Surgical Risk Calculator Research Articles

Editorial comment on “UroArc: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement”

Editorial comment on “UroArc: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement”

An interrater reliability analysis of preoperative mortality risk calculators used for elective high-risk noncardiac surgical patients shows poor to moderate reliability

BackgroundMultiple preoperative calculators are available online to predict preoperative mortality risk for noncardiac surgical patients. However, it is currently unknown how these risk calculators perform across different raters. The current study investigated the interrater reliability of three preoperative mortality risk calculators in an elective high-risk noncardiac surgical patient population to evaluate if these calculators can be safely used for identification of high-risk noncardiac surgical patients for a preoperative multidisciplinary team discussion.MethodsFive anesthesiologists assessed the preoperative mortality risk of 34 high-risk patients using the preoperative score to calculate postoperative mortality risks (POSPOM), the American College of Surgeons surgical risk calculator (SRC), and the surgical outcome risk tool (SORT). In total, 170 calculations per calculator were gathered.ResultsInterrater reliability was poor for SORT (ICC (C.I. 95%) = 0.46 (0.30–0.63)) and moderate for SRC (ICC = 0.65 (0.51–0.78)) and POSPOM (ICC = 0.63 (0.49–0.77). The absolute range of calculated mortality risk was 0.2–72% for POSPOM, 0–36% for SRC, and 0.4–17% for SORT. The coefficient of variation increased in higher risk classes for POSPOM and SORT. The extended Bland–Altman limits of agreement suggested that all raters contributed to the variation in calculated risks.ConclusionThe current results indicate that the preoperative risk calculators POSPOM, SRC, and SORT exhibit poor to moderate interrater reliability. These calculators are not sufficiently accurate for clinical identification and preoperative counseling of high-risk surgical patients. Clinicians should be trained in using mortality risk calculators. Also, clinicians should be cautious when using predicted mortality estimates from these calculators to identify high-risk noncardiac surgical patients for elective surgery.

Morbidity related to major lung thoracoscopic resections in children.

In pediatric thoracic surgery, reported predictors for increased risk are symptoms and active/previous infections (RAP). We investigated the adverse events related to Video-Assisted Thoracic Surgery (VATS) in pediatric patients when considering RAP predictors. A retrospective analysis of pediatric VATS major lung resections in 2008-2021 was conducted at three institutions. We employed the pediatric surgical risk calculator to define patients' preoperative predicted risk (PredR). Postoperative complications were classified according to the Thoracic Morbidity & Mortality (TM&M) system. The observed TM&M rate (ObsR) and the PredR were compared. A subgroup analysis by RAP predictors was conducted. 37 patients (54% female) were included. Mean age and weight were 5.8 years and 22.8 kg. 56.7% had respiratory symptoms, 38.9% active infection and 59.5% history of infections (RAP subpopulations). VATS procedures were lobectomy (n=32), segmentectomy (n=3), bilobectomy (n=1) and pneumonectomy (n=1). The conversion rate was 5.4%. The mean PredR was of 4.43% (±1.8) and the overall ObsR was 45.94% with a median severity of II (I-III). This difference was significant and a higher PredR was not associated with complications development. PredR does not show association among the RAP vs non-RAP group. ObsR showed positive association with RAP, even if it reached statistical significance only for "respiratory symptoms" risk factor. ObsR reflected the number of bronchiectasis patients in our series (n=9), aligning with the hypothesis of "earlier and safer surgery". The risk calculator underestimates VATS morbidity. Multicentre studies will clarify the correlation between inflammation and surgical adverse events.

Comparison of a Risk Calculator With Frailty Indices in Patients Undergoing Lung Cancer Resection.

While frailty has gained attention for its utility in risk stratification, no studies have directly compared them to existing risk calculators. The objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other than home. Patients undergoing anatomic lung resection for primary, nonsmall cell lung cancer were identified within the ACS National Quality Improvement Program (ACS NSQIP) database. Tools were compared for discrimination in the primary outcomes. 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (IQR 59-74) years. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% CI). This included perioperative mortality (AUC 0.74, 95% CI 0.71-0.78), compared to RAI-rev (AUC 0.66, 95% CI 0.62-0.69) and 5-mFI (AUC 0.61, 95% CI 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.

