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Introduction: Prior publications about the association between participation in the American College of Surgeons National Surgical Quality Improvement Program and improved postoperative outcomes have reported mixed results. We aimed to perform a comprehensive analysis of preoperative characteristics and unadjusted and risk-adjusted postoperative complication rates over time in the American College of Surgeons National Surgical Quality Improvement Program dataset., Methods: We used the American College of Surgeons National Surgical Quality Improvement Program database, 2005 to 2018, to analyze preoperative patient characteristics and unadjusted and risk-adjusted rates of adverse postoperative outcomes by year. Expected events were calculated using multiple logistic regression, with each complication as the dependent variable and the 28 non-laboratory preoperative American College of Surgeons National Surgical Quality Improvement Program variables as the independent variables. Annual observed-to-expected ratios for each outcome were used to risk-adjust outcomes over time., Results: The analytic cohort included 7,474,298 operations across 9 surgical specialties. Both the preoperative patient risk and the unadjusted rate of postoperative complications decreased over time. While the observed-to-expected ratio for mortality remained around 1, the observed-to-expected ratios for the other outcomes decreased over time from 2005 to 2018, except for the following cardiac complications: overall morbidity 1.11 (95% confidence interval: 1.10-1.13) to 0.97 (0.96-0.98); pulmonary 1.18 (1.15-1.21) to 0.91 (0.89-0.92); infection 1.19 (1.16-1.21) to 1.01 (1.00-1.01); urinary tract infection 1.29 (1.23-1.34) to 0.87 (0.86-0.89); venous thromboembolism 1.10 (1.03-1.16) to 0.92 (0.90-0.94) ; cardiac 0.76 (0.70-0.81) to 1.04 (1.01-1.07); renal 1.14 (1.08-1.21) to 0.96 (0.93-0.99); stroke 1.12 (1.00-1.25) to 0.98 (0.94-1.03); and bleeding 1.35 (1.33-1.36) to 0.80 (0.79-0.81)., Conclusion: Hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program have experienced a decrease in risk-adjusted postoperative surgical complications over time in all areas except for mortality and cardiac complications., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: Prior studies have associated morbidity following anatomic lung resection with prolonged postoperative length of stay; however, each complication's individual impact on length of stay as a continuous variable has not been studied. The purpose of this study was to determine the risk-adjusted increase in length of stay associated with each individual postoperative complications following anatomic lung resection., Methods: Patients who underwent anatomic lung resection cataloged in the prospectively collected American College of Surgeons National Surgical Quality Improvement Program participant use file, 2005-2018, were targeted. The association between preoperative characteristics, postoperative complications and length of stay in days was tested. A negative binomial model adjusting for the effect of preoperative characteristics and 18 concurrent postoperative complications was used to generate incidence rate ratios. This model was fit to generate risk-adjusted increases in length of stay by complication., Results: Of 32 133 patients, 5065 patients (15.8%) experienced at least one post-operative complication. The most frequent complications were pneumonia (n = 1829, 5.7%), the need for transfusion (n = 1794, 5.6%) and unplanned reintubation (n = 1064, 3.3%). The occurrence of each of the 18 individual complications was associated with significantly increased length of stay. This finding persisted after risk-adjustment, with the greatest risk-adjusted increases being associated with prolonged ventilation (+17.4 days), followed by septic shock (+17.2 days), acute renal failure (+16.5 days) and deep surgical site infection (+13.2 days)., Conclusions: All 18 postoperative complications studied following anatomic lung resection were associated with significant risk-adjusted increases in length of stay, ranging from an increase of 17.4 days with prolonged ventilation to 2.6 days following the need for transfusion., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

Objective: We sought to evaluate how implementing a thoracic enhanced recovery after surgery (ERAS) protocol impacted surgical outcomes after elective anatomic lung resection., Background: The effect of implementing the ERAS Society/European Society of Thoracic Surgery thoracic ERAS protocol on postoperative outcomes throughout an entire health care system has not yet been reported., Methods: This was a prospective cohort study within one health care system (January 2019-March, 2023). A thoracic ERAS protocol was implemented on May 1, 2021 for elective anatomic lung resections, and postoperative outcomes were tracked using the electronic health record and Vizient data. The primary outcome was overall morbidity; secondary outcomes included individual complications, length of stay, opioid use, chest tube duration, and total cost. Patients were grouped into pre-ERAS and post-ERAS cohorts. Bivariable comparisons were performed using independent t -test, χ 2 , or Fisher exact tests, and multivariable logistic regression was performed to control for confounders., Results: There were 1007 patients in the cohort; 450 (44.7%) were in the post-ERAS group. Mean age was 66.2 years; most patients were female (65.1%), white (83.8%), had a body mass index between 18.5 and 29.9 (69.7%), and were ASA class 3 (80.6%). Patients in the postimplementation group had lower risk-adjusted rates of any morbidity, respiratory complication, pneumonia, surgical site infection, arrhythmias, infections, opioid usage, ICU use, and shorter postoperative length of stay (all P <0.05)., Conclusions: Postoperative outcomes were improved after the implementation of an evidence-based thoracic ERAS protocol throughout the health care system. This study validates the ERAS Society/European Society of Thoracic Surgery guidelines and demonstrates that simultaneous multihospital implementation can be feasible and effective., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: The purpose of this study was to determine whether the work relative value unit (workRVU) of a patient's operation can be useful as a measure of surgical complexity for the risk adjustment of surgical outcomes., Methods: We retrospectively analyzed the American College of Surgeon's National Surgical Quality Improvement Program database (2005-2018). We examined the associations of workRVU of the patient's primary operation with preoperative patient characteristics and associations with postoperative complications. We performed forward selection multiple logistic regression analysis to determine the predictive importance of workRVU. We then generated prediction models using patient characteristics with and without workRVU and compared c-indexes to assess workRVU's additive predictive value., Results: 7,507,991 operations were included. Patients who were underweight, functionally dependent, transferred from an acute care hospital, had higher American Society of Anesthesiologists class or who had medical comorbidities had operations with higher workRVU (all P < 0.0001). The subspecialties with the highest workRVU were neurosurgery (mean = 22.2), thoracic surgery (mean = 21.1), and vascular surgery (mean = 18.8) (P < 0.0001). For all postoperative complications, mean workRVU was higher for patients with the complication