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Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

Background: The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been de...

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Published in:Computers in Biology & Medicine Vol. 57; pp. 54 - 66
Main Authors: Cooper, Jennifer N, Wei, Lai, Fernandez, Soledad A, Minneci, Peter C, Deans, Katherine J
Format: research Journal Article
Published: Elsevier B.V. 2015
Online Access:View this record in EBSCOhost
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      dt: 2015
      vid: 57
      pid: 82545
      pub: Elsevier B.V.
      place: Philadelphia, Pennsylvania
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        atl: Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.
      aug:
        au:
          Cooper, Jennifer N
          Wei, Lai
          Fernandez, Soledad A
          Minneci, Peter C
          Deans, Katherine J
      sug:
      ab: Background: The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children.Methods: We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity.Results: The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination.Conclusion: Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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