected Emergency Laparotomy and Laparoscopic Scottish Audit (ELLSA) database. The primary and secondary outcomes were 30-day mortality and postoperative intensive care unit (ICU) admission, respectively.

RESULTS

A total of 609 patients were included. Median age was 65 years, 52.7% were female, the overall mortality was 9.9% and 23.8% were admitted to ICU. Both ESS and NELA were equally accurate in predicting 30-day mortality (c-statistic=0.78 (95% confidence interval (CI), 0.71-0.85) for ESS and c-statistic=0.83 (95% CI, 0.77-0.88) for NELA, p=0.196) and predicting postoperative ICU admission (c-statistic=0.76 (95% CI, 0.71-0.81) for ESS and 0.80 (95% CI, 0.76-0.85) for NELA, p=0.092).

CONCLUSIONS

In the UK population, ESS and NELA both predict 30-day mortality and ICU admission with no statistically significant difference but with higher c-statistics for NELA score. Both scores have certain advantages, with ESS being validated for a wider range of outcomes.

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.

Evaluation of a modified emergency surgical acuity score in predicting operative and non-operative mortality and morbidity in an acute surgical unit

BACKGROUND

Emergency general surgery (EGS) patients have an increased risk of mortality and morbidity compared to other surgical patients. Limited risk assessment tools exist for use in both operative and non-operative EGS patients. We assessed the accuracy of a modified Emergency Surgical Acuity Score (mESAS) in EGS patients at our institution.

METHODS

A retrospective cohort study from an acute surgical unit at a tertiary referral hospital was performed. Primary endpoints assessed included death before discharge, length of stay (LOS) >5 days and unplanned readmission within 28 days. Operative and non-operative patients were analysed separately. Validation was performed using the area under the receiver operating characteristic (AUROC), Brier score and Hosmer-Lemeshow test.

RESULTS

A total of 1763 admissions between March 2018 and June 2021 were included for analysis. The mESAS was an accurate predictor of both death before discharge (AUROC 0.979, Brier score 0.007, Hosmer-Lemeshow P = 0.981) and LOS >5 days (0.787, 0.104, and 0.253, respectively). The mESAS was less accurate in predicting readmission within 28 days (0.639, 0.040, and 0.887, respectively). The mESAS retained its predictive ability for death before discharge and LOS >5 days in the split cohort analysis.

CONCLUSION

This study is the first to validate a modified ESAS in a non-operatively managed EGS population internationally and the first to validate the mESAS in Australia. The mESAS accurately predicts death before discharge and prolonged LOS for all EGS patients, providing a highly useful tool for surgeons and EGS units worldwide.

Frailty Index (mFI-5) Predicts Resource Utilization after Nonruptured Endovascular Aneurysm Repair

INTRODUCTION

The 5- factor frailty index (mFI-5) has reliably predicted outcomes after vascular surgeries. The purpose of this study was to determine the performance of this index in aortic endovascular surgery ( endovascular aneurysm repair [EVAR]) MATERIALS AND METHODS: The American College of Surgeons' National Surgical Quality Improvement Program Database (NSQIP) was retrospectively analyzed for patients undergoing nonruptured EVAR between 2015 and 2019. Outcomes were assessed using bivariate analysis (Mann Whitney U test, chi-squared test, and t-test) and multivariate logistic regression analysis.

RESULTS

10,450 patients were identified with a mean age of 73.59 (SD 8.93) y. 8222 (78.7%) were performed for large diameter with the remaining indications including dissection, symptomatic, and embolization/thrombosis. 30-d mortality was 1.3%. Univariate analysis showed that mFI-5≥0.6 was associated with higher rates of prolonged hospital stay (18.8% versus 5.7%, P < 0.001, reference mFI-5 = 0), readmission (12.3% versus 5.9%, P < 0.001), mortality (3.6 % versus 1.2%, P = 0.01), intensive care unit (ICU) length of stay more than 3 d (7.2% versus 2.7%, P < 0.001). Female gender higher age, indication for surgery, and mFI-5 were all associated with increased mortality. Multivariate logistic regression showed that mFI-5 remained as a significant predictor with mFI-5≥0.6 predicting a close to 3 times higher odds for 30-d mortality (odds ratio OR 2.83, P = 0.003), ICU length of stay >3 d (OR 2.48, P < 0.001), >7 d hospital stay (OR 3.94, P < 0.001), readmission (OR 2.16, P < 0.001), and pneumonia (OR 4.2, P < 0.001) CONCLUSIONS: The modified frailty index (mFI-5) is a good predictor for postoperative complications and hospital resource utilization after nonruptured EVAR.

