Accuracy of the ACS NSQIP Surgical Risk Calculator for Predicting Postoperative Complications in Gastric Cancer Following Open Gastrectomy

INTRODUCTION

The prediction of complications before gastric surgery is of utmost importance in shared decision making and proper counseling of the patient in order to minimize postoperative complications. Our aim was to evaluate the predictive validity of American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) risk calculator in gastric cancer patients who underwent gastrectomy.

METHODS

Preoperative assessment data of 432 patients were retrospectively reviewed and manually entered into the calculator. The accuracy of the calculator was evaluated using Pearson's chi-squared test, C-statistic, Brier score, and Hosmer-Lemeshow test.

RESULTS

The lowest Brier scores were observed in urinary tract infection, renal failure, venous thromboembolism, pneumonia, and cardiac complications. Best results were obtained for predicting sepsis, discharge to rehabilitation facility, and death (low Brier scores, C-statistic >.7, and Hosmer-Lemeshow P > .05).

CONCLUSION

The calculator had a strong performance in predicting sepsis, discharge to the rehabilitation facility, and death. However, it performed poor in predicting the most commonly observed events (any or serious complication and surgical site infection).

Comparison of Acute Physiology and Chronic Health Evaluation (APACHE) II and American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) scoring system in predicting postoperative mortality in patients undergoing emergency laparotomy: A retrospective study

Background and Aims

There is paucity of studies on preoperative risk assessment tools in patients undergoing emergency surgery. The present study evaluated the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator and American Society of Anesthesiologists (ASA) physical status (PS) classification system in patients undergoing emergency exploratory laparotomy.

Methods

This retrospective study included 60 adult patients who underwent emergency exploratory laparotomy for perforation peritonitis. The clinical details, ASA PS classification, laboratory investigations and postoperative course of patients were retrieved from their medical records. Based on these details, APACHE II and ACS-NSQIP were calculated for the patients. The study's primary outcome was the accuracy of the preoperative APACHE II, ACS-NSQIP risk calculator and ASA PS class in predicting the postoperative 30-day mortality of patients.

Results

The area under the curve (AUC) of APACHE II, ACS-NSQIP score, and ASA PS classification for mortality 30 days after surgery was 0.737, 0.694 and 0.601, respectively. The P value for the Hosmer-Lemeshow (H-L) test of scoring systems was 0.05, 0.25 and 0.05, respectively. AUC for postoperative complications was 0.799 for APACHE II, 0.683 for ACS-NSQIP and 0.601 for ASA PS classification. H-L test of these scoring systems for complications after surgery revealed P values of 0.62, 0.36 and 0.53, respectively.

Conclusion

Compared to the ACS-NSQIP and ASA PS classification system, the APACHE II score has a better discriminative ability for postoperative complications and mortality in adult patients undergoing emergency exploratory laparotomy.

Current use of the National Surgical Quality Improvement Program surgical risk calculator in academic surgery: a mixed-methods study

Background

This study aims to quantitatively assess use of the NSQIP surgical risk calculator (NSRC) in contemporary surgical practice and to identify barriers to use and potential interventions that might increase use.

Materials and methods

We performed a cross-sectional study of surgeons at seven institutions. The primary outcomes were self-reported application of the calculator in general clinical practice and specific clinical scenarios as well as reported barriers to use.

Results

In our sample of 99 surgeons (49.7% response rate), 73.7% reported use of the NSRC in the past month. Approximately half (51.9%) of respondents reported infrequent NSRC use (<20% of preoperative discussions), while 14.3% used it in ≥40% of preoperative assessments. Reported use was higher in nonelective cases (30.2% vs 11.1%) and in patients who were ≥65 years old (37.1% vs 13.0%), functionally dependent (41.2% vs 6.6%), or with surrogate consent (39.9% vs 20.4%). NSRC use was not associated with training status or years in practice. Respondents identified a lack of influence on the decision to pursue surgery as well as concerns regarding the calculator's accuracy as barriers to use. Surgeons suggested improving integration to workflow and better education as strategies to increase NSRC use.

