title: Simultaneous Prediction of New Morbidity, Mortality, and Survival Without New Morbidity From Pediatric Intensive Care: A New Paradigm for Outcomes Assessment.
abstract: OBJECTIVE
  Assessments of care including quality assessments adjusted for physiological status should include the development of new morbidities as well as mortalities. We hypothesized that morbidity, like mortality, is associated with physiological dysfunction and could be predicted simultaneously with mortality.
  
  
  METHODS
  Prospective cohort study from December 4, 2011, to April 7, 2013.
  
  
  METHODS
  General and cardiac/cardiovascular PICUs at seven sites.
  
  
  METHODS
  Randomly selected PICU patients from their first PICU admission.
  
  
  METHODS
  None.
  
  
  RESULTS
  Among 10,078 admissions, the unadjusted morbidity rates (measured with the Functional Status Scale and defined as an increase of ≥ 3 from preillness to hospital discharge) were 4.6% (site range, 2.6-7.7%) and unadjusted mortality rates were 2.7% (site range, 1.3-5.0%). Morbidity and mortality were significantly (p < 0.001) associated with physiological instability (measured with the Pediatric Risk of Mortality III score) in dichotomous (survival and death) and trichotomous (survival without new morbidity, survival with new morbidity, and death) models without covariate adjustments. Morbidity risk increased with increasing Pediatric Risk of Mortality III scores and then decreased at the highest Pediatric Risk of Mortality III values as potential morbidities became mortalities. The trichotomous model with covariate adjustments included age, admission source, diagnostic factors, baseline Functional Status Scale, and the Pediatric Risk of Mortality III score. The three-level goodness-of-fit test indicated satisfactory performance for the derivation and validation sets (p > 0.20). Predictive ability assessed with the volume under the surface was 0.50 ± 0.019 (derivation) and 0.50 ± 0.034 (validation) (vs chance performance = 0.17). Site-level standardized morbidity ratios were more variable than standardized mortality ratios.
  
  
  CONCLUSIONS
  New morbidities were associated with physiological status and can be modeled simultaneously with mortality. Trichotomous outcome models including both morbidity and mortality based on physiological status are suitable for research studies and quality and other outcome assessments. This approach may be applicable to other assessments presently based only on mortality.
authors.affiliation: 1Department of Pediatrics, Children's National Medical Center and the George Washington University School of Medicine and Health Sciences, Washington, DC. 2Department of Child Health, Phoenix Children's Hospital and the University of Arizona School of Medicine, Phoenix, AZ. 3Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT. 4Department of Pediatrics, Children's National Medical Center, Washington, DC. 5Department of Pediatrics, Children's Hospital of Michigan, Detroit, MI. 6Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA. 7Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA. 8Department of Pediatrics, Children's National Medical Center, Washington, DC. 9Department of Pediatrics, University of Michigan, Ann Arbor, MI. 10Department of Child Health, Phoenix Children's Hospital and University of Arizona College of Medicine-Phoenix, Phoenix, AZ. 11Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA. 12Department of Pediatrics, University of California at Los Angeles, Los Angeles, CA. 13Department of Pediatrics, Washington University School of Medicine, St. Louis, MO. 14Department of Biochemistry, Washington University School of Medicine, St. Louis, MO. 15Pediatric Trauma and Critical Illness Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institutes of Health, Bethesda, MD.
authors.forename: Murray M
authors.initials: MM
authors.lastname: Pollack
authors.forename: Richard
authors.initials: R
authors.lastname: Holubkov
authors.forename: Tomohiko
authors.initials: T
authors.lastname: Funai
authors.forename: John T
authors.initials: JT
authors.lastname: Berger
authors.forename: Amy E
authors.initials: AE
authors.lastname: Clark
authors.forename: Kathleen
authors.initials: K
authors.lastname: Meert
authors.forename: Robert A
authors.initials: RA
authors.lastname: Berg
authors.forename: Joseph
authors.initials: J
authors.lastname: Carcillo
authors.forename: David L
authors.initials: DL
authors.lastname: Wessel
authors.forename: Frank
authors.initials: F
authors.lastname: Moler
authors.forename: Heidi
authors.initials: H
authors.lastname: Dalton
authors.forename: Christopher J L
authors.initials: CJ
authors.lastname: Newth
authors.forename: Thomas
authors.initials: T
authors.lastname: Shanley
authors.forename: Rick E
authors.initials: RE
authors.lastname: Harrison
authors.forename: Allan
authors.initials: A
authors.lastname: Doctor
authors.forename: Tammara L
authors.initials: TL
authors.lastname: Jenkins
authors.forename: Robert
authors.initials: R
authors.lastname: Tamburro
authors.forename: J Michael
authors.initials: JM
authors.lastname: Dean
mesh_terms: D002648:Child; D002675:Child, Preschool; D016638:Critical Illness / Q000401:mortality*; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015278:Intensive Care Units, Pediatric / Q000706:statistics & numerical data*; D007902:Length of Stay; D008297:Male; D015233:Models, Statistical; D009017:Morbidity; D017063:Outcome Assessment, Health Care / Q000379:methods*; D011446:Prospective Studies; D012372:ROC Curve; D012307:Risk Factors; D016019:Survival Analysis