Biomaterial Database

Objective assessment of changing mortality risks in pediatric intensive care unit patients. 1991

U E Ruttimann, and M M Pollack

Diagnostic Systems Branch, National Institute of Dental Research, National Institutes of Health, Bethesda, MD.

OBJECTIVE To develop and validate a mortality risk predictor based on physiologic data that estimates daily the probability of a patient dying within the next 24 hrs as that probability changes with disease and recovery. METHODS Nine pediatric ICUs in tertiary care centers. METHODS Data from 1,401 patients (116 deaths, 5,521 days of care) were used for predictor development, and 1,227 patients (105 deaths, 4,597 days of care) provided data for predictor validation. METHODS The predictor was developed by logistic regression analysis using the Pediatric Risk of Mortality scores of all previous days as potential predictor variables. Performance was measured by the area under the receiver operating characteristic curve (Az), and by the comparison of the daily predicted vs. observed patient status in five mortality risk groups (less than 0.01, 0.01 to 0.05, 0.05 to 0.15, 0.15 to 0.3, greater than 0.3) using chi-square goodness-of-fit tests. RESULTS Only the most recent and the admission day Pediatric Risk of Mortality scores (with a weighting ratio of 3:1) contributed significantly (p less than .05) to the prediction. The overall prediction attained an accuracy of Az = 0.904. The daily number and distribution of survivors and nonsurvivors in the five mortality risk groups were well predicted in the total sample (chi 2 [5 degrees of freedom] = 2.51; p greater than .75), and each ICU separately (chi 2 [5 degrees of freedom] range 2.41 to 7.96; all p greater than .15). This dynamic predictor improved (p less than .01) ICU outcome prediction over an admission-day predictor and, in the opinion of the authors, is essential for pediatric ICU efficiency analysis. CONCLUSIONS The predictor is valid for assessing the 24-hr mortality risk in pediatric ICU patients hospitalized in other tertiary care institutions, different from those used for predictor development. The predicted mortality risks allow prospective patient stratification into risk groups. The ability of this predictor to follow risk changes over time expands its applicability over static predictors by enabling the charting of patient courses, and permitting ICU efficiency analysis.

UI	MeSH Term	Description	Entries
D009026	Mortality	All deaths reported in a given population.	CFR Case Fatality Rate,Crude Death Rate,Crude Mortality Rate,Death Rate,Age Specific Death Rate,Age-Specific Death Rate,Case Fatality Rate,Decline, Mortality,Determinants, Mortality,Differential Mortality,Excess Mortality,Mortality Decline,Mortality Determinants,Mortality Rate,Mortality, Differential,Mortality, Excess,Age-Specific Death Rates,Case Fatality Rates,Crude Death Rates,Crude Mortality Rates,Death Rate, Age-Specific,Death Rate, Crude,Death Rates,Determinant, Mortality,Differential Mortalities,Excess Mortalities,Mortalities,Mortality Declines,Mortality Determinant,Mortality Rate, Crude,Mortality Rates,Rate, Age-Specific Death,Rate, Case Fatality,Rate, Crude Death,Rate, Crude Mortality,Rate, Death,Rate, Mortality,Rates, Case Fatality
D010043	Outcome and Process Assessment, Health Care	Evaluation procedures that focus on both the outcome or status (OUTCOMES ASSESSMENT) of the patient at the end of an episode of care - presence of symptoms, level of activity, and mortality; and the process (ASSESSMENT, PROCESS) - what is done for the patient diagnostically and therapeutically.	Outcome and Process Assessment (Health Care),Donabedian Model,Donabedian Triad,Outcome and Process Assessment,Structure Process Outcome Triad,Model, Donabedian,Triad, Donabedian
D011336	Probability	The study of chance processes or the relative frequency characterizing a chance process.	Probabilities
D011379	Prognosis	A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.	Prognostic Factor,Prognostic Factors,Factor, Prognostic,Factors, Prognostic,Prognoses
D002648	Child	A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL.	Children
D005544	Forecasting	The prediction or projection of the nature of future problems or existing conditions based upon the extrapolation or interpretation of existing scientific data or by the application of scientific methodology.	Futurology,Projections and Predictions,Future,Predictions and Projections
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012306	Risk	The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.	Relative Risk,Relative Risks,Risk, Relative,Risks,Risks, Relative
D012720	Severity of Illness Index	Levels within a diagnostic group which are established by various measurement criteria applied to the seriousness of a patient's disorder.	Illness Index Severities,Illness Index Severity
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model