Improvement Program Surgical Risk Calculator Research Articles

Validation of the American College of Surgeons surgical risk calculator for thoracic surgery.

Advances in medicine and surgical techniques make it possible to operate on selected comorbid elderly patients for whom risk assessment is essential. We aimed to validate the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator specifically for thoracic surgery. This study retrospectively included 283 consecutive patients who all underwent various thoracic surgeries at our center. Considering "serious complication" as the most important outcome, we compared the predicted risk scores with the observed incidence of 30-day morbidity and mortality. We calculated the area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals for each outcome and utilized the Brier score to check the calibration and complication odds ratios above vs. below average risk in all score outcomes with the number of occurred events. In our study population, most patients were <65 years old (48%), predominantly male (63%), and overweight or obese (48%). In addition, 13% had severe chronic obstructive pulmonary disease (COPD), and 75% were categorized as American Society of Anesthesiologists (ASA) class III or higher. For "serious complication", AUROC was 59%, and events were equal in patients with above or below average risk scores (P=0.96). AUROC was 67% for "any complication" and 58% for "return to OR", expressing no useful predictive value. The Brier score and odds ratios were low for all outcomes. Dyspnea, ASA class, COPD, and body mass index as single postoperative risk predictors significantly improved the basic model consisting of the logit of the risk calculator alone. Thus, the calculator alone did not perform as well as these single variables did. The ACS NSQIP surgical risk calculator exhibited low sensitivity, specificity, and low AUROC for postoperative 30-day morbidity and mortality in our study cohort. Therefore, we think it cannot be considered as valid risk estimation tool for general thoracic surgery.

Assessing the effectiveness of ACS surgical risk calculator versus P-POSSUM in predicting mortality and morbidity for major hepatobiliary surgery: An observational study.

Risk assessment is difficult yet would provide valuable data for both the surgeons and the patients in major hepatobiliary surgeries. An ideal risk calculator should improve workflow through efficient, timely, and accurate risk stratification. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC) and Portsmouth Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (P-POSSUM) are surgical risk stratification tools used to assess postoperative morbidity. In this study, preoperative data from 300 patients undergoing major hepatobiliary surgeries performed at a single tertiary university hospital were retrospectively collected from electronic patient records and entered into the ACS-SRC and P-POSSUM systems, and the resulting risk scores were calculated and recorded accordingly. The ACS-NSQIP-M1 (C-statistics = 0.725) and M2 (C-statistics = 0.791) models showed better morbidity discrimination ability than P-POSSUM-M1 (C-statistics = 0.672) model. The P-POSSUM-M2 (C-statistics = 0.806) model showed better differentiation success in morbidity than other models. The ACS-NSQIP-M1 (C-statistics = 0.888) and M2 (C-statistics = 0.956) models showed better mortality discrimination than P-POSSUM-M1 (C-statistics = 0.776) model. The P-POSSUM-M2 (C-statistics = 0.948) model showed better mortality differentiation success than the ACS-NSQIP-M1 and P-POSSUM-M1 models. The use of ACS-SRC and P-POSSUM calculators for major hepatobiliary surgeries offers quantitative data to assess risks for both the surgeon and the patient. Integrating these calculators into preoperative evaluation practices can enhance decision-making processes for patients. The results of the statistical analyses indicated that the P-POSSUM-M2 model for morbidity and the ACS-NSQIP-M2 model for mortality exhibited superior overall performance.

American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator as a Predictor of Postoperative Outcomes After Adult Spinal Deformity Surgery: A Retrospective Cohort Analysis.