Conclusions

Many surgeons reported use of the NSRC, but few used it frequently. Surgeons reported more frequent use in nonelective cases and frail patients, suggesting the calculator is of greater utility for high-risk patients. Surgeons raised concerns about perceived accuracy and suggested additional education as well as integration of the calculator into the electronic health record.

What If We Do Not Operate? Outcomes of Nonoperatively Managed Emergency General Surgery Patients

INTRODUCTION

Although two-thirds of patients with emergency general surgery (EGS) conditions are managed nonoperatively, their long-term outcomes are not well described. We describe outcomes of nonoperative management in a cohort of older EGS patients and estimate the projected risk of operative management using the NSQIP Surgical Risk Calculator (SRC).

MATERIALS AND METHODS

We studied single-center inpatients aged 65 y and more with an EGS consult who did not undergo an operation (January 2019-December 2020). For each patient, we recorded the surgeon's recommendation as either an operation was "Not Needed" (medical management preferred) or "Not Recommended" (risk outweighed benefits). Our main outcome of interest was mortality at 30 d and 1 y. Our secondary outcome of interest was SRC-projected 30-day postoperative mortality risk (median % [interquartile range]), calculated using hypothetical low-risk and high-risk operations.

RESULTS

We included 204 patients (60% female, median age 75 y), for whom an operation was "Not Needed" in 81% and "Not Recommended" in 19%. In this cohort, 11% died at 30 d and 23% died at 1 y. Mortality was higher for the "Not Recommended" cohort (37% versus 5% at 30 d and 53% versus 16% at 1 y, P < 0.05). The SRC-projected 30-day postoperative mortality risk was 3.7% (1.3-8.7) for low-risk and 5.8% (2-11.8) for high-risk operations.

CONCLUSIONS

Nonoperative management in older EGS patients is associated with very high risk of short-term and long-term mortality, particularly if a surgeon advised that risks of surgery outweighed benefits. The SRC may underestimate risk in the highest-risk patients.

Evaluating Postoperative Morbidity in Patients Undergoing Pelvic Reconstructive Surgery Using the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator

OBJECTIVE

The aim of this study was to evaluate the ability of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator to predict surgical morbidity in patients undergoing pelvic reconstructive surgery.

METHODS

This was a retrospective study of patients who underwent pelvic reconstructive surgery from 2014 to 2017. Preoperative risk factors were abstracted from medical records and entered into the ACS NSQIP surgical risk calculator. The Current Procedural Terminology code that produced the largest risk was used and compared with actual patient outcomes. Demographic, clinical, and surgical characteristics were analyzed descriptively. Logistic regression evaluated significant factors associated with each outcome; prediction capability of the risk calculator was assessed.

RESULTS

Seven hundred thirty-one surgical cases were reviewed. The cohort was predominantly younger than 65 years (58.7%), white (77.4%), multiparous (81.1%), and overweight (64.7%); 76.3% were American Society of Anesthesiologists class 2, and 70.2% had vaginal surgery. There was no difference in median risk scores between those with and without postoperative event. Two hundred twenty-one (30.3%) experienced "any serious complication," with 89% of these due to urinary tract infection. Incidence of urinary tract infection was 27%; readmission was 3.2%, and 3.6% returned to the operating room. Decreasing age was predictive of return to the operating room (P < 0.001), and increasingly worse functional status predicted discharge to nursing or skilled rehabilitation facility (P < 0.001).

CONCLUSIONS

The ACS NSQIP surgical risk calculator is an overall poor predictor of actual outcomes in a sample of patients who underwent pelvic reconstructive surgery, perhaps because of low prevalence of serious events. A more accurate surgical risk calculator is needed for this patient population.

What is the Accuracy of the ACS-NSQIP Surgical Risk Calculator in Emergency Abdominal Surgery? A Meta-Analysis

BACKGROUND

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator provides an estimation of 30-d post-operative complications including mortality. This tool has the potential to both aid in decision-making for patients and their families and also in optimizing the clinical management of high-risk patients. However, it's utility in patients requiring emergency abdominal surgery has shown to be inconsistent outside of NSQIP participating institutions. This study undertook a meta-analysis to assess the calculator's accuracy in predicting mortality in these patients.

