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Is the ACS-NSQIP Risk Calculator Accurate in Predicting Adverse Postoperative Outcomes in the Emergency Setting? An Italian Single-center Preliminary Study.

ABSTRACT: BACKGROUND:The ACS-NSQIP surgical risk calculator (SRC) is an open-access online tool that estimates the chance for adverse postoperative outcomes. The risk is estimated based on 21 patient-related variables and customized for specific surgical procedures. The purpose of this monocentric retrospective study is to validate its predictive value in an Italian emergency setting. METHODS:From January to December 2018, 317 patients underwent surgical procedures for acute cholecystitis (n?=?103), appendicitis (n?=?83), gastrointestinal perforation (n?=?45), and intestinal obstruction (n?=?86). Patients' personal risk was obtained and divided by the average risk to calculate a personal risk ratio (RR). Areas under the ROC curves (AUC) and Brier score were measured to assess both the discrimination and calibration of the predictive model. RESULTS:The AUC was 0.772 (95%CI 0.722-0.817, p??1.24, RR?>?1.52, and RR?>?2.63 predicted the onset of serious complications (sensitivity?=?60.47%, specificity?=?64.07%; NPV?=?81%), death (sensitivity?=?82.76%, specificity?=?62.85%; NPV?=?97%), and discharge to nursing or rehab facility (sensitivity?=?80.00%, specificity?=?69.12%; NPV?=?95%), respectively. CONCLUSIONS:The calculator appears to be accurate in predicting adverse postoperative outcomes in our emergency setting. A RR cutoff provides a much more practical method to forecast the onset of a specific type of complication in a single patient.

SUBMITTER: Scotton G

PROVIDER: S-EPMC7527359 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Is the ACS-NSQIP Risk Calculator Accurate in Predicting Adverse Postoperative Outcomes in the Emergency Setting? An Italian Single-center Preliminary Study.

Scotton Giovanni G Del Zotto Giulio G Bernardi Laura L Zucca Annalisa A Terranova Susanna S Fracon Stefano S Paiano Lucia L Cosola Davide D Biloslavo Alan A de Manzini Nicolò N

World journal of surgery 20200724 11

<h4>Background</h4>The ACS-NSQIP surgical risk calculator (SRC) is an open-access online tool that estimates the chance for adverse postoperative outcomes. The risk is estimated based on 21 patient-related variables and customized for specific surgical procedures. The purpose of this monocentric retrospective study is to validate its predictive value in an Italian emergency setting.<h4>Methods</h4>From January to December 2018, 317 patients underwent surgical procedures for acute cholecystitis ( ...[more]

PMID: 32710123

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Project description:The aim of this study was to evaluate the benefit of adding hypoalbuminemia to the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) Surgical Risk Calculator when predicting postoperative outcomes in colorectal cancer patients.The ACS-NSQIP Surgical Risk Calculator offers qualified risk evaluation in surgical decision-making and informed patient consent. To date, malnutrition defined as hypoalbuminemia, an important independent surgical risk factor in colorectal cancer, is not included.This is a retrospective, multi-institutional study of ACS-NSQIP patients (n?=?18,532) who received colorectal surgery from 2009 to 2012. Models were constructed for predicting postoperative mortality and morbidity using the risk factors of the ACS-NSQIP Surgical Risk Calculator before and after adding hypoalbuminemia as a risk factor. The 2 models' performance was then compared using c-statistics and Brier scores. The ACS-NSQIP database in 2008 was used for validation of the created models.The prevalence of hypoalbuminemia (27.8%) is higher in colorectal cancer, when compared with other most common cancers. In univariate analyses, hypoalbuminemia was significantly associated with postoperative mortality and morbidity in colorectal cancer patients. In multivariate logistic regression analyses, 15 postoperative complications, including mortality and serious morbidities, were significantly predicted by hypoalbuminemia. Most of the models with hypoalbuminemia showed better performance and validation in predicting postoperative complications than those without hypoalbuminemia.In colorectal cancer, hypoalbuminemia, with levels below 3.5?g/dL, serves as an excellent assessment tool and preoperative predictor of postoperative outcomes. When combined with hypoalbuminemia as a risk factor, the ACS-NSQIP Surgical Risk Calculator offers more accurate information and estimation of surgical risks to patients and surgeons when choosing treatment options.

