Developing laboratory indicators to predict the probability of in-hospital progression to severe disease in elderly patients with influenza A virus infection.
- 1Department of Clinical Laboratory, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
- 2Department of Clinical Laboratory, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China. [email protected].
Abstract
Background
The progression of respiratory viral infections to severe disease, and the associated high mortality in elderly patients, pose a significant clinical challenge. Currently, effective warning tools specific for predicting severe influenza are lacking.Objectives
To preliminarily develop a laboratory‑based prediction model using routine clinical laboratory markers to estimate the risk of in-hospital progression to severe disease in elderly patients with influenza A virus infection.Methods
This retrospective cohort study was conducted at Shanghai Fourth People's Hospital, affiliated with Tongji University, from March 2023 to February 2025. Data were extracted from the hospital's electronic medical record system. A total of 198 elderly patients (aged > 60 years) with laboratory-confirmed influenza A virus infection were enrolled; those co-infected with other respiratory pathogens were excluded. All patients had complete demographic and outcome data, and laboratory biomarkers were measured within 24 h of admission. The primary outcome was progression to severe influenza, defined according to the Chinese "Diagnosis and Treatment Protocol for Influenza" (2025 Edition). Patients were randomly split into a training set and a validation set in a 7:3 ratio. Elastic net regression was employed to select candidate variables for constructing a prognostic model of disease severity. Model performance was evaluated based on discrimination and calibration.Results
Of the 198 elderly hospitalized patients with influenza A virus infection, 50 developed severe disease, while 148 remained non-severe. There were no significant differences in age or sex between the two groups. However, 16 clinical laboratory markers, including procalcitonin, interleukin-6, and albumin, showed significant differences. Using a machine learning-based variable selection procedure, we identified white blood cell count, procalcitonin, interleukin-6, and myoglobin and incorporated them into the prediction model. The model showed discriminatory performance in both the training set (C-index: 0.851, 95% CI: 0.797-0.905) and the validation set (C-index: 0.819, 95% CI: 0.709-0.929). Calibration analysis suggested potentially acceptable calibration at day 14.Conclusions
We developed and preliminarily evaluated a laboratory‑based prognostic model to predict the risk of in-hospital progression to severe disease in elderly patients with influenza A virus infection. The model showed moderate discriminative ability in this single‑center cohort. Given the modest sample size and limited number of events, all results should be interpreted with caution and considered preliminary and exploratory. It will be important to conduct external validation in larger, independent cohorts.Trial registration
The study protocol was retrospectively registered with the Chinese Clinical Trial Registry (ChiCTR2500112511) on November 14, 2025, after the study had been conducted.
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BibTeX
@article{xiu2026developing,
title = {{Developing laboratory indicators to predict the probability of in-hospital progression to severe disease in elderly patients with influenza A virus infection.}},
author = {Liu Xiu and Feng Bianying and Zhang Zhifan and Zhou Hao and Zhou Nana and Man Qiuhong},
journal = {BMC infectious diseases},
year = {2026},
month = jul,
doi = {10.1186/s12879-026-14007-w},
url = {https://europepmc.org/article/MED/42902174},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Developing laboratory indicators to predict the probability of in-hospital progression to severe disease in elderly patients with influenza A virus infection.
AU - Liu Xiu
AU - Feng Bianying
AU - Zhang Zhifan
AU - Zhou Hao
AU - Zhou Nana
AU - Man Qiuhong
PY - 2026
DA - 2026/07/01
JO - BMC infectious diseases
DO - 10.1186/s12879-026-14007-w
UR - https://europepmc.org/article/MED/42902174
AB - Background The progression of respiratory viral infections to severe disease, and the associated high mortality in elderly patients, pose a significant clinical challenge. Currently, effective warning tools specific for predicting severe influenza are lacking.Objectives To preliminarily develop a laboratory‑based prediction model using routine clinical laboratory markers to estimate the risk of in-hospital progression to severe disease in elderly patients with influenza A virus infection.Methods This retrospective cohort study was conducted at Shanghai Fourth People's Hospital, affiliated with Tongji University, from March 2023 to February 2025. Data were extracted from the hospital's electronic medical record system. A total of 198 elderly patients (aged > 60 years) with laboratory-confirmed influenza A virus infection were enrolled; those co-infected with other respiratory pathogens were excluded. All patients had complete demographic and outcome data, and laboratory biomarkers were measured within 24 h of admission. The primary outcome was progression to severe influenza, defined according to the Chinese "Diagnosis and Treatment Protocol for Influenza" (2025 Edition). Patients were randomly split into a training set and a validation set in a 7:3 ratio. Elastic net regression was employed to select candidate variables for constructing a prognostic model of disease severity. Model performance was evaluated based on discrimination and calibration.Results Of the 198 elderly hospitalized patients with influenza A virus infection, 50 developed severe disease, while 148 remained non-severe. There were no significant differences in age or sex between the two groups. However, 16 clinical laboratory markers, including procalcitonin, interleukin-6, and albumin, showed significant differences. Using a machine learning-based variable selection procedure, we identified white blood cell count, procalcitonin, interleukin-6, and myoglobin and incorporated them into the prediction model. The model showed discriminatory performance in both the training set (C-index: 0.851, 95% CI: 0.797-0.905) and the validation set (C-index: 0.819, 95% CI: 0.709-0.929). Calibration analysis suggested potentially acceptable calibration at day 14.Conclusions We developed and preliminarily evaluated a laboratory‑based prognostic model to predict the risk of in-hospital progression to severe disease in elderly patients with influenza A virus infection. The model showed moderate discriminative ability in this single‑center cohort. Given the modest sample size and limited number of events, all results should be interpreted with caution and considered preliminary and exploratory. It will be important to conduct external validation in larger, independent cohorts.Trial registration The study protocol was retrospectively registered with the Chinese Clinical Trial Registry (ChiCTR2500112511) on November 14, 2025, after the study had been conducted.
SN - 1471-2334
ER -