METHODS

A literature search of PubMed, Medline and Cochrane databases was conducted between October 2019 to April 2020. The PubMed, Medline and Cochrane Databases were searched for relevant studies. The search strategy included studies from January 2013 to April 2020. Studies including elective surgery were excluded. A random effects model was used and fitted using restricted maximum likelihood estimation. The O:E ratio was used to validate the calculator's accuracy in predicting mortality.

RESULTS

Six studies were included in the meta-analysis, with a total of 1835 patients undergoing emergency intra-abdominal surgery. The summary estimate of the O:E ratio of the ACS-NSQIP surgical risk calculator in predicting 30-d post-operative mortality was 1.06 (95% CI 0.74-1.51). There was significant heterogeneity between studies with a Cochrane Q of 11.96 (P = 0.04) and I² = 57.5%.

CONCLUSIONS

The ACS-NSQIP surgical risk calculator is a reliable predictor of mortality in this external cohort and has potential to be utilised in the multi-disciplinary care of patients undergoing emergency abdominal surgery.

Scoring Systems May be Effective in Predicting Mortality Associated with Palliative Emergency Gastrointestinal Surgery: A Retrospective Observational Study

Background

Palliative emergency gastrointestinal surgery is associated with significant morbidity and mortality and weighing up the benefits and harms during the decision-making may be challenging. There are very few studies on surgery in palliative patient population. The aim of this retrospective study was to evaluate morbidity and mortality after palliative emergency gastrointestinal surgery and the usability of scoring systems in predicting the outcome.

Methods

Consecutive adult patients undergoing palliative emergency surgery at a tertiary hospital during the period 2015 to 2016 were included. Pre- and post-operative functional status, morbidity and mortality of patients were assessed. The predictive value of the American Society of Anesthesiologists (ASA) classification, the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator (ACS NSQIP SRC) and Palliative index (PI) in estimating morbidity and mortality were determined.

Results

A total of 93 patients (age 69 [28–92] years, 51% female) were included. Typical indications for surgery were bowel obstruction (52%) and securing food intake (30%). Pre-operatively two patients (2.2%) were totally dependent in daily activities, while post-operatively the respective share was 34% at discharge from hospital. The incidence of post-operative complications was 37% and 14% died during the hospital stay. One-, three-month and one-year mortality rates were 41%, 63% and 87%, respectively. While ASA score, PI score and ACS NSQIP did not predict post-operative morbidity, both ASA score and ACS NSQIP SRC predicted post-operative mortality.

Conclusions

Palliative emergency laparotomy is associated with significant post-operative mortality and morbidity. Scorings, such as ASA score and ACS NSQIP SRC predict mortality in this patient population.

Readmission for venous thromboembolism after emergency general surgery is underreported and influenced by insurance status

BACKGROUND

Prior studies of venous thromboembolism (VTE) after emergency general surgery (EGS) are not nationally representative nor do they fully capture readmissions to different hospitals. We hypothesized that different-hospital readmission accounted for a significant number of readmissions with VTE after EGS and that predictive factors would be different for same- and different-hospital readmissions.

METHODS

The 2014 Nationwide Readmissions Database was queried for nonelective EGS hospitalizations. The outcomes were readmission to the index or different hospitals within 180 days with VTE. Multivariate logistic regressions identified risk factors for readmission to index and different hospitals with VTE, reported as odds ratios with their 95% confidence intervals. Patients were excluded if during the index admission they expired, developed a VTE, had a vena cava filter placed, or did not have at least 180 days of follow-up.

RESULTS

Of 1,584,605 patients meeting inclusion criteria, 1.3% (n = 20,963) of patients were readmitted within 180 days with a VTE. Of these, 28% (n = 5,866) were readmitted to a different hospital. Predictors overall for readmission with VTE were malignancy, prolonged hospitalization, age, and being publicly insured. However, predictors for readmission to a different hospital are based on hospital characteristics, including for-profit status, or procedure type.

CONCLUSIONS

Nearly one in three readmissions with VTE after EGS occurs at a different hospital and may be missed by current quality metrics that only capture same-hospital readmission. Such metrics may underestimate for-profit hospital postoperative VTE rates relative to public and nonprofit hospitals, potentially affecting benchmarking and reimbursement. Postdischarge VTE rate is associated with insurance status. These findings have implications for policy and prevention programming design.