In recent years, there has been an outpouring of scoring systems that were built to predict outcomes after various surgical procedures; however, research validating these studies in spinal surgery is quite limited. In this study, we evaluated the predictability of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator (ACS NSQIP SRC) for various postoperative outcomes after spinal deformity surgery. A retrospective chart review was conducted to identify patients who underwent spinal deformity surgery at our hospital between January 1, 2014, and December 31, 2022. Demographic and clinical data necessary to use the ACS NSQIP SRC and postoperative outcomes were collected for these patients. Predictability was analyzed using the area under the curve (AUC) of receiver operating characteristic curves and Brier scores. Among the 159 study patients, the mean age was 64.5 ± 9.5 years, mean body mass index was 31.9 ± 6.6, and 95 (59.7%) patients were women. The outcome most accurately predicted by the ACS NSQIP SRC was postoperative pneumonia (observed = 5.0% vs predicted = 3.2%, AUC = 0.75, Brier score = 0.05), but its predictability still fell below the acceptable threshold. Other outcomes that were underpredicted by the ACS NSQIP SRC were readmission within 30 days (observed = 13.8% vs predicted = 9.0%, AUC = 0.63, Brier score = 0.12), rate of discharge to nursing home or rehabilitation facilities (observed = 56.0% vs predicted = 46.6%, AUC = 0.59, Brier = 0.26), reoperation (observed 11.9% vs predicted 5.4%, AUC = 0.60, Brier = 0.11), surgical site infection (observed 9.4% vs predicted 3.5%, AUC = 0.61, Brier = 0.05), and any complication (observed 33.3% vs 19%, AUC = 0.65, Brier = 0.23). Predicted and observed length of stay were not significantly associated (β = 0.132, P = .47). The ACS NSQIP SRC is a poor predictor of outcomes after spinal deformity surgery.

The efficacy of the National Surgical Quality Improvement Program surgical risk calculator in head and neck surgery: A meta-analysis.

The National Surgical Quality Improvement Program surgical risk calculator (SRC) estimates the risk for postoperative complications. This meta-analysis assesses the efficacy of the SRC in the field of head and neck surgery. A systematic review identified studies comparing the SRC's predictions to observed outcomes following head and neck surgeries. Predictive accuracy was assessed using receiver operating characteristic curves (AUCs) and Brier scoring. Nine studies totaling 1774 patients were included. The SRC underpredicted the risk of all outcomes (including any complication [observed (ob) = 35.9%, predicted (pr) = 21.8%] and serious complication [ob = 28.7%, pr = 17.0%]) except mortality (ob = 0.37%, pr = 1.55%). The observed length of stay was more than twice the predicted length (p < 0.02). Discrimination was acceptable for postoperative pneumonia (AUC = 0.778) and urinary tract infection (AUC = 0.782) only. Predictive accuracy was low for all outcomes (Brier scores ≥0.01) and comparable for patients with and without free-flap reconstructions. The SRC is an ineffective instrument for predicting outcomes in head and neck surgery.

Comparison of Acute Physiology and Chronic Health Evaluation (APACHE) II and American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) scoring system in predicting postoperative mortality in patients undergoing emergency laparotomy: A retrospective study.

There is paucity of studies on preoperative risk assessment tools in patients undergoing emergency surgery. The present study evaluated the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II, American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator and American Society of Anesthesiologists (ASA) physical status (PS) classification system in patients undergoing emergency exploratory laparotomy. This retrospective study included 60 adult patients who underwent emergency exploratory laparotomy for perforation peritonitis. The clinical details, ASA PS classification, laboratory investigations and postoperative course of patients were retrieved from their medical records. Based on these details, APACHE II and ACS-NSQIP were calculated for the patients. The study's primary outcome was the accuracy of the preoperative APACHE II, ACS-NSQIP risk calculator and ASA PS class in predicting the postoperative 30-day mortality of patients. The area under the curve (AUC) of APACHE II, ACS-NSQIP score, and ASA PS classification for mortality 30 days after surgery was 0.737, 0.694 and 0.601, respectively. The P value for the Hosmer-Lemeshow (H-L) test of scoring systems was 0.05, 0.25 and 0.05, respectively. AUC for postoperative complications was 0.799 for APACHE II, 0.683 for ACS-NSQIP and 0.601 for ASA PS classification. H-L test of these scoring systems for complications after surgery revealed P values of 0.62, 0.36 and 0.53, respectively. Compared to the ACS-NSQIP and ASA PS classification system, the APACHE II score has a better discriminative ability for postoperative complications and mortality in adult patients undergoing emergency exploratory laparotomy.

