Clinical Features and Prediction Model of Secondary Infection Risk in Adult Patients With Chronic Respiratory Diseases: A Case-Control Study.

He Qing Huang, Hong Lu, Yi Ling Chen, Mei Li Shen, Zu Tao Chen, Jie Xu

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Article in Journal of clinical medicine research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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6 authors.

He Qing HuangCenter of Clinical Laboratory, the First Affiliated Hospital of Soochow University, Suzhou, China.

Hong LuCenter of Clinical Laboratory, the First Affiliated Hospital of Soochow University, Suzhou, China.

Yi Ling ChenDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China.

Mei Li ShenMedical Department, Nanjing Dinfectome Technology Inc., Nanjing, China.

Zu Tao ChenDepartment of Infectious Diseases, The First Affiliated Hospital of Soochow University, Suzhou, China.

Jie XuCenter of Clinical Laboratory, the First Affiliated Hospital of Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There are limited investigations on the general pathogen features, clinical characteristics, and predicted clinical markers of secondary lower respiratory tract infection of chronic respiratory disorders. Methods: A total of 154 adult inpatients with chronic respiratory diseases between 2019 and 2022 were enrolled. Clinical data were retrospectively collected and analyzed. Multivariate logistic regression analysis was used to analyze the susceptibility factors of infection secondary to chronic respiratory diseases. Results: Among the patients with chronic respiratory diseases, the most prevalent condition was chronic obstructive pulmonary disease (44.2%, 68/154). Cough, expectoration, chest tightness, and wheezing were the predominant symptoms irrespective of infection. Conclusions: This study focused and explored the common features between secondary infections of various chronic respiratory diseases. The prediction model is expected to enable timely detection and treatment of secondary infections in clinical practice.

Indexed as

Chronic respiratory diseaseClinical featuresLower respiratory tract infectionPrediction modelSecondary infection

Identifiers

PMID41367826

DOI10.14740/jocmr6291

PMCPMC12683938

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.