LEVEL OF EVIDENCE

Epidemiological study, level III.

Risk-adjustment models in patients undergoing head and neck surgery with reconstruction

BACKGROUND

With the current focus on value-based outcomes and reimbursement models, perioperative risk adjustment is essential. Specialty surgical outcomes are not well predicted by the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP); the Head and Neck-Reconstructive Surgery NSQIP was created as a specialty-specific platform for patients undergoing head and neck surgery with flap reconstruction. This study aims to investigate risk prediction models in these patients.

METHODS

The Head and Neck-Reconstructive Surgery NSQIP collected data on patients undergoing head and neck surgery with flap reconstruction from August 1, 2012 to October 20, 2016. Multivariable logistic regression models were created for 9 outcomes (postoperative ventilator dependence, pneumonia, superficial recipient surgical site infection, presence of tracheostomy/nasoenteric (NE)/gastrostomy/gastrojejunostomy(G/GJ) tube 30 days postoperatively, conversion from NE to G/GJ tube, unplanned return to the operating room, length of stay > 7 days). External validation was completed with a more contemporary cohort.

RESULTS

A total of 1095 patients were included in the modelling cohort and 407 in the validation cohort. Models performed well predicting tracheostomy, NE, G/GJ tube presence at 30 days postoperatively and conversion from NE to G/GJ tube (c-indices = 0.75-0.91). Models for postoperative pneumonia, superficial recipient surgical site infection, ventilator dependence > 48 h, and length of stay > 7 days were fair (concordance [c]-indices = 0.63-0.69). The predictive model for unplanned return to the operating room was poor (c-index = 0.58).

CONCLUSIONS AND RELEVANCE

Reliable and discriminant risk prediction models were able to be created for postoperative outcomes using the specialty-specific Head and Neck-Reconstructive Surgery Specific NSQIP.

Prospective validation of the Emergency Surgery Score in emergency general surgery: An Eastern Association for the Surgery of Trauma multicenter study

BACKGROUND

The Emergency Surgery Score (ESS) was recently developed and retrospectively validated as an accurate mortality risk calculator for emergency general surgery. We sought to prospectively validate ESS, specifically in the high-risk nontrauma emergency laparotomy (EL) patient.

METHODS

This is an Eastern Association for the Surgery of Trauma multicenter prospective observational study. Between April 2018 and June 2019, 19 centers enrolled all adults (aged >18 years) undergoing EL. Preoperative, intraoperative, and postoperative variables were prospectively and systematically collected. Emergency Surgery Score was calculated for each patient and validated using c-statistic methodology by correlating it with three postoperative outcomes: (1) 30-day mortality, (2) 30-day complications (e.g., respiratory/renal failure, infection), and (3) postoperative intensive care unit (ICU) admission.

RESULTS

A total of 1,649 patients were included. The mean age was 60.5 years, 50.3% were female, and 71.4% were white. The mean ESS was 6, and the most common indication for EL was hollow viscus perforation. The 30-day mortality and complication rates were 14.8% and 53.3%; 57.0% of patients required ICU admission. Emergency Surgery Score gradually and accurately predicted 30-day mortality; 3.5%, 50.0%, and 85.7% of patients with ESS of 3, 12, and 17 died after surgery, respectively, with a c-statistic of 0.84. Similarly, ESS gradually and accurately predicted complications; 21.0%, 57.1%, and 88.9% of patients with ESS of 1, 6, and 13 developed postoperative complications, with a c-statistic of 0.74. Emergency Surgery Score also accurately predicted which patients required intensive care unit admission (c-statistic, 0.80).

CONCLUSION

This is the first prospective multicenter study to validate ESS as an accurate predictor of outcome in the EL patient. Emergency Surgery Score can prove useful for (1) perioperative patient and family counseling, (2) triaging patients to the intensive care unit, and (3) benchmarking the quality of emergency general surgery care.

LEVEL OF EVIDENCE

Prognostic study, level III.

