Predictor Of Postoperative Mortality Research Articles

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Baseline Frailty Measured by the Risk Analysis Index and 30-Day Mortality After Surgery for Spinal Malignancy: Analysis of a Prospective Registry (2011-2020).

To evaluate the prognostic utility of baseline frailty, measured by the Risk Analysis Index (RAI), for prediction of postoperative mortality among patients with spinal malignancy (SM) undergoing resection. SM surgery cases were queried from the American College of Surgeons - National Surgical Quality Improvement Program database (2011-2020). The relationship between preoperative RAI frailty score and increasing rate of primary endpoint (mortality or discharge to hospice within 30 days, "mortality/hospice") were assessed. Discriminatory accuracy was assessed by computation of C-statistics (with 95% confidence interval [CI]) in receiver operating characteristic (ROC) curve analysis. A total of 2,235 cases were stratified by RAI score: 0-20, 22.7%; 21-30, 11.9%; 31-40, 54.7%; and ≥ 41, 10.7%. The rate of mortality/hospice was 6.5%, which increased linearly with increasing RAI score (p < 0.001). RAI was also associated with increasing rates of major complication, extended length of stay, and nonhome discharge (all p < 0.05). The RAI demonstrated acceptable discriminatory accuracy for prediction of primary endpoint (C-statistic, 0.717; 95% CI, 0.697-0.735). In pairwise ROC comparison, RAI demonstrated superiority versus modified frailty index-5 and chronological age (p < 0.001). Preoperative frailty, as measured by RAI, is a robust predictor of mortality/ hospice after SM surgery. The frailty score may be applied in clinical settings using a user-friendly calculator, deployed here: https://nsgyfrailtyoutcomeslab.shinyapps.io/spinalMalignancyRAI/.

Perioperative mortality rate and its predictors after emergency laparatomy at Debre Markos comprehensive specialized hospital, Northwest Ethiopia: 2023: retrospective follow-up study.

Emergency laparatomy is abdominal surgery associated with a high rate of mortality. There are few reports on rates and predictors of postoperative mortality, whereas disease related or time specific studies are limited. Understanding the rate and predictors of mortality in the first 30 days (perioperative period) is important for evidence based decision and counseling of patients. This study aimed to estimate the perioperative mortality rate and its predictors after emergency laparatomy at Debre Markos Comprehensive Specialized Hospital, Northwest Ethiopia, 2023. This was a Hospital-based retrospective follow-up study conducted at Debre Markos Comprehensive Specialized Hospital in Ethiopia among patients who had undergone emergency laparatomy between January 1, 2019 and December 31, 2022. Sample of 418 emergency laparatomy patients selected with simple random sampling technique were studied. The data were extracted from March 15, 2023 to April 1, 2023 using a data extraction tool, cleaned, and entered into Epi-Data software version 3.1 before being exported to STATA software version 14.1 for analysis. Predictor variables with P value < 0.05 in multivariable Cox regression were reported. Data of 386 study participants (92.3% complete charts) were analyzed. The median survival time was 18 days [IQR: (14, 29)]. The overall perioperative mortality rate in the cohort during the 2978 person-days of observations was 25.5 per 1000 person-days of follow-up [95% CI: (20.4, 30.9))]. Preoperative need for vasopressor [AHR: 1.8 (95% CI: (1.11, 2.98))], admission to intensive care unit [AHR: 2.0 (95% CI: (1.23, 3.49))], longer than three days of symptoms [AHR: 2.2 (95% CI: (1.15, 4.02))] and preoperative sepsis [AHR: 1.8 (95% CI: (1.05, 3.17))] were identified statistically significant predictors of perioperative mortality after emergency laparatomy. The perioperative mortality rate is high. Preoperative need for vasopressors, admission to intensive care unit, longer than three days of symptoms and preoperative sepsis were predictors of increased perioperative mortality rate.

Postoperative Outcomes After Emergency Surgery in COVID-19 Patients: An Ambispective Matched Cohort Study.