than those without (all P < 0.0001). For eight of 12 postoperative complications, workRVU entered the logistic regression models as a predictor variable in the 1st to 4th steps. Addition of workRVU as a preoperative predictive variable improved the c-index of the prediction models., Conclusions: WorkRVU was associated with sicker patients and patients experiencing postoperative complications and was an important predictor of postoperative complications. When added to a prediction model including patient characteristics, it only marginally improved prediction. This is possibly because workRVU is associated with patient characteristics., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Postoperative length of stay is a meaningful patient-centered outcome and an important determinant of healthcare costs. The Surgical Risk Preoperative Assessment System preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict postoperative length of stay has not been assessed. We aimed to determine whether the Surgical Risk Preoperative Assessment System variables could accurately predict postoperative length of stay up to 30 days in a broad inpatient surgical population., Methods: This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database from 2012 to 2018. A model using the Surgical Risk Preoperative Assessment System variables and a 28-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables, were fit to the analytical cohort (2012-2018) using multiple linear regression and compared using model performance metrics. Internal chronological validation of the Surgical Risk Preoperative Assessment System model was conducted using training (2012-2017) and test (2018) datasets., Results: We analyzed 3,295,028 procedures. The adjusted R 2 for the Surgical Risk Preoperative Assessment System model fit to this cohort was 93.3% of that for the full model (0.347 vs 0.372). In the internal chronological validation of the Surgical Risk Preoperative Assessment System model, the adjusted R 2 for the test dataset was 97.1% of that for the training dataset (0.3389 vs 0.3489)., Conclusion: The parsimonious Surgical Risk Preoperative Assessment System model can preoperatively predict postoperative length of stay up to 30 days for inpatient surgical procedures almost as accurately as a model using all 28 American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables and has shown acceptable internal chronological validation., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Introduction: Unplanned reoperation is an undesirable outcome with considerable risks and an increasingly assessed quality of care metric. There are no preoperative prediction models for reoperation after an index surgery in a broad surgical population in the literature. The Surgical Risk Preoperative Assessment System (SURPAS) preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict unplanned reoperation has not been assessed. This study's objective was to determine whether the SURPAS model could accurately predict unplanned reoperation., Methods: This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database, 2012-2018. An unplanned reoperation was defined as any unintended operation within 30 d of an initial scheduled operation. The 8-variable SURPAS model and a 29-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program nonlaboratory preoperative variables, were developed using multiple logistic regression and compared using discrimination and calibration metrics: C-indices (C), Hosmer-Lemeshow observed-to-expected plots, and Brier scores (BSs). The internal chronological validation of the SURPAS model was conducted using "training" (2012-2017) and "test" (2018) datasets., Results: Of 5,777,108 patients, 162,387 (2.81%) underwent an unplanned reoperation. The SURPAS model's C-index of 0.748 was 99.20% of that for the full model (C = 0.754). Hosmer-Lemeshow plots showed good calibration for both models and BSs were similar (BS = 0.0264, full; BS = 0.0265, SURPAS). Internal chronological validation results were similar for the training (C = 0.749, BS = 0.0268) and test (C = 0.748, BS = 0.0250) datasets., Conclusions: The SURPAS model accurately predicted unplanned reoperation and was internally validated. Unplanned reoperation can be integrated into the SURPAS tool to provide preoperative risk assessment of this outcome, which could aid patient risk education., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Considerable variability exists between surgeons' assessments of a patient's individual preoperative surgical risk. Surgical risk calculators are not routinely used despite their validation. We sought to compare thoracic surgeons' prediction of patients' risk of postoperative adverse outcomes vs a surgical risk calculator, the Surgical Risk Preoperative Assessment System (SURPAS). We developed vignettes from 30 randomly selected patients who underwent thoracic surgery in the American College of Surgeons' National Surgical Quality Improvement Program database. Twelve thoracic surgeons estimated patients' preoperative risks of postoperative morbidity and mortality. These were compared to SURPAS estimates of the same vignettes. C-indices and Brier scores were calculated for the surgeons' and SURPAS estimates. Agreement between surgeon estimates was examined using intraclass correlation coefficients (ICCs). Surgeons estimated higher morbidity risk compared to SURPAS for low-risk patients (ASA classes 1-2, 11.5% vs 5.1%, P ≤ 0.001) and lower morbidity risk compared to SURPAS for high-risk patients (ASA class 5, 37.6% vs 69.8%, P < 0.001). This trend also occurred in high-risk patients for mortality (ASA 5, 11.1% vs 44.3%, P < 0.001). C-indices for SURPAS vs surgeons were 0.84 vs 0.76 (P = 0.3) for morbidity and 0.98 vs 0.85 (P = 0.001) for mortality. Brier scores for SURPAS vs surgeons were 0.1579 vs 0.1986 for morbidity (P = 0.03) and 0.0409 vs 0.0543 for mortality (P = 0.006). ICCs showed that surgeons had moderate risk agreement for morbidity (ICC = 0.654) and mortality (ICC = 0.507). Thoracic surgeons and patients could benefit from using a surgical risk calculator to better estimate patients' surgical risks during the informed consent process., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Comorbidities and postoperative complications increase mortality, making early recognition and management critical. It is useful to understand how they are associated with one another. This study assesses associations between comorbidities, complications, and mortality., Methods: We calculated associations between comorbidities, complications, and 30-day mortality using the 2012-2018 ACS-NSQIP database. We examined the association between mortality and number of complications which complications were most associated with mortality., Results: 5,777,108 patients were included. 30-day mortality was 0.95%. For most comorbidities or postoperative complications, patients with these had higher mortality than patients without. Having ≥ 1 complication increased mortality risk by 32.5-fold (6.5% vs. 0.2%). Mortality rate significantly increased with increasing number of complications, particularly after two or more complications. Bleeding and sepsis were associated with the most deaths., Conclusion: The 30-day mortality rate was < 1% but was 32-fold higher in patients with complications and increased rapidly for patients with ≥ 2 complications. Bleeding and sepsis were the most prominent complications associated with mortality., (© 2022. The Author(s) under exclusive licence to Société Internationale de Chirurgie.)

