Practical Guide to Surgical Data Sets: National Surgical Quality Improvement Program (NSQIP) and Pediatric NSQIP

Raval, Mehul V.; Pawlik, Timothy M.

doi:10.1001/jamasurg.2018.0486

Cited by 133 publications

(72 citation statements)

References 7 publications

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“…NSQIP is a national surgical outcomes data set administered by the American College of Surgeons (ACS) . NSQIP collects data from over 700 centers across the United States and Canada using trained clinical reviewers at each participating site.…”

Section: Methodsmentioning

confidence: 99%

“…Variables specific to the patient's perioperative presentation were included, such as preoperative WBC count level, presence of preoperative sepsis or shock, preoperative imaging of either imaging confirmed or imaging indeterminate appendicitis, and specimen pathology results. WBC count was evaluated within clinically important parameters as a categorical variable using traditional cut-offs of below normal (<4.5), normal (4.5-11), elevated (11)(12)(13)(14)(15)(16)(17)(18)(19)(20), and significantly elevated (>20). The primary goal of this study was to determine independent risk factors associated with the pathologic diagnosis of cancer after appendectomy in patients with imaging-confirmed or indeterminate appendicitis with additional focus on the contribution of age to the final model.…”

Section: Variablesmentioning

confidence: 99%

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Risk of appendiceal cancer in patients undergoing appendectomy for appendicitis in the era of increasing nonoperative management

McCarty

Fields

et al. 2019

Journal of Surgical Oncology

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Background and Objectives Management practices for acute appendicitis are changing. In cases of nonoperative treatment, the risk of missed or delayed diagnosis of malignancy should be considered. We aimed to identify predictors associated with appendiceal cancer diagnosis after appendectomy for acute appendicitis. Materials and Methods This retrospective cohort study was performed using the National Surgical Quality Improvement Program (NSQIP) appendectomy‐targeted data set from 2016 to 2017. A total of 21 069 patients with imaging‐confirmed or imaging indeterminate appendicitis who underwent appendectomy were included. Logistic regression was used to identify predictors of cancer diagnosis. Results Increasing age had an increasing monotonic relationship with the odds of pathologic cancer diagnosis after appendectomy (age 50‐59 odds ratio [OR], 2.08, 95% confidence interval [CI], 1.28‐3.39, P = .003; age 60‐69 OR, 2.89, 95% CI, 1.72‐4.83, P < .001; age 70‐79 OR, 3.85, 95% CI, 2.08‐7.12, P < .001; age >80 OR, 5.32, 95% CI, 2.38‐11.9, P < .001). Other significant predictors included obesity, morbid obesity, normal preoperative white blood cell count, and imaging indeterminate for appendicitis. Conclusions When counseling patients regarding operative vs nonoperative treatment options for management of acute appendicitis, the rising risk of a delayed or missed cancer diagnosis with increasing age must be discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Variablesmentioning

confidence: 99%

Risk of appendiceal cancer in patients undergoing appendectomy for appendicitis in the era of increasing nonoperative management

McCarty

Fields

et al. 2019

Journal of Surgical Oncology

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“…10 Although it is not designed to be broadly representative of surgical trends in the United States, the NSQIP represents currently more than 1 million annual cases performed at almost 700 hospitals. 11 As a result, the database has been used to study nationwide trends in operative therapy, ranging from axillary lymph node dissection rates among breast cancer patients to types of reconstruction after cystectomy for bladder cancer. 12e14 Thus, querying the NSQIP database presents a unique opportunity to examine broad patterns in thyroid operations before and after the release of the 2015 ATA guidelines.…”

Section: Introductionmentioning

confidence: 99%

The 2015 American Thyroid Association guidelines are associated with an increasing rate of hemithyroidectomy for thyroid cancer