Purpose There is limited data from the Indian subcontinent regarding the surgical outcomes of coronavirus disease (COVID-19) patients. In this observational study, we aimed to evaluate the postoperative outcomesafter emergency surgery in COVID-19 patients compared to concurrent age and gender-matched controls. We also sought to analyze the possible predictors of postoperative mortality in COVID-19 patients. Methods This matched cohort study was conductedin a tertiary care teaching hospital in central India, between 1st July 2021 and 30th June 2022. COVID-19-positive patients undergoing emergency surgery under anesthesia were recruited as cases. Age and gender-matched COVID-19-negative patients undergoing a similar nature of surgery in the same period served as concurrent controls. The cases and controls were compared for the 30-day mortality and perioperative complications. Results The COVID-19-positive surgical cohort had a 12.3 times greater 30-day postoperative overall mortality risk as compared to a matched cohort of patients with a negative COVID-19 test. A positive COVID-19 status was associated with more postoperative complications of acute respiratory distress syndrome (ARDS), sepsis, shock, and persistent hyperglycemia. On analysis of predictors of mortality, the presence of preoperative dyspnea, ARDS, American Society of Anesthesiologists Physical Status (ASA-PS) Class IIIE/IVE, increase in sequential organ failure assessment (SOFA) score, Quick SOFA>1, higher creatinine, bilirubin, and lower albumin were observed to be associated with increased mortality. Conclusions Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients undergoing emergency surgery is significantly associated with higher postoperative complications and increased 30-day postoperative mortality.

Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study.

Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models. Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed. Eight hundred forty-six of 10578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively). Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Predictors of mortality in chronic subdural hematoma evacuation.

Chronic subdural hematoma (cSDH) is one of the most common types of intracranial hemorrhages, particularly in the elderly. Despite extensive research regarding cSDH diagnosis and treatment, there is conflicting data on predictors of postoperative mortality (POM). We conducted a large retrospective review of patients who underwent a cSDH evacuation at a single urban institution between 2015 and 2022. Data were collected from the electronic medical record on prior comorbidities, anticoagulation use, mental status on presentation, preoperative labs, and preoperative/postoperative imaging parameters. Univariate and multivariate analyses were conducted to analyze predictors of mortality. Mortality during admission for this cohort was 6.1%. Univariate analysis showed the mortality rate was higher in those presenting with a history of dialysis. In addition, those who presented with altered mental status, were intubated, and lower GCS scores had higher rates of POM. Usage of Coumadin was correlated with higher rates of POM. Examination of preoperative labs showed that patients who presented with anemia or thrombocytopenia had higher POM. Imaging data showed that cSDH volume and greatest dimension were correlated with higher rates of POM. Finally, patients that were not extubated postoperatively had higher rates of POM. Multivariate analysis showed that only altered mental status and being not being extubated postoperatively were correlated with a higher risk of mortality. In summation, we demonstrated that altered mental status and failure to extubate were independent predictors or mortality in cSDH evacuation. Interestingly, patient age was not a significant predictor of mortality.

Systemic Atrioventricular Valve Surgery in Patients With Congenitally Corrected Transposition of the Great Vessels.

Limited data exist regarding the long-term outcomes of systemic atrioventricular valve (SAVV) intervention (morphologic tricuspid valve) in congenitally corrected transposition (ccTGA). The goal of this study was to evaluate the mid- and long-term outcomes of SAVV surgery in ccTGA. We performed a retrospective review of 108 ccTGA patients undergoing SAVV surgery from 1979 to 2022. The primary outcome was a composite endpoint of mortality, cardiac transplantation, or ventricular assist device implantation. The secondary outcome was long-term systemic right ventricular ejection fraction (SVEF). Cox proportional hazard and linear regression models were used to analyze survival and late SVEF data. The median age at surgery was 39.5 years (Q1-Q3: 28.8-51.0 years), and the median preoperative SVEF was 39% (Q1-Q3: 33.2%-45.0%). Intrinsic valve abnormality was the most common mechanism of SAVV regurgitation (76.9%). There was 1 early postoperative mortality (0.9%). Postoperative complete heart block occurred in 20 patients (18.5%). The actuarial 5-, 10-, and 20-year freedom from death or transplantation was 92.4%, 79.1%, and 62.9%. The 10- and 20-year freedom from valve reoperation was 100% and 93% for mechanical prosthesis compared with 56.6% and 15.7% for bioprosthesis (P< 0.0001). Predictors of postoperative mortality were age at operation (P=0.01) and preoperative SVEF (P=0.04). Preoperative SVEF (P< 0.001), complex ccTGA (P=0.02), severe SAVV regurgitation (P=0.04), and preoperative creatinine (P=0.003) were predictors of late postoperative SVEF. SAVV surgery remains a valuable option for the treatment of patients with ccTGA, with low early mortality and satisfactory long-term outcomes, particularly in those with SVEF≥40%. Timely referral and accurate patient selection are the keys to better long-term outcomes.

