Epidemiological and pathological characterization of acute respiratory infections

This research comprehensively investigates the epidemiological features and pathogen profile of acute respiratory infections (ARI) in Shihezi City, Xinjiang. A pivotal aspect of this study is the construction of a Bayes discriminant function for principal pathogen infections. This innovative methodology aims to furnish a robust scientific basis for the prevention and clinical management of ARI, potentially guiding more effective strategies in both public health and clinical settings. We compiled and examined data from January 2020 to June 2023, pertaining to patients admitted with acute respiratory infections at the First Affiliated Hospital of Shihezi University. This investigation focused on discerning patterns in epidemiology and pathogen etiology. Among 2110 cases of acute respiratory infections (ARI), 1736 underwent pathogenetic testing. Of these, 595 cases tested positive for at least one pathogen, marking a positivity rate of 34.27%. Viral detections, at a rate of 27.47%, were notably higher than bacterial detections, which stood at 6.51%. The most prevalent viruses identified were Human respiratory syncytial virus (hRSV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Human adenovirus (HAdV), while the dominant bacterial pathogens included Klebsiella pneumoniae, Haemophilus influenzae, and Staphylococcus aureus. Co-infections were observed in 76 cases, accounting for 12.77% of positive diagnoses, predominantly involving hRSV in conjunction with other pathogens. In cases of acute bronchiolitis, hRSV was the most frequent pathogen, contributing to 23.10% of such cases. Similarly, in severe pneumonia cases, SARS-CoV-2 was predominant, accounting for 25.4% of these infections. The group with bacterial positivity exhibited elevated levels of C-reactive protein (CRP, 19.17 mg/L) and neutrophilic granulocyte percentage (NE%, 54.7%). The Bayes discriminant function demonstrated an initial validation accuracy of 74.9% and a cross-validation accuracy of 63.7%. The study underscores that hRSV, SARS-CoV-2, and HAdV are the primary pathogens in acute respiratory infections in the Shihezi region. Pathogen susceptibility exhibits variation across different age groups, with a higher pathogen detection rate in children compared to adults. The Bayes discriminant function shows significant promise in the classification and diagnosis of major pathogenic infections.

Authors

Institutions

Publication Details

Journal
Apmis
Published
2024-10-24
DOI
https://doi.org/10.1111/apm.13484
Citations
1
Primary Topic
Respiratory viral infections research
Type
article
Field-Weighted Citation Impact
0.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Epidemiological and pathological characterization of acute respiratory infections

Wenying He, Mengyun Xu, Songsong Xie, Jie Chen et al.
1 citations
Apmis
Respiratory viral infections research
0.39
article

Epidemiological and pathological characterization of acute respiratory infections

Wenying He, Mengyun Xu, Songsong Xie, Jie Chen, Zhenggang Ren, Bahejianati Nuerbolati
article en
1 citations

Abstract

This research comprehensively investigates the epidemiological features and pathogen profile of acute respiratory infections (ARI) in Shihezi City, Xinjiang. A pivotal aspect of this study is the construction of a Bayes discriminant function for principal pathogen infections. This innovative methodology aims to furnish a robust scientific basis for the prevention and clinical management of ARI, potentially guiding more effective strategies in both public health and clinical settings. We compiled and examined data from January 2020 to June 2023, pertaining to patients admitted with acute respiratory infections at the First Affiliated Hospital of Shihezi University. This investigation focused on discerning patterns in epidemiology and pathogen etiology. Among 2110 cases of acute respiratory infections (ARI), 1736 underwent pathogenetic testing. Of these, 595 cases tested positive for at least one pathogen, marking a positivity rate of 34.27%. Viral detections, at a rate of 27.47%, were notably higher than bacterial detections, which stood at 6.51%. The most prevalent viruses identified were Human respiratory syncytial virus (hRSV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Human adenovirus (HAdV), while the dominant bacterial pathogens included Klebsiella pneumoniae, Haemophilus influenzae, and Staphylococcus aureus. Co-infections were observed in 76 cases, accounting for 12.77% of positive diagnoses, predominantly involving hRSV in conjunction with other pathogens. In cases of acute bronchiolitis, hRSV was the most frequent pathogen, contributing to 23.10% of such cases. Similarly, in severe pneumonia cases, SARS-CoV-2 was predominant, accounting for 25.4% of these infections. The group with bacterial positivity exhibited elevated levels of C-reactive protein (CRP, 19.17 mg/L) and neutrophilic granulocyte percentage (NE%, 54.7%). The Bayes discriminant function demonstrated an initial validation accuracy of 74.9% and a cross-validation accuracy of 63.7%. The study underscores that hRSV, SARS-CoV-2, and HAdV are the primary pathogens in acute respiratory infections in the Shihezi region. Pathogen susceptibility exhibits variation across different age groups, with a higher pathogen detection rate in children compared to adults. The Bayes discriminant function shows significant promise in the classification and diagnosis of major pathogenic infections.

ApmisVol. 133(1)
Shihezi University (CN), First Affiliated Hospital of Shihezi University Medical College (CN)
Reduced inequalities
Openalex Percentile: Top 33%
Respiratory viral infections research
0.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.