Gait speed assessment as a prognostic tool for morbidity and mortality in vulnerable older adult patients following vascular surgery

IntroductionPredicting the risk associated with vascular surgery in older adult patients has become increasingly challenging, primarily due to limitations in existing risk assessment tools. This study aimed to evaluate the utility of gait speed, a clinical indicator of frailty, in enhancing the prediction of mortality and morbidity in older adult patients undergoing vascular surgery. MethodsA single-center prospective cohort study was conducted, involving older adult patients undergoing vascular surgery at four tertiary care hospitals between 2021 and 2022. Eligible patients were aged 80 years or older and scheduled for surgical treatment of peripheral arterial disease of the lower limbs (IIb Leriche-Le Fontaine). The primary factor of interest was gait speed, defined as taking more than 6 s to walk 5 meters. The primary outcomes were in-hospital postoperative mortality and major morbidity. ResultsThe cohort comprised 131 patients with a mean age of 82.8 ± 1.4 years, with 34 % being female. Before vascular surgery, 60 patients (46 %) were categorized as slow walkers. Slow walkers were more likely to be female (43 % vs. 25 %, p < 0.03) and diabetic (50 % vs. 28 %, p < 0.01). Among the patients, 30 (23 %) experienced the primary composite outcome of mortality or major morbidity following vascular surgery. After adjusting for the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) Surgical Risk Calculator, slow gait speed independently predicted the composite outcome (odds ratio: 3.05; 95 % confidence interval: 1.23 to 7.54). ConclusionsGait speed is a straightforward and effective test that can help identify a subgroup of frail older adult patients at an elevated and incremental risk of mortality and major morbidity after vascular surgery. While gait speed remains a valuable clinical indicator of frailty, it is important to recognize that the broader context of mobility plays a pivotal role in postoperative outcomes.

Evaluation of the ACS NSQIP surgical risk calculator in patients undergoing common bile duct exploration.

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator is a risk stratification tool to help predict risks of postoperative complications, which is important for informed decision-making. The purpose of this study was to evaluate the accuracy of the calculator in predicting postoperative complications in patients undergoing common bile duct (CBD) exploration. A retrospective chart review was completed for 305 patients that underwent open and laparoscopic CBD exploration at a single institution from 2010 to 2018. Patient demographics and preoperative risk factors were entered into the calculator, and the predicted complication risks were compared with observed complication rates. Brier score, C-statistic, and Hosmer-Lemeshow regression analysis were used to assess discrimination and calibration. The observed rate exceeded the predicted rate for any complication (35.1% vs. 21%), return to operating room (5.9% vs. 3.6%), death (3.3% vs. 1%), and sepsis (3% vs. 2.4%). The model performed best in predicting serious complication (Brier 0.087, C-statistic 0.818, Hosmer-Lemeshow 0.695), surgical site infection (Brier 0.068, C-statistic 0.670, Hosmer-Lemeshow 0.292), discharge to rehabilitation facility (Brier 0.041, C-statistic 0.907, Hosmer-Lemeshow 0.638), and death (Brier 0.028, C-statistic 0.898, Hosmer-Lemeshow 0.004). In multivariable analysis, there was no statistically significant predicted complication type that affected the type of surgery. The calculator was accurate in predicting serious complication, surgical site infection, discharge to rehabilitation facility, and death. However, the model displayed poor predictive ability in all other complications that were analyzed.

A Prospective Multicenter Comparison of Trauma and Injury Severity Score, American Society of Anesthesiologists Physical Status, and National Surgical Quality Improvement Program Calculator's Ability to Predict Operative Trauma Outcomes.

Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours. A prospective, observational multicenter study (9/2018-2/2020) of trauma patients ≥18 years undergoing operation within 24 hours of admission was performed. Multiple logistic regression was used to create models predicting mortality utilizing the variables within TRISS, ASA-PS, and NSQIP-SRC, respectively. Linear regression was used to create models predicting LOS and negative binomial regression to create models predicting complications. From 4 level I trauma centers, 1213 patients were included. The Brier Score for each model predicting mortality was found to improve accuracy in the following order: 0.0370 for ASA-PS, 0.0355 for NSQIP-SRC, 0.0301 for TRISS, 0.0291 for TRISS+ASA-PS, and 0.0234 for TRISS+NSQIP-SRC. However, when comparing TRISS alone to TRISS+ASA-PS ( P = .082) and TRISS+NSQIP-SRC ( P = .394), there was no significant improvement in mortality prediction. NSQIP-SRC more accurately predicted both LOS and complications compared to TRISS and ASA-PS. TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.

Impact of a dietitian-led very low calorie diet clinic on perioperative risk for patients with obesity awaiting elective, non-bariatric surgery: A retrospective cohort study

BackgroundDespite a lack of evidence that intentional weight loss reduces the risk of postoperative complications, adults with obesity are commonly asked to lose weight before elective surgery. We hypothesized that patients undertaking dietitian-led preoperative, very low calorie diet treatment could reduce perioperative surgery risks, as per validated risk scoring systems. The purpose of this study was to measure the impact of a dietitian-led preoperative very low calorie diet clinic on the American Society of Anesthesiologists physical status scores and National Surgical Quality Improvement Program Surgical Risk Calculator scores for patients with obesity awaiting non-bariatric elective surgery. MethodsThis retrospective cohort study included patients referred to the preoperative dietitian-led very low calorie diet clinic before elective surgical procedures over a 2-year-9-month period. The dietitian prescribed individualized, very low calorie diet-based treatment. Primary outcomes were changes in the American Society of Anesthesiologists and Surgical Risk Calculator scores from pretreatment until surgery. ResultsA total of 141 eligible participants (48 ± 13.4 years, 76% women, body mass index 41.7 ± 6.3 kg/m2) demonstrated clinically significant weight loss (mean 7.1 ± 6.1kg, 5.2% body weight, P < .001). Median treatment duration was 13 weeks (interquartile range 6.2–19.2 weeks). Five participants (3.5%) avoided surgery due to weight loss-related improvements in their condition. American Society of Anesthesiologists scores improved for 16% (n = 22/141) of participants. Overall, the median surgical risk calculator estimated risk of ‘serious’ and ‘any’ postoperative complication reduced from 4.8% to 3.9% (P < .001) and 6% to 5.1% (P < .001), respectively. Reduction in all Surgical Risk Calculator scores occurred, including surgical site infection, re-admission, and cardiac events (P < .05). ConclusionThe dietitian-led preoperative, very low calorie diet clinic improved American Society of Anesthesiologists and Surgical Risk Calculator scores for non-bariatric elective surgery patients with obesity. Randomized controlled trials comparing this approach with a control group are warranted.

Validation of the American College of Surgeons National Surgical Quality Improvement Program risk predictor in an Australian general surgical cohort.

The National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator from the American College of Surgeons is a widely available tool for peri-operative risk prediction. This study investigates its predictive performance in an Australian setting. A single-centre retrospective external validation study was conducted at a tertiary referral centre in New South Wales, Australia. Data from a general surgical cohort in a 2-year period from 2020 to 2021 was collected from the NSQIP database and entered into the NSQIP calculator. The predictive performance of the calculator was analysed across the standard 14 NSQIP postoperative outcome measures at 30 days. There were 2121 patient records analysed using tests of accuracy and in the discrimination and calibration domains. The overall predictive performance of the NSQIP calculator was reasonable. There was greater accuracy at lower-risk predictions. At higher-risk predictions, Readmission, Death, and Discharge to Nursing or Rehab Facility, and Length of Stay were overestimated, whilst other outcomes were underestimated. This study demonstrates reasonable overall performance of the NSQIP calculator in the context of this cohort and provides data to support the need for locally adapted and validated risk prediction tools for use by Australian perioperative physicians.

