Surgical Risk Calculator Research Articles

Impact of Prehabilitation on Postoperative Mortality and the Need for Non-Home Discharge in High-Risk Surgical Patients.

The preoperative period is an important target for interventions (eg Surgical Prehabilitation and Readiness [SPAR]) that can improve postoperative outcomes for older patients with comorbidities. To determine whether a preoperative multidisciplinary prehabilitation program (SPAR) reduces postoperative 30-day mortality and the need for non-home discharge in high-risk surgical patients, surgical patients enrolled in a prehabilitation program targeting physical activity, pulmonary function, nutrition, and mindfulness were compared with historical control patients from 1 institution's American College of Surgeons (ACS) NSQIP database. SPAR patients were propensity score-matched 1:3 to pre-SPAR NSQIP patients, and their outcomes were compared. The ACS NSQIP Surgical Risk Calculator was used to compare observed-to-expected ratios for postoperative outcomes. A total of 246 patients were enrolled in SPAR. A 6-month compliance audit revealed that overall patient adherence to the SPAR program was 89%. At the time of analysis, 118 SPAR patients underwent surgery with 30 days of follow-up. Compared with pre-SPAR NSQIP patients (n = 4,028), SPAR patients were significantly older with worse functional status and more comorbidities. Compared with propensity score-matched pre-SPAR NSQIP patients, SPAR patients had significantly decreased 30-day mortality (0% vs 4.1%, p = 0.036) and decreased need for discharge to postacute care facilities (6.5% vs 15.9%, p = 0.014). Similarly, SPAR patients exhibited decreased observed 30-day mortality (observed-to-expected ratio 0.41) and need for discharge to a facility (observed-to-expected ratio 0.56) compared with their expected outcomes using the ACS NSQIP Surgical Risk Calculator. The SPAR program is safe and feasible and may reduce postoperative mortality and the need for discharge to postacute care facilities in high-risk surgical patients.

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

MP72-12 ARTIFICIAL INTELLIGENCE ANALYSIS OF PRE-OPERATIVE ECGS CAN PREDICT POST-OPERATIVE DEATH AND MAJOR CARDIAC COMPLICATIONS

You have accessJournal of UrologyCME1 Apr 2023MP72-12 ARTIFICIAL INTELLIGENCE ANALYSIS OF PRE-OPERATIVE ECGS CAN PREDICT POST-OPERATIVE DEATH AND MAJOR CARDIAC COMPLICATIONS Vidit Sharma, Harrison Gottlich, Christine Lohse, Abhinav Khanna, Elizabeth Habermann, Suraj Kapa, Zachi Attia, Stephen Boorjian, and Bradley Leibovich Vidit SharmaVidit Sharma More articles by this author , Harrison GottlichHarrison Gottlich More articles by this author , Christine LohseChristine Lohse More articles by this author , Abhinav KhannaAbhinav Khanna More articles by this author , Elizabeth HabermannElizabeth Habermann More articles by this author , Suraj KapaSuraj Kapa More articles by this author , Zachi AttiaZachi Attia More articles by this author , Stephen BoorjianStephen Boorjian More articles by this author , and Bradley LeibovichBradley Leibovich More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003340.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Approximately 1% of patients undergoing non-cardiac surgery die or have a major adverse cardiac event(MACE) within 30 days. Only 20% of surgeons use online risk calculators to risk-stratify patients due to increased clinical burden. Herein, we investigate if our institution's previously created AI algorithms on pre-operative electrocardiograms(ECGs) can serve as a point-of-care risk stratification for post-operative complications. METHODS: From our institutional National Surgical Quality Improvement Program(NSQIP) data, we identified 128,992 non-cardiac surgeries performed on 116,702 distinct patients between 2006-2020. We identified pre-operative ECGs within 90 days of surgery; AI algorithms were used to derive variables in (Table 1). We evaluated the relationship of AI ECG features with the following 30-day outcomes: death and MACE (stroke, cardiac event, or death). AI ECG features were evaluated in conjunction with NSQIP Surgical Risk Calculator (SRC) variables using multivariable logistic regression models to predict these outcomes, using 500-sample bootstrap-corrected c-indexes. RESULTS: A total of 50,254(39%) surgeries(13% Urologic) had pre-operative ECGs available. The AI ECG features (Table 1) collectively had a c-index of 0.755 and 0.719, respectively, for 30-day death(n=414; 0.8%) and MACE(n=676; 1.4%). After adjusting for NSQIP SRC variables, AI ECG variables remained significantly associated with death and MACE (Table 2). The final model with AI ECG features and NSQIP SRC variables had a c-index of 0.902 death) and 0.845(MACE). CONCLUSIONS: AI features of pre-operative ECGs were significantly associated with death or MACE within 30-days of non-cardiac surgery. Clinical implementation of these algorithms to pre-operatively identify high risk surgical candidates warrants further study. Source of Funding: No sources of funding to disclose © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1029 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Vidit Sharma More articles by this author Harrison Gottlich More articles by this author Christine Lohse More articles by this author Abhinav Khanna More articles by this author Elizabeth Habermann More articles by this author Suraj Kapa More articles by this author Zachi Attia More articles by this author Stephen Boorjian More articles by this author Bradley Leibovich More articles by this author Expand All Advertisement PDF downloadLoading ...

