Laboratory medicine · 2026
Construction of a new reflex fluorescence platelet count confirmation model based on red blood cell and platelet parameters.
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Zhu Wenjun, Zhang Zijian, Jiang Jinan, Chong Huimin, Deng Shaoli, Li Jinmi
Аннотация
INTRODUCTION: We sought to clarify the interference of red blood cell (RBC) and platelet (PLT) parameters on PLT count by impedance method results and establish an intelligent reflex fluorescence PLT count (PLT-F) confirmation model using PLT-F as the reference to quantify the model's clinical efficacy.
METHODS: A retrospective analysis was performed on routine blood data from 11 283 patients (June 2023 to February 2024) at our hospital. Patients were stratified into a training set (n = 7898) and verification set (n = 3385) using Laboman AI, version 1.0-312, software (Shanghai Hyson Meikang Medical Electronics Co Ltd). A decision tree algorithm was employed to construct a new model that integrates RBC and PLT parameters for reflex PLT-F confirmation, and the model's effectiveness was evaluated.
RESULTS: The combined 3-factor model achieved optimal performance for PLT-F reflex confirmation. Its overall false-negative proportion, release rate, total effective rate, and negative predictive value reached 2.9%, 55.2%, 71.1%, and 94.7%, respectively, in the training set, with matching values of 4.2%, 52.4%, 66.1%, and 91.9%, respectively, in the validation set.
DISCUSSION: This study established a novel reflex PLT-F confirmation model based on RBC and PLT parameters. The model achieved a 52.4% sample release rate, with a corresponding overall false-negative proportion of 4.2% in the validation set, presenting a clear trade-off between laboratory operational efficiency and diagnostic safety.
METHODS: A retrospective analysis was performed on routine blood data from 11 283 patients (June 2023 to February 2024) at our hospital. Patients were stratified into a training set (n = 7898) and verification set (n = 3385) using Laboman AI, version 1.0-312, software (Shanghai Hyson Meikang Medical Electronics Co Ltd). A decision tree algorithm was employed to construct a new model that integrates RBC and PLT parameters for reflex PLT-F confirmation, and the model's effectiveness was evaluated.
RESULTS: The combined 3-factor model achieved optimal performance for PLT-F reflex confirmation. Its overall false-negative proportion, release rate, total effective rate, and negative predictive value reached 2.9%, 55.2%, 71.1%, and 94.7%, respectively, in the training set, with matching values of 4.2%, 52.4%, 66.1%, and 91.9%, respectively, in the validation set.
DISCUSSION: This study established a novel reflex PLT-F confirmation model based on RBC and PLT parameters. The model achieved a 52.4% sample release rate, with a corresponding overall false-negative proportion of 4.2% in the validation set, presenting a clear trade-off between laboratory operational efficiency and diagnostic safety.