Evaluation of the ACS-NSQIP Surgical Risk Calculator in Patients with Hepatic Metastases from Colorectal Cancer Undergoing Liver Resection

BackgroundThe American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC) has been designed to predict morbidity and mortality and help stratify surgical patients. This study evaluates the performance of the SRC for patients undergoing surgery for colorectal liver metastases (CRLM). MethodsSRC was retrospectively computed for patients undergoing liver or simultaneous colon and liver surgery for colorectal cancer (CRC) in two high tertiary referral centres from 2011 to 2020. C-statistics and Brier score were calculated as a mean of discrimination and calibration respectively, for both group and for every level of surgeon adjustment score (SAS) for liver resections in case of simultaneous liver-colon surgery. An AUC ≥ 0.7 shows acceptable discrimination; a Brier score next to 0 means the prediction tool has good calibration. ResultsFour hundred ten patients were included, 153 underwent simultaneous resection, and 257 underwent liver-only resections. For simultaneous surgery, the ACS-NSQIP SRC showed good calibration and discrimination only for cardiac complication (AUC = 0.720, 0.740, and 0.702 for liver resection unadjusted, SAS-2, and SAS-3 respectively; 0.714 for colon resection; and Brier score = 0.04 in every case). For liver-only surgery, it only showed good calibration for cardiac complications (Brier score = 0.03). The SRC underestimated the incidence of overall complications, pneumonia, cardiac complications, and the length of hospital stay. ConclusionsACS-NSQIP SRC showed good predicting capabilities only for 1 out of 5 evaluated outcomes; therefore, it is not a reliable tool for patients undergoing liver surgery for CRLM, both in the simultaneous and staged resections.

Development and Validation of a Machine Learning Model to Identify Patients Before Surgery at High Risk for Postoperative Adverse Events

Identifying patients at high risk of adverse outcomes prior to surgery may allow for interventions associated with improved postoperative outcomes; however, few tools exist for automated prediction. To evaluate the accuracy of an automated machine-learning model in the identification of patients at high risk of adverse outcomes from surgery using only data in the electronic health record. This prognostic study was conducted among 1 477 561 patients undergoing surgery at 20 community and tertiary care hospitals in the University of Pittsburgh Medical Center (UPMC) health network. The study included 3 phases: (1) building and validating a model on a retrospective population, (2) testing model accuracy on a retrospective population, and (3) validating the model prospectively in clinical care. A gradient-boosted decision tree machine learning method was used for developing a preoperative surgical risk prediction tool. The Shapley additive explanations method was used for model interpretability and further validation. Accuracy was compared between the UPMC model and National Surgical Quality Improvement Program (NSQIP) surgical risk calculator for predicting mortality. Data were analyzed from September through December 2021. Undergoing any type of surgical procedure. Postoperative mortality and major adverse cardiac and cerebrovascular events (MACCEs) at 30 days were evaluated. Among 1 477 561 patients included in model development (806 148 females [54.5%; mean [SD] age, 56.8 [17.9] years), 1 016 966 patient encounters were used for training and 254 242 separate encounters were used for testing the model. After deployment in clinical use, another 206 353 patients were prospectively evaluated; an additional 902 patients were selected for comparing the accuracy of the UPMC model and NSQIP tool for predicting mortality. The area under the receiver operating characteristic curve (AUROC) for mortality was 0.972 (95% CI, 0.971-0.973) for the training set and 0.946 (95% CI, 0.943-0.948) for the test set. The AUROC for MACCE and mortality was 0.923 (95% CI, 0.922-0.924) on the training and 0.899 (95% CI, 0.896-0.902) on the test set. In prospective evaluation, the AUROC for mortality was 0.956 (95% CI, 0.953-0.959), sensitivity was 2148 of 2517 patients (85.3%), specificity was 186 286 of 203 836 patients (91.4%), and negative predictive value was 186 286 of 186 655 patients (99.8%). The model outperformed the NSQIP tool as measured by AUROC (0.945 [95% CI, 0.914-0.977] vs 0.897 [95% CI, 0.854-0.941], for a difference of 0.048), specificity (0.87 [95% CI, 0.83-0.89] vs 0.68 [95% CI, 0.65-0.69]), and accuracy (0.85 [95% CI, 0.82-0.87] vs 0.69 [95% CI, 0.66, 0.72]). This study found that an automated machine learning model was accurate in identifying patients undergoing surgery who were at high risk of adverse outcomes using only preoperative variables within the electronic health record, with superior performance compared with the NSQIP calculator. These findings suggest that using this model to identify patients at increased risk of adverse outcomes prior to surgery may allow for individualized perioperative care, which may be associated with improved outcomes.

