Abstract
MEASUREMENTS AND MAIN RESULTS: Mechanically ventilated ICU patients were screened for severe hypoxemic respiratory failure (Murray lung injury score of ≥ 3). Survival to hospital discharge was the dependent variable. Clinical predictors within 24 hours of onset of severe hypoxemia were considered as the independent variables. An area under the curve and a Hosmer-Lemeshow goodness-of-fit test were used to assess discrimination and calibration. A logistic regression model was developed in the derivation cohort (2005-2007). The model was validated in an independent cohort (2008-2010). Among 79,341 screened patients, 1,032 met inclusion criteria. Mortality was 41% in the derivation cohort (n = 464) and 35% in the validation cohort (n = 568). The final model included hematologic malignancy, cirrhosis, aspiration, estimated dead space, oxygenation index, pH, and vasopressor use. The area under the curve of the model was 0.85 (0.82-0.89) and 0.79 (0.75-0.82) in the derivation and validation cohorts, respectively, and showed good calibration. A modified model, including only physiologic variables, performed similarly. It had comparable performance in patients with acute respiratory distress syndrome and outperformed previous prognostic models.
CONCLUSIONS: A model using comorbid conditions and physiologic variables on the day of developing severe hypoxemic respiratory failure can predict hospital mortality.
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.
DESIGN: Electronic medical record analysis.
SETTING: Medical, surgical, and mixed ICU setting at a tertiary care institution.
PATIENTS: Mechanically-ventilated ICU patients.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 308-317 |
| Number of pages | 10 |
| Journal | Critical Care Medicine |
| Volume | 43 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2015 |
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Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients*. / Pannu, Sonal R.; Moreno Franco, Pablo; Li, Guangxi; Malinchoc, Michael; Wilson, Gregory; Gajic, Ognjen.
In: Critical Care Medicine, Vol. 43, No. 2, 01.02.2015, p. 308-317.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Development and validation of severe hypoxemia associated risk prediction model in 1,000 mechanically ventilated patients*
AU - Pannu, Sonal R.
AU - Moreno Franco, Pablo
AU - Li, Guangxi
AU - Malinchoc, Michael
AU - Wilson, Gregory
AU - Gajic, Ognjen
PY - 2015/2/1
Y1 - 2015/2/1
N2 - MEASUREMENTS AND MAIN RESULTS: Mechanically ventilated ICU patients were screened for severe hypoxemic respiratory failure (Murray lung injury score of ≥ 3). Survival to hospital discharge was the dependent variable. Clinical predictors within 24 hours of onset of severe hypoxemia were considered as the independent variables. An area under the curve and a Hosmer-Lemeshow goodness-of-fit test were used to assess discrimination and calibration. A logistic regression model was developed in the derivation cohort (2005-2007). The model was validated in an independent cohort (2008-2010). Among 79,341 screened patients, 1,032 met inclusion criteria. Mortality was 41% in the derivation cohort (n = 464) and 35% in the validation cohort (n = 568). The final model included hematologic malignancy, cirrhosis, aspiration, estimated dead space, oxygenation index, pH, and vasopressor use. The area under the curve of the model was 0.85 (0.82-0.89) and 0.79 (0.75-0.82) in the derivation and validation cohorts, respectively, and showed good calibration. A modified model, including only physiologic variables, performed similarly. It had comparable performance in patients with acute respiratory distress syndrome and outperformed previous prognostic models.CONCLUSIONS: A model using comorbid conditions and physiologic variables on the day of developing severe hypoxemic respiratory failure can predict hospital mortality.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.DESIGN: Electronic medical record analysis.SETTING: Medical, surgical, and mixed ICU setting at a tertiary care institution.PATIENTS: Mechanically-ventilated ICU patients.
AB - MEASUREMENTS AND MAIN RESULTS: Mechanically ventilated ICU patients were screened for severe hypoxemic respiratory failure (Murray lung injury score of ≥ 3). Survival to hospital discharge was the dependent variable. Clinical predictors within 24 hours of onset of severe hypoxemia were considered as the independent variables. An area under the curve and a Hosmer-Lemeshow goodness-of-fit test were used to assess discrimination and calibration. A logistic regression model was developed in the derivation cohort (2005-2007). The model was validated in an independent cohort (2008-2010). Among 79,341 screened patients, 1,032 met inclusion criteria. Mortality was 41% in the derivation cohort (n = 464) and 35% in the validation cohort (n = 568). The final model included hematologic malignancy, cirrhosis, aspiration, estimated dead space, oxygenation index, pH, and vasopressor use. The area under the curve of the model was 0.85 (0.82-0.89) and 0.79 (0.75-0.82) in the derivation and validation cohorts, respectively, and showed good calibration. A modified model, including only physiologic variables, performed similarly. It had comparable performance in patients with acute respiratory distress syndrome and outperformed previous prognostic models.CONCLUSIONS: A model using comorbid conditions and physiologic variables on the day of developing severe hypoxemic respiratory failure can predict hospital mortality.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.DESIGN: Electronic medical record analysis.SETTING: Medical, surgical, and mixed ICU setting at a tertiary care institution.PATIENTS: Mechanically-ventilated ICU patients.
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U2 - 10.1097/CCM.0000000000000671
DO - 10.1097/CCM.0000000000000671
M3 - Article
C2 - 25318386
AN - SCOPUS:84925354079
VL - 43
SP - 308
EP - 317
JO - Critical Care Medicine
JF - Critical Care Medicine
SN - 0090-3493
IS - 2
ER -