A multicenter analysis revisiting the PLASMIC score: performance limitations and the impact of age, MCV, and LDH

Department

Internal Medicine

Document Type

Article

Publication Title

Blood Vessels, Thrombosis & Hemostasis

Abstract

The PLASMIC score is widely used to guide early identification of immune thrombotic thrombocytopenic purpura (iTTP), but some studies suggest decreased performance in different populations and low predictive yield of specific components. Refining rapid diagnosis of iTTP may improve outcomes. We conducted a multicenter retrospective analysis of patients who underwent ADAMTS13 testing. We validated the PLASMIC score, evaluated the predictive performance of its individual components using multivariable logistic regression, and compared performance between 2 age groups (< 60 and ≥60 years). We generated modified score variants by excluding low-yield components and/or incorporating lactate dehydrogenase (LDH). We included 482 patients (iTTP, n = 80; controls, n = 402). PLASMIC retained good performance (area under the curve [AUC], 0.854), but sensitivity declined with older age (83% vs 62%). Mean corpuscular volume (MCV) was the only PLASMIC component that lacked independent predictive value (odds ratio [OR], 1.1; P = .68), whereas LDH was a strong independent predictor of iTTP diagnosis (OR, 3.2; P < .001). Excluding MCV ("PLASIC" variant) improved AUC (0.881) and sensitivity (91%), and adding LDH ("PLASIC+L") yielded highest AUC (0.883). All score variants showed reduced performance in older adults. In this largest multicenter validation to date, the PLASMIC score remained a reliable tool, but demonstrated structural weaknesses: diminished performance in older adults, inclusion of a low-yield variable (MCV), and exclusion of a high-performing variable (LDH). The modest gains seen with the PLASIC+L variant underscore the limitations of traditional scoring systems that assign equal weight to unequally informative components. Future iTTP models should prioritize high-yield variables and optimize weighting based on predictive strength to enhance diagnostic precision.

First Page

100163

DOI

10.1016/j.bvth.2026.100163

Volume

3

Issue

2

Publication Date

5-1-2026

PubMed ID

42164818

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