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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.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.Seven percent of patients experienced at least one type of postoperative infection. Models selected contained between 4 and 8 variables and achieved0.91 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.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.

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.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.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.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.

Unplanned hospital admission after intended outpatient surgery is an undesirable outcome. We aimed to develop a prediction model that estimates a patient's risk of conversion from outpatient surgery to inpatient hospitalization.This was a retrospective analysis using the American College of Surgeons National Surgical Quality Improvement Program database, 2005 to 2018. Conversion from outpatient to inpatient surgery was defined as having outpatient surgery and1 day hospital stay. The Surgical Risk Preoperative Assessment System was developed using multiple logistic regression on a training dataset (2005-2016) and compared to a model using the 26 relevant variables in the American College of Surgeons National Surgical Quality Improvement Program. The Surgical Risk Preoperative Assessment System was validated using a testing dataset (2017-2018). Performance statistics and Hosmer-Lemeshow plots were compared. Two high-risk definitions were compared: (1) the maximum Youden index, and (2) the cohort above the tenth decile of risk on the Hosmer-Lemeshow plot. The sensitivities, specificities, positive predictive values, negative predictive values, and accuracies were compared.In all, 2,822,379 patients were included; 3.6% of patients unexpectedly converted to inpatient. The 6-variable Surgical Risk Preoperative Assessment System model performed comparably to the 26-variable American College of Surgeons National Surgical Quality Improvement Program model (c-indices = 0.818 vs. 0.823; Brier scores = 0.0308 vs 0.0306, respectively). The Surgical Risk Preoperative Assessment System performed well on internal validation (c-index = 0.818, Brier score = 0.0341). The tenth decile of risk definition had higher specificity, positive predictive values, and accuracy than the maximum Youden index definition, while having lower sensitivity.The Surgical Risk Preoperative Assessment System accurately predicted a patient's risk of unplanned outpatient-to-inpatient conversion. Patients at higher risk should be considered for inpatient surgery, while lower risk patients could safely undergo operations at ambulatory surgery centers.

Robotic thoracic surgery has demonstrated benefits. We aimed to evaluate implementation of a robotic thoracic surgery program on postoperative outcomes at our Veteran's Administration Medical Center (VAMC). We retrospectively reviewed our VAMC database from 2015 to 2021. Patients who underwent surgery with intention to treat lung nodules were included. Primary outcome was patient length of stay (LOS). Patients were grouped by surgical approach and stratified to before and after adoption of robotic surgery. Univariate comparison of postoperative outcomes was performed using Wilcoxon rank sums and chi-squared tests. Multivariate regression was performed to control for ASA class. P values 0.05 were considered significant. Outcomes of 108 patients were assessed. 63 operations (58%) occurred before and 45 (42%) after robotic surgery implementation. There were no differences in patient preoperative characteristics. More patients underwent minimally invasive surgery (MIS) in the post-implementation era than pre-implementation (85% vs. 42%, p 0.001). Robotic operations comprised 53% of operations post-implementation. On univariate analysis, patients in the post-implementation era had a shorter LOS vs. pre-implementation, regardless of surgical approach (mean 4.7 vs. 6.0 days, p = 0.04). On multivariate analysis, patients who underwent MIS had a shorter LOS [median 4 days (IQR 2-6 days) vs. 7 days (6-9 days), p 0.001] and were more likely to be discharged home than to inpatient facilities [OR (95% CI) 13.00 (1.61-104.70), p = 0.02]. Robotic thoracic surgery program implementation at a VAMC decreased patient LOS and increased the likelihood of discharging home. Implementation at other VAMCs may be associated with improvement in some patient outcomes.

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.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.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.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.

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.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.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.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.

Lung transplantation is the mainstay of treatment for patients with end-stage respiratory failure. This study sought to evaluate survival following transplantation compared to the general population and quantify standardized mortality ratios (SMRs) using a nested case-control study design.Control subjects were nonhospitalized inhabitants of the United States identified through the National Longitudinal Mortality Study. Case subjects were adults who underwent lung transplantation between 1990 and 2007 and identified through the Organ Procurement and Transplantation Network. Propensity-matching (5:1, nearest neighbor, caliper = 0.1) was utilized to identify suitable control subjects based on age, sex, race, and location of residency. The primary study endpoint was 10-year survival.About 14,977 lung transplant recipients were matched to 74,885 nonhospitalized US residents. The 10-year survival rate of lung transplant recipients was 28% (95% confidence interval [CI] = 27%-29%). The population expected mortality rate was 19 deaths/100 person-years while the observed ratio was 104 deaths/100 person-years (SMR = 5.39, 95% CI = 5.35-5.43). The largest discrepancies between observed and expected mortality rates were in females (SMR = 5.97), Hispanic (SMR = 10.70), and single lung recipients (SMR = 5.92). SMRs declined over time (1990-1995 = 5.79, 1996-2000 = 5.64, and 2001-2007 = 5.10). Standardized mortality peaks in the first year after transplant and decreases steadily over time.Lung transplant recipients experience a fivefold higher SMR compared to the nonhospitalized population. Long-term mortality rates have experienced consistent decline over time.

