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A minimal net reclassification improvement to assess predictions of intensive care mortality
Yacine Tandjaoui Lambiotte Redondo1, Jérôme Lambert1, Sylvie Chevret2
1INSERM, U717, Paris, F-75010, France Université Paris Diderot, PRES Sorbonne Paris Cité, UMR-S717, Paris, F-75010, France AP-HP Paris, F-75010, France.
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.
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