Performance of the Emergency Surgery Score (ESS) for nonelective infrainguinal open revascularization procedures (NEIOR)

BACKGROUND

The Emergency Surgery Score has been previously validated as a reliable; tool to predict postoperative outcomes in emergency general surgery. The purpose of this study was to assess the performance of the Emergency Surgery Score for infrainguinal open revascularization procedures in the nonelective setting.

METHODS

The American College of Surgeons' National Surgical Quality Improvement Program database was retrospectively analyzed for patients undergoing infrainguinal open revascularization procedures in the nonelective setting between 2015 and 2019. The performance of the Emergency Surgery Score in predicting mortality in each procedure was assessed using receiver operating characteristic analyses.

RESULTS

A total of 5,027 patients underwent infrainguinal open revascularization procedures in the nonelective setting with median age 68 (±11.66 standard deviation), with 1,666 females (33.1%). The 30-day mortality rate was 2.7%. The Emergency Surgery Score correlated with 30-day mortality (area under the curve was 0.738). The Emergency Surgery Score also predicted risk of death/discharge to hospice (area under the curve 0.756), discharge to rehab (area under the curve 0.643), renal failure (area under the curve 0.741), postintervention ventilation requirement (0.684), stroke (0.717), cardiopulmonary arrest (0.657), and septic shock (0.697). A cumulative frequency table of mortality with Emergency Surgery Score was used to partition patients into quartiles of Emergency Surgery Score ≤5, Emergency Surgery Score of 6, Emergency Surgery Score of 7 or 8, and Emergency Surgery Score ≥9. A Cochran-Armitage test showed linear trend toward increased 30-day mortality among the quartiles with increasing Emergency Surgery Score (P < .001), with quartile 4 (Emergency Surgery Score ≥10) having 13 times odds of increased 30-day mortality compared to reference quartile 1 (Emergency Surgery Score ≤4).

CONCLUSION

Emergency Surgery Score performance accurately predicts mortality for infrainguinal open revascularization procedures in the nonelective setting procedures. It may be useful for preoperative risk stratification and for national benchmarking after nonelective open lower extremity procedures.

Performance of the Emergency Surgery Score in Nonelective Lower-Extremity Endovascular Procedures

INTRODUCTION

Multiple studies have validated the Emergency Surgery Score (ESS) as a tool which reliably predicts outcomes after emergency general surgery. The purpose of this study was to assess the performance of the ESS for lower-extremity endovascular procedures in nonelective setting (neLEE).

METHODS

The American College of Surgeons' National Surgical Quality Improvement Program database was retrospectively analyzed for patients undergoing neLEE between 2015 and 2019. The performance of the ESS in predicting mortality in each procedure was assessed using receiver operating characteristic analyses.

RESULTS

Four thousand five hundred and eighty three patients underwent neLEE with median age 68 (±12.3 SD), with 1802 females (39.3%). The ESS correlated with 30-day mortality (area under the curve [AUC] was 0.729), discharge to rehab (AUC 0.638), renal failure (AUC 0.667), postintervention ventilation requirement (AUC 0.680), and stroke (AUC 0.656). The predictive ability of the ESS decreased with increasing age, with the ESS performing best for patients between 60 and 69 y in age (AUC 0.735) and worst for patients above 80 y (AUC 0.650). A Cochran-Armitage test showed linear trend towards increased 30-day mortality among the quartiles with increasing ESS (P < 0.001), with patients with ESS ≥10 having 10 times odds of increased 30-day mortality compared to reference quartile of patients with ESS ≤4 on multivariate analysis.

CONCLUSIONS

The ESS score is associated with 30-day mortality and other complications after neLEE procedures. It can potentially be used as a predictive tool for preoperative risk stratification and can also be used for equitably evaluating standards and outcomes after lower extremity endovascular procedures.