Background: Surgical Risk Preoperative Assessment System (SURPAS) estimates patient's preoperative risk of 12 postoperative complications, yet little is known about associations between these probabilities- We sought to examine relationships between predicted probabilities., Methods: Risk of 12 postoperative complications was calculated using SURPAS and the 2012-2018 ACS-NSQIP database. Pearson correlation coefficients (r) were computed to examine relationships between predicted outcomes. "High-risk" was predicted risk in the 10th decile., Results: 4,777,267 patients were included. 71.1% were not high risk, 10.7% were high risk for 1, and 18.2% were high risk for ≥2 complications. High mortality risk was associated with high risk for pulmonary (r = 0.94), cardiac (r = 0.98), renal (r = 0.93), and stroke (0.96) complications. Patients high-risk for ≥2 complications had the most comorbidities and actual adverse outcomes., Conclusions: High preoperative risk for certain postoperative complications had strong correlations. 18.2% of patients were high-risk for ≥2 complications and could be targeted for risk reduction interventions., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Emerging literature suggests that measures of social vulnerability should be incorporated into surgical risk calculators. The Social Vulnerability Index (SVI) is a measure designed by the CDC that encompasses 15 socioeconomic and demographic variables at the census tract level. We examined whether adding the SVI into a parsimonious surgical risk calculator would improve model performance., Study Design: The eight-variable Surgical Risk Preoperative Assessment System (SURPAS), developed using the entire American College of Surgeons (ACS) NSQIP database, was applied to local ACS-NSQIP data from 2012 to 2018 to predict 12 postoperative outcomes. Patient addresses were geocoded and used to estimate the SVI, which was then added to the model as a ninth predictor variable. Brier scores and c-indices were compared for the models with and without the SVI., Results: The analysis included 31,222 patients from five hospitals. Brier scores were identical for eight outcomes and improved by only one to two points in the fourth decimal place for four outcomes with addition of the SVI. Similarly, c-indices were not significantly different (p values ranged from 0.15 to 0.96). Of note, the SVI was associated with most of the eight SURPAS predictor variables, suggesting that SURPAS may already indirectly capture this important risk factor., Conclusion: The eight-variable SURPAS prediction model was not significantly improved by adding the SVI, showing that this parsimonious tool functions well without including a measure of social vulnerability., (Copyright © 2022 by the American College of Surgeons. Published by Wolters Kluwer Health, Inc. All rights reserved.)

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.

Objectives: Baseline kidney function is a key predictor of postoperative morbidity and mortality. Whether an increased creatinine at the time of surgery, compared with the lowest creatinine in the 3 months before surgery, is associated with poor outcomes has not been evaluated. We examined whether creatinine elevations from "baseline" were associated with adverse postoperative outcomes., Methods: A total of 1486 patients who underwent cardiac surgery at the University of Colorado Hospital between January 2011 and May 2016 met inclusion criteria. "Change in creatinine from baseline" was defined as the difference between the immediate presurgical creatinine value and the lowest creatinine value within 3 months preceding surgery. Outcomes evaluated were in-hospital mortality, postoperative infection, postoperative stroke, development of stage 3 acute kidney injury, intensive care unit length of stay, and hospital length of stay. Outcomes were adjusted using a balancing score to account for differences in patient characteristics., Results: There were significant increases in the odds of postoperative infection (odds ratio, 1.17; confidence interval, 1.02-1.34; per 0.1 mg/dL increase in creatinine), stage 3 acute kidney injury (odds ratio, 1.44; confidence interval; 1.18-1.75), intensive care unit length of stay (odds ratio, 1.13; confidence interval, 1.01-1.26), and hospital length of stay (odds ratio, 1.09; confidence interval, 1.05-1.13). There was a significant increase in mortality in the unadjusted analysis, although not after adjustment using a balancing score. There was no association with postoperative stroke., Conclusions: Elevations in creatinine at the time of surgery above the "baseline" level are associated with increased postoperative morbidity. Baseline creatinine should be established before surgery, and small changes in creatinine should trigger heightened vigilance in the postoperative period., (Copyright © 2020 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Defining a "high risk" surgical population remains challenging. Using the Surgical Risk Preoperative Assessment System (SURPAS), we sought to define "high risk" groups for adverse postoperative outcomes., Materials and Methods: We retrospectively analyzed the 2009-2018 American College of Surgeons National Surgical Quality Improvement Program database. SURPAS calculated probabilities of 12 postoperative adverse events. The Hosmer Lemeshow graphs of deciles of risk and maximum Youden index were compared to define "high risk.", Results: Hosmer-Lemeshow plots suggested the "high risk" patient could be defined by the 10th decile of risk. Maximum Youden index found lower cutoff points for defining "high risk" patients and included more patients with events. This resulted in more patients classified as "high risk" and higher number needed to treat to prevent one complication. Some specialties (thoracic, vascular, general) had more "high risk" patients, while others (otolaryngology, plastic) had lower proportions., Conclusions: SURPAS can define the "high risk" surgical population that may benefit from risk-mitigating interventions., Competing Interests: Disclosure The authors report no conflicts of interest. The authors of this manuscript and the developers of SURPAS have no financial interests in SURPAS. The ACS-NSQIP and participating hospitals are the source of these data; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: The universal Surgical Risk Preoperative Assessment System (SURPAS) prediction models for postoperative adverse outcomes have good accuracy for estimating risk in broad surgical populations and for surgical specialties. The accuracy in individual operations has not yet been assessed. The objective of this study was to evaluate the Surgical Risk Preoperative Assessment System in predicting adverse outcomes for selected individual operations., Methods: The SURPAS models were applied to the top 2 most frequent common procedural terminology codes in 9 surgical specialties and 5 additional common general surgical operations in the 2009 to 2018 database of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Goodness of fit statistics were estimated, including c-indices for discrimination, Hosmer-Lemeshow graphs and P values for calibration, overall observed versus expected event rates, and Brier scores., Results: The total sample size was 2,020,172, which represented 29% of the 6.9 million operations in the ACS NSQIP database. Average c-indices across 12 outcomes were acceptable (≥0.70) for 13 (56.5%) of the 23 operations. Overall observed-to-expected rates were similar for mortality and overall morbidity across the 23 operations. Hosmer-Lemeshow graphs over quintiles of risk comparing observed-to-expected rates of mortality and overall morbidity were similar for 52% and 70% of operations, respectively. Model performance was better in less complex operations and those done in patients with lower preoperative risk., Conclusion: SURPAS displayed accuracy in estimating postoperative adverse events for some of the 23 operations studied, but not all. In the procedures where SURPAS was not accurate, developing disease or operation-specific risk models might be appropriate., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objective: Stroke remains a potentially devastating complication of aortic arch intervention. The value of neurophysiologic intraoperative monitoring (NIOM) in the early identification of stroke is unclear. We evaluated the utility of NIOM for early stroke detection in aortic arch surgery., Methods: Across 8 years at our institution, 365 patients underwent aortic arch surgery with hypothermic circulatory arrest, and 224 cases utilized NIOM. One patient was excluded for intraoperative death. In the remaining cohort, we reviewed the incidence, timing, and location of strokes, and the incidence and nature of NIOM alerts., Results: Hemiarch was performed in 154 patients and total arch replacement in 69 patients. Stroke occurred in 6.3% of all cases (14 out of 223), 15.9% of total arches (11 out of 69), and 2.0% of hemiarches (3 out of 154). There were 33 NIOM alerts (14.8%), and 9 patients had both alerts and stroke. Of these, NIOM deficits plausibly correlated with imaging findings in 7 cases (78%). Of the 5 stroke patients without NIOM alerts, 2 developed neurologic symptoms 3 days or more postoperatively, and infarcts in 3 patients did not result in sensory or motor deficits. Excluding 2 patients with late stroke, the sensitivity of NIOM for stroke detection was 75%, specificity was 88.5%, positive predictive value was 27.3%, and negative predictive value was 97.4%., Conclusions: Despite a low positive predictive value requiring a high level of discrimination when interpreting abnormal findings, NIOM has high sensitivity and specificity for the early stroke detection. Furthermore, its high negative predictive valve is reassuring for low risk of stroke in the absence of alerts., (Copyright © 2020 The American Association for Thoracic Surgery. All rights reserved.)