Is there an outcome penalty linked to guideline-based class-I indications for valvular surgery? long-term analysis of patients with high gradient severe aortic stenosis

Abstract Background Current ACC/AHA guidelines recommend aortic valve replacement (AVR) as class-I triggers in patients with high-gradient severe aortic stenosis (HGSAS) in presence of symptoms and/or left ventricular dysfunction (LVEF&amp;lt;50%). Whether waiting for these triggers could be associated with long-term outcome penalty after AVR remains poorly studied. Objective To examine the impact of guideline-based class-I triggers on long-term post-operative survival in patients with HGSAS. Methods We used an international registry including consecutive patients operated on for HGSAS between 2000 and 2017. 2030 Patients were included in the analysis and retrospectively classified according to the guideline-based indication: no Class-I trigger (no symptoms and LVEF&amp;gt;50%, n=853), Symptoms with LVEF&amp;gt;50% (n=965), or LVEF&amp;lt;50% (regardless of symptoms, n=212). Survival was compared in a multivariate Cox-model and after inverse probability weighting-[IPW] for relevant variables. Finally, we explored the most sensitive left ventricular ejection fraction threshold for predicting mortality risk. Results 10-years survival was better among patients without any class-I trigger than with symptoms or LVEF&amp;lt;50% (67±3 vs 56±3 vs 53±7% respectively, p&amp;lt;0.001). After adjustment for covariates, risk of death increased significantly when patients were operated on with symptoms (HR: 1.48, [95%CI: 1.17-1.86]) or with LVEF&amp;lt;50% (HR: 1.47, [95%CI: 1.05-2.06]) compared to patients with no class-I trigger. Furthermore, LVEF&amp;lt;55% emerged as a more sensitive threshold for the prediction of post-operative mortality in comparison with LVEF&amp;lt;50% (see table), allowing a better separation of survival curves and indicating that patients with LVEF 50-55% are already at risk with long term consequences even after AVR. Interestingly, among asymptomatic patients with LVEF &amp;gt;55%, performing AVR restituted a normal life expectancy (comparable to the Belgian population of same age, see figure). Finally, the outcome penalty after AVR when waiting for symptoms or LVEF&amp;lt;55% was confirmed in IPW analysis (HR: 1.43, [95%CI: 1.13-1.82] and HR: 1.63, [95%CI: 1.19-2.23], respectively). Conclusions Guideline-based Class-I triggers for AVR in patients with HGSAS is associated with profound outcome consequences on long-term postoperative mortality. Our data argue that patients with HGSAS should be operated on before the onset of these triggers. Finally, our data suggest that a threshold of LVEF&amp;lt;55% is a stronger predictor of outcome than LVEF&amp;lt;50%.

Machine learning-based prediction of 30-day major adverse cardiac and cerebrovascular events in non-cardiac surgery patients