Is the ACS-NSQIP Risk Calculator Accurate in Predicting Adverse Postoperative Outcomes in the Emergency Setting? An Italian Single-center Preliminary Study

Background

The ACS-NSQIP surgical risk calculator (SRC) is an open-access online tool that estimates the chance for adverse postoperative outcomes. The risk is estimated based on 21 patient-related variables and customized for specific surgical procedures. The purpose of this monocentric retrospective study is to validate its predictive value in an Italian emergency setting.

Methods

From January to December 2018, 317 patients underwent surgical procedures for acute cholecystitis (n = 103), appendicitis (n = 83), gastrointestinal perforation (n = 45), and intestinal obstruction (n = 86). Patients’ personal risk was obtained and divided by the average risk to calculate a personal risk ratio (RR). Areas under the ROC curves (AUC) and Brier score were measured to assess both the discrimination and calibration of the predictive model.

Results

The AUC was 0.772 (95%CI 0.722–0.817, p < 0.0001; Brier 0.161) for serious complications, 0.887 (95%CI 0.847–0.919, p < 0.0001; Brier 0.072) for death, and 0.887 (95%CI 0.847–0.919, p < 0.0001; Brier 0.106) for discharge to nursing or rehab facility. Pneumonia, cardiac complications, and surgical site infection presented an AUC of 0.794 (95%CI 0.746–0.838, p < 0.001; Brier 0.103), 0.836 (95%CI 0.790–0.875, p < 0.0001; Brier 0.081), and 0.729 (95%CI 0.676–0.777, p < 0.0001; Brier 0.131), respectively. A RR > 1.24, RR > 1.52, and RR > 2.63 predicted the onset of serious complications (sensitivity = 60.47%, specificity = 64.07%; NPV = 81%), death (sensitivity = 82.76%, specificity = 62.85%; NPV = 97%), and discharge to nursing or rehab facility (sensitivity = 80.00%, specificity = 69.12%; NPV = 95%), respectively.

Conclusions

The calculator appears to be accurate in predicting adverse postoperative outcomes in our emergency setting. A RR cutoff provides a much more practical method to forecast the onset of a specific type of complication in a single patient.

Risk factors for mortality after emergency laparotomy: scoping systematic review

BACKGROUND

Emergency laparotomy (EL) is a common procedure with high mortality leading to several efforts to record and reduce mortality. Risk scores currently used by quality improvement programmes either require intraoperative data or are not specific to EL. To be of utility to clinicians/patients, estimation of preoperative risk of mortality is important. We aimed to explore individual preoperative risk factors that might be of use in developing a preoperative mortality risk score.

METHODS

Two independent reviewers identified relevant articles from searches of MEDLINE, EMBASE and Cochrane databases from January 1980 to January 2018. We selected studies that evaluated only preoperative predictive factors for mortality in EL patients.

RESULTS

The search yielded 6648 articles screened, with 22 studies included examining 157 728 patients. The combined post-operative 30-day mortality was 13%. All, but one small study, were at low risk of bias. A meta-analysis of results was not possible due to the heterogeneity of populations and outcomes. Age, American Society of Anesthesiologists, preoperative sepsis, dependency status, current cancer and comorbidities were associated with increased mortality. Acute physiological derangements seen in renal, albumin and complete blood count assays were strongly associated with mortality. Delay to surgery and diabetes did not influence mortality. Higher body mass index was protective.

CONCLUSION

Preoperatively, risk factors identified can be used to develop and update risk scores specific for EL mortality. This scoping review focused on the preoperative setting which helps tailor treatment decisions. It highlights the need for further research to test the relevance of newer risk factors such as frailty and nutrition.

Evaluating the ACS-NSQIP Risk Calculator in Primary GI Neuroendocrine Tumor: Results from the United States Neuroendocrine Tumor Study Group