MP24-14 URORISK: A NOVEL SURGICAL RISK CALCULATOR FOR BLADDER OUTLET PROCEDURES IN OLDER ADULTS

You have accessJournal of UrologyCME1 Apr 2023MP24-14 URORISK: A NOVEL SURGICAL RISK CALCULATOR FOR BLADDER OUTLET PROCEDURES IN OLDER ADULTS Farnoosh Nik-Ahd, Shoujun Zhao, Lufan Wang, W. John Boscardin, and Anne M. Suskind Farnoosh Nik-AhdFarnoosh Nik-Ahd More articles by this author , Shoujun ZhaoShoujun Zhao More articles by this author , Lufan WangLufan Wang More articles by this author , W. John BoscardinW. John Boscardin More articles by this author , and Anne M. SuskindAnne M. Suskind More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003249.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Bladder outlet obstruction (BOO) is common in older adults. As such, many who pursue surgery have additional vulnerabilities affecting surgical risk, including frailty and other comorbidities. No risk calculator for BOO surgery that factors in frailty exists. We developed the first risk calculator for BOO surgeries that includes components of frailty and comorbidity to predict the risk of common complications. METHODS: Medicare beneficiaries undergoing BOO surgery from 2014-2016 were examined using Medicare MedPAR, Outpatient, and Carrier files. Pre-procedure frailty was quantified for each subject using the 93-item Claims-Based Frailty Index (CFI). Each variable in the Charlson Comorbidity Index and CFI was extrapolated into stepwise logistic regression models to determine variables most predictive of surgical complications. The most prognostic variables were grouped into 13 categories. C-statistics, Brier scores, Spiegelhalter p values and model fit curves were calculated to assess model fit. RESULTS: A total of 212,543 subjects were included. BOO surgeries included transurethral resection of the prostate, GreenLight Laser Therapy, transurethral incision of the bladder neck, holmium laser enucleation of the prostate, transurethral microwave thermotherapy, transurethral incision of the prostate, UroLift, and transurethral needle ablation. Most subjects were white (88%), with CFI scores that were not frail (CFI<0.15, N=101,282, 48%) or pre-frail (0.15≤CFI<0.25, N=10,559, 39%). Variables most predictive of complications were congestive heart failure, primary malignancy, secondary or metastatic malignancy, pneumonia/influenza in the past year, neurodegenerative/neurologic diseases, dementia/psychiatric conditions and substance use, renal disease, chronic liver disease, anemias/platelets disorders, chronic lung disease, coronary artery disease, venous/lymphatic/thrombotic disorders, and stroke. Model fit for any complication within 30 days of surgery is shown (Figure). CONCLUSIONS: This is the first risk calculator for BOO surgery that includes predictors of frailty and comorbidity to individualize risk calculation among older adults. This serves as an effective and widely-applicable tool to help guide patient counseling and clinical decision-making. Source of Funding: NIH-NIA R01AG058616 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e323 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Farnoosh Nik-Ahd More articles by this author Shoujun Zhao More articles by this author Lufan Wang More articles by this author W. John Boscardin More articles by this author Anne M. Suskind More articles by this author Expand All Advertisement PDF downloadLoading ...