Abstract Background The number of non-cardiac surgeries performed worldwide has been steadily increasing, presenting a challenge for clinicians to accurately identify patients at high risk of complications and to allocate the appropriate level of perioperative care. Accurate prediction of postoperative mortality is crucial not only for successful patient care, but also for information-based shared decision-making with patients and efficient allocation of medical resources. Purpose In this study, we aimed to develop a novel predictive model using machine learning methods applied to electronic health record data. Our objective is to identify the risk factors most likely to lead to 30-day major adverse cardiac and cerebrovascular events after non-cardiac surgery Methods We conducted a retrospective analysis of data from a single tertiary care institution that included patients aged 65 years or over who underwent non-cardiac surgery from May 2003 and December 2020. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) data was used to build predictive models, which allowed for the utilization of demographic data, as well as preoperative characteristics such as diagnosis, lab results, vital signs, medications, and information on operations and procedures from the electronic health records (EHRs) in a standardized way. We employed machine learning models, which were developed and validated using the OHDSI Patient-Level-Prediction framework. Results We included a total of 47,915 patients to train (75%) and test (25%) our predictive models. To compare prediction performances, we applied gradient boosting machine (GBM), logistic regression (LR), random forest (RF), AdaBoost (AB), and decision tree (DT). Our results for a test data (Fig 1.) showed that the GBM model had the best performance in terms of the area under the receiver operating characteristic curve (AUROC) (0.903) and the area under the precision-recall curve (AUPRC) (0.395). Conclusions Our study demonstrates that applying machine learning algorithms to electronic health record data can effectively identify patients at high risk of major adverse cardiac and cerebrovascular events following non-cardiac surgery. This algorithm has the potential to support clinicians in effectively identifying patients at high risk and provide appropriate perioperative care. Further work is needed to validate and refine the proposed model to ensure its external validity and broader applicability in clinical practice.We plan to validate the proposed model externally by testing it on a cohort of approximately 280,000 patients from other tertiary care institution, and present the results at the 2023 ESC Congress.

Feasibility of using red cell distribution width for prediction of postoperative mortality in severe burn patients: an association with acute kidney injury after surgery.

Severe burns cause pathophysiological processes that result in mortality. A laboratory biomarker, red cell distribution width (RDW), is known as a predictor of mortality in critically-ill patients. We examined the association between RDW and postoperative mortality in severe burn patients. We retrospectively analyzed medical data of 731 severely burned patients who underwent surgery under general anesthesia. We evaluated whether preoperative RDW value can predict 3-month mortality after burn surgery using receiver operating characteristic (ROC) curve analysis, logistic regression, and Cox proportional-hazards regression analysis. Mortality was also analyzed according to preoperative RDW values and incidence of postoperative acute kidney injury (AKI). The 3-month mortality rate after burn surgery was 27.1% (198/731). The area under the ROC curve of preoperative RDW to predict mortality after burn surgery was 0.701 (95% confidence interval [CI], 0.667-0.734; P < 0.001) with a cut-off point of 12.9. The adjusted hazard ratio in patients with RDW > 12.9 was 1.238 (95% CI, 1.138-1.347; P < 0.001). Subgroup analysis showed that the survival rate was 88.8% for the non-AKI group with RDW ≤ 12.9 and 17.6% for the AKI group with RDW > 12.9. Preoperative RDW was considered an independent risk factor for mortality (odds ratio, 1.679; 95% CI, 1.378- 2.046; P < 0.001). Preoperative RDW may predict 3-month postoperative mortality in patients with severe burns, while preoperative RDW > 12.9 and postoperative AKI may further increase mortality after burn surgery.

Machine learning-based prediction of postoperative mortality in emergency colorectal surgery: A retrospective, multicenter cohort study using Tokushukai medical database

BackgroundAlthough prognostic factors associated with mortality in patients with emergency colorectal surgery have been identified, an accurate mortality risk assessment is still necessary to determine the range of therapeutic resources in accordance with the severity of patients. We established machine-learning models to predict in-hospital mortality for patients who had emergency colorectal surgery using clinical data at admission and attempted to identify prognostic factors associated with in-hospital mortality. MethodsThis retrospective cohort study included adult patients undergoing emergency colorectal surgery in 42 hospitals between 2012 and 2020. We employed logistic regression and three supervised machine-learning models: random forests, gradient-boosting decision trees (GBDT), and multilayer perceptron (MLP). The area under the receiver operating characteristics curve (AUROC) was calculated for each model. The Shapley additive explanations (SHAP) values are also calculated to identify the significant variables in GBDT. ResultsThere were 8792 patients who underwent emergency colorectal surgery. As a result, the AUROC values of 0.742, 0.782, 0.814, and 0.768 were obtained for logistic regression, random forests, GBDT, and MLP. According to SHAP values, age, colorectal cancer, use of laparoscopy, and some laboratory variables, including serum lactate dehydrogenase serum albumin, and blood urea nitrogen, were significantly associated with in-hospital mortality. ConclusionWe successfully generated a machine-learning prediction model, including GBDT, with the best prediction performance and exploited the potential for use in evaluating in-hospital mortality risk for patients who undergo emergency colorectal surgery.