Predicting mortality and morbidity in emergency general surgery patients in a Jordanian Tertiary Medical Center: a retrospective validation study of the Emergency Surgery Score (ESS)

OBJECTIVE

The Emergency Surgery Score (ESS) is a predictive tool used to assess morbidity and mortality rates in patients undergoing emergent surgery. This study explores the ESS's predictive ability and reliability in the Jordanian surgical population.

DESIGN

A retrospective validation study.

SETTING

A tertiary hospital in Jordan.

PARTICIPANTS

A database was created including patients who underwent emergent surgery in King Abdullah University Hospital from January 2017 to June 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

Relevant preoperative, intraoperative and postoperative variables were retrospectively and systematically gathered, and the ESS was calculated for each patient accordingly. In addition, a multivariable logistic regression analysis was performed to assess the correlations between the ESS and postoperative mortality and morbidity along with intensive care unit (ICU) admissions.

RESULTS

Out of total of 1452 patients evaluated, 1322 patients were enrolled based on inclusion and exclusion criteria. The mean age of the population was 47.9 years old. 91.9% of the patients were admitted to the surgical ward through the emergency department, while the rest were referred from inpatient and outpatient facilities. The mortality and postoperative complication rates were 3.9% and 13.5%, respectively. Mortality rates increased as the ESS score gradually increased, and the ESS was evaluated as a strong predictor with a c-statistic value of 0.842 (95% CI 0.743 to 0.896). The postoperative complication and ICU admission rate also increased with reciprocal rises in the ESS. They were also evaluated as accurate predictors with a c-statistic value of 0.724 (95% CI 0.682 to 0.765) and a c-statistic value of 0.825 (95% CI 0.784 to 0.866), respectively.

CONCLUSION

The ESS is a robust, accurate predictor of postoperative mortality and morbidity of emergency general surgery patients. Furthermore, it is an all-important tool to enhance emergency general surgery practices, in terms of mitigating risk, quality of care measures and patient counselling.

Emergency Surgery Score as an Effective Risk Stratification Tool for Patients Undergoing Emergency Surgeries: A Narrative Review

Several risk stratification tools have been described for quantifying perioperative morbidity, mortality, and adverse events in patients undergoing elective and emergency surgeries. These tools help in decision-making, determining the prognosis and communicating it with patients and family members, and planning admissions to the intensive care units (ICU) if necessary. Emergency surgery poses quite a unique challenge in terms of deranged physiology, age, and comorbid conditions, and often carries a higher incidence of morbidity and mortality. Very few risk stratification tools are available to reliably predict the risk posed by emergency surgical interventions. One of the recently described tools is the Emergency Surgery Score (ESS), which comprises three demographic variables, 10 comorbidities, and nine laboratory variables, the scores of which add up to 29. Several studies have demonstrated that ESS reliably predicts morbidity, mortality, and the need for ICU admission, predicting infectious complications like pneumonia and renal failure. In this review, we analyze the current literature to investigate the efficacy and reliability of ESS as a risk stratification tool for patients undergoing emergency surgeries.

Development and Validation of a Multivariable Prediction Model for Postoperative Intensive Care Unit Stay in a Broad Surgical Population

IMPORTANCE

Despite limited capacity and expensive cost, there are minimal objective data to guide postoperative allocation of intensive care unit (ICU) beds. The Surgical Risk Preoperative Assessment System (SURPAS) uses 8 preoperative variables to predict many common postoperative complications, but it has not yet been evaluated in predicting postoperative ICU admission.

OBJECTIVE

To determine if the SURPAS model could accurately predict postoperative ICU admission in a broad surgical population.

DESIGN, SETTING, AND PARTICIPANTS

This decision analytical model was a retrospective, observational analysis of prospectively collected patient data from the 2012 to 2018 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, which were merged with individual patients' electronic health record data to capture postoperative ICU use. Multivariable logistic regression modeling was used to determine how the 8 preoperative variables of the SURPAS model predicted ICU use compared with a model inputting all 28 preoperatively available NSQIP variables. Data included in the analysis were collected for the ACS NSQIP at 5 hospitals (1 tertiary academic center, 4 academic affiliated hospitals) within the University of Colorado Health System between January 1, 2012, and December 31, 2018. Included patients were those undergoing surgery in 9 surgical specialties during the 2012 to 2018 period. Data were analyzed from May 29 to July 30, 2021.