Current use of the National Surgical Quality Improvement Program surgical risk calculator in academic surgery: a mixed-methods study.

This study aims to quantitatively assess use of the NSQIP surgical risk calculator (NSRC) in contemporary surgical practice and to identify barriers to use and potential interventions that might increase use. We performed a cross-sectional study of surgeons at seven institutions. The primary outcomes were self-reported application of the calculator in general clinical practice and specific clinical scenarios as well as reported barriers to use. In our sample of 99 surgeons (49.7% response rate), 73.7% reported use of the NSRC in the past month. Approximately half (51.9%) of respondents reported infrequent NSRC use (<20% of preoperative discussions), while 14.3% used it in ≥40% of preoperative assessments. Reported use was higher in nonelective cases (30.2% vs 11.1%) and in patients who were ≥65 years old (37.1% vs 13.0%), functionally dependent (41.2% vs 6.6%), or with surrogate consent (39.9% vs 20.4%). NSRC use was not associated with training status or years in practice. Respondents identified a lack of influence on the decision to pursue surgery as well as concerns regarding the calculator's accuracy as barriers to use. Surgeons suggested improving integration to workflow and better education as strategies to increase NSRC use. Many surgeons reported use of the NSRC, but few used it frequently. Surgeons reported more frequent use in nonelective cases and frail patients, suggesting the calculator is of greater utility for high-risk patients. Surgeons raised concerns about perceived accuracy and suggested additional education as well as integration of the calculator into the electronic health record.

Comparison of National Surgical Quality Improvement Program Surgical Risk Calculator and Trauma and Injury Severity Score Risk Assessment Tools in Predicting Outcomes in High-Risk Operative Trauma Patients.

The Trauma and Injury Severity Score (TRISS) uses anatomic/physiologic variables to predict outcomes. The National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC) includes functional status and comorbidities. It is unclear which of these tools is superior for high-risk trauma patients (American Society of Anesthesiologists Physical Status (ASA-PS) class IV or V). This study compares risk prediction of TRISS and NSQIP-SRC for mortality, length of stay (LOS), and complications for high-risk operative trauma patients. This is a prospective study of high-risk (ASA-PS IV or V) trauma patients (≥18years-old) undergoing surgery at 4 trauma centers. We compared TRISS vs NSQIP-SRC vs NSQIP-SRC + TRISS for ability to predict mortality, LOS, and complications using linear, logistic, and negative binomial regression. Of 284 patients, 48 (16.9%) died. The median LOS was 16days and number of complications was 1. TRISS + NSQIP-SRC best predicted mortality (AUROC: .877 vs .723 vs .843, P = .0018) and number of complications (pseudo-R2/median error (ME) 5.26%/1.15 vs 3.39%/1.33 vs 2.07%/1.41, P < .001) compared to NSQIP-SRC or TRISS, but there was no difference between TRISS + NSQIP-SRC and NSQIP-SRC with LOS prediction (P = .43). For high-risk operative trauma patients, TRISS + NSQIP-SRC performed better at predicting mortality and number of complications compared to NSQIP-SRC or TRISS alone but similar to NSQIP-SRC alone for LOS. Thus, future risk prediction and comparisons across trauma centers for high-risk operative trauma patients should include a combination of anatomic/physiologic data, comorbidities, and functional status.