Preoperative risk modelling for oesophagectomy: A systematic review.

Oesophageal cancer is a frequently observed and lethal malignancy worldwide. Surgical resection remains a realistic option for curative intent in the early stages of the disease. However, the decision to undertake oesophagectomy is significant as it exposes the patient to a substantial risk of morbidity and mortality. Therefore, appropriate patient selection, counselling and resource allocation is important. Many tools have been developed to aid surgeons in appropriate decision-making. To examine all multivariate risk models that use preoperative and intraoperative information and establish which have the most clinical utility. A systematic review of the MEDLINE, EMBASE and Cochrane databases was conducted from 2000-2020. The search terms applied were ((Oesophagectomy) AND (Risk OR predict OR model OR score) AND (Outcomes OR complications OR morbidity OR mortality OR length of stay OR anastomotic leak)). The applied inclusion criteria were articles assessing multivariate based tools using exclusively preoperatively available data to predict perioperative patient outcomes following oesophagectomy. The exclusion criteria were publications that described models requiring intra-operative or post-operative data and articles appraising only univariate predictors such as American Society of Anesthesiologists score, cardiopulmonary fitness or pre-operative sarcopenia. Articles that exclusively assessed distant outcomes such as long-term survival were excluded as were publications using cohorts mixed with other surgical procedures. The articles generated from each search were collated, processed and then reported in accordance with PRISMA guidelines. All risk models were appraised for clinical credibility, methodological quality, performance, validation, and clinical effectiveness. The initial search of composite databases yielded 8715 articles which reduced to 5827 following the deduplication process. After title and abstract screening, 197 potentially relevant texts were retrieved for detailed review. Twenty-seven published studies were ultimately included which examined twenty-one multivariate risk models utilising exclusively preoperative data. Most models examined were clinically credible and were constructed with sound methodological quality, but model performance was often insufficient to prognosticate patient outcomes. Three risk models were identified as being promising in predicting perioperative mortality, including the National Quality Improvement Project surgical risk calculator, revised STS score and the Takeuchi model. Two studies predicted perioperative major morbidity, including the predicting postoperative complications score and prognostic nutritional index-multivariate models. Many of these models require external validation and demonstration of clinical effectiveness. Whilst there are several promising models in predicting perioperative oesophagectomy outcomes, more research is needed to confirm their validity and demonstrate improved clinical outcomes with the adoption of these models.

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.

High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP data

BackgroundsNew methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP). MethodsAll 2012–2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012–2017 data. 2018 data was used as independent performance evaluation. Results431,148 patients were included in the 2012–2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications). ConclusionsWe developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.

Creating a surgical risk calculator for benign hysterectomy using classical lasso regression

Creating a surgical risk calculator for benign hysterectomy using classical lasso regression

Surgical mortality in patients in extremis: futility in emergency abdominal surgery

BackgroundThe number of older patients with multiple comorbidities in the emergency service is increasingly frequent, which implies the risk of incurring in futile surgical interventions. Some interventions generate false expectations of survival or quality of life in patients and families and represent a negligible therapeutic benefit in patients whose chances of survival are minimal. In order to address this dilemma, we describe mortality in a cohort of patients undergoing emergency laparotomy with a risk ≥ 75% per the ACS NSQIP Surgical Risk Calculator.MethodsA retrospective observational study was designed to analyze postoperative mortality and factors associated with postoperative mortality in a cohort of patients undergoing emergency laparotomy between January 2018 and December 2021 in a high-complexity hospital who had a mortality risk ≥ 75% per the ACS NSQIP Surgical Risk Calculator.ResultsA total of 890 emergency laparotomies were performed during the study period, and 50 patients were included for the analysis. Patient median age was 82.5 (IQR: 18.25) years old and 33 (66.00%) were male. The most frequent diagnoses were mesenteric ischemia 21 (42%) and secondary peritonitis 18 (36%). Mortality in the series was 92%. Twenty-four (54.34%) died within the first 24 h of the postoperative period; 11 (23.91%) within 72 h and 10 (21.73%) within 30 days. APACHE II and SOFA scores were statistically significantly higher in patients who died.ConclusionsAll available tools should be used to make decisions, with the most reliable and objective information possible, and be particularly vigilant in patients at extreme risk (mortality risk greater than 75% according to ACS NSQIP Surgical Risk Calculator) to avoid futility and its consequences. The available information should be shared with the patient, the family, or their guardians through an assertive and empathetic communication strategy. It is necessary to insist on a culture of surgical ethics based on reflection and continuous improvement in patient care and to know how to accompany them in order to have a proper death.

