Dataset Information

Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP.

ABSTRACT: In 2012, Medicare began cutting reimbursement for hospitals with high readmission rates. We sought to define the incidence and risk factors associated with readmission after surgery.A total of 230,864 patients discharged after general, upper gastrointestinal (GI), small and large intestine, hepatopancreatobiliary (HPB), vascular, and thoracic surgery were identified using the 2011 American College of Surgeons National Surgical Quality Improvement Program. Readmission rates and patient characteristics were analyzed. A predictive model for readmission was developed among patients with length of stay (LOS) 10 days or fewer and then validated using separate samples.Median patient age was 56 years; 43% were male, and median American Society of Anesthesiologists (ASA) class was 2 (general surgery: 2; upper GI: 3; small and large intestine: 2; HPB: 3; vascular: 3; thoracic: 3; P < 0.001). The median LOS was 1 day (general surgery: 0; upper GI: 2; small and large intestine: 5; HPB: 6; vascular: 2; thoracic: 4; P < 0.001). Overall 30-day readmission was 7.8% (general surgery: 5.0%; upper GI: 6.9%; small and large intestine: 12.6%; HPB: 15.8%; vascular: 11.9%; thoracic: 11.1%; P < 0.001). Factors strongly associated with readmission included ASA class, albumin less than 3.5, diabetes, inpatient complications, nonelective surgery, discharge to a facility, and the LOS (all P < 0.001). On multivariate analysis, ASA class and the LOS remained most strongly associated with readmission. A simple integer-based score using ASA class and the LOS predicted risk of readmission (area under the receiver operator curve 0.702).Readmission among patients with the LOS 10 days or fewer occurs at an incidence of at least 5% to 16% across surgical subspecialties. A scoring system on the basis of ASA class and the LOS may help stratify readmission risk to target interventions.

SUBMITTER: Lucas DJ

PROVIDER: S-EPMC4623433 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP.

Lucas Donald J DJ Haider Adil A Haut Elliot E Dodson Rebecca R Wolfgang Christopher L CL Ahuja Nita N Sweeney John J Pawlik Timothy M TM

Annals of surgery 20130901 3

<h4>Objective</h4>In 2012, Medicare began cutting reimbursement for hospitals with high readmission rates. We sought to define the incidence and risk factors associated with readmission after surgery.<h4>Methods</h4>A total of 230,864 patients discharged after general, upper gastrointestinal (GI), small and large intestine, hepatopancreatobiliary (HPB), vascular, and thoracic surgery were identified using the 2011 American College of Surgeons National Surgical Quality Improvement Program. Readmi ...[more]

PMID: 24022435

Similar Datasets

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.

Project description:Study designRetrospective cohort study.ObjectiveSpine surgery has been increasingly performed in the outpatient setting, providing greater control over cost, efficiency, and resource utilization. However, research evaluating the safety of this trend is limited. The objective of this study is to compare 30-day readmission, reoperation, and morbidity for patients undergoing lumbar disc arthroplasty (LDA) in the inpatient versus outpatient settings.MethodsPatients who underwent LDA from 2005 to 2018 were identified using the ACS-NSQIP (American College of Surgeons National Surgical Quality Improvement Program) database. Regression was utilized to compare readmission, reoperation, and morbidity between surgical settings, and to evaluate for predictors thereof.ResultsWe identified 751 patients. There were no significant differences between inpatient and outpatient LDA in rates of readmission, reoperation, or morbidity on univariate or multivariate analyses. There were also no significant differences in rates of specific complications. Inpatient operative time (138 ± 75 minutes) was significantly (P < .001) longer than outpatient operative time (106 ± 43 minutes). In multivariate analysis, diabetes (P < .001, OR = 7.365), baseline dyspnea (P = .039, OR = 6.447), and increased platelet count (P = .048, OR = 1.007) predicted readmission. Diabetes (P = .016, OR = 6.533) and baseline dyspnea (P = .046, OR = 13.814) predicted reoperation. Baseline dyspnea (P = .021, OR = 8.188) and ASA (American Society of Anesthesiologists) class ≥3 (P = .014, OR = 3.515) predicted morbidity. Decreased hematocrit (P = .008) and increased operative time (P = .003) were associated with morbidity in univariate analysis, but not in multivariate analysis.ConclusionsReadmission, reoperation, and morbidity were statistically similar between surgical setting, indicating that LDA can be safely performed in the outpatient setting. Higher ASA class and specific comorbidities predicted poorer 30-day outcomes. These findings can guide choice of surgical setting given specific patient factors.

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:The Centers for Medicare and Medicaid Services have implemented penalties for hospitals with above-average readmission rates under the Hospital Readmissions Reductions Program. These changes will likely be extended to affect postoperative readmissions in the future.To identify variables that place patients at risk for readmission, develop a predictive nomogram, and validate this nomogram.Retrospective review and prospective validation of a predictive nomogram. A predictive nomogram was developed with the linear predictor method using the American College of Surgeons National Surgical Quality Improvement Program database paired with institutional billing data for patients who underwent nonemergent inpatient general surgery procedures. The nomogram was developed from August 1, 2006, through December 31, 2011, in 2799 patients and prospectively validated from November 1, 2013, through December 19, 2013, in 255 patients at a single academic institution. Area under the curve and positive and negative predictive values were calculated.The outcome of interest was readmission within 30 days of discharge following an index hospitalization for a surgical procedure.Bleeding disorder (odds ratio, 2.549; 95% CI, 1.464-4.440), long operative time (odds ratio, 1.601; 95% CI, 1.186-2.160), in-hospital complications (odds ratio, 16.273; 95% CI, 12.028-22.016), dependent functional status, and the need for a higher level of care at discharge (odds ratio, 1.937; 95% CI, 1.176-3.190) were independently associated with readmission. The nomogram accurately predicted readmission (C statistic?=?0.756) in a prospective evaluation. The negative predictive value was 97.9% in the prospective validation, while the positive predictive value was 11.1%.Development of an online calculator using this predictive model will allow us to identify patients who are at high risk for readmission at the time of discharge. Patients with increased risk may benefit from more intensive postoperative follow-up in the outpatient setting.

Dataset Information

Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP.

Publications

Assessing readmission after general, vascular, and thoracic surgery using ACS-NSQIP.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets