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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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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: Blood transfusion has been associated with worse outcomes in adult trauma patients with traumatic brain injury (TBI). However, the effects in injured children have not been evaluated. We hypothesize that blood transfusion is also associated with worse outcomes in children with TBI., Methods: A retrospective review of the trauma database at two Level I pediatric trauma centers was performed. We reviewed all patients 18 years and younger with TBI, who survived at least 24 hours, from 2002 to 2011. Exclusion criteria include those who underwent craniotomy, thoracotomy, exploratory laparotomy, and any orthopedic procedure., Results: A total of 1,607 children with TBI were included in the study population (mean age, 6.4 [5.7] years; 65% male), 178 of whom received a blood transfusion. Mean Injury Severity Score (ISS) was 16.5 (9.1). Patients who received a transfusion had a higher ISS than those who did not (26.7 vs. 15.3). After controlling for age, sex, ISS, Glasgow Coma Scale (GCS) score on presentation, and mechanism of injury, patients who received a blood transfusion were more likely to be admitted to the intensive care unit (p < 0.0001), less likely to survive to hospital discharge (p = 0.02), more likely to be discharged to a rehabilitation facility (p = 0.01) and be dependent on caretakers at follow-up (p < 0.0001), as well as more likely to develop urinary tract infection (p = 0.02) and bacteremia (p = 0.02) during their hospital stay. These differences in outcomes among those who did and did not receive a blood transfusion began to disappear in patients with a nadir hemoglobin of less than 8.0 g/dL., Conclusion: Pediatric patients sustaining TBI who receive blood transfusion and do not require operative intervention have worse outcomes compared with patients who do not receive transfusion. This includes an increased risk of death. These data suggest that a transfusion trigger of hemoglobin level at 8.0 g/dL in injured children with TBI may be beneficial., Level of Evidence: Epidemiologic study, level III. Therapeutic study, level IV.

Objectives: Population-level control of modifiable cardiovascular disease (CVD) risk factors is suboptimal. The objectives of this study were (1) to demonstrate the use of electronically downloaded electronic health record (EHR) data to assess guideline concordance in a large cohort of primary care patients, (2) to provide a contemporary assessment of blood pressure (BP) and low-density lipoprotein (LDL) noncontrol in primary care, and (3) to demonstrate the effect of risk adjustment of rates of noncontrol of BP and LDL for differences in patient mix on these clinic-level performance measures., Methods: This was an observational comparative effectiveness study that included 232,172 adult patients ≥18 years old with ≥1 visit within 2 years in 33 primary care clinics with EHRs. The main measures were rates of BP and LDL noncontrol based on current guidelines and were calculated from electronically downloaded EHR data. Rates of noncontrol were risk-adjusted using multivariable models of patient-level variables., Results: Overall, 16.0% of the 227,122 patients with known BP and 14.9% of the 136,771 patients with known LDL were uncontrolled. Clinic-level, risk-adjusted BP noncontrol ranged from 7.7% to 26.5%, whereas that for LDL ranged from 5.8% to 23.6%. Rates of noncontrol exceeded an achievable benchmark for 85% (n = 28) and 79% (n = 26) of the 33 clinics for BP and LDL, respectively. Risk adjustment significantly influences clinic rank order for rate of noncontrol., Conclusions: We demonstrated that the use of electronic collection of data from a large cohort of patients from fee-for-service primary care clinics is feasible for the audit of and feedback on BP and LDL noncontrol. Rates of noncontrol for most clinics are substantially higher than those achievable. Risk adjustment of noncontrol rates results in a rank-order of clinics very different from that achieved with nonadjusted data.

Considerable variability exists between surgeons' assessments of a patient's individual preoperative 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 vs 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 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 ≤ 0.001) and lower morbidity risk compared to SURPAS for high-risk patients (ASA class 5, 37.6% vs 69.8%, P < 0.001). This trend also occurred in high-risk patients for mortality (ASA 5, 11.1% vs 44.3%, P < 0.001). C-indices for SURPAS vs surgeons were 0.84 vs 0.76 (P = 0.3) for morbidity and 0.98 vs 0.85 (P = 0.001) for mortality. Brier scores for SURPAS vs surgeons were 0.1579 vs 0.1986 for morbidity (P = 0.03) and 0.0409 vs 0.0543 for mortality (P = 0.006). ICCs showed that surgeons had moderate risk agreement for morbidity (ICC = 0.654) and mortality (ICC = 0.507). Thoracic surgeons and patients could benefit from using a surgical risk calculator to better estimate patients' surgical risks during the informed consent process. [ABSTRACT FROM AUTHOR]