Transforming the perioperative medicine care model: The Singapore experience.

More than 300 million surgeries are performed worldwide annually. Established perioperative centres in the UK, USA and Australia have demonstrated the impact of improving perioperative care in reducing costs, increasing patient satisfaction and improving population health. Likewise, the surgical burden of care in Asia is increasing, but with sociocultural, economic and epigenetic differences compared to the west. As Singapore's largest hospital, the Singapore General Hospital pre-admission perioperative clinic sees about 20,000 patients annually. We aim to illustrate Singapore General Hospital's perioperative model of care to contribute to the paucity of literature describing perioperative programme implementation within Asia, and to encourage the cross-sharing of perioperative practices internationally. Our perioperative framework navigates risk assessment, risk counselling, and mitigation of health, medical and functional risks to better patients' perioperative outcomes and population health. We have implemented evidence-based pathways for common conditions such as anaemia and malnutrition, including a multidisciplinary programme for the elderly to tackle frailty and reduce length of stay. We describe how we have enhanced local risk profiling with the Combined Assessment of Risk Encountered in Surgery surgical risk calculator derived locally using a gradient boosting machine learning model. Finally, we report clinical outcomes of these interventions and discuss further challenges and new initiatives at each tier of our perioperative model. Our perioperative care model provides a framework that other centres can adopt to promote value-driven care, while catering for differences in the Asian population, thereby promoting evidence-based improvements in the area of perioperative medicine.

What If We Do Not Operate? Outcomes of Nonoperatively Managed Emergency General Surgery Patients

IntroductionAlthough two-thirds of patients with emergency general surgery (EGS) conditions are managed nonoperatively, their long-term outcomes are not well described. We describe outcomes of nonoperative management in a cohort of older EGS patients and estimate the projected risk of operative management using the NSQIP Surgical Risk Calculator (SRC). Materials and methodsWe studied single-center inpatients aged 65 y and more with an EGS consult who did not undergo an operation (January 2019-December 2020). For each patient, we recorded the surgeon's recommendation as either an operation was “Not Needed” (medical management preferred) or “Not Recommended” (risk outweighed benefits). Our main outcome of interest was mortality at 30 d and 1 y. Our secondary outcome of interest was SRC-projected 30-day postoperative mortality risk (median % [interquartile range]), calculated using hypothetical low-risk and high-risk operations. ResultsWe included 204 patients (60% female, median age 75 y), for whom an operation was “Not Needed” in 81% and “Not Recommended” in 19%. In this cohort, 11% died at 30 d and 23% died at 1 y. Mortality was higher for the “Not Recommended” cohort (37% versus 5% at 30 d and 53% versus 16% at 1 y, P < 0.05). The SRC-projected 30-day postoperative mortality risk was 3.7% (1.3-8.7) for low-risk and 5.8% (2-11.8) for high-risk operations. ConclusionsNonoperative management in older EGS patients is associated with very high risk of short-term and long-term mortality, particularly if a surgeon advised that risks of surgery outweighed benefits. The SRC may underestimate risk in the highest-risk patients.

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.

External Validation of the American College of Surgeons Surgical Risk Calculator in Elderly Patients Undergoing General Surgery Operations.