UroARC: A novel surgical risk calculator for older adults undergoing pelvic organ prolapse and stress urinary incontinence surgery.

Surgeries for pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are commonly performed in older adults, many of whom are also frail. A surgical risk calculator for older adults undergoing POP/SUI surgeries that incorporates frailty, a factor known to increase the risk of surgical complications, would be helpful for preoperative counseling but currently does not exist. Medicare Carrier, Outpatient, and MedPAR files were examined for beneficiaries undergoing POP and SUI surgery between 2014 and 2016. A total of 15 POP/SUI categories were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty in Medicare data, and Charlson Comorbidity Index were deconstructed into their individual variables, and individual variables were entered into stepwise logistic regression models to determine which variables were most highly predictive of 30-day complications and 1-year mortality. To verify the prognostic accuracy for each model for surgical complications of interest, calibration curves and tests of model fit, including C-statistic, Brier scores, and Spiegelhalter p values, were determined. In total, 108 479 beneficiaries were included. Among these, 4.7% had CFI scores consistent with mild to severe frailty (CFI≥0.25). A total of 13 prognostic variable categories were found to be most highly predictive of postoperative complications. Calibration curves for each outcome of interest showed models were well-fit. Most models demonstrated high c-statistic values (≥0.7) and high Spiegelhalter p values (≥0.9), indicating good model calibration and excellent discrimination, and low Brier scores (<0.02), indicating high model accuracy. Urologic surgery for older Adults Risk Calculator serves as a novel surgical risk calculator that is readily accessible to both patients and clinicians that specifically factors in components of frailty. Furthermore, this calculator accounts for the heterogeneity of an aging population and can assist in individualized surgical decision-making for these common procedures.

UroARC: A Novel Surgical Risk Calculator for Older Adults Undergoing Suprapubic Tube Placement

ObjectivesTo develop a surgical risk calculator for older adults undergoing suprapubic tube (SPT) placement that specifically factors in frailty, a key predictor of surgical risk in this vulnerable and heterogenous population. MethodsMedicare MedPAR, Outpatient, and Carrier files for beneficiaries undergoing SPT placement between 2014-2016 were examined. The Claims-Based Frailty Index (CFI), a validated measure of frailty, was used to calculate baseline frailty for each beneficiary. Stepwise regression models were used for each variable within the CFI and Charlson Comorbidity Index to determine the variables most highly predictive of postoperative complications. The most highly predictive variables were then combined into parsimonious categories. To ensure the prognostic accuracy for each outcome, calibration curves and tests of model fit, including C-statistics, Brier scores, and Spiegelhalter p-values were calculated. ResultsA total of 26,999 beneficiaries were included. Among these, 39.1% were pre-frail, 36.8% were mildly frail, and 12.3% were moderately-to-severely frail. Thirteen prognostic variable categories were deemed highly predictive of postoperative complications of interest. All models demonstrated low Brier scores, indicating high model accuracy, and high C-statistic and Spiegelhalter p-values, consistent with excellent model discrimination and calibration, respectively. Excellent model fit was seen on calibration curves for each outcome. ConclusionsUroARC is a novel surgical tool for older adults undergoing SPT placement that specifically factors in frailty. This risk calculator has high accuracy, calibration, and discrimination, and serves as a valuable resource to patients and clinicians for those undergoing consideration for SPT placement.

Predicting Complications in Head and Neck Surgery: Comparing Calculators to Surgeons.

Objectives: Surgical outcomes determine national ranking, reputation, and funding, and are often assessed with objective surgical risk calculators (SRCs). Surgeons' assessments are not considered. This study aims to determine if surgeons or SRCs are more accurate in predicting outcomes. Methods: This prospective cohort study identified a surgeon's assessment on a patient's risk preoperatively. The patient's risk was also calculated using the SRC. Predictions were compared to patient outcomes and to each other to assess whether surgeons or the SRC were more accurate. Results: Of the 101 patients included, 37 (36.6%) experienced a complication of any kind and 18 (17.8%) experienced a serious complication. Smoking resulted in a 2.49 times higher overall complication rate (P = .04). Laryngectomy patients experienced the highest rate of complications (P = .02) compared to those undergoing free flap reconstruction [odds ratio (OR) 0.9] or any other surgery (OR 0.26). Both surgeons and the American College of Surgeons (ACS) tool performed poorly on the prediction of the outcome of any complication, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.51 [95% confidence interval (CI): 0.39-0.62] and 0.58 (95% CI: 0.47-0.70), respectively, which was not statistically significant (P = .34). For the prediction of the outcome of serious complication, the AUC for surgeons and the ACS tool were 0.55 (95% CI: 0.41-0.69) and 0.60 (95% CI: 0.46-0.74), respectively, which was not statistically significant (P = .58). Conclusions: Neither validated risk calculators nor surgeons are accurate in predicting perioperative risk. The only risk factor that contributes to improving predictions for complications is preoperative smoking, although age and type of surgery are also significant predictors. Risk calculators may therefore not be appropriate metrics for assessing hospital performance. These findings can help guide preoperative counseling and may help in the development of more accurate predictive tools as the healthcare field continues to incorporate artificial intelligence into surgical planning.

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.

Comparison of a risk calculator with frailty indices in patients undergoing lung cancer resection.

Perioperative risk stratification is an essential component of preoperative planning for cancer surgery. While frailty has gained attention for its utility in risk stratification, no studies have directly compared it to existing risk calculators. Therefore, the objective of this study was to compare the risk stratification of the American College of Surgeons Surgical Risk Calculator (ACS-SRC), the Revised Risk Analysis Index (RAI-rev), and the Modified Frailty Index (5-mFI). The primary outcomes were 30-day postoperative morbidity, 30-day postoperative mortality, unplanned readmission, unplanned reoperation, and discharge disposition other-than-home. Patients undergoing anatomic lung resection for primary, non-small cell lung cancer were identified within the American College of Surgeons National Quality Improvement Program (ACS NSQIP) database. The ACS-SRC, RAI-rev, and 5-mFI tools were used to predict adverse postoperative events. Tools were compared for discrimination in the primary outcomes. 9663 patients undergoing anatomic lung resection for cancer between 2012 and 2014 were included. The cohort was 53.1% female. Median age at diagnosis was 67 (interquartile range = 59-74) years. Cardiothoracic surgeons performed 89% and general surgeons performed 11.0% of the operations. Perioperative morbidity and mortality rates were 10.9% (n = 1048) and 1.6% (n = 158). Rates of 30-day postoperative unplanned readmission and reoperation were 7.5% (n = 725) and 4.8% (n = 468). The ACS-SRC had the highest discrimination for all measured outcomes, as measured by the area under the receiver operating curve (AUC) and corresponding confidence interval (95% confidence interval [CI]). This included perioperative mortality (AUC = 0.74, 95% CI = 0.71-0.78), compared to RAI-rev (AUC = 0.66, 95% CI = 0.62-0.69) and 5-mFI (AUC = 0.61, 95% CI = 0.57-0.65; p < 0.001). The RAI-rev and 5-mFI had similar discrimination for all measured outcomes. ACS-SRC was the perioperative risk stratification tool with the highest predictive discrimination for adverse, 30-day, postoperative events for patients with cancer treated with anatomic lung resection.

Assessing surgical risk calculators on hernia repair candidates with cirrhosis.