Background: The Surgical Risk Preoperative Assessment System (SURPAS) uses eight variables to accurately predict postoperative complications but has not been sufficiently studied in emergency surgery. We evaluated SURPAS in emergency surgery, comparing it to the Emergency Surgery Score (ESS)., Methods: SURPAS and ESS estimates of 30-day mortality and overall morbidity were calculated for emergency operations in the 2009-2018 ACS-NSQIP database and compared using observed-to-expected plots and rates, c-indices, and Brier scores. Cases with incomplete data were excluded., Results: In 205,318 emergency patients, SURPAS underestimated (8.1%; 35.9%) while ESS overestimated (10.1%; 43.8%) observed mortality and morbidity (8.9%; 38.8%). Each showed good calibration on observed-to-expected plots. SURPAS had better c-indices (0.855 vs 0.848 mortality; 0.802 vs 0.755 morbidity), while the Brier score was better for ESS for mortality (0.0666 vs. 0.0684) and for SURPAS for morbidity (0.1772 vs. 0.1950)., Conclusions: SURPAS accurately predicted mortality and morbidity in emergency surgery using eight predictor variables., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objectives: Severe acute kidney injury (AKI) is a known risk factor for infection and mortality. However, whether stage 1 AKI is a risk factor for infection has not been evaluated in adults. We hypothesized that stage 1 AKI following cardiac surgery would independently associate with infection and mortality., Methods: In this retrospective propensity score-matched study, we evaluated 1620 adult patients who underwent nonemergent cardiac surgery at the University of Colorado Hospital from 2011 to 2017. Patients who developed stage 1 AKI by Kidney Disease Improving Global Outcomes creatinine criteria within 72 hours of surgery were matched to patients who did not develop AKI. The primary outcome was an infection, defined as a new surgical-site infection, positive blood or urine culture, or development of pneumonia. Secondary outcomes included in-hospital mortality, stroke, and intensive care unit (ICU) and hospital length of stay (LOS)., Results: Stage 1 AKI occurred in 293 patients (18.3%). Infection occurred in 20.9% of patients with stage 1 AKI compared with 8.1% in the no-AKI group (P < .001). In propensity-score matched analysis, stage 1 AKI independently associated with increased infection (odds ratio [OR]; 2.24, 95% confidence interval [CI], 1.37-3.17), ICU LOS (OR, 2.38; 95% CI, 1.71-3.31), and hospital LOS (OR, 1.30; 95% CI, 1.17-1.45)., Conclusions: Stage 1 AKI is independently associated with postoperative infection, ICU LOS, and hospital LOS. Treatment strategies focused on prevention, early recognition, and optimal medical management of AKI may decrease significant postoperative morbidity., (Copyright © 2019 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.)

Background: Postoperative complications, length of index hospital stay, and unplanned hospital readmissions are important metrics reflecting surgical care quality. Postoperative infections represent a substantial proportion of all postoperative complications. We examined the relationships between identification of postoperative infection prehospital and posthospital discharge, length of stay, and unplanned readmissions in the American College of Surgeons National Surgical Quality Improvement Program database across nine surgical specialties., Methods: The 30-day postoperative infectious complications including sepsis, surgical site infections, pneumonia, and urinary tract infection were analyzed in the American College of Surgeons National Surgical Quality Improvement Program inpatient data during the period from 2012 to 2017. General, gynecologic, vascular, orthopedic, otolaryngology, plastic, thoracic, urologic, and neurosurgical inpatient operations were selected., Results: Postoperative infectious complications were identified in 5.2% (137,014/2,620,450) of cases; 81,929 (59.8%) were postdischarge. The percentage of specific complications identified postdischarge were 73.4% of surgical site infections (range across specialties 63.7-93.1%); 34.9% of sepsis cases (27.4-58.1%); 26.5% of pneumonia cases (18.9%-36.3%); and 53.2% of urinary tract infections (48.3%-88.0%). The relative risk of readmission among patients with postdischarge versus predischarge surgical site infection, sepsis, pneumonia, or urinary tract infection was 5.13 (95% confidence interval: 4.90-5.37), 9.63 (8.93-10.40), 10.79 (10.15-11.45), and 3.32 (3.07-3.60), respectively. Over time, mean length of stay decreased but postdischarge infections and readmission rates significantly increased., Conclusion: Most postoperative infectious complications were diagnosed postdischarge. These were associated with an increased risk of readmission. The trend toward shorter length of stay over time was observed along with an increase both in the percentage of infections detected after discharge and the rate of unplanned related postoperative readmissions over time. Postoperative surveillance of infections should extend beyond hospital discharge of surgical patients., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The Surgical Risk Preoperative Assessment System accurately predicts postoperative complications in elective surgery using only 8 preoperative variables, but its performance in emergency surgery has not been evaluated. We hypothesized that the Surgical Risk Preoperative Assessment System accurately predicts postoperative complications in emergency surgery and compared its performance to that of the American College of Surgeons Surgical Risk Calculator., Methods: We calculated the Surgical Risk Preoperative Assessment System and the American College of Surgeons Surgical Risk Calculator risk estimates in a random sample of 1,010 emergency surgery cases from the American College of Surgeons National Surgical Quality Improvement Program 2014 to 2017 database. Risk estimates were compared with known outcomes. Analyses included the Hosmer-Lemeshow goodness of fit graphs and P values, c-indexes, and Brier scores., Results: Results were similar between the Surgical Risk Preoperative Assessment System and the American College of Surgeons Surgical Risk Calculator for mortality, urinary tract infection, cardiac, venous thromboembolism, and renal complications. The American College of Surgeons Surgical Risk Calculator underestimated morbidity compared with the Surgical Risk Preoperative Assessment System (16.04% American College of Surgeons Surgical Risk Calculator vs 24.88% Surgical Risk Preoperative Assessment System vs 24.3% observed). Both calculators overestimated readmission (7.67% American College of Surgeons Surgical Risk Calculator vs 5.18% Surgical