Modified GLIM Status as a Predictor of Morbidity and Mortality After Radical Cystectomy: A Propensity Matched Analysis

IntroductionTo assess the modified Global Leadership Initiative on Malnutrition (mGLIM) status as a predictor of postoperative mortality and morbidity in patients undergoing Radical Cystectomy (RC). Methods and MaterialsThe American College of Surgeons - National Surgical Quality Improvement Program (ACS-NSQIP) dataset was used to select patients who underwent RC between the years 2011 to 2020. A positive mGLIM status includes patients with preoperative albumin levels < 3.5 g/L or patients with ≥ 10% weight loss over 6 months or BMI ≤ 21 kg/m2. We compared prolonged length of stay, mortality, major morbidity and Clavien-Dindo complications between mGLIM positive and negative patients. A multivariable logistic regression model was also performed to control for possible confounders. Furthermore, a sensitivity analysis was performed by propensity score matching. ResultsOur cohort consisted of 12,760 patients who underwent RC. The matched cohort yielded 4864 matched patients. After propensity score matching, patients with a positive mGLIM status had higher odds of prolonged length of stay (OR = 1.99, 95%CI [1.75, 2.27]), mortality (OR 1.56, 95%CI [1.08, 2.26]), major morbidity (OR = 1.69, 95%CI [1.51, 1.90]), Clavien-Dindo class I and II (OR = 1.77, 95%CI [1.58, 1.99]), and lower odds of Clavien-Dindo class III (OR = 0.72, 95%CI [0.57, 0.92]) as compared to those with a negative mGLIM status (P < .018). ConclusionA positive mGLIM status is associated with prolonged hospital stay, morbidity, and mortality following RC. This indicates that the mGLIM criteria could serve as an independent predictor of morbidity and mortality in an attempt to optimize patient counseling and preoperative care.

Perioperative mortality and its predictors among patients undergoing emergency laparotomy at selected southern Ethiopian governmental hospitals, 2022: a multicenter prospective cohort study.

To assess perioperative mortality and its predictors among patients undergoing emergency laparotomy at selected southern Ethiopian governmental hospitals. A multicenter prospective cohort study was conducted, and data were collected at selected hospitals after obtaining ethical approval from the Institutional Review Board. Data were analyzed using SPSS version 26. The rate of postoperative complications after emergency laparotomy surgery was 39.3%, with an in-hospital mortality rate of 8.4% and a length of hospital stay of 9±6.5 days. The predictors of postoperative mortality were the age of the patient greater than 65 [adjusted odds ratio (AOR)=8.46, 95% CI=1.3-57.1], presence of intraoperative complications (AOR=7.26, 95% CI=1.3-41.3), and postoperative ICU admission (AOR=8.5, 95% CI=1.5-49.6). Our study revealed a significant level of postoperative complications and in-hospital mortality. The identified predictors should be sorted and applied to the preoperative optimization, risk assessment, and standardization of effective postoperative care following emergency laparotomy.