Several risk calculators have been developed and deployed to help surgeons estimate the mortality risk that comes with performing hernia repair surgery on patient with severe liver disease. This study seeks to evaluate the accuracy of these risk calculators on patients with cirrhosis and identify the most suitable population of patient to use these calculators on. The American College of Surgeons National Surgery Quality Improvement Program (NSQIP) 2013-2021 datasets were queried for patients who underwent hernia repair surgery. Mayo Clinic's "Post-operative Mortality Risk in Patients with Cirrhosis" risk calculator, Model for End-Stage Liver Disease (MELD) calculator, NSQIP's Surgical Risk Calculator, and a surgical 5-item modified frailty index were assessed to determine whether they accurately predict mortality following abdominal hernia repair. In total, 1368 patients met inclusion criteria. Receiver operating characteristic (ROC) curve analysis of the 4 mortality risk calculators resulted in the following: NSQIP Surgical Risk Calculator = 0.803 (p < 0.001); "Post-operative Mortality Risk in Patients with Cirrhosis" with an etiology of "Alcoholic or Cholestatic" yielded an AUC = 0.722 (p < 0.001); MELD score yielded an AUC = 0.709 (p < 0.001); and the modified 5-item frailty index yielded an AUC = 0.583 (p = 0.04). The NSQIP Surgical Risk Calculator more accurately predicts 30-day mortality in patients with ascites undergoing hernia repair. However, if the patient is missing one of the 21 input variables required by this calculator, Mayo Clinic's 30-day mortality calculator should be consulted before the more widely used MELD score.

Anticipated Morbidities Assessed by NSQIP Surgical Risk Calculator in Patients with Pneumoperitoneum Undergoing Laparotomy

Background: The American College of Surgery National Surgical Quality Improvement Program (NSQIP) calculator is a tool to assess the risk stratification of a patient undergoing major surgical procedures. We used this calculator to assess morbidities in patients with pneumoperitoneum. Method: A prospective study of patients with pneumoperitoneum undergoing exploratory laparotomy was conducted in the General Surgery Department, Liaquat National Hospital. Surgical risks were calculated preoperatively and were compared with the actual outcomes. Results: The NSQIP surgical risk calculator calculated morbidities in comparison with actual morbidities, which were surgical site infection in 28 (17.8%) vs. 49 (31.2%) patients, urinary tract infection in 26 (16.6%) vs. 4 (2.5%), pneumonia in 28 (17.8%) vs. 24 (15.3%) and cardiac event in 25 (15.9%) vs. 14 (8.9%). Conclusion: The NSQIP surgical risk calculator adequately anticipated morbidities such as SSI, UTI, pneumonia, and cardiac events in comparison with actual morbidities.

An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement. The UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations. Models were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 60.1% [59.6%-60.4%], 63.4% [63.2%-63.4%] and 66.6% [66.2%-66.9%] for the linear models and 51.5% [49.4%-53.4%], 63.2% [61.2%-65.0%] and 62.6% [60.7%-64.5%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.3% [60.0%-60.4%], 78.7% [78.7%-78.7%] and 80.0% [79.9%-80.0%] for the linear models and 59.4% [58.2%-60.9%], 78.8% [77.8%-79.8%] and 79.8% [78.8%-80.9%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 50.1% [49.6%-50.6%], 69.2% [69.1%-69.2%] and 68.4% [68.0%-68.5%] for the linear models and 51.0% [49.7%-52.4%], 69.7% [.5%-70.8%] and 69.7% [68.6%-70.8%] for the deep learning SNP, clinical and combined models, respectively. In this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy.

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.

A comparative assessment of ACS NSQIP-predicted and actual surgical risk outcomes of robotic transhiatal esophagectomy for esophageal adenocarcinoma resection at a high volume institution.

Esophageal adenocarcinoma incidence is increasing in Western nations. There has been a shift toward minimally invasive approaches for transhiatal esophagectomy (THE). This study compares the outcomes of robotic THE for esophageal adenocarcinoma resection at our institution with the predicted metrics from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). With Institutional Review Board (IRB) approval, we prospectively followed 83 patients who underwent robotic THE from 2012 to 2023. Predicted outcomes were determined using the ACS NSQIP Surgical Risk Calculator. Our outcomes were compared with these predicted outcomes and with general outcomes for transhiatal esophagectomy reported in ACS NSQIP, which includes a mix of surgical approaches. The median age of patients was 70years, with a body mass index (BMI) of 26.4kg/m2 and a male prevalence of 82%. The median length of stay was 7days. The rates of any complications and in-hospital mortality were 16% and 5%, respectively. Seven patients (8%) were readmitted within a 30-day postoperative window. The median survival is anticipated to surpass 95months. Our outcomes were generally aligned with or surpassed the predicted ACS NSQIP metrics. The extended median survival of over 95months highlights the potential effectiveness of robotic THE in the resection of esophageal adenocarcinoma. Further exploration into its long-term survival benefits and outcomes is warranted, along with studies that provide a more direct comparison between robotic and other surgical approaches.

