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J Crit Care. 2015 Dec;30(6):1287-94. doi: 10.1016/j.jcrc.2015.09.002. Epub 2015 Sep 4.

Modeling for critically ill patients: An introduction for beginners.

Author information

1
Medical Intensive Care Unit, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris Descartes Sorbonne Paris Cité, Paris, France.
2
Centre d'Investigation Clinique-0991 INSERM, Université Paris Descartes Sorbonne Paris Cité, Paris, France.
3
Centre d'Investigation Clinique-1166 INSERM, Hôpital La Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Université Pierre et Marie Curie, Paris, France.
4
Medical Intensive Care Unit, Hôpital Raymond Poincarré, Assistance Publique-Hôpitaux de Paris, Université Versailles-Saint Quentin, Garches, France.
5
Medical Intensive Care Unit, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Université Paris Descartes Sorbonne Paris Cité, Paris, France. Electronic address: christophe.faisy@egp.aphp.fr.

Abstract

Models are mathematical tools used to describe real-world features. Therapeutic interventions in the field of critical care medicine may easily be translated into such models. Indeed, numerous variables influencing drug pharmacokinetics and pharmacodynamics are systematically documented in the intensive care unit over time. Organ failure, fluid shifts, other drug administration, and renal replacement therapy may cause changes in physiological values, such as body weight and composition, temperature, serum protein levels, arterial pH, and renal or hepatic function. Trials assessing the efficacy and safety of novel drugs usually exclude critically ill patients, and guidelines regarding drug dosage rarely apply to such patients. Modeling in the critically ill may allow physicians to inform decisions related to therapeutic interventions, particularly relating to infectious diseases. However, few clinicians are familiar with these methods. Here, we present a current overview of population pharmacokinetic and pharmacodynamic models applicable in critically ill patients aimed at nonspecialists and then emphazize recent potential of modeling for optimizing treatments and care in the intensive care unit.

KEYWORDS:

Bayesian network; Critically ill patients; Multicompartmental model; Pharmacodynamics; Pharmacokinetics; Predictive model

PMID:
26719063
DOI:
10.1016/j.jcrc.2015.09.002
[Indexed for MEDLINE]

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