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Journal Article Epublish 1 citation

PubMed: 39811157 DOI: 10.3389/fmed.2024.1518222

Construction of geriatric hypoalbuminemia predicting model for hypoalbuminemia patients with and without pneumonia and explainability analysis.

Front Med (Lausanne) 11:1518222

Authors: Ziqi Liu, Qi Kang, Zhilong Mi, Yuan Yuan, Tiantian Sang, Binghui Guo, Zhiming Zheng, Ziqiao Yin, Wei Tian

Affiliations

  • School of Mathematical Sciences, Beihang University, Beijing, China.
  • Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing, China.
  • Department of Geriatrics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.
  • Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China.
  • Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing, China. Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China. Zhongguancun Laboratory, Beijing, China.
  • Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State Key Laboratory of Software Development Environment, Beihang University, Beijing, China. Institute of Artificial Intelligence, Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, China. Zhongguancun Laboratory, Beijing, China. Hangzhou Internation Innovation Institute of Beihang University, Hangzhou, China.

Abstract

Background and objectives

Pneumonia portrays a critical health concern in geriatrics. Geriatric pneumonia can lead to changes on other complications, in which hypoalbuminemia is a common complication. However, few studies have looked at the impact of pneumonia on the course of hypoalbuminemia and predicting. This study aims to predicting hypoalbuminemia in geriatric pneumonia and non-pneumonia patients and exploring the clinical difference between the two groups.

Materials and methods

This retrospective study enrolled 42 pneumonia patients group and 76 non-pneumonia patients group. The indicators difference of different groups were analyzed, then a mutual information-grey relational coefficient gradual fusion model was constructed to predict hypoalbuminemia in the future by the indicators of vital signs, N-Terminal Pro-Brain Natriuretic Peptide, blood routine examination and urine routine examination at admission. Through the sensitivity analysis of model, we analysed the important of four examines in patients with and without pneumonia.

Results

The predicted accuracy of our gradual fusion model was 0.954, which improve the prediction accuracy by nearly 17.6% compared with the classical machine learning method. The AUC of gradual fusion model was 0.96 and 0.9 in hypoalbuminemia patients with and without pneumonia. The sensitivity analysis of gradual fusion model showed blood routine examine was most important to predict hypoalbuminemia in patients with pneumonia, while urine routine examine was most important to predict hypoalbuminemia in non-pneumonia patients.

Conclusion

The changes in the blood of patients with hypoalbuminemia combined with pneumonia were more significant than that of patients with hypoalbuminemia alone, which was characterized by abnormal excretion due to low protein. We suggested doctors should pay more attention to blood routine results when preventing hypoalbuminemia in patients with pneumonia and pay more attention to urine routine examine results when preventing hypoalbuminemia in patients without pneumonia.

MeSH

Blood, Pneumonia, Hypoalbuminemia