Predicting late anemia in critical illness

Eric B Milbrandt , Gilles Clermont , Javier Martinez , Alex Kersten , Malik T Rahim and Derek C Angus

The CRISMA Laboratory (Clinical Research, Investigation, and Systems Modeling of Acute Illness), Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Pittsburgh, PA 15261, USA

author email corresponding author email

Critical Care 2006, 10:R39doi:10.1186/cc4847


Published:	28 February 2006

Abstract

Introduction

Identifying critically ill patients most likely to benefit from pre-emptive therapies will become increasingly important if therapies are to be used safely and cost-effectively. We sought to determine whether a predictive model could be constructed that would serve as a useful decision support tool for the pre-emptive management of intensive care unit (ICU)-related anemia.

Methods

Our cohort consisted of all ICU patients (n = 5,170) admitted to a large tertiary-care academic medical center during the period from 1 July 2000 to 30 June 2001. We divided the cohort into development (n = 3,619) and validation (n = 1,551) sets. Using a set of demographic and physiologic variables available within six hours of ICU admission, we developed models to predict patients who either received late transfusion or developed late anemia. We then constructed a point system to quantify, within six hours of ICU admission, the likelihood of developing late anemia.

Results

Models showed good discrimination with receiver operating characteristic curve areas ranging from 0.72 to 0.77, although predicting late transfusion was consistently less accurate than predicting late anemia. A five-item point system predicted likelihood of late anemia as well as existing clinical trial inclusion criteria but resulted in pre-emptive intervention more than two days earlier.

Conclusion

A rule-based decision support tool using information available within six hours of ICU admission may lead to earlier and more appropriate use of blood-sparing strategies.