Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients

Sonal R. Pannu; Pablo Moreno Franco; Guangxi Li; Michael Malinchoc; Gregory Wilson; Ognjen Gajic

doi:10.1097/CCM.0000000000000671

Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients

Sonal R. Pannu, Pablo Moreno Franco, Guangxi Li, Michael Malinchoc, Gregory Wilson, Ognjen Gajic

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

Objectives: Patients with severe, persistent hypoxemic respiratory failure have a higher mortality. Early identification is critical for informing clinical decisions, using rescue strategies, and enrollment in clinical trials. The objective of this investigation was to develop and validate a prediction model to accurately and timely identify patients with severe hypoxemic respiratory failure at high risk of death, in whom novel rescue strategies can be efficiently evaluated.

Original language	English (US)
Pages (from-to)	308-317
Number of pages	10
Journal	Critical care medicine
Volume	43
Issue number	2
DOIs	https://doi.org/10.1097/CCM.0000000000000671
State	Published - Feb 1 2015

Keywords

Hypoxemic respiratory failure
Mortality risk prediction
Prediction model
Refractory hypoxemia
Severe hypoxemia

ASJC Scopus subject areas

Critical Care and Intensive Care Medicine

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10.1097/CCM.0000000000000671

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title = "Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients",

abstract = "Objectives: Patients with severe, persistent hypoxemic respiratory failure have a higher mortality. Early identification is critical for informing clinical decisions, using rescue strategies, and enrollment in clinical trials. The objective of this investigation was to develop and validate a prediction model to accurately and timely identify patients with severe hypoxemic respiratory failure at high risk of death, in whom novel rescue strategies can be efficiently evaluated.",

keywords = "Hypoxemic respiratory failure, Mortality risk prediction, Prediction model, Refractory hypoxemia, Severe hypoxemia",

author = "Pannu, {Sonal R.} and Franco, {Pablo Moreno} and Guangxi Li and Michael Malinchoc and Gregory Wilson and Ognjen Gajic",

note = "Publisher Copyright: {\textcopyright} 2015 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins.",

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T1 - Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients

AU - Pannu, Sonal R.

AU - Franco, Pablo Moreno

AU - Li, Guangxi

AU - Malinchoc, Michael

AU - Wilson, Gregory

AU - Gajic, Ognjen

PY - 2015/2/1

Y1 - 2015/2/1

N2 - Objectives: Patients with severe, persistent hypoxemic respiratory failure have a higher mortality. Early identification is critical for informing clinical decisions, using rescue strategies, and enrollment in clinical trials. The objective of this investigation was to develop and validate a prediction model to accurately and timely identify patients with severe hypoxemic respiratory failure at high risk of death, in whom novel rescue strategies can be efficiently evaluated.

AB - Objectives: Patients with severe, persistent hypoxemic respiratory failure have a higher mortality. Early identification is critical for informing clinical decisions, using rescue strategies, and enrollment in clinical trials. The objective of this investigation was to develop and validate a prediction model to accurately and timely identify patients with severe hypoxemic respiratory failure at high risk of death, in whom novel rescue strategies can be efficiently evaluated.

KW - Hypoxemic respiratory failure

KW - Mortality risk prediction

KW - Prediction model

KW - Refractory hypoxemia

KW - Severe hypoxemia

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ER -

Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients

Abstract

Keywords

ASJC Scopus subject areas

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