MP32-09 A PREDICTOR OF COMPLICATIONS AFTER RADICAL CYSTECTOMY: THE MODIFIED GLIM STATUS IN A PROPENSITY MATCHED ANALYSIS OF THE AMERICAN COLLEGE OF SURGEONS - NATIONAL SURGICAL QUALITY IMPROVEMENT PROGRAM DATA

You have accessJournal of UrologyCME1 Apr 2023MP32-09 A PREDICTOR OF COMPLICATIONS AFTER RADICAL CYSTECTOMY: THE MODIFIED GLIM STATUS IN A PROPENSITY MATCHED ANALYSIS OF THE AMERICAN COLLEGE OF SURGEONS - NATIONAL SURGICAL QUALITY IMPROVEMENT PROGRAM DATA Elia Abou Chawareb, Christian Habib Ayoub, Christelle Lattouf, Albert El Hajj, and Jose M. El asmar Elia Abou ChawarebElia Abou Chawareb More articles by this author , Christian Habib AyoubChristian Habib Ayoub More articles by this author , Christelle LattoufChristelle Lattouf More articles by this author , Albert El HajjAlbert El Hajj More articles by this author , and Jose M. El asmarJose M. El asmar More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003265.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Nutritional status and malnutrition are important predictors of morbidity and mortality after surgery. For this reason, we aim to assess the modified Global Leadership Initiative on Malnutrition (mGLIM) status as a predictor of post-operative mortality and morbidity in patients undergoing Radical Cystectomy (RC). METHODS: Using the American College of Surgeons - National Surgical Quality Improvement Program (ACS-NSQIP) dataset, we selected patients who underwent RC between the years 2011-2020. A positive mGLIM status includes patients with preoperative albumin levels <3.5 g/L, or patients with ≥ 10% weight loss over 6 months or BMI ≤ 21 kg/m2. We compared prolonged length of stay, mortality, major morbidity and Clavien-Dindo complications between mGLIM positive and negative patients. We also performed multivariable logistic regression models and propensity score matching to control for possible confounders. RESULTS: The unmatched cohort consisted of 13,585 patients whereas the matched cohort yielded 5,610 matched patients who underwent RC. Patients with a positive mGLIM status were more likely to be older, with a higher ASA class, and with comorbidities (p<0.001). After multivariable analysis and propensity score matching, patients with a positive mGLIM status had higher odds of prolonged length of stay (OR=2.29), mortality (OR 1.97), major morbidity (OR=1.82), Clavien-Dindo class I and II (OR=1.90), and Clavien-Dindo class ≥ IV (OR=1.14) as compared to those with a negative mGLIM status (p<0.04). CONCLUSIONS: A positive mGLIM status is associated with prolonged hospital stay, morbidity, and mortality following RC. This highlights the importance of optimizing patient pre-operative nutritional counseling. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e443 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Elia Abou Chawareb More articles by this author Christian Habib Ayoub More articles by this author Christelle Lattouf More articles by this author Albert El Hajj More articles by this author Jose M. El asmar More articles by this author Expand All Advertisement PDF downloadLoading ...

Evaluation of lymphopenia as a predictor of postoperative mortality and major complications in patients undergoing surgery for metastatic spine tumors.

Lymphopenia is often seen in advanced metastatic disease and has been associated with poor postoperative outcomes. Limited research has been done to validate this metric in patients with spinal metastases. The objective of this study was to evaluate the capability of preoperative lymphopenia to predict 30-day mortality, overall survival (OS), and major complications in patients undergoing surgery for metastatic spine tumors. A total of 153 patients who underwent surgery for metastatic spine tumor between 2012 and 2022 and met the inclusion criteria were examined. Electronic medical record chart review was conducted to obtain patient demographics, comorbidities, preoperative laboratory values, survival time, and postoperative complications. Preoperative lymphopenia was defined as < 1.0 K/μL based on the institution's laboratory cutoff value and within 30 days prior to surgery. The primary outcome was 30-day mortality. Secondary outcomes were OS up to 2 years and 30-day postoperative major complications. Outcomes were assessed with logistic regression. Survival analyses were done using the Kaplan-Meier method with log-rank test and Cox regression. Receiver operating characteristic curves were plotted to classify the predictive ability of lymphocyte count as a continuous variable on outcome measures. Lymphopenia was identified in 47% of patients (72 of 153). The overall 30-day mortality rate was 9% (13 of 153). In logistic regression analysis, lymphopenia was not associated with 30-day mortality (OR 1.35, 95% CI 0.43-4.21; p = 0.609). The mean OS in this sample was 15.6 months (95% CI 13.9-17.3 months), with no significant difference between patients with lymphopenia and those with no lymphopenia (p = 0.157). Cox regression analysis did not show an association between lymphopenia and survival (HR 1.44, 95% CI 0.87-2.39; p = 0.161). The major complication rate was 26% (39 of 153). In univariable logistic regression analysis, lymphopenia was not associated with the development of a major complication (OR 1.44, 95% CI 0.70-3.00; p = 0.326). Finally, receiver operating characteristic curves generated poor discrimination between lymphocyte count and all outcomes, including 30-day mortality (area under the curve 0.600, p = 0.232). This study does not support prior research that had shown an independent association between low preoperative lymphocyte level and poor postoperative outcomes following surgery for metastatic spine tumors. Although lymphopenia may be used to predict outcomes in other tumor-related surgeries, this metric may not hold a similar predictive capability in the population undergoing surgery for metastatic spine tumors. Further research into reliable prognostic tools is needed.

