Development and Validation of a Multivariable Prediction Model for Postoperative Intensive Care Unit Stay in a Broad Surgical Population.

IMPORTANCE: Despite limited capacity and expensive cost, there are minimal objective data to guide postoperative allocation of intensive care unit (ICU) beds. The Surgical Risk Preoperative Assessment System (SURPAS) uses 8 preoperative variables to predict many common postoperative complications, but it has not yet been evaluated in predicting postoperative ICU admission. OBJECTIVE: To determine if the SURPAS model could accurately predict postoperative ICU admission in a broad surgical population. DESIGN, SETTING, AND PARTICIPANTS: This decision analytical model was a retrospective, observational analysis of prospectively collected patient data from the 2012 to 2018 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database, which were merged with individual patients' electronic health record data to capture postoperative ICU use. Multivariable logistic regression modeling was used to determine how the 8 preoperative variables of the SURPAS model predicted ICU use compared with a model inputting all 28 preoperatively available NSQIP variables. Data included in the analysis were collected for the ACS NSQIP at 5 hospitals (1 tertiary academic center, 4 academic affiliated hospitals) within the University of Colorado Health System between January 1, 2012, and December 31, 2018. Included patients were those undergoing surgery in 9 surgical specialties during the 2012 to 2018 period. Data were analyzed from May 29 to July 30, 2021. EXPOSURE: Surgery in 9 surgical specialties, including general, gynecology, orthopedic, otolaryngology, plastic, thoracic, urology, vascular, and neurosurgery. MAIN OUTCOMES AND MEASURES: Use of ICU care up to 30 days after surgery. RESULTS: A total of 34 568 patients were included in the analytical data set: 32 032 (92.7%) in the cohort without postoperative ICU use and 2545 (7.4%) in the cohort with postoperative ICU use (no ICU use: mean [SD] age, 54.9 [16.6] years; 18 188 women [56.8%]; ICU use: mean [SD] age, 60.3 [15.3] years; 1333 men [52.4%]). For the internal chronologic validation of the 7-variable SURPAS model, data from 2012 to 2016 were used as the training data set (n = 24 250, 70.2% of the total sample size of 34 568) and data from 2017 to 2018 were used as the test data set (n = 10 318, 29.8% of the total sample size of 34 568). The C statistic improved in the test data set compared with the training data set (0.933; 95% CI, 0.924-0.941 vs 0.922; 95% CI, 0.917-0.928), whereas the Brier score was slightly worse in the test data set compared with the training data set (0.045; 95% CI, 0.042-0.048 vs 0.045; 95% CI, 0.043-0.047). The SURPAS model compared favorably with the model inputting all 28 NSQIP variables, with both having good calibration between observed and expected outcomes in the Hosmer-Lemeshow graphs and similar Brier scores (model inputting all variables, 0.044; 95% CI, 0.043-0.048; SURPAS model, 0.045; 95% CI, 0.042-0.046) and C statistics (model inputting all variables, 0.929; 95% CI, 0.925-0.934; SURPAS model, 0.925; 95% CI, 0.921-0.930). CONCLUSIONS AND RELEVANCE: Results of this decision analytical model study revealed that the SURPAS prediction model accurately predicted postoperative ICU use across a diverse surgical population. These results suggest that the SURPAS prediction model can be used to help with preoperative planning and resource allocation of limited ICU beds.

Using the Surgical Risk Preoperative Assessment System to Define the "High Risk" Surgical Patient.

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.

Accuracy of the surgical risk preoperative assessment system universal risk calculator in predicting risk for patients undergoing selected operations in 9 specialty areas.

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.

Relationships between predischarge and postdischarge infectious complications, length of stay, and unplanned readmissions in the ACS NSQIP database.

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.

A Pilot Study of Patient-Reported Outcome Measures Across a Broad Sample of Surgical Patients.

