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Objective: We sought to evaluate how implementing a thoracic enhanced recovery after surgery (ERAS) protocol impacted surgical outcomes after elective anatomic lung resection. Background: The effect of implementing the ERAS Society/European Society of Thoracic Surgery thoracic ERAS protocol on postoperative outcomes throughout an entire health care system has not yet been reported. Methods: This was a prospective cohort study within one health care system (January 2019–March, 2023). A thoracic ERAS protocol was implemented on May 1, 2021 for elective anatomic lung resections, and postoperative outcomes were tracked using the electronic health record and Vizient data. The primary outcome was overall morbidity; secondary outcomes included individual complications, length of stay, opioid use, chest tube duration, and total cost. Patients were grouped into pre-ERAS and post-ERAS cohorts. Bivariable comparisons were performed using independent t-test, χ², or Fisher exact tests, and multivariable logistic regression was performed to control for confounders. Results: There were 1007 patients in the cohort; 450 (44.7%) were in the post-ERAS group. Mean age was 66.2 years; most patients were female (65.1%), white (83.8%), had a body mass index between 18.5 and 29.9 (69.7%), and were ASA class 3 (80.6%). Patients in the postimplementation group had lower risk-adjusted rates of any morbidity, respiratory complication, pneumonia, surgical site infection, arrhythmias, infections, opioid usage, ICU use, and shorter postoperative length of stay (all P< 0.05). Conclusions: Postoperative outcomes were improved after the implementation of an evidence-based thoracic ERAS protocol throughout the health care system. This study validates the ERAS Society/European Society of Thoracic Surgery guidelines and demonstrates that simultaneous multihospital implementation can be feasible and effective. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Objective: To develop parsimonious prediction models for postoperative mortality, overall morbidity, and 6 complication clusters applicable to a broad range of surgical operations in adult patients. Summary Background Data: Quantitative risk assessment tools are not routinely used for preoperative patient assessment, shared decision making, informed consent, and preoperative patient optimization, likely due in part to the burden of data collection and the complexity of incorporation into routine surgical practice. Methods: Multivariable forward selection stepwise logistic regression analyses were used to develop predictive models for 30-day mortality, overall morbidity, and 6 postoperative complication clusters, using 40 preoperative variables from 2,275,240 surgical cases in the American College of Surgeons National Surgical Quality Improvement Program data set, 2005 to 2012. For the mortality and overall morbidity outcomes, prediction models were compared with and without preoperative laboratory variables, and generic models (based on all of the data from 9 surgical specialties) were compared with specialty-specific models. In each model, the cumulative c-index was used to examine the contribution of each added predictor variable. C-indexes, Hosmer-Lemeshow analyses, and Brier scores were used to compare discrimination and calibration between models. Results: For the mortality and overall morbidity outcomes, the prediction models without the preoperative laboratory variables performed as well as the models with the laboratory variables, and the generic models performed as well as the specialty-specific models. The c-indexes were 0.938 for mortality, 0.810 for overall morbidity, and for the 6 complication clusters ranged from 0.757 for infectious to 0.897 for pulmonary complications. Across the 8 prediction models, the first 7 to 11 variables entered accounted for at least 99% of the c-index of the full model (using up to 28 nonlaboratory predictor variables). Conclusions: Our results suggest that it will be possible to develop parsimonious models to predict 8 important postoperative outcomes for a broad surgical population, without the need for surgeon specialty-specific models or inclusion of laboratory variables. [ABSTRACT FROM AUTHOR]