The deimplementation of laboratory testing in low-risk patients as recommended by the American society of anesthesiologists: An ACS-NSQIP longitudinal analysis.

BACKGROUND: In 2012, the American Society of Anesthesiologists (ASA) published guidelines recommending against routine preoperative laboratory testing for low-risk patients to reduce unnecessary medical expenditures. The aim of this study was to assess the change in routine preoperative laboratory testing in low-risk versus higher-risk patients before and after release of these guidelines. METHODS: The ACS-NSQIP database, 2005-2018, was separated into low-risk versus higher-risk patients based upon a previously published stratification. The guideline implementation date was defined as January 2013. Changes in preoperative laboratory testing over time were compared between low- and higher-risk patients. A difference-in-differences model was applied. The primary outcome included any laboratory test obtained ≤90 days prior to surgery. RESULTS: Of 7,507,991 patients, 972,431 (13.0%) were defined as low-risk and 6,535,560 (87.0%) higher-risk. Use of any preoperative laboratory test declined in low-risk patients from 66.5% before to 59.6% after guidelines, a 6.9 percentage point reduction, versus 93.0%-91.9% in higher-risk patients, a 1.1 percentage point reduction (p < 0.0001, comparing percentage point reductions). After risk-adjustment, the adjusted odds ratio for having any preoperative laboratory test after versus before the guidelines was 0.77 (95% CI 0.76-0.78) in low-risk versus 0.93 (0.92-0.94) in higher-risk patients. In low-risk patients, lack of any preoperative testing was not associated with worse outcomes. CONCLUSIONS: While a majority of low-risk patients continue to receive preoperative laboratory testing not recommended by the ASA, there has been a decline after implementation of guidelines. Continued effort should be directed at the deimplementation of routine preoperative laboratory testing for low-risk patients.

Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data.

OBJECTIVE: To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data. BACKGROUND: Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner. However, there are no specific models for risk-adjusting infectious complication rates using EHR data. METHODS: Preoperative EHR data from 30,639 patients (2013-2019) were linked to the American College of Surgeons National Surgical Quality Improvement Program preoperative data and postoperative infection outcomes data from 5 hospitals in the University of Colorado Health System. EHR data included diagnoses, procedures, operative variables, patient characteristics, and medications. Lasso and the knockoff filter were used to perform controlled variable selection. Outcomes included surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia up to 30 days postoperatively. RESULTS: Among >15,000 candidate predictors, 7 were chosen for the surgical site infection model and 6 for each of the urinary tract infection, sepsis, and pneumonia models. Important variables included preoperative presence of the specific outcome, wound classification, comorbidities, and American Society of Anesthesiologists physical status classification. The area under the receiver operating characteristic curve for each model ranged from 0.73 to 0.89. CONCLUSIONS: Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.

Social vulnerability is associated with higher risk-adjusted rates of postoperative complications in a broad surgical population.

OBJECTIVE: The purpose of this study was to determine if an association between Social Vulnerability Index (SVI) and risk-adjusted complications exists in a broad spectrum of surgical patients. SUMMARY BACKGROUND DATA: Growing evidence supports the impact of social circumstances on surgical outcomes. SVI is a neighborhood-based measure accounting for sociodemographic factors putting communities at risk. METHODS: This was a multi-hospital, retrospective cohort study including a sample of patients within one healthcare system (2012-2017). Patient addresses were geocoded to determine census tract of residence and estimate SVI. Patients were grouped into low SVI (score<75) and high SVI (score≥75) cohorts. Perioperative variables and postoperative outcomes were tracked and compared using local ACS-NSQIP data. Multivariable logistic regression was performed to generate risk-adjusted odds ratios of postoperative complications in the high SVI cohort. RESULTS: Overall, 31,224 patients from five hospitals were included. Patients with high SVI were more likely to be racial minorities, have 12/18 medical comorbidities, have high ASA class, be functionally dependent, be treated at academic hospitals, and undergo emergency operations (all p â€‹< â€‹0.05). Patients with high SVI had significantly higher rates of 30-day mortality, overall morbidity, respiratory, cardiac and infectious complications, urinary tract infections, postoperative bleeding, non-home discharge, and unplanned readmissions (all p â€‹< â€‹0.05). After risk-adjustment, only the associations between high SVI and mortality and unplanned readmission became non-significant. CONCLUSIONS: High SVI was associated with multiple adverse outcomes even after risk adjustment for preoperative clinical factors. Targeted preventative interventions to mitigate risk of these specific complications should be considered in this high-risk population.

