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.
Electronic medical record analysis.
Medical, surgical, and mixed ICU setting at a tertiary care institution.
Mechanically-ventilated ICU patients.
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.
A model using comorbid conditions and physiologic variables on the day of developing severe hypoxemic respiratory failure can predict hospital mortality.
Supplemental Digital Content is available in the text.
1Division of Pulmonary Care and Critical Medicine, Ohio State University, Wexner Medical Center, Columbus, OH.
2METRIC, Multidisciplinary Epidemiology and Translational Research in Intensive Care, Emergency and Perioperative Medicine, Mayo Clinic, Rochester, MN.
3Division of Pulmonary Care and Critical Medicine, Mayo Clinic, Jacksonville, FL.
4Division of Pulmonary Care and Critical Medicine, Mayo Clinic, Rochester, MN.
5Division of Pulmonary Care & Critical Medicine, Guang An Men Hospital, China Academy of Chinese Medical Science, Beijing, China.
6Department of Biostatistics, Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN.
* See also p. 481.
Dr. Pannu generated the hypothesis, collected data, designed the study, and contributed to analysis and article writing. Dr. Moreno Franco participated in data collection and article writing. Dr. Li performed data analysis and statistics. Dr. Malinchoc performed some statistics. Mr. Wilson was the main contributor to data extraction. Dr. Gajic supervised all aspects and contributed to hypothesis generation, study design, analysis, and article writing.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).
Dr. Gajic received support for article research from the National Institutes of Health. The remaining authors have disclosed that they do not have any potential conflicts of interest.
Address requests for reprints to: Sonal R. Pannu, MD, Division of Pulmonary Care and Critical Medicine, Ohio State University, Wexner Medical Center, 473 West 12th Avenue, Suite 201B DHLRI, Columbus, OH 43210. E-mail: firstname.lastname@example.org