Comparing National Inpatient Sample and National Surgical Quality Improvement Program

Somani, Sulaiman; Capua, John Di; Kim, Jun; Kothari, Parth; Lee, Nathan J.; Leven, Dante M.; Cho, Samuel K.

doi:10.1097/brs.0000000000001850

Cited by 19 publications

(2 citation statements)

References 54 publications

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“…Cardiac complications were predicted by age, bleeding disorder, sex, smoking history, cardiac comorbidity, and high ASA physical status classification. These findings are echoed heavily by domain knowledge in prior spine literature [20,21]. While ASA physical status classification was included as a feature of prediction of cardiac complication, it was not included as a feature used for prediction of VTE, wound complication, and mortality by our models.…”

Section: Discussionmentioning

confidence: 92%

Predicting Surgical Complications in Adult Patients Undergoing Anterior Cervical Discectomy and Fusion Using Machine Learning

et al. 2018

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ObjectiveMachine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF).MethodsArtificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. ResultsA total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). ConclusionANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

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Section: Discussionmentioning

confidence: 92%

Predicting Surgical Complications in Adult Patients Undergoing Anterior Cervical Discectomy and Fusion Using Machine Learning

et al. 2018

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“…These studies reported successful outcomes but are limited by comparatively small samples sizes, short-term follow-up and outcome measures, and significant practitioner and patient selection bias. More recently, data derived from multi-institutional database sources have emerged, including analysis of the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) database [6,8,14] and other regional surgical registry databases [15]. These data consistently suggest that outpatient ACDF is associated with equivalent or favorable short-term complication and hospital readmission rates, with significantly lower payor costs when compared with traditional inpatient ACDF [16].…”

Section: Introductionmentioning

confidence: 99%