EXPOSURE

Surgery in 9 surgical specialties, including general, gynecology, orthopedic, otolaryngology, plastic, thoracic, urology, vascular, and neurosurgery.

MAIN OUTCOMES AND MEASURES

Use of ICU care up to 30 days after surgery.

RESULTS

A total of 34 568 patients were included in the analytical data set: 32 032 (92.7%) in the cohort without postoperative ICU use and 2545 (7.4%) in the cohort with postoperative ICU use (no ICU use: mean [SD] age, 54.9 [16.6] years; 18 188 women [56.8%]; ICU use: mean [SD] age, 60.3 [15.3] years; 1333 men [52.4%]). For the internal chronologic validation of the 7-variable SURPAS model, data from 2012 to 2016 were used as the training data set (n = 24 250, 70.2% of the total sample size of 34 568) and data from 2017 to 2018 were used as the test data set (n = 10 318, 29.8% of the total sample size of 34 568). The C statistic improved in the test data set compared with the training data set (0.933; 95% CI, 0.924-0.941 vs 0.922; 95% CI, 0.917-0.928), whereas the Brier score was slightly worse in the test data set compared with the training data set (0.045; 95% CI, 0.042-0.048 vs 0.045; 95% CI, 0.043-0.047). The SURPAS model compared favorably with the model inputting all 28 NSQIP variables, with both having good calibration between observed and expected outcomes in the Hosmer-Lemeshow graphs and similar Brier scores (model inputting all variables, 0.044; 95% CI, 0.043-0.048; SURPAS model, 0.045; 95% CI, 0.042-0.046) and C statistics (model inputting all variables, 0.929; 95% CI, 0.925-0.934; SURPAS model, 0.925; 95% CI, 0.921-0.930).

CONCLUSIONS AND RELEVANCE

Results of this decision analytical model study revealed that the SURPAS prediction model accurately predicted postoperative ICU use across a diverse surgical population. These results suggest that the SURPAS prediction model can be used to help with preoperative planning and resource allocation of limited ICU beds.

The Emergency Surgery Score is a powerful predictor of outcomes across multiple surgical specialties: Results of a retrospective nationwide analysis

BACKGROUND

The Emergency Surgery Score was recently validated in a prospective multicenter study as an accurate predictor of mortality in emergency general surgery patients. The Emergency Surgery Score is easily calculated using multiple demographic, comorbidity, laboratory, and acuity of disease variables. We aimed to investigate whether the Emergency Surgery Score can predict 30-day postoperative mortality across patients undergoing emergency surgery in multiple surgical specialties.

METHODS

Our study is a retrospective cohort study using data from the national American College of Surgeons National Surgical Quality Improvement Program database (2007-2017). We included patients that underwent emergency gynecologic, urologic, thoracic, neurosurgical, orthopedic, vascular, cardiac, and general surgical procedures. The Emergency Surgery Score was calculated for each patient, and the correlation between the Emergency Surgery Score and 30-day mortality was assessed for each specialty using the c-statistics methodology.

RESULTS

Of 6,485,915 patients, 173,890 patients were included. The mean age was 60 years, 50.6% were female patients, and the overall mortality was 9.7%. The Emergency Surgery Score predicted mortality best in emergency gynecologic, general, and urologic surgery (c-statistics: 0.97, 0.87, 0.81, respectively). The Emergency Surgery Score predicted mortality moderately well in emergency thoracic, neurosurgical, orthopedic, and vascular surgery (c-statistics 0.73-0.79). For example, the mortality of gynecology patients with an Emergency Surgery Score of 5, 9, and 13 was 2%, 27%, and 50%, respectively. The Emergency Surgery Score performed poorly in cardiac surgery.

CONCLUSION

The Emergency Surgery Score accurately predicts mortality across patients undergoing emergency surgery in multiple surgical specialties, especially general, gynecologic, and urologic surgery. The Emergency Surgery Score can prove useful for perioperative patient counseling and for benchmarking the quality of surgical care.

Validation of the Emergency Surgery Score (ESS) in a Greek patient population: a prospective bi-institutional cohort study

Purpose

The Emergency Surgery Score (ESS) is a reliable point-based score that predicts mortality and morbidity in emergency surgery patients. However, it has been validated only in the U.S. patients. We aimed to prospectively validate ESS in a Greek patient population.