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.

Study and validation of an explainable machine learning-based mortality prediction following emergency surgery in the elderly: A prospective observational study

IntroductionThe heterogeneity of procedures and the variety of comorbidities of the patients undergoing surgery in an emergency setting makes perioperative risk stratification, planning, and risk mitigation crucial. In this optic, Machine Learning has the capability of deriving data-driven predictions based on multivariate interactions of thousands of instances. Our aim was to cross-validate and test interpretable models for the prediction of post-operative mortality after any surgery in an emergency setting on elderly patients. MethodsThis study is a secondary analysis derived from the FRAILESEL study, a multi-center (N = 29 emergency care units), nationwide, observational prospective study with data collected between 06-2017 and 06–2018 investigating perioperative outcomes of elderly patients (age≥65 years) undergoing emergency surgery. Demographic and clinical data, medical and surgical history, preoperative risk factors, frailty, biochemical blood examination, vital parameters, and operative details were collected and the primary outcome was set to the 30-day mortality. ResultsOf the 2570 included patients (50.66% males, median age 77 [IQR = 13] years) 238 (9.26%) were in the non-survivors group. The best performing solution (MultiLayer Perceptron) resulted in a test accuracy of 94.9% (sensitivity = 92.0%, specificity = 95.2%). Model explanations showed how non-chronic cardiac-related comorbidities reduced activities of daily living, low consciousness levels, high creatinine and low saturation increase the risk of death following surgery. ConclusionsIn this prospective observational study, a robustly cross-validated model resulted in better predictive performance than existing tools and scores in literature. By using only preoperative features and by deriving patient-specific explanations, the model provides crucial information during shared decision-making processes required for risk mitigation procedures.

Multi-center validation of machine learning model for preoperative prediction of postoperative mortality

Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.

Comparison of POSSUM and SAPS II in Prediction of Postoperative Mortality in Hollow Viscus Perforation.

There have been very few studies in the literature assessing various scoring systems to predict mortality in patients with hollow viscous perforation. Scoring systems like POSSUM and SAPS II are among the most widely validated risk predictors. Objective of the study was to compare POSSUM and SAPS II in prediction of mortality in patients undergoing surgery for hollow viscus perforation. Prospective observational study was conducted at Department of Surgery, Tribhuvan University Teaching Hospital, Kathmandu, Nepal, over a period of 18 months. Ethical approval was obtained from the Institutional Review Board of Institute of Medicine. Informed consent was taken from all the patients. Patients aged less than 16 years, discharged on request and patients in whom no perforation found during surgery were excluded from the study. Among 121 patients enrolled in the study, in-hospital mortality was seen in 19 patients (17.0%). Mean POSSUM score in survivors was 39.7 ± 7.3 and in non-survivors was 52.8 ± 5.8 (p < 0.001). Similarly mean SAPS II score was 16.4 ± 9.7 in survivors and 41.8 ± 6.4 in non-survivors ( p < 0.001). Area under ROC curve was higher for SAPS II (0.964) as compared to POSSUM (0.906) suggesting that SAPS was better. Both POSSUM and SAPS II provided good discrimination between survivors and non survivors in patients undergoing surgery for hollow viscus perforation. SAPS II showed better sensitivity and specificity than POSSUM in predicting mortality.