Preoperative risk stratification in the elderly surgical patient is an essential part of contemporary perioperative care and can be done with the use of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). However, data on the generalizability of the ACS-SRC in the elderly is scarce. In this study, we report an external validation of the ACS-RC in a geriatric cohort. A retrospective analysis of a prospectively maintained database was performed including patients aged > 65 who underwent general surgery procedures during 2012−2017 in a Greek academic centre. The predictive ability of the ACS-SRC for post-operative outcomes was tested with the use of Brier scores, discrimination, and calibration metrics. 471 patients were included in the analysis. 30-day postoperative mortality was 3.2%. Overall, Brier scores were lower than cut-off values for almost all outcomes. Discrimination was good for serious complications (c-statistic: 0.816; 95% CI: 0.762−0.869) and death (c-statistic: 0.824; 95% CI: 0.719−0.929). The Hosmer-Lemeshow test showed good calibration for all outcomes examined. Predicted and observed length of stay (LOS) presented significant differences for emergency and for elective cases. The ACS-SRC demonstrated good predictive performance in our sample and can aid preoperative estimation of multiple outcomes except for the prediction of post-operative LOS.

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.

Perioperative outcomes of robotic lobectomy for early-stage non-small cell lung cancer in elderly patients.

Minimally invasive surgery has become the standard for the early-stage non-small cell lung cancer (NSCLC). The appropriateness of the kind of lung resection for the elderly patients is still debated. We retrospectively reviewed patients with older than 75 years who underwent robotic lobectomy between May 2016 to June 2022. We selected 103 patients who met the inclusion criteria of the study. The preoperative cardiorespiratory functional evaluations were collected, and the risk of postoperative complications was calculated according to the Charlson Comorbidity Index, the American College of Surgery surgical risk calculator (ACS-NSQIP), EVAD score, and American Society of Anesthesiology (ASA) score. The patients were divided in two groups according to the presence of postoperative complications. Forty-three patients were female, and 72.8% of the total population were former or active smokers. Thirty-five patients reported postoperative complications. The analysis of the two groups showed that the predicted postoperative forced expiratory volumes in the first second (FEV1) and forced vital capacity (FVC) were significantly lower in patients presenting postoperative complications (p=0.04). Moreover, the upstaging rate and the unexpected nodal metastases were higher in the postoperative complication groups. Robotic-assisted lobectomy for early-stage lung cancer is a safe and feasible approach in selected elderly patients. The factors that could predict the complication rate was the predicted postoperative FEV1 and the nodal disease.

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.

What's New in Musculoskeletal Tumor Surgery.

What's New in Musculoskeletal Tumor Surgery.

Mortality Risk Stratification in Emergency Surgery for Obstructive Colon Cancer-External Validation of International Scores, American College of Surgeons National Surgical Quality Improvement Program Surgical Risk Calculator (SRC), and the Dedicated Score of French Surgical Association (AFC/OCC Score).

Background: The increased rates of postoperative mortality after emergency surgery for obstructive colon cancer (OCC) require the use of risk-stratification scores. The study purpose is to external validate the surgical risk calculator (SRC) and the AFC/OCC score and to create a score for risk stratification. Patients and methods: Overall, 435 patients with emergency surgery for OCC were included in this retrospective study. We used statistical methods suitable for the aimed purpose. Results: Postoperative mortality was 11.72%. SRC performance: strong discrimination (AUC = 0.864) and excellent calibration (11.80% predicted versus 11.72% observed); AFC/OCC score performance: adequate discrimination (AUC = 0.787) and underestimated mortality (6.93% predicted versus 11.72% observed). We identified nine predictors of postoperative mortality: age > 70 years, CHF, ECOG > 2, sepsis, obesity or cachexia, creatinine (aN) or platelets (aN), and proximal tumors (AUC = 0.947). Based on the score, we obtained four risk groups of mortality rate: low risk (0.7%)—0–2 factors, medium risk (12.5%)—3 factors, high risk (40.0%)—4 factors, very high risk (84.4%)—5–6 factors. Conclusions: The two scores were externally validated. The easy identification of predictors and its performance recommend the mortality score of the Clinic County Emergency Hospital of Galați/OCC for clinical use.