Non-Parametric Combined Reference Regions and Prediction of Clinical Risk
| Autor: | Ron Cialic, John M. Higgins, Roy Malka, Carlo Brugnara |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Accuracy and precision Multivariate statistics Clinical Biochemistry Population Clinical Chemistry Tests Interval (mathematics) 030204 cardiovascular system & hematology Statistics Nonparametric 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Reference Values Statistics medicine Humans 030212 general & internal medicine education education.field_of_study medicine.diagnostic_test business.industry Biochemistry (medical) Univariate Nonparametric statistics Complete blood count Reproducibility of Results Articles Middle Aged Reference Standards Blood Cell Count Female Reference Region business Biomarkers |
| Zdroj: | Clin Chem |
| ISSN: | 1530-8561 |
| Popis: | Background Many clinical decisions depend on estimating patient risk of clinical outcomes by interpreting test results relative to reference intervals, but standard application of reference intervals suffers from two major limitations that reduce the accuracy of clinical decisions: (1) each test result is assessed separately relative to a univariate reference interval, ignoring the rich pathophysiologic information in multivariate relationships, and (2) reference intervals are intended to reflect a population’s biological characteristics and are not calibrated for outcome prediction. Methods We developed a combined reference region (CRR), derived CRRs for some pairs of complete blood count (CBC) indices (RBC, MCH, RDW, WBC, PLT), and assessed whether the CRR could enhance the univariate reference interval’s prediction of a general clinical outcome, 5-year mortality risk (MR). Results The CRR significantly improved MR estimation for 21/21 patient subsets defined by current univariate reference intervals. The CRR identified individuals with >2-fold increase in MR in many cases and uniformly improved the accuracy for all five pairs of tests considered. Overall, the 95% CRR identified individuals with a >7× increase in 5-year MR. Conclusions The CRR enhances the accuracy of the prediction of 5-year MR relative to current univariate reference intervals. The CRR generalizes to higher numbers of tests or biomarkers, as well as to clinical outcomes more specific than MR, and may provide a general way to use existing data to enhance the accuracy and precision of clinical decisions. |
| Databáze: | OpenAIRE |
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