Methods

All patients who underwent an emergent laparotomy were prospectively included over a 15-month period. A systematic chart review was performed to collect relevant preoperative, intraoperative, and postoperative variables based on which the ESS was calculated for each patient. The relationship between ESS and 30-day mortality, morbidity (i.e., the occurrence of at least one complication), and the need for intensive care unit (ICU) admission was evaluated and compared between the Greek and U.S. patients using the c-statistics methodology. The study was registered on "Research Registry" with the unique identifying number 5901.

Results

A total of 214 patients (102 Greek) were included. The mean age was 64 years, 44% were female, and the median ESS was 7. The most common indication for surgery was hollow viscus perforation (25%). The ESS reliably and incrementally predicted mortality (c-statistics = 0.79 [95% CI 0.67–0.90] and 0.83 [95% CI 0.74–0.92]), morbidity (c-statistics = 0.83 [95% CI 0.76–0.91] and 0.79 [95% CI 0.69–0.88]), and ICU admission (c-statistics = 0.88 [95% CI 0.81–0.96] and 0.84 [95% CI 0.77–0.91]) in both Greek and U.S. patients.

Conclusion

The correlation between the ESS and the surgical outcomes was statistically significant in both Greek and U.S. patients undergoing emergency laparotomy. ESS could prove globally useful for preoperative patient counseling and quality-of-care benchmarking.

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.

Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients

BACKGROUND

The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the performance of POTTER in the emergency general surgery (EGS) population in particular.

METHODS

All patients who underwent EGS in the 2017 American College of Surgeons NSQIP database were included. The performance of POTTER in predicting 30-day postoperative mortality, morbidity, and 18 specific complications was assessed using the c-statistic metric. As a subgroup analysis, the performance of POTTER in predicting the outcomes of patients undergoing emergency laparotomy was assessed.

RESULTS

A total of 59,955 patients were included. Median age was 50 years and 51.3% were women. POTTER predicted mortality (c-statistic = 0.93) and morbidity (c-statistic = 0.83) extremely well. Among individual complications, POTTER had the highest performance in predicting septic shock (c-statistic = 0.93), respiratory failure requiring mechanical ventilation for 48 hours or longer (c-statistic = 0.92), and acute renal failure (c-statistic = 0.92). Among patients undergoing emergency laparotomy, the c-statistic performances of POTTER in predicting mortality and morbidity were 0.86 and 0.77, respectively.

CONCLUSIONS

POTTER is an interpretable, accurate, and user-friendly predictor of 30-day outcomes in patients undergoing EGS. POTTER could prove useful for bedside counseling of patients and their families and for benchmarking of EGS care.

The Emergency surgery score (ESS) accurately predicts outcomes of emergency surgical admissions at a Saudi academic health center

BACKGROUND

The emergency surgery score (ESS) has emerged as a tool to predict outcomes in emergency surgery (EGS) patients. Our study examines the ability of ESS to predict outcomes in EGS admissions.

METHODS

All EGS admissions to King Saud University Medical City (KSUMC) from January 2017 to October 2019 were included. ESS was calculated for each patient. Correlations between ESS and 30-day mortality and complications were evaluated.

RESULTS

1607 patients were included. 30-day mortality rate was 2.2% while complication rate was 18.7%. Mortality increased as ESS increased, from 0.3% for ESS≤2, to 30.1% for ESS >10, with a c-statistic of 0.88. Complication rates were 2.2%, 40%, and 100% at ESS of 0, 6, and 15, respectively, with a c-statistic of 0.82.

CONCLUSIONS

ESS accurately predicted outcomes at our tertiary center. ESS could be useful in identifying high risk EGS admissions and in benchmarking quality of care across Saudi institutions.

Performance of the Emergency Surgery Score (ESS) Across Different Emergency General Surgery Procedures

BACKGROUND

The Emergency Surgery Score (ESS) has been previously validated as a reliable tool to predict postoperative outcomes in emergency general surgery (EGS). The purpose of this study is to assess the differential performance of the ESS in specific EGS procedures.