The ACS established an online risk calculator to help surgeons make patient-specific estimates of postoperative morbidity and mortality. Our objective was to assess the accuracy of the ACS-NSQIP calculator for estimating risk after curative intent resection for primary GI neuroendocrine tumors (GI-NETs). Adult patients with GI-NET who underwent complete resection from 2000 to 2017 were identified using a multi-institutional database, including data from eight academic medical centers. The ability of the NSQIP calculator to accurately predict a particular outcome was assessed using receiver operating characteristic curves and the area under the curve (AUC). Seven hundred three patients were identified who met inclusion criteria. The most commonly performed procedures were resection of the small intestine with anastomosis (N = 193, 26%) and partial colectomy with anastomosis (N = 136, 18%). The majority of patients were younger than 65 years (N = 482, 37%) and ASA Class III (N = 337, 48%). The most common comorbidities were diabetes (N = 128, 18%) and hypertension (N = 395, 56%). Complications among these patients based on ACS NSQIP definitions included any complication (N = 132, 19%), serious complication (N = 118, 17%), pneumonia (N = 7, 1.0%), cardiac complication (N = 1, 0.01%), SSI (N = 80, 11.4%), UTI (N = 17, 2.4%), venous thromboembolism (N = 18, 2.5%), renal failure (N = 16, 2.3%), return to the operating room (N = 27, 3.8%), discharge to nursing/rehabilitation (N = 22, 3.1%), and 30-day mortality (N = 9, 1.3%). The calculator provided reasonable estimates of risk for pneumonia (AUC = 0.721), cardiac complication (AUC = 0.773), UTI (AUC = 0.716), and discharge to nursing/ rehabilitation (AUC = 0.779) and performed poorly (AUC < 0.7) for all other complications Fig. 1). The ACS-NSQIP risk calculator estimates a similar proportion of risk to actual events in patients with GI-NET but has low specificity for identifying the correct patients for many types of complications. The risk calculator may require modification for some patient populations.

Clinically Meaningful Laboratory Protocols Reduce Hospital Charges Based on Institutional and ACS-NSQIP® Risk Calculators in Hepatopancreatobiliary Surgery

Postoperative laboratory testing is an underrecognized but substantial contributor to health-care costs. We aimed to develop and validate a clinically meaningful laboratory (CML) protocol with individual risk stratification using generalizable and institution-specific predictive analytics to reduce laboratory testing and maximize cost savings for low-risk patients. An institutionally based risk model was developed for pancreaticoduodenectomy and hepatectomy, and an ACS-NSQIP®–based model was developed for distal pancreatectomy. Patients were stratified in each model to the CML by individual risk of major complications, readmission, or death. Clinical outcomes and estimated cost savings were compared with those of a historical cohort with standard of care. Over 34 months, 394 patients stratified to the CML for pancreaticoduodenectomy or hepatectomy saved an estimated $803,391 (44.4%). Over 13 months, 52 patients stratified to the CML for distal pancreatectomy saved an estimated $81,259 (30.5%). Clinical outcomes for 30-day major complications, readmission, and mortality were unchanged after implementation of either model. Predictive analytics can target low-risk patients to reduce laboratory testing and improve cost savings, regardless of whether an institutional or a generalized risk model is implemented. Broader application is important in patient-centered health care and should transition from predictive to prescriptive analytics to guide individual care in real time.

Life after 90: Predictors of mortality and performance of the ACS-NSQIP risk calculator in 4,724 nonagenarian patients undergoing emergency general surgery

BACKGROUND

The decision to emergently operate on nonagenarian patients (NONAs) can be complex due to the uncertainty about outcomes and goals of care at this advanced age. We sought to study: (1) the outcomes and predictors of mortality for NONAs undergoing emergency general surgery (EGS) and (2) the accuracy of ACS-NSQIP mortality risk calculator in this special population.

METHODS

Using the 2007 to 2015 ACS-NSQIP database, we included all patients older than 90 years of age who underwent an emergent operation with a Current Procedural Terminology (CPT) code for "digestive system." Multivariable logistic regression analyses were performed to identify independent predictors of 30-day mortality. NONAs' mortality rates for different combinations of risk factors were also studied and compared to the ACS-NSQIP calculator-predicted mortality rates.

RESULTS

Out of a total of 4,456,809 patients, 4,724 NONAs were included. The overall 30-day patient mortality and morbidity rates were 21% and 45%, respectively. In multivariable analyses, several independent predictors of 30-day mortality were identified, including recent history of weight loss, history of steroid use, smoking, functional dependence, hypoalbuminemia and sepsis or septic shock. The mortality among NONAs with a history of steroid use and a recent history of weight loss was 100%. Similarly, the mortality of NONAs with recent history of weight loss who presented with preoperative septic shock was 93%. The ACS-NSQIP calculator significantly and consistently underestimated the risk of mortality in all NONAs undergoing EGS.