Risk Preoperative Assessment System vs 4.1% observed)., Conclusion: Both calculators predicted mortality, urinary tract infection, cardiac, venous thromboembolism, and renal complications well, but readmissions relatively poorly. The Surgical Risk Preoperative Assessment System estimated overall morbidity accurately, while the American College of Surgeons Surgical Risk Calculator underestimated this risk., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Thrombocytopenia is a risk factor for morbidity and mortality in critically ill patients, and is common after cardiopulmonary bypass (CPB). In this study, we evaluate whether thrombocytopenia after CPB is an independent risk factor for postoperative morbidity and mortality., Methods: We retrospectively evaluated 1364 patients requiring CPB at the University of Colorado Hospital between January 2011 and May 2016. Platelet nadir, absolute change in platelets, and percent change in platelets were modeled as continuous variables. Patients with postoperative thrombocytopenia (defined a nadir <75 × 10 3 /μL within 72 hours) were also compared with patients without thrombocytopenia in a propensity-matched model. The primary outcome was in-hospital mortality, and secondary outcomes included postoperative infection, postoperative acute kidney injury (AKI), postoperative stroke, and prolonged intensive care unit (ICU) and hospital lengths of stay (LOS)., Results: Postoperative thrombocytopenia occurred in 356 (26.0%) patients. In multivariable analysis, platelet nadir was significantly inversely associated with mortality (odds ratio [OR], 0.955; 95% confidence interval [CI], 0.934-0.975; P < .001), postoperative infection (OR, 0.992; 95% CI, 0.986-0.999; P = .03), AKI (all stage) (OR, 0.993; 95% CI, 0.988-0.998; P = .01), AKI (stage 3) (OR, 0.966; 95% CI, 0.951-0.982; P < .001), postoperative stroke (OR, 0.974; 95% CI, 0.956-0.992; P = .006), prolonged ICU stay (OR, 0.986; 95% CI, 0.981-0.991; P < .001), and hospital LOS (OR, 0.998; 95% CI, 0.997-0.999; P = .001). Percent change in platelets from baseline was also significantly associated with all primary and secondary outcomes., Conclusions: Postoperative thrombocytopenia is independently associated with postoperative mortality, AKI, infection, stroke, and prolonged ICU and hospital LOS. Serial platelet monitoring may help identify patients at higher risk of postoperative complications. Further studies investigating strategies to reduce postoperative thrombocytopenia, including reducing CPB time, are needed., (Copyright © 2020 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR)., Methods: We used an elastic-net model to estimate regression coefficients and carry out variable selection. International classification of disease codes (ICD-9), common procedural terminology (CPT) codes, medications, and CPT-specific complication event rate were included as predictors., Results: Of 6840 patients, 922 (13.5%) had at least one of the 18 complications tracked by NSQIP. The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93., Conclusions: Using machine learning on EHR postoperative data linked to NSQIP outcomes data, a model with 163 predictors from the EHR identified complications well at our institution., Competing Interests: Declaration of competing interest The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: The novel Surgical Risk Preoperative Assessment System (SURPAS) requires entry of five predictor variables (the other three variables of the eight-variable model are automatically obtained from the electronic health record or a table look-up), provides patient risk estimates compared to national averages, is integrated into the electronic health record, and provides a graphical handout of risks for patients. The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC)., Methods: Predicted risk of postoperative mortality and morbidity was calculated using both SURPAS and ACS-SRC for 1,006 randomly selected 2007-2016 ACS National Surgical Quality Improvement Program (NSQIP) patients with known outcomes. C-indexes, Hosmer-Lemeshow graphs, and Brier scores were compared between SURPAS and ACS-SRC., Results: ACS-SRC risk estimates for overall morbidity underestimated risk compared to observed postoperative overall morbidity, particularly for the highest risk patients. SURPAS accurately estimates morbidity risk compared to observed morbidity., Conclusions: SURPAS risk predictions were more accurate than ACS-SRC's for overall morbidity, particularly for high risk patients., Summary: The accuracy of the SURPAS tool was compared to that of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). SURPAS risk predictions were more accurate than those of the ACS-SRC for overall morbidity, particularly for high risk patients., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: The objective of this study was to determine the effects of using the Surgical Risk Preoperative Assessment System (SURPAS) on patient satisfaction and surgeon efficiency in the surgical informed consent process, as compared to surgeons' "usual" consent process., Study Design: Patient perception of the consent process was assessed via survey in 2 cohorts: 10 surgeons in different specialties used their "usual" consent process for 10 patients; these surgeons were then taught to use SURPAS, and they used it during the informed consent process of 10 additional patients. The data were compared using Fisher's exact test and the Cochran-Mantel-Haenszel test., Results: One hundred patients underwent the "usual" consent process (USUAL), and 93 underwent SURPAS-guided consent (SURPAS). Eighty-two percent of SURPAS were "very satisfied" and 18% were "satisfied" with risk discussion vs 16% and 72% of USUAL, respectively. Of those who used SURPAS, 75.3% reported the risk discussion made them "more comfortable" with surgery vs 19% of USUAL, and 90.3% of SURPAS users reported "somewhat" or "greatly decreased" anxiety vs 20% of USUAL. All p values were <0.0001. Among SURPAS patients, 97.9% reported "enough time spent discussing risks" vs 72.0% of USUAL patients., Conclusions: The SURPAS tool improved the informed consent process for patients compared with the "usual" consent process, in terms of patient satisfaction, ie making patients feel more comfortable and less anxious about their impending operations. Providers should consider integrating the SURPAS tool into their preoperative consent process., (Copyright © 2020 American College of Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: Patients with end-stage renal disease (ESRD) are at high risk for cardiac disease requiring surgery, and have been shown to have increased surgical risks. There have been significant improvements in ESRD