A minimal net reclassification improvement to assess predictions of intensive care mortality. (February 2016)

• Record Type: Journal Article
• Authors: Redondo, Yacine Tandjaoui Lambiotte; Lambert, Jérôme; Chevret, Sylvie
• Is Part Of: Statistical methods in medical research. Volume 25:Number 1(2016:Feb.)
• Page Start: 413
• Page End: 429
• Publication Date: 2016-02
• Subjects: Reclassification methods -- mortality prediction -- severity scores; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280212459389
• Languages: English
• ISSNs: 0962-2802

Abstract

Objective: In assessing the improved discrimination of a new prognostic score, the "Net Reclassification Improvement" from reclassification methods appears of interest. We propose a measure that takes into account improvements in predicted probabilities to assess and allows testing the additional predictive ability of a new scoring system Y compared to a reference score X. Study design and settings: To assess and test the improvement in mortality prediction of (X + Y) compared to X, we defined a minimal net reclassification improvement that restricted improvements in predicted probabilities according to some positive threshold δ. Both absolute and relative improvements were considered. A simulation study was performed to assess its performances in a range of practical situations. We then applied our measures to real intensive care unit data. Results: Expectedly, minimal net reclassification improvement increased with the effect size of Y and decreased with the value of δ. Using relative improvements allowed erasing the influence of the population mortality. For given effect sizes of X and Y, the difference in all measures of reclassification decreased when a correlation between X and Y was introduced. Conclusion: Reclassification methods, particularly the minimal net reclassification improvement, seem to be clinically relevant when used with continuous clinical data with no known threshold.