CONCLUSION

Most NONAs undergoing EGS survive the hospital stay and the first 30 postoperative days, even in the presence of significant preexisting comorbidities. However, the combination of recent weight loss with either steroid use or septic shock nearly ensures mortality and should be used in the discussions with patients and families before a decision to operate is made. The ACS-NSQIP surgical risk calculator should be used with caution in these high-risk patients.

LEVEL OF EVIDENCE

Prognostic study, level III.

Cardiac biomarkers improve prediction performance of the combination of American Society of Anesthesiologists physical status classification and Americal College of Surgeons National Surgical Quality Improvement Program calculator for postoperative mortality in elderly patients: a pilot study

BACKGROUND

Our previous research has shown American Society of Anaesthesiologists physical status classification (ASA) score and Americal College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) calculator to have the most accuracy in the prediction of postoperative mortality.

AIMS

The aim of our research was to define the most reliable combination of cardiac biomarkers with ASA and ACS NSQIP.

METHODS

We have included a total of 78 patients. ASA score has been determined in standard fashion, while we used the available interactive calculator for the ACS NSQIP score. Biomarkers BIRC5, H-FABP, and hsCRP have been measured in specialized laboratories.

RESULTS

All of the deceased patients had survivin (BIRC5) > 4.00 pg/ml, higher values of H-FABP and hsCRP and higher estimated levels of ASA and ACS NSQIP (P = 0.0001). ASA and ACS NSQIP alone had AUC of, respectively, 0.669 and 0.813. The combination of ASA and ACS NSQIP had AUC = 0.841. Combination of hsCRP with the two risk scores had AUC = 0.926 (95% CI 0.853-1.000, P < 0.0001). If we add three cardiac biomarkers to this model, we get AUC as high as 0.941 (95% CI 0.876-1.000, P < 0.0001). The correction of statistical models with comorbidities (CIRS-G score) did not change the accuracy of prediction models that we have provided.

DISCUSSION

Addition of ACS NSQIP and biomarkers adds to the accuracy of ASA score, which has already been proved by other authors.

CONCLUSION

Cardiac biomarker hsCRP can be used as the most reliable cardiac biomarker; however, the "multimarker approach" adds the most to the accuracy of the combination of clinical risk scores.

Rates of Serious Complications Estimated by the ACS-NSQIP Surgical Risk Calculator in Predicting Oncologic Outcomes of Patients Treated with Pancreaticoduodenectomy for Pancreatic Head Cancer

BACKGROUND

The purpose of this study was to validate the predictive value of the oncologic outcome in addition to the validation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator in patients treated with pancreaticoduodenectomy (PD) or pylorus-preserving pancreaticoduodenectomy (PPPD) for pancreatic head cancer.

METHODS

From June 2005 to December 2014, 199 patients underwent PD or PPPD for pancreatic head cancer. Medical records were retrospectively reviewed for investigating general patient characteristics and any comorbid diseases. The calculated perioperative complication risks from the ACS NSQIP calculator were compared with observed complication rates. In a propensity score matching analysis, disease-free survival (DFS) and overall survival (OS) were estimated according to calculated severe complication rate (CSCR).

RESULTS

The CSCR > 17.9% (n = 69) and CSCR < 17.9% (n = 130) groups were significantly different considering number of the retrieved lymph nodes (22.95 ± 14.0 vs 18.80 ± 10.1, p = 0.029), histologic grade (p = 0.0235), and incidence of lymphovascular invasion (p = 0.026). The CSCR < 17.9% group had longer DFS (17.0 vs. 11.0 months, p = 0.015), but the OS was similar between the groups (39.0 vs. 23.0 months, p = 0.48). In the 1:2 propensity score analysis, the CSCR < 17.9% group had longer DFS and OS (DFS 26.0 vs. 11.0 months, p = 0.009; OS 44.0 vs. 26.0 months, p = 0.023).

CONCLUSION

The ACS NSQIP surgical risk calculator predicts surgical risk in patients with pancreatic head cancer who undergo PD or PPPD. Furthermore, this tool can help predict the prognosis of surgically treated pancreatic head cancer.