management, surgical techniques, and patient selection over the past 10 y. We evaluated rates of serious postoperative outcomes in stable, well-dialyzed patients with ESRD undergoing nonemergent cardiac surgery compared to the general cardiac surgery population., Methods: In this propensity-score matched study, we evaluated 1451 adult patients who underwent nonemergent cardiac surgery at the University of Colorado Hospital (UCH) between 2011 and 2016. Patients with ESRD were compared to nonESRD patients. The primary outcome was a composite endpoint, including 30-d mortality, stroke, postoperative infection, and prolonged intensive care unit (ICU) length of stay (LOS)., Results: A total of 35 patients with ESRD met inclusion criteria. These select patients were younger with few comorbidities than the nonESRD population. There were no statistically significant differences in the composite outcome between ESRD and nonESRD patients in the propensity-matched analysis (OR 0.70, CI 0.29-1.72, P = 0.44). There were no significant differences or trends for in-hospital mortality, postoperative stroke, infection, ICU LOS, or hospital LOS between the patients with and without ESRD., Conclusions: Stable ESRD patients undergoing nonemergent surgery are not at increased risk of major postoperative complications when compared to those without ESRD. Well-compensated ESRD patients should not be excluded from surgical consideration., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Unplanned postoperative readmissions are associated with high costs, may indicate poor care quality, and present a substantial opportunity for healthcare quality improvement. Patients want to know their risk of unplanned readmission, and surgeons need to know the risk to adequately counsel their patients. The Surgical Risk Preoperative Assessment System tool was developed from the American College of Surgeons National Surgical Quality Improvement Program dataset and is a parsimonious model using 8 predictor variables. Surgical Risk Preoperative Assessment System is applicable to >3,000 operations in 9 surgical specialties, predicts 30-day postoperative mortality and morbidity, and is incorporated into our electronic health record., Methods: A Surgical Risk Preoperative Assessment System model was developed using logistic regression. It was compared to the 28 nonlaboratory variables model from the American College of Surgeons National Surgical Quality Improvement Program 2012 to 2017 dataset using the c-index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration., Results: Of 4,861,370 patients, 188,150 (3.98%) experienced unplanned readmission related to the index operation. The Surgical Risk Preoperative Assessment System model's c-index, 0.728, was 99.3% of that of the full model's, 0.733; the Hosmer-Lemeshow plots indicated good calibration; and the Brier score was 0.0372 for Surgical Risk Preoperative Assessment System and 0.0371 for the full model., Conclusion: The 8 variable Surgical Risk Preoperative Assessment System model detects patients at risk for postoperative unplanned, related readmission as accurately as the full model developed from all 28 nonlaboratory preoperative variables in the American College of Surgeons National Surgical Quality Improvement Program dataset. Therefore, unplanned readmission can be integrated into the existing Surgical Risk Preoperative Assessment System tool providing moderately accurate prediction of postoperative readmission., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Acute kidney injury (AKI) following open aortic arch surgery is a frequent complication associated with increased morbidity and mortality. The primary purpose of this study was to evaluate risk factors for postoperative AKI in patients who underwent open aortic arch surgery utilizing hypothermic circulatory arrest (HCA)., Materials and Methods: Included were 295 patients undergoing surgery between January 2011 and March 2018. AKI was defined according to Kidney Disease: Improving Global Outcomes guidelines. Preoperative and intraoperative variables were stratified by no AKI versus any AKI, and bivariate analysis was performed. Multivariable logistic regression analysis used statistically and clinically significant characteristics from the bivariate analysis., Results: Of the 295 patients, 93 (32%) developed AKI. In the bivariate analysis, significant predictors of AKI included the following: history of hypertension (P < 0.001), diabetes (P = 0.03), operative urgency (P = 0.009), cardiopulmonary bypass (CPB) time (P < 0.0001), HCA time (0.02), total intraoperative transfusions (P = 0.002), and concomitant procedures (coronary artery bypass grafting, or mitral/tricuspid interventions, P = 0.0009). In the multivariable analysis, significant predictors of AKI were history of hypertension (P = 0.03) and CPB time (P = 0.02). Age, operative urgency, circulatory arrest time, and any intraoperative transfusion were not significant in the multivariable analysis., Conclusion: In conclusion, given that CPB time is the only modifiable risk factor identified in the analysis, approaches to reducing bypass time should continue to be the focus of decreasing risk for postoperative AKI in HCA cases., (Copyright © 2019. Published by Elsevier Inc.)

Background: Population ascertainment of postoperative urinary tract infections (UTIs) is time-consuming and expensive, as it often requires manual chart review. Using the American College of Surgeons National Surgical Quality Improvement Program UTI status of patients who underwent an operation at the University of Colorado Hospital, we sought to develop an algorithm for identifying UTIs using data from the electronic health record., Methods: Data were split into training (operations occurring between 2013-2015) and test (operations in 2016) sets. A binomial generalized linear model with an elastic-net penalty was used to fit the model and carry out variables selection. International classification of disease codes, common procedural terminology codes, antibiotics, catheterization, and common procedural terminology-specific UTI event rates were included as predictors. The Youden's J statistic was used to determine the optimal classification threshold., Results: Of 6,840 patients, 134 (2.0%) had a UTI. The model achieved 92% specificity, 80% sensitivity, 100% negative predictive value, 16% positive predictive value, and an area under the curve of 0.94 using a decision threshold of 0.03., Conclusions: A model with 14 predictors from the electronic health record identifies UTIs well, and it could be used to scale up UTI surveillance or to estimate the impact of large-scale interventions on UTI rates., (Copyright © 2018. Published by Elsevier Inc.)