Evaluation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator in Predicting Adverse Outcomes among Asian Colorectal Surgical Patients

Bernardo, Ma Jeanesse Constantino MD; So, Jerel Lee Tan; Ngo, Reginald Jethro Y MD, FACS; Lao, Linnelle Stacy T MD; Cunanan, Sammy C MD, FACS Author Information

Performance Assessment of the American College of Surgeons Risk Calculator in Metastatic Spinal Tumor Surgery.

This was a retrospective cohort study. The objective of this study was to assess the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator performance in patients undergoing surgery for metastatic spine disease. Patients with spinal metastases may require surgical intervention for cord compression or mechanical instability. The ACS-NSQIP calculator was developed to assist surgeons with estimating 30-day postoperative complications based on patient-specific risk factors and has been validated within several surgical patient populations. We included 148 consecutive patients at our institution who underwent surgery for metastatic spine disease between 2012 and 2022. Our outcomes were 30-day mortality, 30-day major complications, and length of hospital stay (LOS). Predicted risk, determined by the calculator, was compared with observed outcomes using receiver operating characteristic curves with area under the curve (AUC) and Wilcoxon signed-rank tests. Analyses were repeated using individual corpectomy and laminectomy Current Procedural Terminology (CPT) codes to determine procedure-specific accuracy. Based on the ACS-NSQIP calculator, there was good discrimination between observed and predicted 30-day mortality incidence overall (AUC=0.749), as well as in corpectomy cases (AUC=0.745) and laminectomy cases (AUC=0.788). Poor 30-day major complication discrimination was seen in all procedural cohorts, including overall (AUC=0.570), corpectomy (AUC=0.555), and laminectomy (AUC=0.623). The overall median observed LOS was similar to predicted LOS (9 vs. 8.5d, P =0.125). Observed and predicted LOS were also similar in corpectomy cases (8 vs. 9d; P =0.937) but not in laminectomy cases (10 vs. 7d, P =0.012). The ACS-NSQIP risk calculator was found to accurately predict 30-day postoperative mortality but not 30-day major complications. The calculator was also accurate in predicting LOS following corpectomy but not laminectomy. While this tool may be utilized to predict risk short-term mortality in this population, its clinical value for other outcomes is limited.

Preparing for participation in the centers for Medicare and Medicaid Services' bundle care payment initiative-advanced for major bowel surgery.

As healthcare costs rise, there is an increasing emphasis on alternative payment models to improve care efficiency. The bundled payment represents an alternative reimbursement model gaining popularity within the surgical sphere. We aimed to assess where the largest opportunities for care improvement lay and how best to identify patients at high risk of suffering costly complications. We utilized itemized CMS claims data for a retrospective cohort of patients between 2014 and 2016 who met inclusion criteria for the Major Bowel Bundled Payment Program and performed a cost analysis to identify opportunities for improved care efficiency. Based on the results of this cost analysis, we identified readmissions as a target for improvement. We then assessed whether the American College of Surgeons' National Surgical Quality Improvement Program surgical risk calculator (ACS NSQIP SRC) could accurately identify patients within our bundled payment population who were at high risk of readmission using a logistic regression model. Our study cohort included 252 patients. Readmissions accounted for 12.8% of the average total care episode cost with a coefficient of variation of 2.72, thereby representing the most substantial, non-fixed cost for our bundled payment patients. Patients readmitted within their 90-day care episode were 2.53 times more likely to be high-cost (>$60,000) than patients not readmitted. However, the ACS NSQIP SRC did not accurately predict patients at high risk of readmission within the first 30 days with an AUROC of 0.58. Our study highlights the importance of reducing readmissions as a central component of improving care for bowel surgery bundled payment patients. Preventing such readmissions requires accurate identification of patients at high risk of readmission; however, current risk prediction models lack the adaptability necessary for this task.

Visual Preoperative Risk Depiction Tools for Shared Decision-making: A Pilot Study from the Surgeon's Perspective.