Predicting Postoperative Complications after Acute Care Surgery: How Accurate is the ACS NSQIP Surgical Risk Calculator?

The ACS NSQIP Surgical Risk Calculator (SRC) is an evidence-based clinical tool commonly used for evaluating postoperative risk. The goal of this study was to validate SRC-predicted complications by comparing them with observed outcomes in the acute care surgical setting. In this study, pre- and postoperative data from 1693 acute care surgeries (hernia repair, enterolysis, intestinal incision/excision and enterectomy, gastrectomy, debridement, colectomy, appendectomy, cholecystectomy, gastrorrhaphy, and incision and drainage of soft tissue, breast abscesses, and removal of foreign bodies) performed at a Level I trauma center over a five-year time period were abstracted. Predictions for any and serious complications were based on SRC were compared with observed outcomes using various measures of diagnostic. When evaluated as one group, the SRC had good discriminative power for predicting any and serious complications after acute care surgeries (Area Under the Curve (AUC) 0.79, 0.81). In addition, the SRC met Brier score requirements for an informative model overall. However, the predictive accuracy of the SRC varied for various procedures within the acute care patient population. For serious complications, the diagnostic measures ranged from an AUC of 0.61 and negative likelihood ratio of 0.716 for incision & drainage soft tissue to AUC of 0.91 and negative likelihood ratio of 0.064 for gastrorrhaphy. Length of stay was significantly underestimated by the SRC overall (8.56 days, P < 0.01) and for individual procedures. The SRC performs well at predicting complications after acute care surgeries overall; however, there is great variability in performance between procedure types. Further refinements in risk stratification may improve SRC predictions.

Emergent Laparoscopic Ventral Hernia Repairs

BACKGROUND

Emergent repairs of incarcerated and strangulated ventral hernia repairs (VHR) are associated with higher perioperative morbidity and mortality than those repaired electively. Despite increasing utilization of minimally invasive techniques in elective repairs, the role for laparoscopy in emergent VHR is not well defined, and its feasibility has been demonstrated only in single center studies.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database (2009-2016) was queried for emergent VHR. Laparoscopic and open techniques were compared using univariate and multivariate analyses.

RESULTS

A total of 11,075 patients who underwent emergent ventral and incisional hernia repairs were identified: 85.5% open ventral hernia repair (OVHR), 14.5% laparoscopic ventral hernia repair (LVHR). Patients who underwent emergent OVHRs were older, more comorbid, and more likely to be septic at the time of surgery than those undergoing emergent LVHRs. Emergent OVHR patients were more likely to have minor complications (22.1% versus 11.0%; OR 1.7; 95% CI 1.069-2.834). After controlling for confounding variables, LVHR and OVHR had similar outcomes, with the exception of higher rates of superficial surgical site infection in OVHR (5.0% versus 1.8%; odd's ratio (OR) 2.7; 95% confidence interval (CI) 1.176-6.138). Following multivariate analysis, laparoscopic approach demonstrated similar outcomes in major complications, reoperation, and 30-d mortality compared to open repairs. However, when controlling for other confounding factors, LVHR had reduced length of stay compared to OVHR (6.7 versus 4.0 d; 1.6 d longer, standard error 0.77, P < 0.03).

CONCLUSIONS

Emergent LVHR is associated with fewer superficial surgical site infection and shorter length of stay than OVHR but no difference in major complications, reoperation or 30-d mortality is associated with LVHR in the emergency setting.

Quality Improvement in Anesthesiology - Leveraging Data and Analytics to Optimize Outcomes

Quality improvement is at the heart of practice of anesthesiology. Objective data are critical for any quality improvement initiative; when possible, a combination of process, outcome, and balancing metrics should be evaluated to gauge the value of an intervention. Quality improvement is an ongoing process; iterative reevaluation of data is required to maintain interventions, ensure continued effectiveness, and continually improve. Dashboards can facilitate rapid analysis of data and drive decision making. Large data sets can be useful to establish benchmarks and compare performance against other providers, practices, or institutions. Audit and feedback strategies are effective in facilitating positive change.