METHODS

The American College of Surgeons' National Surgical Quality Improvement Program database was retrospectively analyzed for patients undergoing EGS between 2007 and 2017. Patients who underwent the following EGS procedures were identified: laparoscopic appendectomy, laparoscopic cholecystectomy, surgery for small bowel obstruction (SBO), colectomy, and incarcerated ventral or inguinal hernia repair. The performance of the ESS in predicting mortality in each procedure was assessed using receiver operating characteristic analyses.

RESULTS

A total of 467,803 patients underwent EGS (mean age 50 ± 19.9 y, females 241,330 [51.6%]), of which 191,930 (41%) underwent laparoscopic appendectomy, 40,353 (8.6%) underwent laparoscopic cholecystectomy, and 35,152 (7.5%) patients underwent surgery for SBO. The ESS correlated extremely well with mortality for patients who underwent laparoscopic appendectomy (area under the curve (AUC) 0.91), laparoscopic cholecystectomy (AUC 0.91), lysis of adhesions for SBO (AUC 0.83), colectomy (AUC 0.83), and incarcerated hernia repair (AUC 0.85).

CONCLUSIONS

ESS performance accurately predicts mortality across a wide range of EGS procedures, and its use should be encouraged for preoperative patient counseling and for nationally benchmarking the quality of care of EGS.

Overall management of emergency general surgery patients during the surge of the COVID-19 pandemic: an analysis of procedures and outcomes from a teaching hospital at the worst hit area in Spain

Objective

To assess how the COVID-19 outbreak has affected emergency general surgery (EGS) care during the pandemic, indications for surgery, types of procedures, perioperative course, and final outcomes.

Methods

This is a retrospective study of EGS patients during the pandemic period. The main outcome was 30-day morbidity and mortality according to severity and COVID-19 infection status. Secondary outcomes were changes in overall management. A logistic regression analysis was done to assess factors predictive of mortality.

Results

One hundred and fifty-three patients were included. Half of the patients with an abdominal ultrasound and/or CT scan had signs of severity at diagnosis, four times higher than the previous year. Non-COVID patients underwent surgery more often than the COVID group. Over 1/3 of 100 operated patients had postoperative morbidity, versus only 15% the previous year. The most common complications were septic shock, pneumonia, and ARDS. ICU care was required in 17% of patients, and was most often required in the SARS-CoV-2-infected group, which also had a higher morbidity and mortality. The 30-day mortality in the surgical series was of 7%, with no differences with the previous year. The strongest independent predictors of overall mortality were age > 70 years, ASA III–IV, ESS > 9, and SARS-CoV-2 infection.

Conclusions

Non-operative management (NOM) was undertaken in a third of patients, and only 14% of operated patients had a perioperative confirmation of -CoV-2 infection. The severity and morbidity of COVID-19-infected patients was much higher. Late presentations for medical care may have added to the high morbidity of the series.

The emergency surgery score (ESS) and outcomes in elderly patients undergoing emergency laparotomy: A post-hoc analysis of an EAST multicenter study

INTRODUCTION

We sought to evaluate whether the Emergency Surgery Score (ESS) can accurately predict outcomes in elderly patients undergoing emergent laparotomy (EL).

METHODS

This is a post-hoc analysis of an EAST multicenter study. Between April 2018 and June 2019, all adult patients undergoing EL in 19 participating hospitals were prospectively enrolled, and ESS was calculated for each patient. Using the c-statistic, the correlation between ESS and mortality, morbidity, and need for ICU admission was assessed in three patient age cohorts (65-74, 75-84, ≥85 years old).

RESULTS

715 patients were included, of which 52% were 65-74, 34% were 75-84, and 14% were ≥85 years old; 51% were female, and 77% were white. ESS strongly correlated with postoperative mortality (c-statistic:0.81). Mortality gradually increased from 0% to 20%-60% at ESS of 2, 10 and 16 points, respectively. ESS predicted mortality, morbidity, and need for ICU best in patients 65-74 years old (c-statistic:0.81, 0.75, 0.83 respectively), but its performance significantly decreased in patients ≥85 years (c-statistic:0.72, 0.64, 0.67 respectively).

CONCLUSION

ESS is an accurate predictor of outcome in the elderly EL patient 65-85 years old, but its performance decreases for patients ≥85. Consideration should be given to modify ESS to better predict outcomes in the very elderly patient population.