BACKGROUND: Patient-reported outcomes (PROs) have the potential to aid in surgical decision-making, predict surgical outcomes, assess recovery, and evaluate long-term success. We performed a pilot study testing the ability to use PROs in a broad surgical population in preparation for wide spread use. MATERIAL AND METHODS: Surgical patients completed five Patient-Reported Outcome Measurement Information System (PROMIS) measures during their preoperative encounter in the preanesthesia clinic and again postoperatively via emailed link. Preoperative to postoperative changes in PROMIS scores, factors related to completion of postoperative measures, intercorrelations between PROMIS measures, and numbers of patients with normal function, and mild, moderate, and severe deficits in PROMIS scores were analyzed. RESULTS: A total of 393 patients undergoing surgery in 8 specialties completed preoperative PROMIS measures; 239 (60.8%) completed them postoperatively. Physical function (P < 0.0001), pain (P < 0.0001), and cognitive function (P = 0.03) PROMIS scores significantly worsened after surgery but not mental PROMIS scores (P = 0.48). Hispanic and sicker patients had lower completion rates of postoperative measures. Intercorrelations were very high (>0.80) among the physical function and self-efficacy for activities of daily living PROMIS measures. Physical function and pain PROMIS measures had the largest number of patients in the "severe" range after surgery. CONCLUSIONS: Patients across a broad surgical population completed PROMIS measures successfully, both preoperatively and postoperatively, although the postoperative completion rate was lower than other studies reported in the literature. PROMIS scores were reflective of the effects of surgery. Some of the PROMIS measures were highly correlated suggesting that some measures could be eliminated or replaced with measures assessing other important effects of surgery. Consideration could be made to alert health care providers about patients having PROs in the "severe" range for potential intervention.

A comparison of the new, parsimonious tool Surgical Risk Preoperative Assessment System (SURPAS) to the American College of Surgeons (ACS) risk calculator in emergency surgery.

BACKGROUND: The Surgical Risk Preoperative Assessment System accurately predicts postoperative complications in elective surgery using only 8 preoperative variables, but its performance in emergency surgery has not been evaluated. We hypothesized that the Surgical Risk Preoperative Assessment System accurately predicts postoperative complications in emergency surgery and compared its performance to that of the American College of Surgeons Surgical Risk Calculator. METHODS: We calculated the Surgical Risk Preoperative Assessment System and the American College of Surgeons Surgical Risk Calculator risk estimates in a random sample of 1,010 emergency surgery cases from the American College of Surgeons National Surgical Quality Improvement Program 2014 to 2017 database. Risk estimates were compared with known outcomes. Analyses included the Hosmer-Lemeshow goodness of fit graphs and P values, c-indexes, and Brier scores. RESULTS: Results were similar between the Surgical Risk Preoperative Assessment System and the American College of Surgeons Surgical Risk Calculator for mortality, urinary tract infection, cardiac, venous thromboembolism, and renal complications. The American College of Surgeons Surgical Risk Calculator underestimated morbidity compared with the Surgical Risk Preoperative Assessment System (16.04% American College of Surgeons Surgical Risk Calculator vs 24.88% Surgical Risk Preoperative Assessment System vs 24.3% observed). Both calculators overestimated readmission (7.67% American College of Surgeons Surgical Risk Calculator vs 5.18% Surgical Risk Preoperative Assessment System vs 4.1% observed). CONCLUSION: Both calculators predicted mortality, urinary tract infection, cardiac, venous thromboembolism, and renal complications well, but readmissions relatively poorly. The Surgical Risk Preoperative Assessment System estimated overall morbidity accurately, while the American College of Surgeons Surgical Risk Calculator underestimated this risk.

The value of the "Surgical Risk Preoperative Assessment System" (SURPAS) in preoperative consultation for elective surgery: a pilot study.

BACKGROUND: Risk assessment is essential to informed decision making in surgery. Preoperative use of the Surgical Risk Preoperative Assessment System (SURPAS) providing individualized risk assessment, may enhance informed consent. We assessed patient and provider perceptions of SURPAS as a risk assessment tool. METHODS: A convergent mixed-methods study assessed SURPAS's trial implementation, concurrently collecting quantitative and qualitative data, separately analyzing it, and integrating the results. Patients and providers were surveyed and interviewed on their opinion of how SURPAS impacted the preoperative encounter. Relationships between patient risk and patient and provider assessment of SURPAS were examined. RESULTS: A total of 197 patients were provided their SURPAS postoperative risk estimates in nine surgeon's clinics. Of the total patients, 98.8% reported they understood their surgical risks very or quite well after exposure to SURPAS; 92.7% reported SURPAS was very helpful or helpful. Providers shared that 83.4% of the time they reported SURPAS was very or somewhat helpful; 44.7% of the time the providers reported it changed their interaction with the patient and this change was beneficial 94.3% of the time. As patient risk increased, providers reported that SURPAS was increasingly helpful (p < 0.0001). CONCLUSIONS: Patients and providers reported the use of SURPAS helpful and informative during the preoperative risk assessment of patients, thus improving the surgical decision making process. Patients thought that SURPAS was helpful regardless of their risk level, whereas providers thought that SURPAS was more helpful in higher risk patients.