Project description:Study designA retrospective study at a single academic institution.ObjectiveThe purpose of this study is to utilize machine learning to predict hospital length of stay (LOS) and discharge disposition following adult elective spine surgery, and to compare performance metrics of machine learning models to the American College of Surgeon's National Surgical Quality Improvement Program's (ACS NSQIP) prediction calculator.Summary of background dataA total of 3678 adult patients undergoing elective spine surgery between 2014 and 2019, acquired from the electronic health record.MethodsPatients were divided into three stratified cohorts: cervical degenerative, lumbar degenerative, and adult spinal deformity groups. Predictive variables included demographics, body mass index, surgical region, surgical invasiveness, surgical approach, and comorbidities. Regression, classification trees, and least absolute shrinkage and selection operator (LASSO) were used to build predictive models. Validation of the models was conducted on 16% of patients (N=587), using area under the receiver operator curve (AUROC), sensitivity, specificity, and correlation. Patient data were manually entered into the ACS NSQIP online risk calculator to compare performance. Outcome variables were discharge disposition (home vs. rehabilitation) and LOS (days).ResultsOf 3678 patients analyzed, 51.4% were male (n=1890) and 48.6% were female (n=1788). The average LOS was 3.66 days. In all, 78% were discharged home and 22% discharged to rehabilitation. Compared with NSQIP (Pearson R2 =0.16), the predictions of poisson regression ( R2 =0.29) and LASSO ( R2 =0.29) models were significantly more correlated with observed LOS ( P =0.025 and 0.004, respectively). Of the models generated to predict discharge location, logistic regression yielded an AUROC of 0.79, which was statistically equivalent to the AUROC of 0.75 for NSQIP ( P =0.135).ConclusionThe predictive models developed in this study can enable accurate preoperative estimation of LOS and risk of rehabilitation discharge for adult patients undergoing elective spine surgery. The demonstrated models exhibited better performance than NSQIP for prediction of LOS and equivalent performance to NSQIP for prediction of discharge location.

Project description:BackgroundSeventy percent of surgical procedures are currently performed in the outpatient setting. Although the American Society of Anesthesiologists (ASA) Physical Classification ability to predict risk has been evaluated for in-patient surgeries, an evaluation in outpatient surgeries has yet to be performed. The major goal of the current study is to determine if the ASA classification is an independent predictor for morbidity and mortality for outpatient surgeries.MethodsThe 2005 through 2016 NSQIP Participant Use Data Files were queried to extract all patients scheduled for outpatient surgery. ASA PS class was the primary independent variable of interest. The primary outcome was 30-day medical complications, defined as having one or more of the following postoperative outcomes: (1) deep vein thrombosis, (2) pulmonary embolism, (3) reintubation, (4) failure to wean from ventilator, (5) renal insufficiency, (6) renal failure, (7) stroke, (8) cardiac arrest, (9) myocardial infarction, (10) pneumonia, (11) urinary tract infection, (12) systemic sepsis or septic shock. Mortality was also evaluated as a separate outcome.ResultsA total of 2,089,830 cases were included in the study. 24,777 (1.19%) patients had medical complications and 1,701 (0.08%) died within 30 days. ASA PS IV patients had a much greater chance of dying when compared to healthy patients, OR (95%CI) of 89 (55 to 143), P < 0.001. Nonetheless, over 30,000 ASA PS IV patients had surgery in the outpatient setting. Multivariable analysis demonstrated a stepwise independent association between ASA PS class and medical complications (C statistic = 0.70), mortality (C statistic = 0.74) and readmissions (C statistic = 0.67). Risk stratifying ability was maintained across surgical procedures and anesthesia techniques.ConclusionsASA PS class is a simple risk stratification tool for surgeries in the outpatient setting. Patients with higher ASA PS classes subsequently developed medical complications or mortality at a greater frequency than patients with lower ASA PS class after outpatient surgery. Our results suggest that the ambulatory setting may not be able to match the needs of high-risk patients.

Project description:Study designRetrospective cohort study of the American College of Surgeons National Surgical Quality Improvement Program database from 2005 to 2010 that included 1861 patients who had undergone elective posterior lumbar fusion.ObjectiveTo characterize factors that were independently associated with increased hospital length of stay (LOS) in patients who had undergone elective posterior lumbar fusion.Summary of background dataPosterior lumbar spine fusion is a common surgical procedure used to treat lumbar spine pathology. LOS is an important clinical variable and a major determinant of inpatient hospital costs. There is lack of studies in the literature using multivariate analysis to examine specifically the predictors of LOS after elective posterior lumbar fusion.MethodsPatients who underwent elective posterior lumbar fusion from 2005 to 2010 were identified in the American College of Surgeons National Surgical Quality Improvement Program database. Preoperative and intraoperative variables were extracted for each case and a multivariate linear regression was performed to assess the contribution of each variable to LOS.ResultsA total of 1861 patients who had undergone elective posterior lumbar fusion were identified. The average age for patients in this cohort was 60.6 ± 13.9 years (mean ± standard deviation) with a body mass index of 30.3 ± 6.2 kg/m. Of the total patients, 44.7% of patients were male. LOS was in the range from 0 days to 51 days. Multivariate linear regression identified age (P < 0.001), morbid obesity (body mass index ≥ 40 kg/m, P < 0.001), American Society of Anesthesiologists class (P = 0.001), operative time (P < 0.001), multilevel procedure (P = 0.001), and intraoperative transfusion (P < 0.001) as significant predictors of extended LOS.ConclusionThe identified preoperative and intraoperative variables associated with extended LOS after elective posterior lumbar fusion may be helpful to clinicians for patient counseling and postoperative planning.Level of evidence3.

Dataset Information

Is the ACS-NSQIP Risk Calculator Accurate in Predicting Adverse Postoperative Outcomes in the Emergency Setting? An Italian Single-center Preliminary Study.

Publications

Is the ACS-NSQIP Risk Calculator Accurate in Predicting Adverse Postoperative Outcomes in the Emergency Setting? An Italian Single-center Preliminary Study.

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