Multi-parameter prediction model based on blood routine in children with influenza A
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Supported by Navy Family Planning Project (21JSZ05) and Project of Naval Medical University (Second Military Medical University) (2023MS031).

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    Abstract:

    Objective To establish and validate a risk prediction model based on multiple blood routine parameters for preliminary differential diagnosis of influenza A and influenza like illness (ILI) in children. Methods Children with influenza A (n=2 686) and ILI (n=1 369) who were treated in Department of Pediatrics, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Jul. 1, 2022 to Jun. 30, 2023 were enrolled, and their clinical and laboratory features were collected for retrospective analysis. According to age, patients were divided into 2 subgroups: 1 year≤age≤6 years and 6 years<age≤16 years. Patients in each subgroup were randomly divided into training set (70%) and internal validation set (30%). Children with influenza A (n=204) and ILI (n=404) who were treated in Department of Pediatrics of The Second Affiliated Hospital of Naval Medical University (Second Military Medical University) and Naval Hospital of PLA Eastern Theater Command from Jul. 1, 2022 to Jun. 30, 2023 were selected as the external validation set. Multivariate logistic regression analysis was performed on the training set to obtain the independent influencing factors of influenza A. The prediction model based on these factors were displayed as a nomogram. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and decision curve analysis (DCA) were used to evaluate the performance of the model from 3 aspects: discrimination, calibration, and clinical practicality, respectively. The diagnostic performance of the model was verified in both internal validation set and external validation set. Results In the subgroup of 1 year≤age≤6 years, age, white blood cell count, lymphocyte count and C reactive protein were the independent influencing factors of influenza A (all P<0.01); the area under the curve (AUC) value of the established nomogram prediction model for identifying influenza A was 0.746 in the training set, 0.771 in the internal validation set, and 0.753 in the external validation set; the predicted probability of the model was highly consistent with the actual probability (P=0.216); and taking intervention measures within a threshold probability range of 16%-60% could yield net benefits. In the subgroup of 6 years<age≤16 years, gender, white blood cell count and lymphocyte count were the independent influencing factors of influenza A (all P<0.01); the AUC value of the established nomogram prediction model for identifying influenza A was 0.733, 0.747 in the internal validation set, and 0.753 in the external validation set; the predicted probability of the model was highly consistent with the actual probability (P=0.06); and taking intervention measures within a threshold probability range of 12%-58% could yield net benefits. Conclusion This risk prediction model based on easily obtainable blood routine parameters shows good diagnostic performance for influenza A, with high accuracy and clinical practicality.

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History
  • Received:May 29,2024
  • Revised:August 27,2024
  • Adopted:
  • Online: November 25,2024
  • Published: November 20,2024
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