• Surgical Risk Preoperative Assessment System (SURPAS) is a surgical risk calculator. • It uses 8 pre-operative variables to calculate 12 post-operative adverse events. • We used SURPAS to create two definitions of the "high risk" surgical patient. • The deciles approach and maximum Youden index create different "high risk" groups. • The definitions allow providers to target risk mitigation at high risk patients. 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. 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." 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. SURPAS can define the "high risk" surgical population that may benefit from risk-mitigating interventions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Introduction: The Surgical Risk Preoperative Assessment System is a parsimonious, universal surgical risk calculator integrated into our local electronic health record. We determined how many of its eight preoperative risk predictor variables could be automatically obtained from the electronic health record. This has implications for the usability and adoption of Surgical Risk Preoperative Assessment System, serving as an example of use of electronic health record data for populating clinical decision support tools. Methods: We quantified the availability and accuracy in the electronic health record of the eight Surgical Risk Preoperative Assessment System predictor variables (patient age, American Society of Anesthesiology physical status classification, functional health status, sepsis, work Relative Value Unit, in-/outpatient operation, surgeon specialty, emergency status) at the patient's preoperative encounter of 5205 patients entered into the American College of Surgeons National Surgical Quality Improvement Program. Accuracy was determined by comparing the electronic health record data to the same patient's National Surgical Quality Improvement Program data, used as the "gold standard." Acceptable accuracy was defined as a Kappa statistic or Pearson correlation coefficient ≥0.8 when comparing electronic health record and National Surgical Quality Improvement Program data. Acceptable availability was defined as presence of the variable in the electronic health record at the preoperative encounter ≥95% of the time. Results: Of the eight predictor variables, six had acceptable accuracy. Only preoperative sepsis and functional health status had Kappa statistics <0.8. However, only patient age and surgeon specialty were ≥95% available in the electronic health record at the preoperative visit. Conclusions: Processes need to be developed to populate more of the Surgical Risk Preoperative Assessment System preoperative predictor variables in the patient's electronic health record prior to the preoperative visit to lessen the burden on the busy surgeon and encourage more widespread use of Surgical Risk Preoperative Assessment System. [ABSTRACT FROM AUTHOR]

Unplanned reoperation is an undesirable outcome with considerable risks and an increasingly assessed quality of care metric. There are no preoperative prediction models for reoperation after an index surgery in a broad surgical population in the literature. The Surgical Risk Preoperative Assessment System (SURPAS) preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict unplanned reoperation has not been assessed. This study's objective was to determine whether the SURPAS model could accurately predict unplanned reoperation. This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database, 2012-2018. An unplanned reoperation was defined as any unintended operation within 30 d of an initial scheduled operation. The 8-variable SURPAS model and a 29-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program nonlaboratory preoperative variables, were developed using multiple logistic regression and compared using discrimination and calibration metrics: C-indices (C), Hosmer–Lemeshow observed-to-expected plots, and Brier scores (BSs). The internal chronological validation of the SURPAS model was conducted using "training" (2012-2017) and "test" (2018) datasets. Of 5,777,108 patients, 162,387 (2.81%) underwent an unplanned reoperation. The SURPAS model's C-index of 0.748 was 99.20% of that for the full model (C = 0.754). Hosmer–Lemeshow plots showed good calibration for both models and BSs were similar (BS = 0.0264, full; BS = 0.0265, SURPAS). Internal chronological validation results were similar for the training (C = 0.749, BS = 0.0268) and test (C = 0.748, BS = 0.0250) datasets. The SURPAS model accurately predicted unplanned reoperation and was internally validated. Unplanned reoperation can be integrated into the SURPAS tool to provide preoperative risk assessment of this outcome, which could aid patient risk education. • Reoperations increase morbidity and may be a quality issue. • Preoperative prediction of reoperation is informative to surgeons and patients. • 8-variable Surgical Risk Preoperative Assessment System model predicts reoperation as well as 29-variable National Surgical Quality Improvement Program model. • The Surgical Risk Preoperative Assessment System model shows good internal chronological validation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]