Considerable variability exists between surgeons' assessments of a patient's individual pre-operative 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 versus 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 (NSQIP) 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=

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.

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.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.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.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.

Although some studies have compared esophagectomy outcomes by technique or approach, there is opportunity to strengthen our knowledge surrounding these outcomes. We aimed to perform a comprehensive comparison of esophagectomy postoperative complications.We retrospectively reviewed the American College of Surgeons National Surgical Quality Improvement Program database (2007-2018). Esophagectomies were identified using Current Procedural Terminology codes and grouped by operative technique (Ivor Lewis, transhiatal, McKeown) and surgical approach (minimally invasive vs open esophagectomy). Twelve postoperative complications were compared. Significant complications underwent risk adjustment using multivariate logistic regression.Analysis was performed on 13,457 esophagectomies: 11,202 (83.2%) open and 2255 (16.8%) minimally invasive. There were 7611 (56.6%) Ivor Lewis, 3348 (24.9%) transhiatal, and 2498 (18.6%) McKeown procedures. There were significant differences among the surgical techniques in 6 of 12 risk-adjusted complications. When comparing the outcomes of minimally invasive techniques, there were only significant differences in 2 of 12 complications: overall morbidity (minimally invasive Ivor Lewis 30.5%, minimally invasive transhiatal 43.4%, minimally invasive McKeown 40.3%, P = .0009) and infections (minimally invasive Ivor Lewis 15.4%, minimally invasive transhiatal 26.0%, minimally invasive McKeown 25.3%, P = .0003). Patients who underwent minimally invasive surgery were less likely to have overall morbidity (odds ratio, 0.68; 95% confidence interval, 0.62-0.75), respiratory complications (odds ratio, 0.77; 95% confidence interval, 0.68-0.87), urinary tract infection (odds ratio, 0.61; 95% confidence interval, 0.43-0.88), renal complications (odds ratio, 0.52; 95% confidence interval, 0.34-0.81), bleeding complications (odds ratio, 0.36; 95% confidence interval, 0.30-0.43), and nonhome discharge (odds ratio, 0.54; 95% confidence interval, 0.45-0.64), and had shorter length of stay (9.7 vs 13.2 days, P .0001).Patients undergoing minimally invasive esophagectomy have lower rates of postoperative complications regardless of esophagectomy techniques. The minimally invasive approach was associated with reduced complication variance among 3 common esophagectomy techniques.

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.

A new postoperative esophagectomy care pathway was recently implemented at our institution. Practice pattern change among provider teams can prove challenging; therefore, we sought to study the barriers and facilitators toward pathway implementation at the provider level.This qualitative study was guided by the Theoretical Domains Framework (TDF) to study the adoption and implementation of a post-esophagectomy care pathway. Sixteen in-depth interviews were conducted with providers involved with the pathway. Matrix analysis was used to analyze the data.Providers included attending surgeons (n = 6), advanced practice providers (n = 8), registered dietitian (n = 1), and clinic staff (n = 1). TDF domains that were salient across our findings included knowledge, beliefs about consequences, social influences, and environmental context and resources. Identified facilitators included were electronic health record tools, such as note templates including pathway components and a pathway-specific order set, patient satisfaction, and preliminary data indicating clinical benefits such as a reduced anastomotic leak rate. The major barrier reported was a hesitance to abandon previous practice patterns, most prevalent at the attending surgeon level.The TDF enabled us to identify and understand the individuals' perceived barriers and facilitators toward adoption and implementation of a postoperative esophagectomy pathway. This analysis can help guide and improve adoption of surgical patient care pathways among providers.

