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Methods Inf Med 2013; 52(06): 494-502
DOI: 10.3414/ME12-01-0093
Original Articles
Schattauer GmbH

Disease-based Modeling to Predict Fluid Response in Intensive Care Units

A. S. Fialho
1   Massachusetts Institute of Technology, Engineering Systems Division, Cambridge, MA, USA
,
L. A. Celi
2   Beth Israel Deaconess Medical Center, Division of Pulmonary, Critical Care and Sleep Medicine, Boston, MA, USA
,
F. Cismondi
1   Massachusetts Institute of Technology, Engineering Systems Division, Cambridge, MA, USA
,
S. M. Vieira
3   Technical University of Lisbon, Instituto Superior Técnico, Dept. of Mechanical Engineering,CIS/IDMEC – LAETA, Lisbon, Portugal
,
S. R. Reti
4   Beth Israel Deaconess Medical Centre, Harvard Medical School, Division of Clinical Informatics, Department of Medicine, Boston, MA, USA
,
J. M. C. Sousa
3   Technical University of Lisbon, Instituto Superior Técnico, Dept. of Mechanical Engineering,CIS/IDMEC – LAETA, Lisbon, Portugal
,
S. N. Finkelstein
1   Massachusetts Institute of Technology, Engineering Systems Division, Cambridge, MA, USA
› Author Affiliations
Further Information

Publication History

received: 05 October 2012

accepted: 30 May 2013

Publication Date:
20 January 2018 (online)

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Summary

Objective: To compare general and disease-based modeling for fluid resuscitation and vasopressor use in intensive care units.

Methods: Retrospective cohort study in -volving 2944 adult medical and surgical intensive care unit (ICU) patients receiving fluid resuscitation. Within this cohort there were two disease-based groups, 802 patients with a diagnosis of pneumonia, and 143 patients with a diagnosis of pancreatitis. Fluid resuscitation either progressing to subsequent vasopressor administration or not was used as the primary outcome variable to compare general and disease-based modeling.

Results: Patients with pancreatitis, pneumonia and the general group all shared three common predictive features as core variables, arterial base excess, lactic acid and platelets. Patients with pneumonia also had non-invasive systolic blood pressure and white blood cells added to the core model, and pancreatitis patients additionally had temperature. Disease-based models had significantly higher values of AUC (p < 0.05) than the general group (0.82 f± 0.02 for pneumonia and 0.83 ± 0.03 for pancreatitis vs. 0.79 ± 0.02 for general patients).

Conclusions: Disease-based predictive mod -eling reveals a different set of predictive variables compared to general modeling and improved performance. Our findings add support to the growing body of evidence advantaging disease specific predictive modeling.

Keywords

Disease-based modeling - fluid resuscitation - intensive care units - decision modeling
 

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