Use of Surgical Risk Preoperative Assessment System (SURPAS) and Patient Satisfaction During Informed Consent for Surgery.

BACKGROUND: The objective of this study was to determine the effects of using the Surgical Risk Preoperative Assessment System (SURPAS) on patient satisfaction and surgeon efficiency in the surgical informed consent process, as compared to surgeons' "usual" consent process. STUDY DESIGN: Patient perception of the consent process was assessed via survey in 2 cohorts: 10 surgeons in different specialties used their "usual" consent process for 10 patients; these surgeons were then taught to use SURPAS, and they used it during the informed consent process of 10 additional patients. The data were compared using Fisher's exact test and the Cochran-Mantel-Haenszel test. RESULTS: One hundred patients underwent the "usual" consent process (USUAL), and 93 underwent SURPAS-guided consent (SURPAS). Eighty-two percent of SURPAS were "very satisfied" and 18% were "satisfied" with risk discussion vs 16% and 72% of USUAL, respectively. Of those who used SURPAS, 75.3% reported the risk discussion made them "more comfortable" with surgery vs 19% of USUAL, and 90.3% of SURPAS users reported "somewhat" or "greatly decreased" anxiety vs 20% of USUAL. All p values were <0.0001. Among SURPAS patients, 97.9% reported "enough time spent discussing risks" vs 72.0% of USUAL patients. CONCLUSIONS: The SURPAS tool improved the informed consent process for patients compared with the "usual" consent process, in terms of patient satisfaction, ie making patients feel more comfortable and less anxious about their impending operations. Providers should consider integrating the SURPAS tool into their preoperative consent process.

Systematic Review of Preoperative Risk Discussion in Practice.

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.

Accurate Preoperative Prediction of Discharge Destination Using 8 Predictor Variables: A NSQIP Analysis.

BACKGROUND: With inpatient length of stay decreasing, discharge destination after surgery can serve as an important metric for quality of care. Additionally, patients desire information on possible discharge destination. Adequate planning requires a multidisciplinary approach, can reduce healthcare costs and ensure patient needs are met. The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious risk assessment tool using 8 predictor variables developed from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) dataset. SURPAS is applicable to more than 3,000 operations in adults in 9 surgical specialties, predicts important adverse outcomes, and is incorporated into our electronic health record. We sought to determine whether SURPAS can accurately predict discharge destination. STUDY DESIGN: A "full model" for risk of postoperative "discharge not to home" was developed from 28 nonlaboratory preoperative variables from ACS NSQIP 2012-2017 dataset using logistic regression. This was compared with the 8-variable SURPAS model using the C index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration. RESULTS: Of 5,303,519 patients, 447,153 (8.67%) experienced a discharge not to home. The SURPAS model's C index, 0.914, was 99.24% of that of the full model's (0.921); the Hosmer-Lemeshow plots indicated good calibration and the Brier score was 0.0537 and 0.0514 for the SUPAS and full model, respectively. CONCLUSIONS: The 8-variable SURPAS model preoperatively predicts risk of postoperative discharge to a destination other than home as accurately as the 28 nonlaboratory variable ACS NSQIP full model. Therefore, discharge destination can be integrated into the existing SURPAS tool, providing accurate outcomes to guide decision-making and help prepare patients for their postoperative recovery.

Comparison of accuracy of prediction of postoperative mortality and morbidity between a new, parsimonious risk calculator (SURPAS) and the ACS Surgical Risk Calculator.

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.

Refining the predictive variables in the "Surgical Risk Preoperative Assessment System" (SURPAS): a descriptive analysis.

