The Pediatric Risk of Mortality Score: Update 2015.
OBJECTIVE
Severity of illness measures have long been used in pediatric critical care. The Pediatric Risk of Mortality is a physiologically based score used to quantify physiologic status, and when combined with other independent variables, it can compute expected mortality risk and expected morbidity risk. Although the physiologic ranges for the Pediatric Risk of Mortality variables have not changed, recent Pediatric Risk of Mortality data collection improvements have been made to adapt to new practice patterns, minimize bias, and reduce potential sources of error. These include changing the outcome to hospital survival/death for the first PICU admission only, shortening the data collection period and altering the Pediatric Risk of Mortality data collection period for patients admitted for "optimizing" care before cardiac surgery or interventional catheterization. This analysis incorporates those changes, assesses the potential for Pediatric Risk of Mortality physiologic variable subcategories to improve score performance, and recalibrates the Pediatric Risk of Mortality score, placing the algorithms (Pediatric Risk of Mortality IV) in the public domain.


METHODS
Prospective cohort study from December 4, 2011, to April 7, 2013.


RESULTS
Among 10,078 admissions, the unadjusted mortality rate was 2.7% (site range, 1.3-5.0%). Data were divided into derivation (75%) and validation (25%) sets. The new Pediatric Risk of Mortality prediction algorithm (Pediatric Risk of Mortality IV) includes the same Pediatric Risk of Mortality physiologic variable ranges with the subcategories of neurologic and nonneurologic Pediatric Risk of Mortality scores, age, admission source, cardiopulmonary arrest within 24 hours before admission, cancer, and low-risk systems of primary dysfunction. The area under the receiver operating characteristic curve for the development and validation sets was 0.88 ± 0.013 and 0.90 ± 0.018, respectively. The Hosmer-Lemeshow goodness of fit statistics indicated adequate model fit for both the development (p = 0.39) and validation (p = 0.50) sets.


CONCLUSIONS
The new Pediatric Risk of Mortality data collection methods include significant improvements that minimize the potential for bias and errors, and the new Pediatric Risk of Mortality IV algorithm for survival and death has excellent prediction performance.
1Department of Pediatrics, Children's National Medical Center and the George Washington University School of Medicine and Health Sciences, Washington DC. 2Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT. 3Department of Pediatrics, Children's National Medical Center, Washington DC. 4Department of Pediatrics, Children's Hospital of Michigan, Detroit, MI. 5Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA. 6Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA. 7Department of Pediatrics, University of California at Los Angeles, Los Angeles, CA. 8Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA. 9Department of Child Health, Phoenix Children's Hospital and University of Arizona College of Medicine-Phoenix, Phoenix, AZ. 10Department of Pediatrics, University of Michigan, Ann Arbor, MI. 11Pediatric Trauma and Critical Illness Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Institutes of Health (NIH), Bethesda, MD.
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Pollack
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Holubkov
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David L
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Robert A
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Christopher J L
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Rick E
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Carcillo
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D000367:Age Factors; D000465:Algorithms; D002675:Child, Preschool; D017052:Hospital Mortality; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015278:Intensive Care Units, Pediatric / Q000706:statistics & numerical data*; D011446:Prospective Studies; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012720:Severity of Illness Index