Dataset Information

A Comparison of Common Plastic Surgery Operations Using the NSQIP and TOPS Databases.

ABSTRACT: Both the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) and the American Society of Plastic Surgeons Tracking Operations and Outcomes for Plastic Surgeons (TOPS) databases track 30-day outcomes. Methods:Using the 2008-2016 TOPS and NSQIP databases, we compared patient characteristics and postoperative outcomes for 5 common plastic surgery procedures. A weighted TOPS population was used to mirror the NSQIP population in clinical and demographic characteristics to compare postoperative outcomes. Results:We identified 154,181 cases. Compared with NSQIP patients, TOPS patients were more likely to be younger (47.9 versus 50.0 years), have American Society of Anesthesiologists class I-II (92.1% versus 74.6%), be outpatient (66.0% versus 49.3%), and be smokers (18.7% versus 11.7%). TOPS had extensive missing data: body mass index (40.6%), American Society of Anesthesiologists class (34.9%), diabetes (39.3%), and smoking status (37.2%). NSQIP was missing <1% of all shared categories except race (15.6%). The entire TOPS cohort versus only TOPS patients without missing data had higher rates of dehiscence (5.1% versus 3.5%) and infection (2.1% versus 1.7%). TOPS versus NSQIP patients had higher dehiscence rates (5.1% versus 1.0%) but lower rates of return to the operating room (3.1% versus 6.6%), infection (2.1% versus 3.0%), and medical complications (0.3% versus 2.2%). Nonweighted and weighted TOPS cohorts had similar 30-day outcomes. Conclusions:NSQIP and TOPS populations are different in characteristics and outcomes, likely due to differences in collection methodology and the types physicians using the databases. The strengths of each dataset can be used together for research and quality improvement.

SUBMITTER: Veith J

PROVIDER: S-EPMC7572021 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A Comparison of Common Plastic Surgery Operations Using the NSQIP and TOPS Databases.

Veith Jacob J Collier Willem W Simpson Andrew A Magno-Padron David D Mast Bruce B Murphy Robert X RX Agarwal Jayant J Kwok Alvin A

Plastic and reconstructive surgery. Global open 20200527 5

Both the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) and the American Society of Plastic Surgeons Tracking Operations and Outcomes for Plastic Surgeons (TOPS) databases track 30-day outcomes.<h4>Methods</h4>Using the 2008-2016 TOPS and NSQIP databases, we compared patient characteristics and postoperative outcomes for 5 common plastic surgery procedures. A weighted TOPS population was used to mirror the NSQIP population in clinical and demographic character ...[more]

PMID: 33133901

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Project description:ImportanceFrailty is a measure of decreased physiological reserve that is associated with morbidity and mortality in major elective and emergency general surgery operations, independent of chronological age. To date, the association of frailty with outcomes in ambulatory general surgery has not been established.ObjectiveTo determine the association between frailty and perioperative morbidity in patients undergoing ambulatory general surgery operations.Design, setting, and participantsA retrospective cohort study was conducted of 140?828 patients older than 40 years of age from the 2007-2010 American College of Surgeons National Surgical Quality Improvement Program Participant Use File who underwent ambulatory and 23-hour-stay hernia, breast, thyroid, or parathyroid surgery. Data analysis was performed from August 18, 2016, to June 21, 2017.Main outcomes and measuresThe association between the National Surgical Quality Improvement Program modified frailty index and perioperative morbidity was determined via multivariable logistic regression with random-effects modeling to control for clustering within Current Procedural Terminology codes.ResultsA total of 140?828 patients (80?147 women and 60?681 men; mean [SD] age, 59.3 [12.0] years) underwent ambulatory hernia (n?=?71?455), breast (n?=?51?267), thyroid, or parathyroid surgery (n?=?18?106). Of these patients, 2457 (1.7%) experienced any type of perioperative complication and 971 (0.7%) experienced serious perioperative complications. An increasing modified frailty index was associated with a stepwise increase in the incidence of complications. In multivariable analysis adjusting for age, sex, race/ethnicity, anesthesia type, tobacco use, renal failure, corticosteroid use, and clustering by Current Procedural Terminology codes, an intermediate modified frailty index score (0.18-0.35, corresponding to 2-3 frailty traits) was associated with statistically significant odds ratios of 1.70 (95% CI, 1.54-1.88; P?<?.001) for any complication and 2.00 (95% CI, 1.72-2.34; P?<?.001) for serious complications. A high modified frailty index score (?0.36, corresponding to ?4 frailty traits) was associated with statistically significant odds ratios of 3.35 (95% CI, 2.52-4.46; P?<?.001) for any complication and 3.95 (95% CI, 2.65-5.87; P?<?.001) for serious complications. Anesthesia with local and monitored anesthesia care was the only modifiable covariate associated with decreased odds of serious 30-day complications, with an adjusted odds ratio of 0.66 (95% CI, 0.53-0.81; P?<?.001).Conclusions and relevanceFrailty is associated with increased perioperative morbidity in common ambulatory general surgery operations, independent of age, type of anesthesia, and other comorbidities. Surgeons should consider frailty rather than chronological age when counseling and selecting patients for elective ambulatory surgery.

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.

Dataset Information

A Comparison of Common Plastic Surgery Operations Using the NSQIP and TOPS Databases.

Publications

A Comparison of Common Plastic Surgery Operations Using the NSQIP and TOPS Databases.

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