BACKGROUND: The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious set of models providing accurate preoperative prediction of common adverse outcomes for individual patients. However, focus groups with surgeons and patients have developed a list of questions about and recommendations for how to further improve SURPAS's usability and usefulness. Eight issues were systematically evaluated to improve SURPAS. METHODS: The eight issues were divided into three groups: concerns to be addressed through further analysis of the database; addition of features to the SURPAS tool; and the collection of additional outcomes. Standard multiple logistic regression analysis was performed using the 2005-2015 American College of Surgeons National Surgical Quality Improvement Participant Use File (ACS NSQIP PUF) to refine models: substitution of the preoperative sepsis variable with a procedure-related risk variable; testing of an indicator variable for multiple concurrent procedure codes in complex operations; and addition of outcomes to increase clinical applicability. Automated risk documentation in the electronic health record and a patient handout and supporting documentation were developed. Long term functional outcomes were considered. RESULTS: Model discrimination and calibration improved when preoperative sepsis was replaced with a procedure-related risk variable. Addition of an indicator variable for multiple concurrent procedures did not significantly improve the models. Models were developed for a revised set of eleven adverse postoperative outcomes that separated bleeding/transfusion from the cardiac outcomes, UTI from the other infection outcomes, and added a predictive model for unplanned readmission. Automated documentation of risk assessment in the electronic health record, visual displays of risk for providers and patients and an "About" section describing the development of the tool were developed and implemented. Long term functional outcomes were considered to be beyond the scope of the current SURPAS tool. CONCLUSION: Refinements to SURPAS were successful in improving the accuracy of the models, while reducing manual entry to five of the eight variables. Adding a predictor variable to indicate a complex operation with multiple current procedure codes did not improve the accuracy of the models. We developed graphical displays of risk for providers and patients, including a take-home handout and automated documentation of risk in the electronic health record. These improvements should facilitate easier implementation of SURPAS.

Accurate preoperative prediction of unplanned 30-day postoperative readmission using 8 predictor variables.

BACKGROUND: Unplanned postoperative readmissions are associated with high costs, may indicate poor care quality, and present a substantial opportunity for healthcare quality improvement. Patients want to know their risk of unplanned readmission, and surgeons need to know the risk to adequately counsel their patients. The Surgical Risk Preoperative Assessment System tool was developed from the American College of Surgeons National Surgical Quality Improvement Program dataset and is a parsimonious model using 8 predictor variables. Surgical Risk Preoperative Assessment System is applicable to >3,000 operations in 9 surgical specialties, predicts 30-day postoperative mortality and morbidity, and is incorporated into our electronic health record. METHODS: A Surgical Risk Preoperative Assessment System model was developed using logistic regression. It was compared to the 28 nonlaboratory variables model from the American College of Surgeons National Surgical Quality Improvement Program 2012 to 2017 dataset using the c-index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration. RESULTS: Of 4,861,370 patients, 188,150 (3.98%) experienced unplanned readmission related to the index operation. The Surgical Risk Preoperative Assessment System model's c-index, 0.728, was 99.3% of that of the full model's, 0.733; the Hosmer-Lemeshow plots indicated good calibration; and the Brier score was 0.0372 for Surgical Risk Preoperative Assessment System and 0.0371 for the full model. CONCLUSION: The 8 variable Surgical Risk Preoperative Assessment System model detects patients at risk for postoperative unplanned, related readmission as accurately as the full model developed from all 28 nonlaboratory preoperative variables in the American College of Surgeons National Surgical Quality Improvement Program dataset. Therefore, unplanned readmission can be integrated into the existing Surgical Risk Preoperative Assessment System tool providing moderately accurate prediction of postoperative readmission.

Use of the consolidated framework for implementation research to guide dissemination and implementation of new technologies in surgery.

BACKGROUND: Improving surgical outcomes is important to patients, providers, and healthcare systems. Understanding best methods to ensure evidence based practices are successfully implemented and sustained in clinical practices leads to improved care. Dissemination and implementation (D&I) science facilitates the successful pathway from clinical trials to sustained implementation. METHODS: We describe D&I science, introduce the consolidated framework for implementation research (CFIR), a D&I framework, and provide an example of how CFIR was utilized to facilitate the translational process from design adaptations to implementation, broad utilization by clinicians, and sustainability of the SUrgical Risk Preoperative Assessment System (SURPAS) tool into regular clinical practice. SURPAS creates data-driven individualized risk assessments of common adverse postoperative outcomes to enhance the informed consent process, shared decision making, and consequently improved surgical outcomes. The CFIR provided a structured systematic way to identify constructs influencing the D&I of SURPAS, including adaptations for the process and tool. RESULTS: We identified three domains, each with specific constructs, that participants believed would strongly influence effectiveness of SURPAS implementation efforts: the importance of patients' perspectives (outer setting); the quality of SURPAS (intervention characteristic); and integration of SURPAS into the electronic health record (inner setting). Additionally, providers' positive attitudes toward and support of SURPAS (characteristics of individuals); and the ease of integration of SURPAS into the workflow (process), were also identified. Tension emerged between patients' preference of the provision of risk information and providers' concern about additional clinic time required for formal risk discussion with low-risk patients. CONCLUSIONS: Systematically identifying constructs from the beginning of the design through the implementation process can guide design of a multi-component strategy for future large-scale implementation by assessing the relative impact of factors on implementation using the CFIR framework. In the example studied, this allows key stakeholders to ensure success of D&I of SURPAS at multiple levels and times, continuously optimizing the process.