Interventions to improve perinatal outcomes among migrant women in high-income countries: a systematic review protocol.

INTRODUCTION: Women who are migrants and who are pregnant or postpartum are at high risk of poorer perinatal outcomes compared with host country populations due to experiencing numerous additional stressors including social exclusion and language barriers. High-income countries (HICs) host many migrants, including forced migrants who may face additional challenges in the peripartum period. Although HICs' maternity care systems are often well developed, they are not routinely tailored to the needs of migrant women. The primary objective will be to determine what interventions exist to improve perinatal outcomes for migrant women in HICs. The secondary objective will be to explore the effectiveness of these interventions by exploring the impact on perinatal outcomes. The main outcomes of interest will be rates of preterm birth, birth weight, and number of antenatal or postnatal appointments attended. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocols guidelines. EMBASE, EMCARE, MEDLINE and PsycINFO, CENTRAL, Scopus, CINAHL Plus, and Web of Science, as well as grey literature sources will be searched from inception up to December 2022. We will include randomised controlled trials, quasi-experimental and interventional studies of interventions, which aim to improve perinatal outcomes in any HIC. There will be no language restrictions. We will exclude studies presenting only qualitative outcomes and those including mixed populations of migrant and non-migrant women. Screening and data extraction will be completed by two independent reviewers and risk of bias will be assessed using the Quality Assessment Tool for Quantitative Studies. If a collection of suitably comparable outcomes is retrieved, we will perform meta-analysis applying a random effects model. Presentation of results will comply with guidelines in the Cochrane Handbook of Systematic Reviews of Interventions and the PRISMA statement. ETHICS AND DISSEMINATION: Ethical approval is not required. Results will be submitted for peer-reviewed publication and presented at national and international conferences. The findings will inform the work of the Lancet Migration European Hub. PROSPERO REGISTRATION NUMBER: CRD42022380678.

Emergency thoracic surgery patients have worse risk-adjusted outcomes than non-emergency patients.

BACKGROUND: Outcomes for patients undergoing emergency thoracic operations have not been well described. This study was designed to compare postoperative outcomes among patients undergoing emergency versus nonemergency thoracic operations. METHODS: We retrospectively analyzed the American College of Surgeons National Surgical Quality Improvement Program database (2005-2018). We identified patients who underwent emergency thoracic operations using current procedural technology codes. Patients were then sorted into 1 of 4 cohorts: lung and chest wall, hiatal hernia, esophagus, and pericardium. Emergency versus nonemergency outcomes were compared. Univariate logistic regression was performed with "emergency status" as the independent variable and 30-day postoperative outcomes as the dependent variables. Multiple logistic regression models were performed to control for preoperative factors. RESULTS: Of 90,398 thoracic operations analyzed, 4,044 (4.5%) were emergency. Common emergency operations were pericardial window (n = 580, 10.2%), laparoscopic hiatal hernia repair (n = 366, 8.9%), thoracoscopic partial lung decortication (n = 334, 8.1%), thoracoscopic wedge resection (n = 301, 7.3%), thoracoscopic total lung decortication (n = 256, 6.2%), and open repair of hiatal hernia without mesh (n = 254, 6.2%). In all 4 cohorts, 30-day postoperative complications occurred more frequently after emergency surgery. After controlling for patient characteristics, 8 complications were more frequent after emergency lung and chest wall surgery, 5 complications were more frequent after emergency hiatal hernia surgery, and 3 complications were more frequent after emergency pericardium surgery. Risk-adjusted complications were not different after emergency esophageal surgery. CONCLUSION: Patients undergoing emergency thoracic operations have worse risk-adjusted outcomes than those undergoing nonemergency thoracic operations. Subset analysis is needed to determine what factors contribute to increased adverse outcomes in specific patient populations.

Formative evaluation of the development and implementation of the automated surveillance of postoperative infections tool.

BACKGROUND: The gold standard for detecting postoperative complications uses databases like the American College of Surgeons National Surgical Quality Improvement Program, a multi-centered database based on manual chart review. However, their limitations and costs have led many centers to discontinue participation. Novel techniques to detect postoperative complications must be developed and implemented with surgeon involvement, which is paramount to their adoption. We sought to assess surgeons' opinions of a newly developed postoperative complication detection tool, the Automated Surveillance of Postoperative Infections, within the contextual clinical environment. METHODS: This was a multi-site qualitative formative evaluation of surgeon perceptions of the Automated Surveillance of Postoperative Infections. We conducted semi-structured interviews and focus groups with surgeons and presented the Automated Surveillance of Postoperative Infections concept. Important domains and constructs, as categorized by Consolidated Framework for Implementation Research, were identified to support the successful adoption and implementation of the Automated Surveillance of Postoperative Infections. RESULTS: Twenty-four surgeons with 10 surgical subspecialties were interviewed. The following 4 main themes were found: (1) perception of the Automated Surveillance of Postoperative Infections tool-to provide important data that can improve and support clinical outcomes; (2) environment for implementation-description of factors to support or impede implementation; (3) adaptability of the Automated Surveillance of Postoperative Infections-to work with the complexity of surgical cases; and (4) the Automated Surveillance of Postoperative Infections report format and details. CONCLUSIONS: We successfully captured the perspectives and suggestions of surgeons to improve the Automated Surveillance of Postoperative Infections and potential barriers during the initial development phase. Barriers included fear of punitive action from reports and complex surgical cases. Facilitators identified were the need to improve clinical outcomes and organizational support. The results of this formative evaluation will be used to further develop Automated Surveillance of Postoperative Infections, starting with a prototype, the Automated Surveillance of Postoperative Infections 1.0.

Preoperative risk factors and postoperative complications associated with mortality after outpatient surgery in a broad surgical population: an analysis of 2.8 million ACS-NSQIP patients.

BACKGROUND: Thirty-day mortality after outpatient surgery is unexpected and undesired. We investigated preoperative risk factors, operative variables, and postoperative complications associated with 30-day death after outpatient surgery. METHODS: Using the 2005 to 2018 American College of Surgeons National Surgical Quality Improvement Program database, we evaluated 30-day mortality rate trends over time after outpatient operations. We analyzed associations between 37 preoperative variables, operation time, hospital length of stay, and 9 postoperative complications with mortality rate using χ2 analyses for categorical data and tests for continuous data. We used forward selection logistic regression models to determine the best predictors of mortality preoperatively and postoperatively. We also separately analyzed mortality by age group. RESULTS: A total of 2,822,789 patients were included. The 30-day mortality rate did not change significantly over time (P = .34, Cochran-Armitage trend test), remaining steady at around 0.06%. The most significant preoperative predictors of mortality included the patient having disseminated cancer, decreased functional health status, increased American Society of Anesthesiology Physical Status classification, increased age, and ascites, accounting for 95.8% (0.837/0.874) of the full model c-index. The most significant postoperative complications associated with increased risk of mortality included having cardiac (26.95% yes vs 0.04% no), pulmonary (10.25% vs 0.04%), stroke (9.22% vs 0.06%), and renal (9.33% vs 0.06%) complications. Postoperative complications conferred a greater risk for mortality than preoperative variables. Mortality risk increased incrementally with age, particularly past age 80. CONCLUSION: The operative mortality rate after outpatient surgery has not changed over time. Patients over 80 years with disseminated cancer, decreased functional health status, or increased ASA class should generally be considered for inpatient surgery. However, there might be some circumstances where outpatient surgery could be considered.