The objective of this study was to evaluate the educational impact following the implementation of a robotic thoracic surgery program on cardiothoracic (CT) surgery trainees. We hypothesized that the introduction of a robotic thoracic surgery program would adversely affect the CT surgery resident experience, decreasing operative involvement and subsequent competency of surgical procedures. CT surgery residents and thoracic surgery attendings from a single academic institution were administered a recurring, electronic survey from September 2019 to September 2020 following each robotic thoracic surgery case. Surveys evaluated resident involvement and operative performance. This study was exempt from review by our Institutional Review Board. Attendings and residents completed surveys for 86 and 75 cases, respectively. Residents performed > 50% of the operation independently at the surgeon console in 66.2 and 73.3% of cases according to attending and resident responses, respectively. The proportion of trainees able to perform > 75% of the operation increased with each increasing year in training (p = 0.002). Based on the Global Evaluative Assessment of Robotic Skills grading tool, third-year residents averaged higher scores compared to first-year residents (22.9 versus 17.4 out of 30 possible points, p

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.

Rectal prolapse is relatively uncommon in male patients. The aim of this study was to compare males and females who underwent rectal prolapse surgery.Retrospective analysis of the ACS NSQIP public use file.Among 12,220 patients, 978 (8%) were male and 11,242 (92%) were female. Males were younger, 56 (38-73) vs. 71 (58-83) years, less often white (83% vs. 71%), had lower ASA scores, and underwent more laparoscopic (33% vs. 27%), more open (33% vs. 29%), and less perineal (33% vs 44%) procedures (all p 0.05). Morbidity (9.9% vs. 10.0%), reoperation (3.4% vs. 3.1%), and readmission (5.7% vs. 6.0%) were not different for males and females. In subgroup analysis by surgical procedure type, there remained no outcome differences. Propensity matched analysis revealed no difference in the use of laparoscopic, open, or perineal procedures.Males with rectal prolapse are younger, have a different racial distribution, a lower surgical risk profile, and undergo different surgical procedures than females, which appears to be driven by patient age and surgical risk assessment.

The extraordinary demands of managing the COVID-19 pandemic has disrupted the world's ability to care for patients with thoracic malignancies. As a hospital's COVID-19 population increases and hospital resources are depleted, the ability to provide surgical care is progressively restricted, forcing surgeons to prioritize among their cancer populations. Representatives from multiple cancer, surgical, and research organizations have come together to provide a guide for triaging patients with thoracic malignancies as the impact of COVID-19 evolves as each hospital.

Background Informed consent is an ethical imperative of surgical practice. This requires effective communication of procedural risks to patients and is learned during residency. No systematic review has yet examined current risk disclosure. This systematic review aims to use existing published information to assess preoperative provision of risk information by surgeons. Methods Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses as a guide, a standardized search in Ovid MEDLINE, Embase, CINHAL, and PubMed was performed. Three reviewers performed the study screening, with 2-reviewer consensus required at each stage. Studies containing objective information concerning preoperative risk provision in adult surgical patients were selected for inclusion. Studies exclusively addressing interventions for pediatric patients or trauma were excluded, as were studies addressing risks of anesthesia. Results The initial search returned 12,988 papers after deduplication, 33 of which met inclusion criteria. These studies primarily evaluated consent through surveys of providers, record reviews and consent recordings. The most ubiquitous finding of all study types was high levels of intra-surgeon variation in what risk information is provided to patients preoperatively. Studies recording consents found the lowest rates of risk disclosure. Studies using multiple forms of investigation corroborated this, finding disparity between verbally provided information vs chart documentation. Conclusions The wide variance in what information is provided to patients preoperatively inhibits the realization of the ethical and practical components of informed consent. The findings of this review indicate that significant opportunities exist for practice improvement. Future development of surgical communication tools and techniques should emphasize standardizing what risks are shared with patients.

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.

Neighborhood measures of social vulnerability encompassing multiple sociodemographic factors can be used to quantify disparities in outcomes. We hypothesize patients with high Social Vulnerability Index (SVI) are at increased risk of morbidity following colectomy.We used local 2012-2017 National Surgical Quality Improvement Program (NSQIP) data to study colectomy patients, examining associations between SVI and postoperative outcomes.We included 976 patients from five hospitals. High SVI (75th percentile) was associated with increased postoperative morbidity on unadjusted analysis (OR 1.84, 95% CI 1.35-2.52, p 0.001); this association persisted after adjusting for demographics and comorbidities (OR 1.63, 95% CI 1.15-2.31, p = 0.005). The association with SVI was not significant after adjusting for perioperative risk modifiers such as emergent presentation (OR 1.37, 95% CI 0.95-1.98, p = 0.10).High social vulnerability is associated with increased postoperative complications. This effect appears mediated by perioperative risk factors, suggesting potential to improve outcomes by facilitating timely surgical intervention.