Assessment of attitudes towards future implementation of the "Surgical Risk Preoperative Assessment System" (SURPAS) tool: a pilot survey among patients, surgeons, and hospital administrators.

BACKGROUND: Risk assessment in surgery is essential to guide treatment decisions but is highly variable in practice. Providing formal preoperative risk assessment to surgical teams and patients may optimize understanding of risk. Implementation of the Surgical Risk Preoperative Assessment System (SURPAS), an innovative real time, universal, preoperative tool providing individualized risk assessment, may enhance informed consent and reduce adverse outcomes. To ensure optimal development and implementation of SURPAS we performed an in-depth pre-implementation evaluation of SURPAS at an academic tertiary referral center in Colorado. METHODS: Four focus groups with 24 patients, three focus groups with 29 surgical providers and clinic administrators, and five individual interviews with administrative officials were conducted to elicit their perspectives about the development and implementation of SURPAS. Qualitative data collection and analyses, utilizing a Matrix Analysis approach were used to explore insights regarding SURPAS. RESULTS: Participants were positive about SURPAS and provided suggestions to improve and address concerns regarding it. For healthcare personnel three major themes emerged: 1) The SURPAS tool - Important work especially for high risk patients, yet not a substitute for clinical judgment; 2) Benefits of SURPAS to the risk assessment process - Improves the processes, enhances patients' participation in shared decision-making process, and creates a permanent record; and 3) Facilitators and barriers of implementation of SURPAS - Easy to incorporate into clinical practice in spite of surgical providers' resistance to adoption of new technology. For patients three major themes emerged: 1) Past experience of preoperative risk assessment discussions - Patients were not made aware of possible complications that occurred; 2) The SURPAS tool - All patients liked SURPAS and believed having printed material would be useful to guide discussions and facilitate remembering conversations with the providers; and 3) Potential concerns with having risk assessment information - Patients were mixed in deciding to have an operation with high risks. CONCLUSIONS: Systematically capturing data from the beginning of the implementation process from key stakeholders (patients, surgical providers, clinical staff, and administrators) that includes adaptations to the tool and implementation process will help to inform pragmatic approaches for implementing the SURPAS tool in various settings, scaling-up, and sustaining it.

Temporal Trends, Determinants, and Outcomes of Inpatient versus Outpatient Arteriovenous Fistula Operations.

BACKGROUND: As high healthcare costs are increasing scrutinized, a movement toward reducing patient hospital admissions and lengths of stay has emerged, particularly for operations that may be performed safely in the outpatient setting. Our aim is to describe recent temporal trends in the proportion of dialysis access procedures performed on an inpatient versus outpatient basis and to determine the effects of these changes on perioperative morbidity and mortality. METHODS: The 2005-2008 American College of Surgeons National Surgical Quality Improvement Program database was queried for all primary arteriovenous fistula (AVF) procedures using current procedural terminology codes. Changes in the proportions of inpatient versus outpatient operations performed by year, as well as the associated 30-day postoperative morbidity and mortality, were analyzed using univariable statistics and multivariable logistic regression. RESULTS: Two thousand nine hundred fifty AVF procedures were performed over the study period. Overall, 71.7% (n = 2,114) were performed on an outpatient basis. Inpatient procedures were associated with higher 30-day morbidity (10.5% vs. 4.5%) and mortality (2.8% vs. 0.7%) than outpatient procedures (both, P < 0.001). There was a significant increase in the proportion of procedures performed on an outpatient basis over time (2005: 56% vs. 2008: 75%; P < 0.001). There were no changes in postoperative morbidity or mortality for inpatient or outpatient AVF over time (P ≥ 0.36). Independent determinants of having an inpatient procedure included younger age (OR 0.99), increasing ASA class (ASA IV OR 1.56), congestive heart failure (OR 3.32), recent ascites (OR 3.25), poor functional status (OR 3.22), the presence of an open wound (OR 1.91), and recent sepsis (OR 6.06) (all, P < 0.01). Acute renal failure (OR 2.60) and current dialysis (OR 1.44) were also predictive (P < 0.001). After correcting for baseline differences between groups, the adjusted OR for both morbidity (aOR 1.93, 95% CI 1.38-2.69) and mortality (aOR 2.85, 95% CI 1.36-5.95) remained significantly higher for inpatient versus outpatient AVF. CONCLUSIONS: Dialysis access operations are increasingly being performed on an outpatient basis, with stable perioperative outcomes. Inpatient procedures are associated with worse outcomes, likely because they are reserved for patients with acute illnesses, serious comorbidities, and poor functional status. Overall, for appropriately selected patients, the movement toward performing more elective dialysis access operations on an outpatient basis is associated with acceptable outcomes.

