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Objective: Improvement of surgical care is dependent upon evidence-based practices (EBPs), policies, procedures, and innovations. The objective of this study was to understand and synthesize the use of implementation science (IS) in surgical care., Summary Background Data: This article summarizes the existing literature to identify the frequency and types of EBPs selected for surgical care, IS frameworks that guided the published research, and prominent facilitators and barriers., Methods: A modified version of the Arksey and O'Malley framework and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews Checklist were used to provide the guidance and standards to conduct this scoping review. We queried: Ovid MEDLINE; American Psychological Association PsycINFO; Embase; Cumulated Index to Nursing and Allied Health Literature; Web of Science; and Google Scholar for manuscripts published January 2001 - June 2023., Results: The initial search found 3,674 citations of which 129 met inclusion criteria. The heterogeneity and volume of innovations within the surgical IS field were vast. The most frequent innovations were in peri-operative care, safety in surgery, and Enhanced Recovery After Surgery. Six constructs were identified as both major facilitators and barriers: support from leadership; surgeon and staff knowledge regarding EBPs; relationship/team building; environmental context; data; and resources., Conclusion: Identifying these implementation factors used in the surgical field enables us to determine variables that support and inhibit the adoption and implementation of new practices, support practice change, enhance quality and equity of surgical care, and identify research gaps for future IS in surgical care., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

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

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., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Lung transplantation survival estimates are traditionally reported as fixed 1-, 5-, and 10-year mortality rates. Alternatively, this study aims to demonstrate how conditional survival models can provide useful prognostic information tailored to the time a recipient has already survived from the date of transplantation. Recipient data was obtained from the Organ Procurement and Transplantation Network database. Data from 24,820 adult recipients over age 18 who received a lung transplant between 2002 and 2017 were included in the study. Five-year observed conditional survival estimates were calculated by recipient age, sex, race, transplant indication, transplant type ( i.e. , single or double), and renal function at the time of transplantation. Significant variability exists in conditional survival following lung transplantation. Each specific recipient characteristic significantly impacted conditional survival during at least one time point in the first 5 years. Younger age and double lung transplantation were the two most positive predictors of improved conditional survival consistently throughout the 5-year study period. Conditional survival in lung transplantation recipients changes over time and across recipient characteristics. Hazards of mortality are not fixed and need to be dynamically evaluated as a function of time. Conditional survival calculations can provide more accurate prognostic predictions than unconditional survival estimates., Competing Interests: Disclosure: A.S.-P. received financial support from the American Association for Thoracic Surgery Cardiac Surgical Resident Investigator Award. The other authors have no conflicts of interest to report., (Copyright © ASAIO 2023.)

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

Objective: 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.

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

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