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.

Head-to-Head Comparison: P-POSSUM and ACS-NSQIP® in Predicting Perioperative Risk in Robotic Surgery for Gynaecological Cancers.

Purpose: In this retrospective pilot study, we aim to evaluate the accuracy and reliability of the P-POSSUM and ACS-NSQIP surgical risk calculators in predicting postoperative complications in gynaecological-oncological (GO) robotic surgery (RS). Methods: Retrospective data collection undertaken through a dedicated GO database and patient notes at a tertiary referral cancer centre. Following data lock with the actual post-op event/complication, the risk calculators were used to measure predictive scores for each patient. Baseline analysis of 153 patients, based on statistician advice, was undertaken to evaluate P-POSSUM and ACS-NSQIP validity and relevance in GO patients undergoing RS performed. Results: P-POSSUM reports on mortality and morbidity only; ACS-NSQIP reports some individual complications as well. ACS-NSQIP risk prediction was most accurate for venous thromboembolism (VTE) (area under the curve (AUC)-0.793) and pneumonia (AUC-0.657) and it showed 90% accuracy in prediction of five major complications (Brier score 0.01). Morbidity was much better predicted by ACS-NSQIP than by P-POSSUM (AUC-0.608 vs. AUC-0.551) with the same result in mortality prediction (Brier score 0.0000). Moreover, a statistically significant overestimation of morbidity has been shown by the P-POSSUM calculator (p = 0.018). Conclusions: Despite the limitations of this pilot study, the ACS-NSQIP risk calculator appears to be a better predictor of major complications and mortality, making it suitable for use by GO surgeons as an informed consent tool. Larger data collection and analyses are ongoing to validate this further.

Predicting Risk of Post-Operative Morbidity and Mortality following Gynaecological Oncology Surgery (PROMEGO): A Global Gynaecological Oncology Surgical Outcomes Collaborative Led Study.

The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien-Dindo I-II), major morbidity (Clavien-Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities.

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.

Association between self-reported METs and other perioperative cardiorespiratory fitness assessment tools in abdominal surgery—a prospective cross-sectional correlation study

Cardiovascular complications represent a significant proportion of adverse events during the perioperative period, necessitating accurate preoperative risk assessment. This study aimed to investigate the association between well-established risk assessment tools and self-reported preoperative physical performance, quantified by metabolic equivalent (MET) equivalents, in high-risk patients scheduled for elective abdominal surgery. A prospective cross-sectional correlation study was conducted, involving 184 patients admitted to a Gastrointestinal Surgery Department. Various risk assessment tools, including the Revised Cardiac Risk Index (RCRI), Surgical Mortality Probability Model (S-MPM), American University of Beirut (AUB)-HAS2 Cardiovascular Risk Index, and Surgical Risk Calculator (NSQIP-MICA), were utilized to evaluate perioperative risk. Patients self-reported their physical performance using the MET-REPAIR questionnaire. The findings demonstrated weak or negligible correlations between the risk assessment tools and self-reported MET equivalents (Spearman’s ρ = − 0.1 to − 0.3). However, a statistically significant relationship was observed between the ability to ascend two flights of stairs and the risk assessment scores. Good correlations were identified among ASA-PS, S-MPM, NSQIP-MICA, and AUB-HAS2 scores (Spearman’s ρ = 0.3–0.8). Although risk assessment tools exhibited limited correlation with self-reported MET equivalents, simple questions regarding physical fitness, such as the ability to climb stairs, showed better associations. A comprehensive preoperative risk assessment should incorporate both objective and subjective measures to enhance accuracy. Further research with larger cohorts is needed to validate these findings and develop a comprehensive screening tool for high-risk patients undergoing elective abdominal 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.