Background: Hospital readmissions are increasingly viewed as a marker of inferior health care quality and penalized with decreased reimbursement. The timing of, and reasons for, readmissions after esophagectomy for cancer are not well understood. We examined the association of complications to 30-day postoperative-related, unplanned readmission to identify opportunities for improvement in patient care., Methods: We analyzed the American College of Surgeons National Surgical Quality Improvement Program database (2012 to 2015) to characterize 30-day postoperative unplanned readmissions after esophagectomy for cancer using descriptive statistics. Type and timing of readmission after discharge was assessed. A Cox proportional hazards model was developed to identify predictors of readmission., Results: Of 3,723 patients who underwent esophagectomy for cancer, 1,419 (38.1%) experienced ≥1 complication within 30 days. A total of 400 patients (10.7%) experienced related, unplanned readmissions within 30 days of the operation, and postoperative complications were documented in 263 (65.8%). Leading causes of readmission were infectious, pulmonary, and gastrointestinal complications. Of these patients, 155 (59%) were readmitted within 7 days and 236 (90%) within 14 days of discharge. The Cox proportional hazards model identified readmission being associated with occurrence of postdischarge infectious, pulmonary, venous thromboembolic, and urinary tract infection complications, in-hospital urinary tract infection complications, and log-transformation of length of stay (representing increasing length of stay) (all p < 0.05)., Conclusions: Postoperative occurrence of common complications and prolonged length of stay are associated with unplanned readmission after esophagectomy. Most patients are readmitted within 1 week of discharge. Earlier follow-up after discharge may identify patients with complications and facilitate outpatient intervention to prevent readmission., (Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: Hospital readmissions are viewed as a mark of inferior health care quality and are penalized. Unplanned postoperative readmission reason and timing after lung resection are not well understood. We examine related, unplanned readmissions after thoracoscopic versus open anatomic lung resections to identify opportunities to improve patient care., Methods: We analyzed the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) data set, 2012 to 2015, characterizing 30-day related, unplanned postoperative readmissions after anatomic lung resections for primary lung cancer. Risk-adjusted comparison of readmission after thoracoscopic and open resection was performed using propensity matching., Results: Patients (n = 9,510) underwent anatomic lung resections; 4,935 (51.9%) were thoracoscopic resections and 4,575 (48.1%) were open resections. Of the thoracoscopic patients, 10.9% experienced one or more complications, versus 19.4% of patients with open resection (p < 0.0001). Of the thoracoscopic patients 5.5% experienced related, unplanned readmissions versus 7.2% of the patients with open resection (p < 0.001). 24.8% of complications after thoracoscopic approach occurred after discharge, versus 15.5% after open approach (p < 0.0001). Timing of unplanned readmission was similar for both groups. The propensity-matched odds ratio of risk of readmission after thoracoscopic versus open resection was 1.16 (95% confidence interval, 0.949 to 1.411, p = 0.15)., Conclusions: Open anatomic lung resections for primary lung cancer had nearly twice the complication rate but only a slightly higher readmission rate than thoracoscopic resection. More complications occurred after discharge after thoracoscopic than open resections. Most readmissions occurred within 2 weeks after both thoracoscopic and open resections. Risk-adjusted comparison identified no statistically significant difference in risk of related, unplanned readmission after thoracoscopic versus open resections. Future studies should focus on identification of processes of care to decrease complications and unplanned readmissions after lung cancer resection., (Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Background: Nondepolarizing neuromuscular blocking drugs (NNMBDs) are commonly used as an adjunct to general anesthesia. Residual blockade is common, but its potential adverse effects are incompletely known. This study was designed to assess the association between NNMBD use with or without neostigmine reversal and postoperative morbidity and mortality., Methods: This is a retrospective observational study of 11,355 adult patients undergoing general anesthesia for noncardiac surgery at 5 Veterans Health Administration (VA) hospitals. Of those, 8984 received NNMBDs, and 7047 received reversal with neostigmine. The primary outcome was a composite of respiratory complications (failure to wean from the ventilator, reintubation, or pneumonia), which was "yes" if a patient had any of the 3 component events and "no" if they had none. Secondary outcomes were nonrespiratory complications, 30-day and long-term all-cause mortality. We adjusted for differences in patient risk using propensity matched (PM) followed by assessment of the association of interest by logistic regression between the matched pairs as our primary analysis and multivariable logistic regression (MLR) as a sensitivity analysis., Results: Our primary aim was to assess the adverse outcomes in the patients who had received NNMBDs with and without neostigmine. Administration of an NNMBD without neostigmine reversal compared with NNMBD with neostigmine reversal was associated with increased odds of respiratory complications (PM odds ratio [OR], 1.75 [95% confidence interval [CI], 1.23-2.50]; MLR OR, 1.71 [CI, 1.24-2.37]) and a marginal increase in 30-day mortality (PM OR, 1.83 [CI, 0.99-3.37]; MLR OR, 1.78 [CI, 1.02-3.13]). However, there were no statistically significant associations with nonrespiratory complications or long-term mortality. Patients who were administered an NNMBD followed by neostigmine had no differences in outcomes compared with patients who had general anesthesia without an NNMBD., Conclusions: The use of NNMBDs without neostigmine reversal was associated with increased odds of our composite respiratory outcome compared with patients reversed with neostigmine. Based on these data, we conclude that reversal of NNMBDs should become a standard practice if extubation is planned.

Objective: To develop accurate preoperative risk prediction models for multiple adverse postoperative outcomes applicable to a broad surgical population using a parsimonious common set of risk variables and outcomes., Summary Background Data: Currently, preoperative assessment of surgical risk is largely based on subjective clinician experience. We propose a paradigm shift from the current postoperative risk adjustment for cross-hospital comparison to patient-centered quantitative risk assessment during the preoperative evaluation., Methods: We identify the most common and important predictor variables of postoperative mortality, overall morbidity, and 6 complication clusters from previously published prediction analyses that used forward selection stepwise logistic regression. We then refit the prediction models using only the 8 most common and important predictor variables, and compare the discrimination and calibration of these models to the original full-variable models using the c-index, Hosmer-Lemeshow analysis, and Brier scores., Results: Accurate risk models for 30-day outcomes of mortality, overall morbidity, and 6 clusters of complications were developed using a set of 8 preoperative risk variables. C-indexes of the 8 variable models are between 97.9% and 99.2% of those of the full models containing up to 28 variables, indicating excellent discrimination using fewer predictor variables. Hosmer-Lemeshow analyses showed observed to expected event rates to be nearly identical between parsimonious models and full models, both showing good calibration., Conclusions: Accurate preoperative risk assessment of postoperative mortality, overall morbidity, and 6 complication clusters in a broad surgical population can be achieved with as few as 8 preoperative predictor variables, improving feasibility of routine preoperative risk assessment for surgical patients.

