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Penalized count data regression with application to hospital stay after pediatric cardiac surgery

Zhu Wang1, Shuangge Ma2, Michael Zappitelli3

  • 1Department of Research, Connecticut Children's Medical Center, Hartford, CT, USA zwang@connecticutchildrens.org.

Statistical Methods in Medical Research
|April 19, 2014
PubMed

Insights

New statistical methods improve prediction of prolonged hospital stays after pediatric cardiac surgery. Urine biomarkers like NGAL, IL18, and KIM-1 were found to independently predict length of stay (LOS) in children.

Area of Science:

  • Biostatistics
  • Pediatric Cardiology
  • Nephrology

Background:

  • Pediatric cardiac surgery is associated with adverse outcomes, including acute kidney injury (AKI) and extended hospital length of stay (LOS).
  • Early identification and prediction of these outcomes are crucial for improving patient care.
  • Plasma and urine biomarkers show potential for predicting clinical outcomes in this population.

Purpose of the Study:

  • To propose and evaluate novel variable selection methods for Poisson and negative binomial (NB) regression models.
  • To apply these methods to identify predictors of hospital length of stay (LOS) in children undergoing cardiac surgery.
  • To assess the predictive value of specific urine biomarkers for LOS.

Main Methods:

  • Development of penalized regression methods, including extended elastic net (Enet), minimax concave (Mnet), and smoothly clipped absolute deviation (Snet) penalties combined with ridge penalties (EMSnet) for Poisson and NB regression.
  • A unified algorithm was created for simultaneous parameter estimation and variable selection.
  • The proposed methods were evaluated through simulation studies and applied to a multi-center dataset of 311 children undergoing cardiac surgery.

Main Results:

  • Simulation studies demonstrated the advantage of the proposed EMSnet methods, particularly with highly correlated predictors.
  • Application to the pediatric cardiac surgery data identified early postoperative urine biomarkers, including NGAL, IL18, and KIM-1, as independent predictors of LOS.
  • These biomarkers predicted LOS after adjusting for other risk and biomarker variables.

Conclusions:

  • The novel penalized regression methods (EMSnet) offer robust variable selection for count data, especially in the presence of multicollinearity.
  • Early postoperative urine biomarkers (NGAL, IL18, KIM-1) are significant independent predictors of prolonged hospital length of stay (LOS) in pediatric cardiac surgery patients.
  • These findings highlight the potential of non-invasive biomarkers for risk stratification and management in pediatric cardiac surgery.

Keywords:
EnetMnetPoisson regressionSnetnegative binomial regressionvariable selection

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