Development and validation of a multivariable preoperative prediction model for postoperative length of stay in a broad inpatient surgical population.

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 R2 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 R2 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.

A comparison of the National Surgical Quality Improvement Program and the Society of Thoracic Surgery Cardiac Surgery preoperative risk models: a cohort study.

BACKGROUND: Cardiac surgery prediction models and outcomes from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) have not been reported. The authors sought to develop preoperative prediction models and estimates of postoperative outcomes for cardiac surgery using the ACS-NSQIP and compare these to the Society of Thoracic Surgeons Adult Cardiac Surgery Database (STS-ACSD). METHODS: In a retrospective analysis of the ACS-NSQIP data (2007-2018), cardiac operations were identified using cardiac surgeon primary specialty and sorted into cohorts of coronary artery bypass grafting (CABG) only, valve surgery only, and valve+CABG operations using CPT codes. Prediction models were created using backward selection of the 28 non-laboratory preoperative variables in ACS-NSQIP. Rates of nine postoperative outcomes and performance statistics of these models were compared to published STS 2018 data. RESULTS: Of 28 912 cardiac surgery patients, 18 139 (62.8%) were CABG only, 7872 (27.2%) were valve only, and 2901 (10.0%) were valve+CABG. Most outcome rates were similar between the ACS-NSQIP and STS-ACSD, except for lower rates of prolonged ventilation and composite morbidity and higher reoperation rates in ACS-NSQIP (all P <0.0001). For all 27 comparisons (9 outcomes × 3 operation groups), the c-indices for the ACS-NSQIP models were lower by an average of ~0.05 than the reported STS models. CONCLUSIONS: The ACS-NSQIP preoperative risk models for cardiac surgery were almost as accurate as the STS-ACSD models. Slight differences in c-indexes could be due to more predictor variables in STS-ACSD models or the use of more disease- and operation-specific risk variables in the STS-ACSD models.

The association between obesity and postoperative outcomes in a broad surgical population: A 7-year American College of Surgeons National Surgical Quality Improvement analysis.

BACKGROUND: The number of obese surgical patients continues to grow, and yet obesity's association with surgical outcomes is not totally clear. This study examined the association between obesity and surgical outcomes across a broad surgical population using a very large sample size. METHODS: This was an analysis of the 2012 to 2018 American College of Surgeons National Surgical Quality Improvement database, including all patients from 9 surgical specialties (general, gynecology, neurosurgery, orthopedics, otolaryngology, plastics, thoracic, urology, and vascular). Preoperative characteristics and postoperative outcomes were compared by body mass index class (normal weight 18.5-24.9 kg/m2, overweight 25.0-29.9, obese class I 30.0-34.9, obese II 35.0-39.9, obese III ≥40). Adjusted odds ratios were computed for adverse outcomes by body mass index class. RESULTS: A total of 5,572,019 patients were included; 44.6% were obese. Median operative times were marginally higher for obese patients (89 vs 83 minutes, P < .001). Compared to normal weight patients, overweight and obese patients in classes I, II, and III all had higher adjusted odds of developing infection, venous thromboembolism, and renal complications, but they did not exhibit elevated odds of other postoperative complications (mortality, overall morbidity, pulmonary, urinary tract infection, cardiac, bleeding, stroke, unplanned readmission, or discharge not home (except for class III patients). CONCLUSION: Obesity was associated with increased odds of postoperative infection, venous thromboembolism, and renal but not the other American College of Surgeons National Surgical Quality Improvement complications. Obese patients need to be carefully managed for these complications.

Does Work Relative Value Unit Measure Surgical Complexity for Risk Adjustment of Surgical Outcomes?

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.

Preoperative Prediction of Unplanned Reoperation in a Broad Surgical Population.

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.

Effect of Present at Time of Surgery on Unadjusted and Risk-Adjusted Postoperative Complication Rate.