Objective: Postoperative readmissions are frequent in vascular surgery patients, but it is not clear which factors are the main drivers of readmissions. Specifically, the relative contributions of patient comorbidities vs those of operative factors and postoperative complications are unknown. We sought to study the multiple potential drivers of readmission and to create a model for predicting the risk of readmission in vascular patients., Methods: The 2012-2013 American College of Surgeons National Surgical Quality Improvement Program data set was queried for unplanned readmissions in 86,238 vascular patients. Multivariable forward selection logistic regression analysis was used to model the relative contributions of patient comorbidities, operative factors, and postoperative complications for readmission., Results: The unplanned readmission rate was 9.3%. The preoperative model based on patient demographics and comorbidities predicted readmission risk with a low C index of .67; the top five predictors of readmission were American Society of Anesthesiologists class, preoperative open wound, inpatient operation, dialysis dependence, and diabetes mellitus. The postoperative model using operative factors and postoperative complications predicted readmission risk better (C index, .78); postoperative complications were the most significant predictor of readmission, overpowering patient comorbidities. Importantly, postoperative complications identified before discharge from the hospital were not a strong predictor of readmission as the model using predischarge postoperative complications had a similar C index to our preoperative model (.68). However, the inclusion of complications identified after discharge from the hospital appreciably improved the predictive power of the model (C index, .78). The top five predictors of readmission in the final model based on patient comorbidities and postoperative complications were postdischarge deep space infection, superficial surgical site infection, pneumonia, myocardial infection, and sepsis., Conclusions: Readmissions in vascular surgery patients are mainly driven by postoperative complications identified after discharge. Thus, efforts to reduce vascular readmissions focusing on inpatient hospital data may prove ineffective. Our study suggests that interventions to reduce vascular readmissions should focus on prompt identification of modifiable postdischarge complications., (Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)

Objective: To use factor analysis to cluster the 18 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) perioperative complications into a reproducible, smaller number of clinically meaningful groups of postoperative complications, facilitating and streamlining future study and application in live clinical settings., Background: The ACS NSQIP collects and reports on eighteen 30-day postoperative complications (excluding mortality), which are variably grouped in published analyses using ACS NSQIP data. This hinders comparison between studies of this widely used quality improvement dataset., Methods: Factor analysis was used to develop a series of complication clusters, which were then analyzed to identify a parsimonious, clinically meaningful grouping, using 2,275,240 surgical cases in the ACS NSQIP Participant Use File (PUF), 2005 to 2012. The main outcome measures are reproducible, data-driven, clinically meaningful clusters of complications derived from factor solutions., Results: Factor analysis solutions for 5 to 9 latent factors were examined for their percent of total variance, parsimony, and clinical interpretability. Applying the first 2 of these criteria, we identified the 7-factor solution, which included clusters of pulmonary, infectious, wound disruption, cardiac/transfusion, venous thromboembolic, renal, and neurological complications, as the best solution for parsimony and clinical meaningfulness. Applying the last (clinical interpretability), we combined the wound disruption with the infectious clusters resulting in 6 clusters for future clinical applications., Conclusions: Factor analysis of ACS NSQIP postoperative complication data provides 6 clinically meaningful complication clusters in lieu of 18 postoperative morbidities, which will facilitate comparisons and clinical implementation of studies of postoperative morbidities.

Background: Although deviations in intraoperative blood pressure are assumed to be associated with postoperative mortality, critical blood pressure thresholds remain undefined. Therefore, the authors estimated the intraoperative thresholds of systolic blood pressure (SBP), mean blood pressure (MAP), and diastolic blood pressure (DBP) associated with increased risk-adjusted 30-day mortality., Methods: This retrospective cohort study combined intraoperative blood pressure data from six Veterans Affairs medical centers with 30-day outcomes to determine the risk-adjusted associations between intraoperative blood pressure and 30-day mortality. Deviations in blood pressure were assessed using three methods: (1) population thresholds (individual patient sum of area under threshold [AUT] or area over threshold 2 SDs from the mean of the population intraoperative blood pressure values), (2). absolute thresholds, and (3) percent change from baseline blood pressure., Results: Thirty-day mortality was associated with (1) population threshold: systolic AUT (odds ratio, 3.3; 95% CI, 2.2 to 4.8), mean AUT (2.8; 1.9 to 4.3), and diastolic AUT (2.4; 1.6 to 3.8). Approximate conversions of AUT into its separate components of pressure and time were SBP < 67 mmHg for more than 8.2 min, MAP < 49 mmHg for more than 3.9 min, DBP < 33 mmHg for more than 4.4 min. (2) Absolute threshold: SBP < 70 mmHg for more than or equal to 5 min (odds ratio, 2.9; 95% CI, 1.7 to 4.9), MAP < 49 mmHg for more than or equal to 5 min (2.4; 1.3 to 4.6), and DBP < 30 mmHg for more than or equal to 5 min (3.2; 1.8 to 5.5). (3) Percent change: MAP decreases to more than 50% from baseline for more than or equal to 5 min (2.7; 1.5 to 5.0). Intraoperative hypertension was not associated with 30-day mortality with any of these techniques., Conclusion: Intraoperative hypotension, but not hypertension, is associated with increased 30-day operative mortality.

Importance: The literature regarding the occurrence of adverse outcomes following nonobstetric surgery in pregnant compared with nonpregnant women has conflicting findings. Those differing conclusions may be the result of inadequate adjustment for differences between pregnant and nonpregnant women. It remains unclear whether pregnancy is a risk factor for postoperative morbidity and mortality of the woman after general surgery., Objective: To compare the risk of postoperative complications in pregnant vs nonpregnant women undergoing similar general surgical procedures., Design, Setting, and Participants: In this retrospective cohort study, data were obtained from the American College of Surgeons' National Surgical Quality Improvement Program participant user file from January 1, 2006, to December 31, 2011. Propensity-matched females based on 63 preoperative characteristics were matched 1:1 with nonpregnant women undergoing the same operations by general surgeons. Operations performed between January 1, 2006, and December 31, 2011, were analyzed for postoperative adverse events occurring within 30 days of surgery., Main Outcomes and Measures: Rates of 30-day postoperative mortality, overall morbidity, and 21 individual postoperative complications were compared., Results: The unmatched cohorts included 2764 pregnant women (50.5% underwent emergency surgery) and 516,705 nonpregnant women (13.2% underwent emergency surgery) undergoing general surgery. After propensity matching, there were no meaningful differences in all 63 preoperative characteristics between 2539 pregnant and 2539 nonpregnant patients (all standardized differences, <0.1). The 30-day mortality rates were similar (0.4% in pregnant women vs 0.3% in nonpregnant women; P = .82), and the rate of overall morbidity was also not significantly different between pregnant vs nonpregnant patients (6.6% vs 7.4%; P = .30)., Conclusions and Relevance: There was no significant difference in overall morbidity or 30-day mortality rates in pregnant and nonpregnant propensity-matched women undergoing similar general surgical operations. General surgery appears to be as safe for pregnant women as it is for nonpregnant women.