The objectives were to determine factors associated with conversion to open surgery in patients with esophageal cancer who underwent minimally invasive esophagectomy (MIE, including laparo-thoracoscopic and robotic) and the impact of conversion to open surgery on patient outcomes. We included patients from the National Cancer Database with esophageal and gastroesophageal junction cancer who underwent MIE from 2010 to 2015. Patient-, tumor-, and facility-related characteristics as well as short-term and oncologic outcomes were compared between patients who were converted to open surgery and those who underwent successful MIE without conversion to open surgery. Multivariable logistic regression models were used to analyze risk factors for conversion to open surgery from attempted MIE. 7306 patients underwent attempted MIE. Of these patients, 82 of 1487 (5.2%) robotic-assisted esophagectomies were converted to open, compared to 691 of 5737 (12.0%) laparo-thoracoscopic esophagectomies (p

Background Our objective was to evaluate the association of bridge to transplant (BTT) extracorporeal membrane oxygenation (ECMO) on survival after lung transplantation (LTx) and determine the degree to which transplant center volume affects this relationship. Methods Using the United Network for Organ Sharing database, we performed a retrospective cohort study evaluating the survival of patients undergoing LTx between 2005 and 2017. On the basis of previous literature, LTx centers were classified into 3 groups using their average annual LTx volume over the preceding 5 years: less than 25, 25 to 49, and more than 50. Survival of BTT ECMO and non-ECMO patients was analyzed using a log-rank test. Propensity scores for BTT ECMO were calculated, and a weighted proportional hazards model was used to compare BTT ECMO and non-ECMO patients by center volume. Results There were 20,976 patients who met inclusion criteria, with 611 (2.9%) undergoing BTT ECMO. Overall, BTT ECMO was associated with increased posttransplantation hazard of mortality (hazard ratio, 1.37; 95% confidence interval, 1.14 to 1.64). Kaplan-Meier plots by center volume suggest that BTT ECMO–associated mortality may be mitigated at high-volume LTx centers. In the propensity score–weighted proportional hazards model, we determined that when centers perform more than 35 LTxs per year, the increased hazard of BTT ECMO on mortality is no longer observed. Conclusions BTT ECMO can be performed as a bridge to LTx without significantly increasing patient mortality in high-volume centers. Patients undergoing BTT ECMO at LTx centers that perform more than 35 LTxs annually have equivalent mortality to those who do not require ECMO before transplantation.

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.

Background Pulmonary segmentectomy provides an anatomic lung resection while avoiding removal of excess normal lung tissue. This may be beneficial in patients with minimal pulmonary reserve who present with early-stage non-small cell lung cancer (NSCLC). However, the operative performance of a segmentectomy using a video-assisted thoracoscopic approach can be technically challenging. We hypothesized that introduction of the robotic surgical system would facilitate the performance of a segmentectomy as measured by an increase in the proportion of segmentectomies being pursued. Methods We completed a retrospective analysis of thoracoscopic and robotic anatomic lung resections, including lobectomies and segmentectomies, performed in patients with primary lung cancer from the time of initiation of the robotic thoracic surgery program in November 2017 to November 2019. We compared the proportion of thoracoscopic and robotic segmentectomies performed during the first year compared to the second year of the data collection period. Results A total of 138 thoracoscopic and robotic anatomic lung resections were performed for primary lung cancer. Types of lung cancer resected (adenocarcinoma, squamous cell carcinoma, or other), tumor size based on clinical T staging (T1-T4), and tumor location were not significantly different between years (P=0.44, P=0.98, and P=0.26, respectively). The proportion of segmentectomies increased from 8.6% during the first year to 25.0% during the second year (P=0.01). One out of 6 (16.7%) segmentectomies were performed using the robot during the first year versus 15 out of 17 (88.2%) during the second year (P=0.003). Conclusions Use of the robot led to a significant increase in the number of segmentectomies performed in patients undergoing anatomic lung resection. With increasing lung cancer awareness and widely available screening, a greater number of small, early-stage tumors suitable for segmentectomy will likely be detected. We conclude that robotic-assisted surgery may facilitate the challenges of performing a minimally invasive segmentectomy.