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Prediction Model for Critically Ill Patients with Acute Respiratory Distress Syndrome

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  • Zhongheng Zhang
  • Hongying Ni

Abstract

Background and objectives: Acute respiratory distress syndrome (ARDS) is a major cause respiratory failure in intensive care unit (ICU). Early recognition of patients at high risk of death is of vital importance in managing them. The aim of the study was to establish a prediction model by using variables that were readily available in routine clinical practice. Methods: The study was a secondary analysis of data obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. Patients were enrolled between August 2007 and July 2008 from 33 hospitals. Demographics and laboratory findings were extracted from dataset. Univariate analyses were performed to screen variables with p

Suggested Citation

  • Zhongheng Zhang & Hongying Ni, 2015. "Prediction Model for Critically Ill Patients with Acute Respiratory Distress Syndrome," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    • Handle: RePEc:plo:pone00:0120641
    DOI: 10.1371/journal.pone.0120641
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    References listed on IDEAS

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    1. Hosmer, D.W. & Taber, S. & Lemeshow, S., 1991. "The importance of assessing the fit of logistic regression models: A case study," American Journal of Public Health, American Public Health Association, vol. 81(12), pages 1630-1635.
    2. Patrick Royston, 2005. "Multivariable regression models with continuous covariates, with a practical emphasis on fractional polynomials and applications in clinical epidemiology," German Stata Users' Group Meetings 2005 01, Stata Users Group.
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