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. 2016 May;42(5):879-888.
doi: 10.1007/s00134-016-4285-8. Epub 2016 Mar 23.

Development and validation of the pediatric risk estimate score for children using extracorporeal respiratory support (Ped-RESCUERS)

Ryan P Barbaro  1   2 , Philip S Boonstra  3 , Matthew L Paden  4 , Lloyd A Roberts  5 , Gail M Annich  6 , Robert H Bartlett  7 , Frank W Moler  8 , Matthew M Davis  9   8   10   11
Affiliations

Development and validation of the pediatric risk estimate score for children using extracorporeal respiratory support (Ped-RESCUERS)

Ryan P Barbaro et al. Intensive Care Med. 2016 May.

Abstract

Purpose: To develop and validate the Pediatric Risk Estimation Score for Children Using Extracorporeal Respiratory Support (Ped-RESCUERS). Ped-RESCUERS is designed to estimate the in-hospital mortality risk for children prior to receiving respiratory extracorporeal membrane oxygenation (ECMO) support.

Methods: This study used data from an international registry of patients aged 29 days to less than 18 years who received ECMO support from 2009 to 2014. We divided the registry into development and validation datasets by calendar date. Candidate variables were selected for model inclusion if the variable independently changed the mortality risk by at least 2 % in a Bayesian logistic regression model with in-hospital mortality as the outcome. We characterized the model's ability to discriminate mortality with the area under curve (AUC) of the receiver operating characteristic.

Results: From 2009 to 2014, 2458 non-neonatal children received ECMO for respiratory support, with a mortality rate of 39.8 %. The development dataset contained 1611 children receiving ECMO support from 2009 to 2012. The model included the following variables: pre-ECMO pH, pre-ECMO arterial partial pressure of carbon dioxide, hours of intubation prior to ECMO support, hours of admission at ECMO center prior to ECMO support, ventilator type, mean airway pressure, pre-ECMO use of milrinone, and a diagnosis of pertussis, asthma, bronchiolitis, or malignancy. The validation dataset included 438 children receiving ECMO support from 2013 to 2014. The Ped-RESCUERS model from the development dataset had an AUC of 0.690, and the validation dataset had an AUC of 0.634.

Conclusions: Ped-RESCUERS provides a novel measure of pre-ECMO mortality risk. Future studies should seek external validation and improved discrimination of this mortality prediction tool.

Keywords: Extracorporeal membrane oxygenation; Mortality; Pediatric; Risk adjustment; Risk assessment; Severity of illness index.

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Conflict of interest statement

Conflict of Interest: Drs. Bartlett, Paden, and Annich acknowledge that they are on the Extracorporeal Life Support Organization steering committee. The other authors have no conflicts of interest relevant to this article to disclose.

Figures

Figure 1.
Figure 1.
Flow Diagram for Development and Internal Validation Datasets ECMO=Extracorporeal Membrane Oxygenation. In the developmental dataset, all children were analyzed because missing data was imputed.
Figure 2.
Figure 2.
Receiver Operating Characteristic Curves Receiver operating characteristics (ROC) curve dashed line depicts the area under the curve (AUC) for the development sample and solid line ROC curve gives the AUC for the validation data.
See this image and copyright information in PMC

References

    1. Pollack MM, Ruttimann UE, Getson PR, (1987) Accurate prediction of the outcome of pediatric intensive care. A new quantitative method. N Engl J Med 316: 134–139. - PubMed
    1. Pollack MM, Holubkov R, Funai T, Berger JT, Clark AE, Meert K, Berg RA, Carcillo J, Wessel DL, Moler F, Dalton H, Newth CJ, Shanley T, Harrison RE, Doctor A, Jenkins TL, Tamburro R, Dean JM, (2015) Simultaneous Prediction of New Morbidity, Mortality, and Survival Without New Morbidity From Pediatric Intensive Care: A New Paradigm for Outcomes Assessment. Crit Care Med 43: 1699–1709. - PMC - PubMed
    1. Fonarow GC, Pan W, Saver JL, Smith EE, Reeves MJ, Broderick JP, Kleindorfer DO, Sacco RL, Olson DM, Hernandez AF, Peterson ED, Schwamm LH, (2012) Comparison of 30-day mortality models for profiling hospital performance in acute ischemic stroke with vs without adjustment for stroke severity. JAMA 308: 257–264. - PubMed
    1. Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, Ingber MJ, Roman S, Normand SL, (2006) An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure. Circulation 113: 1693–1701. - PubMed
    1. Krumholz HM, Brindis RG, Brush JE, Cohen DJ, Epstein AJ, Furie K, Howard G, Peterson ED, Rathore SS, Smith SC Jr., Spertus JA, Wang Y, Normand SL, (2006) Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation. Circulation 113: 456–462. - PubMed

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