Analysis of Risk Factors for Severe Pneumonia Progression in Chlamydia psittaci Infections: A Retrospective Study.

IF 2 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
International Journal of General Medicine Pub Date : 2025-09-18 eCollection Date: 2025-01-01 DOI:10.2147/IJGM.S545097
Han Hu, Yang Yang, Wei Lu, Xin-Er Huang
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引用次数: 0

Abstract

Introduction: Psittacosis pneumonia, caused by Chlamydia psittaci, is a zoonotic infection with a severe-case mortality rate of 15-20%. This study aimed to identify risk factors for severe pneumonia and evaluate the discriminative performance of corresponding predictive models.

Methods: In this retrospective study, 51 patients with metagenomic next-generation sequencing (mNGS)-confirmed psittacosis pneumonia were classified into severe (n=20) and non-severe (n=31) groups. Demographic characteristics, clinical manifestations, laboratory parameters, and imaging features were collected. Variables were analyzed using univariate screening, variance inflation factor (VIF)-based multicollinearity control, least absolute shrinkage and selection operator (LASSO) regression, and Firth-penalized logistic regression. Receiver operating characteristic (ROC) analysis evaluated predictive performance.

Results: Patients with severe pneumonia were significantly older (p = 0.043) and showed a higher prevalence of underlying diseases (p = 0.025) and cardiovascular diseases (CVD, p = 0.028). They also exhibited persistent lymphocytopenia, progressive decreases in hemoglobin (Hb) levels, and a higher rate of bilateral lung involvement. LASSO regression identified four key predictors: history of CVD, first lymphocyte count (LYM), second Hb measurement, and lactate dehydrogenase (LDH) level. Firth-penalized logistic regression confirmed that a history of CVD (OR = 4.874, 95% CI: 1.270-22.763) and a decreased second Hb measurement (OR = 0.415, 95% CI: 0.169-0.844) were independent risk factors for severe pneumonia. ROC analysis demonstrated that the combination of CVD history and the second Hb measurement achieved a specificity of 90.32%, while the combination of CVD history and LDH exhibited a sensitivity of 90%. The final multivariable model showed good discriminatory performance, with an area under the curve (AUC) of 0.885, sensitivity of 75%, and specificity of 93.50%.

Conclusion: CVD history and progressive Hb decrease are independent risk factors for severe psittacosis pneumonia. Dynamic monitoring of LYM, Hb, LDH, and CVD history facilitates early risk stratification. The multivariate model demonstrates high predictive accuracy and clinical utility.

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鹦鹉热衣原体感染严重肺炎进展的危险因素分析:一项回顾性研究。
由鹦鹉热衣原体引起的肺炎鹦鹉热病是一种人畜共患感染,重症死亡率为15-20%。本研究旨在识别重症肺炎的危险因素,并评估相应预测模型的判别性能。方法:回顾性研究51例经mNGS确认的鹦鹉热肺炎患者,分为重症组(n=20)和非重症组(n=31)。收集患者的人口学特征、临床表现、实验室参数和影像学特征。变量分析采用单变量筛选、基于方差膨胀因子(VIF)的多重共线性控制、最小绝对收缩和选择算子(LASSO)回归以及firth惩罚逻辑回归。受试者工作特征(ROC)分析评估预测效果。结果:重症肺炎患者年龄明显增加(p = 0.043),基础疾病(p = 0.025)和心血管疾病(CVD, p = 0.028)患病率较高。他们还表现出持续性淋巴细胞减少,血红蛋白(Hb)水平进行性下降,双侧肺受累率更高。LASSO回归确定了四个关键预测因素:CVD病史、第一淋巴细胞计数(LYM)、第二次Hb测量和乳酸脱氢酶(LDH)水平。经firth校正的logistic回归证实,心血管疾病史(OR = 4.874, 95% CI: 1.270-22.763)和第二次Hb测量值下降(OR = 0.415, 95% CI: 0.169-0.844)是严重肺炎的独立危险因素。ROC分析显示,CVD史和第二次Hb测量相结合的特异性为90.32%,而CVD史和LDH相结合的敏感性为90%。最终的多变量模型具有良好的判别性能,曲线下面积(AUC)为0.885,灵敏度为75%,特异性为93.50%。结论:CVD病史和进行性Hb下降是严重尘螨肺炎的独立危险因素。动态监测LYM、Hb、LDH和CVD病史有助于早期风险分层。该多变量模型具有较高的预测准确性和临床实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of General Medicine
International Journal of General Medicine Medicine-General Medicine
自引率
0.00%
发文量
1113
审稿时长
16 weeks
期刊介绍: The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas. A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal. As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.
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