Severe sepsis mortality prediction with logistic regression over latent factors

Sepsis is one of the main causes of death for non-coronary ICU (Intensive Care Unit) patients and has become the 10th most common cause of death in western societies. This is a transversal condition affecting immunocompromised patients, critically ill patients, post-surgery patients, patients with AIDS, and the elderly. In western countries, septic patients account for as much as 25% of ICU bed utilization and the pathology affects 1-2% of all hospitalizations. Its mortality rates range from 12.8% for sepsis to 45.7% for septic shock. The prediction of mortality caused by sepsis is, therefore, a relevant research challenge from a medical viewpoint. The clinical indicators currently in use for this type of prediction have been criticized for their poor prognostic significance. In this study, we redescribe sepsis indicators through latent model-based feature extraction, using factor analysis. These extracted indicators are then applied to the prediction of mortality caused by sepsis. The reported results show that the proposed method improves on the results obtained with the current standard mortality predictor, which is based on the APACHE II score.

[1]  Martin J Tobin,et al.  Meta-analysis under the spotlight: Focused on a meta-analysis of ventilator weaning* , 2008, Critical care medicine.

[2]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[3]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[4]  Stanley Lemeshow,et al.  How to Assess Organ Dysfunction in the Intensive Care Unit? The Logistic Organ Dysfunction (LOD) System , 1997 .

[5]  R. Byrick,et al.  Evaluation of predictive ability of APACHE II system and hospital outcome in Canadian intensive care unit patients. , 1995, Critical care medicine.

[6]  Paulo J. G. Lisboa,et al.  Data Mining in Cancer Research [Application Notes] , 2010, IEEE Computational Intelligence Magazine.

[7]  John C. Marshall,et al.  Predisposition, insult/infection, response, and organ dysfunction: A new model for staging severe sepsis , 2009, Critical care medicine.

[8]  Fred H. Hamker,et al.  Septic Shock Diagnosis by Neural Networks and Rule Based Systems , 2002 .

[9]  Jin Gao,et al.  Predicting hospital mortality using APACHE II scores in neurocritically ill patients: a prospective study , 2009, Journal of Neurology.

[10]  E. Draper,et al.  Prognosis in Acute Organ‐System Failure , 1985, Annals of surgery.

[11]  F. Gordo,et al.  Sepsis incidence and outcome: Contrasting the intensive care unit with the hospital ward* , 2007, Critical care medicine.

[12]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[13]  Munish Goyal,et al.  Validation of the Mortality in Emergency Department Sepsis (MEDS) score in patients with the systemic inflammatory response syndrome (SIRS)* , 2008, Critical care medicine.

[14]  W. Knaus,et al.  Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. 1992. , 2009, Chest.

[15]  D. Pittet,et al.  Systemic inflammatory response syndrome, sepsis, severe sepsis and septic shock: Incidence, morbidities and outcomes in surgical ICU patients , 1995, Intensive Care Medicine.

[16]  D. Mannino,et al.  The epidemiology of sepsis in the United States from 1979 through 2000. , 2003, The New England journal of medicine.

[17]  Dorothy T. Thayer,et al.  EM algorithms for ML factor analysis , 1982 .

[18]  Margaret M Parker,et al.  Surviving Sepsis Campaign guidelines for management of severe sepsis and septic shock , 2004, Critical care medicine.

[19]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[20]  Mevlut Ture,et al.  Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease , 2008, Expert Syst. Appl..

[21]  J. Vincent,et al.  The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.

[22]  Nigel H Lovell,et al.  Non-invasive classification of severe sepsis and systemic inflammatory response syndrome using a nonlinear support vector machine: a preliminary study , 2010, Physiological measurement.

[23]  Paulo J. G. Lisboa,et al.  Computational Intelligence in biomedicine: Some contributions , 2010, ESANN.

[24]  J. Vincent,et al.  Dear SIRS, I'm sorry to say that I don't like you... , 1997, Critical care medicine.

[25]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[26]  Paulo J. G. Lisboa,et al.  Application notes: data mining in cancer research , 2010 .

[27]  Wolfgang Härdle,et al.  Applied Multivariate Statistical Analysis: third edition , 2006 .

[28]  D. Pittet,et al.  The natural history of the systemic inflammatory response syndrome (SIRS). A prospective study. , 1995, JAMA.

[29]  Peter Bauer,et al.  Sepsis mortality prediction based on predisposition, infection and response , 2008, Intensive Care Medicine.

[30]  Mukta Paliwal,et al.  Neural networks and statistical techniques: A review of applications , 2009, Expert Syst. Appl..

[31]  E. Draper,et al.  APACHE II: A severity of disease classification system , 1985, Critical care medicine.

[32]  M. Levy,et al.  2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference , 2003, Intensive care medicine.

[33]  K. Mardia.  Measures of multivariate skewness and kurtosis with applications , 1970 .

[34]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[35]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[36]  W. Knaus,et al.  Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. , 1992, Chest.

[37]  C. Sprung,et al.  Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. , 1995, Critical care medicine.