BACKGROUND: Present at the time of surgery (PATOS) is an important measure to collect in postoperative complication surveillance systems because it may affect a patient's risk of a subsequent complication and the estimation of postoperative complication rates attributed to the healthcare system. The American College of Surgeons (ACS) NSQIP started collecting PATOS data for 8 postoperative complications in 2011, but no one has used these data to quantify how this may affect unadjusted and risk-adjusted postoperative complication rates. STUDY DESIGN: This study was a retrospective observational study of the ACS NSQIP database from 2012 to 2018. PATOS data were analyzed for the 8 postoperative complications of superficial, deep, and organ space surgical site infection; pneumonia; urinary tract infection; ventilator dependence; sepsis; and septic shock. Unadjusted postoperative complication rates were compared ignoring PATOS vs taking PATOS into account. Observed to expected ratios over time were also compared by calculating expected values using multiple logistic regression analyses with complication as the dependent variable and the 28 nonlaboratory preoperative variables in the ACS NSQIP database as the independent variables. RESULTS: In 5,777,108 patients, observed event rates for each outcome were reduced by between 6.1% (superficial surgical site infection) and 52.5% (sepsis) when PATOS was taken into account. The observed to expected ratios were similar each year for all outcomes, except for sepsis and septic shock in the early years. CONCLUSIONS: Taking PATOS into account is important for reporting unadjusted event rates. The effect varied by type of complication-lowest for superficial surgical site infection and highest for sepsis and septic shock. Taking PATOS into account was less important for risk-adjusted outcomes (observed to expected ratios), except for sepsis and septic shock.

Inpatient Versus Outpatient Surgery: A Comparison of Postoperative Mortality and Morbidity in Elective Operations.

BACKGROUND: Operations performed outpatient offer several benefits. The prevalence of outpatient operations is growing. Consequently, the proportion of patients with multiple comorbidities undergoing outpatient surgery is increasing. We compared 30-day mortality and overall morbidity between outpatient and inpatient elective operations. METHODS: Using the 2005-2018 ACS-NSQIP database, we evaluated trends in percent of hospital outpatient operations performed over time, and the percent of operations done outpatient versus inpatient by CPT code. Patient characteristics were compared for outpatient versus inpatient operations. We compared unadjusted and risk-adjusted 30-day mortality and morbidity for inpatient and outpatient operations. RESULTS: A total of 6,494,298 patients were included. The proportion of outpatient operations increased over time, from 37.8% in 2005 to 48.2% in 2018. We analyzed the 50 most frequent operations performed outpatient versus inpatient 25-75% of the time (n = 1,743,097). Patients having outpatient operations were younger (51.6 vs 54.6 years), female (70.3% vs 67.3%), had fewer comorbidities, and lower ASA class (I-II, 69.3% vs. 59.9%). On both unadjusted and risk-adjusted analysis, 30-day mortality and overall morbidity were less likely in outpatient versus inpatient operations. CONCLUSION: In this large multi-specialty analysis, we found that patients undergoing outpatient surgery had lower risk of 30-day morbidity and mortality than those undergoing the same inpatient operation. Patients having outpatient surgery were generally healthier, suggesting careful patient selection occurred even with increasing outpatient operation frequency. Patients and providers can feel reassured that outpatient operations are a safe, reasonable option for selected patients.

Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning.

BACKGROUND: Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations. METHODS: The goal of this study was to develop and validate parsimonious, interpretable models for conducting surveillance of postoperative infections using structured electronic health records data. This was a retrospective study using 30,639 unique operations from 5 major hospitals between 2013 and 2019. Structured electronic health records data were linked to postoperative outcomes data from the American College of Surgeons National Surgical Quality Improvement Program. Predictors from the electronic health records included diagnoses, procedures, and medications. Infectious complications included surgical site infection, urinary tract infection, sepsis, and pneumonia within 30 days of surgery. The knockoff filter, a penalized regression technique that controls type I error, was applied for variable selection. Models were validated in a chronological held-out dataset. RESULTS: Seven percent of patients experienced at least one type of postoperative infection. Models selected contained between 4 and 8 variables and achieved >0.91 area under the receiver operating characteristic curve, >81% specificity, >87% sensitivity, >99% negative predictive value, and 10% to 15% positive predictive value in a held-out test dataset. CONCLUSION: Surveillance and reporting of postoperative infection rates can be implemented for all operations with high accuracy using electronic health records data and simple linear regression models.