Intermediate-Acting Nondepolarizing Neuromuscular Blocking Agents and Risk of Postoperative 30-Day Morbidity and Mortality, and Long-term Survival.

BACKGROUND: Nondepolarizing neuromuscular blocking drugs (NNMBDs) are commonly used as an adjunct to general anesthesia. Residual blockade is common, but its potential adverse effects are incompletely known. This study was designed to assess the association between NNMBD use with or without neostigmine reversal and postoperative morbidity and mortality. METHODS: This is a retrospective observational study of 11,355 adult patients undergoing general anesthesia for noncardiac surgery at 5 Veterans Health Administration (VA) hospitals. Of those, 8984 received NNMBDs, and 7047 received reversal with neostigmine. The primary outcome was a composite of respiratory complications (failure to wean from the ventilator, reintubation, or pneumonia), which was "yes" if a patient had any of the 3 component events and "no" if they had none. Secondary outcomes were nonrespiratory complications, 30-day and long-term all-cause mortality. We adjusted for differences in patient risk using propensity matched (PM) followed by assessment of the association of interest by logistic regression between the matched pairs as our primary analysis and multivariable logistic regression (MLR) as a sensitivity analysis. RESULTS: Our primary aim was to assess the adverse outcomes in the patients who had received NNMBDs with and without neostigmine. Administration of an NNMBD without neostigmine reversal compared with NNMBD with neostigmine reversal was associated with increased odds of respiratory complications (PM odds ratio [OR], 1.75 [95% confidence interval [CI], 1.23-2.50]; MLR OR, 1.71 [CI, 1.24-2.37]) and a marginal increase in 30-day mortality (PM OR, 1.83 [CI, 0.99-3.37]; MLR OR, 1.78 [CI, 1.02-3.13]). However, there were no statistically significant associations with nonrespiratory complications or long-term mortality. Patients who were administered an NNMBD followed by neostigmine had no differences in outcomes compared with patients who had general anesthesia without an NNMBD. CONCLUSIONS: The use of NNMBDs without neostigmine reversal was associated with increased odds of our composite respiratory outcome compared with patients reversed with neostigmine. Based on these data, we conclude that reversal of NNMBDs should become a standard practice if extubation is planned.

An institutional analysis of unplanned return to the operating room to identify areas for quality improvement.

BACKGROUND: Unplanned return to the operating room (uROR) has been suggested as a hospital quality indicator. The purpose of this study was to determine reasons for uROR to identify opportunities for patient care improvement. METHODS: uROR reported by our institution's American College of Surgeons National Surgical Quality Improvement Program underwent secondary review. RESULTS: The uROR rate reported by clinical reviewers was 4.3%. Secondary review re-categorized 64.7% as "true uROR" with the most common reasons for uROR being infection (30.9%) and bleeding (23.6%). Remaining cases were categorized as "false uROR" with the most common reasons being inadequate documentation (60.0%) and not directly related to index procedure (16.7%). CONCLUSIONS: Strict adherence to NSQIP definitions results in misidentification of true uROR. This raises concerns for using NSQIP-identified uROR as a hospital quality metric. Improved processes of care to prevent infection and hemorrhage at our institution could reduce the rate of true uROR.