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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.

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.

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.

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.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.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.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.

Chest tubes account for a large proportion of postoperative pain after cardiothoracic operations. The objective of this study was to develop a novel, cost-effective, easy-to-use, lidocaine-eluting coating to reduce pain associated with postoperative chest tubes. A lidocaine-eluting hydrogel was developed by dispersing lidocaine-loaded nanoparticles in an aqueous solution containing gelatin (5%). Glutaraldehyde (1%) was added to crosslink the gelatin into a hydrogel. The hydrogel was dehydrated, resulting in a thin, stable polymer. Sterile lidocaine hydrogel-coated silicone discs and control discs were prepared and surgically implanted in the subcutaneous space of C57B6 mice. Using von Frey filaments, mice underwent preoperative baseline pain testing, followed by pain testing on post-procedure day 1 and 3. On post-procedure day 1, mice implanted with control discs demonstrated no change in pain tolerance compared to baseline, while mice implanted with 20 mg and 80 mg lidocaine-loaded discs demonstrated a 2.4-fold (P = 0.36) and 4.7-fold (P = 0.01) increase in pain tolerance, respectively. On post-procedure day 3, mice implanted with control discs demonstrated a 0.7-fold decrease in pain tolerance compared to baseline, while mice implanted with 20 mg and 80 mg lidocaine-loaded discs demonstrated a 1.8-fold (P = 0.88) and 8.4-fold (P = 0.02) increase in pain tolerance, respectively. Our results demonstrate successful development of a lidocaine-eluting chest tube with hydrogel coating, leading to improved pain tolerance in vivo. The concept of a drug-eluting drain coating has significant importance due to its potential universal application in a variety of drain types and insertion locations.

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.

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.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.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.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.

Observational studies demonstrate a protective effect of statins on the development and progression of esophageal adenocarcinoma. The role of statins in the prevention of reflux-induced esophageal changes remains unknown.Using a mixed gastroduodenal reflux mouse model, we hypothesized that oral administration of simvastatin would attenuate reflux-induced mucosal changes of the distal esophagus.Human Barrett's (CPB) and esophageal adenocarcinoma (FLO1 and OE19) cells were treated with simvastatin. Cell proliferation and apoptosis were evaluated using the MTS proliferation and annexin V apoptosis assays, respectively. A reflux mouse model was generated by performing a side-to-side anastomosis between the gastroesophageal junction and first portion of the duodenum (duodeno-gastroesophageal anastomosis, DGEA). DGEA mice were fed a standard or simvastatin-containing diet following surgery. Mice were euthanized 6 weeks post-operatively.Simvastatin significantly decreased proliferation and increased apoptosis in all cell lines. Compared to control animals, mice undergoing DGEA who were fed a standard diet demonstrated a fourfold increase in mucosal thickness and significant increase in proliferating cells (p 0.0001). DGEA mice fed a simvastatin-containing diet had an attenuated response to reflux, with a significant reduction in mucosal hyperplasia and proliferation (p 0.0001). DGEA mice fed a simvastatin-containing diet demonstrated significant upregulation of procaspase-3 (p = 0.009) and cleaved caspase-3 (p = 0.034) in the distal esophagus.We demonstrate for the first time a reduction in reflux-induced histologic changes of the distal esophagus following oral administration of simvastatin in vivo. These findings identify simvastatin as a potential preventative agent to inhibit the development and progression of reflux-induced esophageal injury.