Development and validation of a model for surveillance of postoperative bleeding complications using structured electronic health records data.

BACKGROUND: Postoperative bleeding complications surveillance is done primarily through manual chart review. The purpose of this study was to develop and validate a detection model for postoperative bleeding complications using structured electronic health records data. METHODS: Patients who underwent operations at 1 of 5 hospitals within our local health system between 2013 and 2019 and whose complications were reported by the American College of Surgeons National Surgical Quality Improvement Program were included. Electronic health records data were linked to American College of Surgeons National Surgical Quality Improvement Program data using personal health identifiers. Electronic health records predictors included diagnosis codes mapped to PheCodes, procedure names, and medications within 30 days after surgery. We defined bleeding events as the transfusion of red blood cell components within 30 days after surgery. The knockoff filter and the lasso were used to develop a model in a training set of operations from January 2013 to March 2017. Performance of each model was tested in a held-out data set of patients who underwent operations from March 2017 to October 2019. RESULTS: A total of 30,639 patients were included; 1,112 patients (3.6%) had a bleeding event. Eight predictor variables were selected by the knockoff filter. When applied to the test set, specificity was 94%, sensitivity was 94%, area under the curve was 0.97, and accuracy was 93%. Calibration was consistent in lower predicted risk patients, whereas the model slightly overpredicted risk in high-risk patients. CONCLUSION: We created a parsimonious, accurate model for identifying patients with bleeding complications. This model can be used to augment manual chart review for surveillance and reporting of perioperative bleeding complications, enabling inclusion of all surgeries in quality improvement efforts.

Associations Between Preoperative Risk, Postoperative Complications, and 30-Day Mortality.

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.

Does Adding a Measure of Social Vulnerability to a Surgical Risk Calculator Improve Its Performance?

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.

Postoperative pulmonary complications with adjuvant regional anesthesia versus general anesthesia alone: a sub-analysis of the Perioperative Research Network study.

BACKGROUND: Adjuvant regional anesthesia is often selected for patients or procedures with high risk of pulmonary complications after general anesthesia. The benefit of adjuvant regional anesthesia to reduce postoperative pulmonary complications remains uncertain. In a prospective observational multicenter study, patients scheduled for non-cardiothoracic surgery with at least one postoperative pulmonary complication surprisingly received adjuvant regional anesthesia more frequently than those with no complications. We hypothesized that, after adjusting for surgical and patient complexity variables, the incidence of postoperative pulmonary complications would not be associated with adjuvant regional anesthesia. METHODS: We performed a secondary analysis of a prospective observational multicenter study including 1202 American Society of Anesthesiologists physical status 3 patients undergoing non-cardiothoracic surgery. Patients were classified as receiving either adjuvant regional anesthesia or general anesthesia alone. Predefined pulmonary complications within the first seven postoperative days were prospectively identified. Groups were compared using bivariable and multivariable hierarchical logistic regression analyses for the outcome of at least one postoperative pulmonary complication. RESULTS: Adjuvant regional anesthesia was performed in 266 (22.1%) patients and not performed in 936 (77.9%). The incidence of postoperative pulmonary complications was greater in patients receiving adjuvant regional anesthesia (42.1%) than in patients without it (30.9%) (site adjusted p = 0.007), but this association was not confirmed after adjusting for covariates (adjusted OR 1.37; 95% CI, 0.83-2.25; p = 0.165). CONCLUSION: After adjusting for surgical and patient complexity, adjuvant regional anesthesia versus general anesthesia alone was not associated with a greater incidence of postoperative pulmonary complications in this multicenter cohort of non-cardiothoracic surgery patients.