Scholarlink

Background <p>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.</p> Objectives <p>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.</p> Methods <p>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 &gt; 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&#xa0;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.</p> Results <p>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.</p> Conclusions <p>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.</p> Trial registration <p>The study protocol was retrospectively registered with the Chinese Clinical Trial Registry (ChiCTR2500112511) on November 14, 2025, after the study had been conducted.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Developing laboratory indicators to predict the probability of in-hospital progression to severe disease in elderly patients with influenza A virus infection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月23日

查看全文
  • Liu Xiu,
  • Feng Bianying,
  • Zhang Zhifan,
  • Zhou Hao,
  • Zhou Nana,
  • Man Qiuhong

摘要

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.