Complications for reduction mammoplasty and skin grafting in a burns setting were sourced from the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator, the American Society of Plastic Surgeons website, peer-reviewed literature, and available clinical data. Pre- and postoperative patient satisfaction data were collected from the published literature on Breast-Q patient-reported outcomes for reduction mammoplasty. Everyday risk comparisons were collected from a general online database search. Three distinct risk depiction tools (spiral, tile, and scatter plot) were developed in the Microsoft Office Suite. Anonymous REDCap surveys were sent to healthcare practitioners for feedback. Twenty-six survey results were collected. Twenty-four respondents (92%) agreed these graphics would be useful for SDM discussions. Nineteen respondents (73%) either agreed or strongly agreed that these graphics depicted risk in a meaningful way. Fifteen respondents (58%) indicated they would use these graphics in daily practice. The majority of respondents preferred the spiral design (58%). Areas for improvement included design simplification and written explanations to accompany graphics. Feedback from the survey was incorporated into the spiral design. Risk visualization tools meaningfully depict surgical risks to improve communication in SDM. This study proposes a tool that can be adapted for many surgical procedures.

Current challenges faced by transgender and gender-diverse patients and providers in anesthesiology.

Current challenges faced by transgender and gender-diverse patients and providers in anesthesiology.

ACS-NSQIP – Surgical risk calculator accurately predicts outcomes of laparotomy in a prospective study at a tertiary hospital in Tanzania

Introduction: The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator is excellent in predicting postoperative complications. Validation in resource-limited settings and applicability in gastrointestinal surgery is still unclear. Objective: The study aimed to determine the accuracy of the ACS-NSQIP surgical risk calculator in predicting the 30days postoperative adverse outcomes among patients who underwent laparotomy. Methods: A Single hospital-based, prospective cohort study was done at Muhimbili National Hospital in Tanzania from April 2021 to December 2021 recruiting patients aged 18 and above who underwent primary laparotomy. SR calculator variables were obtained from patients and entered manually to categorize patients into low and high-risk. Patients were followed up for thirty days for the outcome. The predicted risk was compared with actual occurrence to obtain personal risk ratios. The c-statistics of &gt; 0.7, Brier of score &lt; 0.25 and Index of Prediction Accuracy score were used for discrimination, accuracy, and usefulness of the model respectively. Results: ACS -NSQIP SR-Calculator discriminated well the risks of cardiac complications, re-laparotomy, Anastomotic leak, and death (c-statistic &gt; 0.7) Poor discrimination was observed for the length of hospital stay (c-statistics 0.518). However, SR-calculator shows high calibration potential for all complications with a Brier score &lt; 0.25 (0.002 - 0.144) and an IPA score ranging from 0.225 - 0.969. Conclusion: The ACS -NSQIP SR-calculator accurately predicted postoperative outcomes for patients requiring laparotomy. SR-Calculator is a reliable tool for preoperative shared decision-making and counseling. The model should be adopted to strengthen the healthcare system in a low-income country. What is already known about this subject?  The ACS NSQIP surgical risk calculator is accurate in predicting the outcome of the Surgical procedure.  ACS NSQIP surgical risk calculator was validated in the USA  The SRC is associated with surgical care improvement whenever applied. What are the new findings?  The SRC accurately predicted the 30-day outcomes associated with primary laparotomy except for the length of hospital stay, discharge for other services, and veno-thromboembolic events.  The risk ratio (RR) provides a preliminary estimation of specific complications for a single patient.  Index of prediction Accuracy (IPA) reliably summarizes the discrimination and calibration of the ACS-NSQIP-SR-Calculator.  The SRC can be validated in a resource-limited setting and can be a source of surgical care improvement. How might these results affect future research or surgical practice?  The results of this study will provide a comparison between our institution and other international situations where a calculator had been tested and surgical service improvement locally. Our findings will provide insights into our loco-regional surgical institutions in the low-income country to conduct cross-specialties and institutional research toward the adoption of ACS-NSQIP-SRC into National surgical improvement programs. Informative to the surgical community on the performance of the SR-calculator outside the USA.