Ullmann

Gray

Stefanova

et al. 2019

Surgery

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Background: In an attempt to prevent overly aggressive treatment of low-risk thyroid cancers, the American Thyroid Association changed guideline recommendations in late 2015 to state that either hemithyroidectomy or total thyroidectomy are acceptable operations for these patients. We hypothesized that surgeons would increasingly perform hemithyroidectomy after the release of these guidelines. Methods: The database of the National Surgery Quality Improvement Program was queried to identify all patients with thyroid cancer who were undergoing thyroidectomy between 2009 and 2017. Patients treated before the release of the 2015 American Thyroid Association guidelines were compared with those treated afterward. Temporal trends in operative rates were assessed quarterly using interrupted time series analyses. Results: A total of 35,291 patients were included in the study. Of those, 26,882 (76.2%) were female and 25,193 (71.3%) were white. After the release of the American Thyroid Association guidelines, there was an increase in hemithyroidectomy rate for patients with cancer from 17.3% to 22.0% (P < .001). Interrupted time series analysis controlling for patient factors demonstrated that quarterly growth in the hemithyroidectomy rate accelerated almost 10-fold (P < .001) after publication of the revised guidelines. Of note, there was no corresponding increase in the completion thyroidectomy rate (8.3% versus 7.9%, respectively, P ¼ .213). Patients treated with hemithyroidectomy were more likely to be managed as outpatients (70.8% versus 57.1%, P < .001), had fewer surgical site infections (0.3% versus 0.5%, P ¼ .050), and had fewer unplanned reintubations (0.2% versus 0.4%, P ¼ .005). Conclusion: In hospitals participating in the National Surgery Quality Improvement Program, the hemithyroidectomy rate increased significantly after the release of the 2015 American Thyroid Association guidelines. Surgeons at hospitals that participate in the National Surgery Quality Improvement Program may be changing practice patterns in response to these guidelines.

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“…The National Surgical Quality Improvement Program (NSQIP) is a clinical registry of 30-day outcomes extensively studied in spine surgery. 11,25 Institutional review board approval specific to this study was not sought as the de-identified NSQIP data have been previously exempted for individual review by our institutional review board.…”

Section: Data Sourcementioning

confidence: 99%

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

Karhade

Ogink

Thio

et al. 2018

Neurosurgical Focus

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OBJECTIVEIf not anticipated and prearranged, hospital stay can be prolonged while the patient awaits placement in a rehabilitation unit or skilled nursing facility following elective spine surgery. Preoperative prediction of the likelihood of postoperative discharge to any setting other than home (i.e., nonroutine discharge) after elective inpatient spine surgery would be helpful in terms of decreasing hospital length of stay. The purpose of this study was to use machine learning algorithms to develop an open-access web application for preoperative prediction of nonroutine discharges in surgery for elective inpatient lumbar degenerative disc disorders.METHODSThe American College of Surgeons National Surgical Quality Improvement Program was queried to identify patients who underwent elective inpatient spine surgery for lumbar disc herniation or lumbar disc degeneration between 2011 and 2016. Four machine learning algorithms were developed to predict nonroutine discharge and the best algorithm was incorporated into an open-access web application.RESULTSThe rate of nonroutine discharge for 26,364 patients who underwent elective inpatient surgery for lumbar degenerative disc disorders was 9.28%. Predictive factors selected by random forest algorithms were age, sex, body mass index, fusion, level, functional status, extent and severity of comorbid disease (American Society of Anesthesiologists classification), diabetes, and preoperative hematocrit level. On evaluation in the testing set (n = 5273), the neural network had a c-statistic of 0.823, calibration slope of 0.935, calibration intercept of 0.026, and Brier score of 0.0713. On decision curve analysis, the algorithm showed greater net benefit for changing management over all threshold probabilities than changing management on the basis of the American Society of Anesthesiologists classification alone or for all patients or for no patients. The model can be found here: https://sorg-apps.shinyapps.io/discdisposition/.CONCLUSIONSMachine learning algorithms show promising results on internal validation for preoperative prediction of nonroutine discharges. If found to be externally valid, widespread use of these algorithms via the open-access web application by healthcare professionals may help preoperative risk stratification of patients undergoing elective surgery for lumbar degenerative disc disorders.

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Practical Guide to Surgical Data Sets: National Surgical Quality Improvement Program (NSQIP) and Pediatric NSQIP

Cited by 133 publications

References 7 publications

Risk of appendiceal cancer in patients undergoing appendectomy for appendicitis in the era of increasing nonoperative management

Risk of appendiceal cancer in patients undergoing appendectomy for appendicitis in the era of increasing nonoperative management

The 2015 American Thyroid Association guidelines are associated with an increasing rate of hemithyroidectomy for thyroid cancer

Development of machine learning algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders

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