Evaluating mathematical complexity and incremental clinical value in complete blood count-derived indices.

Authors

Hong-Yin Zhou, Tian-Ji Li, Jia-Min Zou, Yong-Gang Hu, Li Xiao

Published in

Clinical chemistry and laboratory medicine. Nov 16, 2026. Epub Nov 16, 2026.

Abstract

Complete blood count-derived indices have proliferated from simple two-cell ratios to higher-order products that combine neutrophils, lymphocytes, monocytes, platelets and, in some formulas, erythroid variables. Their accessibility is attractive, but greater mathematical complexity does not necessarily provide additional clinical information. This critical narrative review examines whether higher-order indices deliver incremental predictive or decision value beyond their source measurements, simpler ratios and established clinical models. We apply a formula-first framework to distinguish reported labels from executable equations and assess algebraic nesting, denominator dependence, scaling, analytical uncertainty and biological variation. A targeted evidence map evaluates direct comparisons and studies adding an index to a prespecified model, and a PubMed publication snapshot quantifies the scale and nomenclature inflation of this literature. Several named indices reduce to the same mathematical construct when component definitions and units are aligned, whereas similar abbreviations may conceal non-equivalent formulas. Higher-order indices inherit error pathways from every component and may amplify instability through low denominators, unit choices and fixed implicit weighting. Comparative studies show context-dependent rankings rather than a consistent advantage with increasing component count. Evidence for improvement beyond raw components, simpler indices or established models remains limited, and external calibration and prospective impact evaluation are uncommon. Higher-order indices should therefore be treated as candidate mathematical representations, not automatically as new measurements. Routine reporting requires a locked formula, verified computation, transportable decision limits and evidence that the result improves a defined clinical decision.

PMID:
43031444
Bibliographic data and abstract were imported from PubMed on 14 Nov 2026.

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