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Abstract：
We aimed to summarise the prevalence of atypical pathogens in patients with severe pneumonia to understand the prevalence of severe pneumonia caused by atypical pathogens, improve clinical decision-making and guide antibiotic use.
Systematic review and meta-analysis.
PubMed, Embase, Web of Science and Cochrane Library were searched through November 2022.
English language studies enrolled consecutive cases of patients diagnosed with severe pneumonia, with complete aetiological analysis.
We conducted literature retrieval on PubMed, Embase, Web of Science and The Cochrane Library to estimate the prevalence of Chlamydia, Mycoplasma and Legionella in patients with severe pneumonia. After double arcsine transformation of the data, a random-effects model was used for meta-analyses to calculate the pooled prevalence of each pathogen. Meta-regression analysis was also used to explore whether the region, different diagnostic method, study population, pneumonia categories or sample size were potential sources of heterogeneity.
We included 75 eligible studies with 18 379 cases of severe pneumonia. The overall prevalence of atypical pneumonia is 8.1% (95% CI 6.3% to 10.1%) In patients with severe pneumonia, the pooled estimated prevalence of Chlamydia, Mycoplasma and Legionella was 1.8% (95% CI 1.0% to 2.9%), 2.8% (95% CI 1.7% to 4.3%) and 4.0% (95% CI 2.8% to 5.3%), respectively. We noted significant heterogeneity in all pooled assessments. Meta-regression showed that the pneumonia category potentially influenced the prevalence rate of Chlamydia. The mean age and the diagnostic method of pathogens were likely moderators for the prevalence of Mycoplasma and Legionella, and contribute to the heterogeneity of their prevalence.
In severe pneumonia, atypical pathogens are notable causes, especially Legionella. The diagnostic method, regional difference, sample size and other factors contribute to the heterogeneity of prevalence. The estimated prevalence and relative heterogeneity factors can help with microbiological screening, clinical treatment and future research planning.
CRD42022373950.
Systematic review and meta-analysis.
PubMed, Embase, Web of Science and Cochrane Library were searched through November 2022.
English language studies enrolled consecutive cases of patients diagnosed with severe pneumonia, with complete aetiological analysis.
We conducted literature retrieval on PubMed, Embase, Web of Science and The Cochrane Library to estimate the prevalence of Chlamydia, Mycoplasma and Legionella in patients with severe pneumonia. After double arcsine transformation of the data, a random-effects model was used for meta-analyses to calculate the pooled prevalence of each pathogen. Meta-regression analysis was also used to explore whether the region, different diagnostic method, study population, pneumonia categories or sample size were potential sources of heterogeneity.
We included 75 eligible studies with 18 379 cases of severe pneumonia. The overall prevalence of atypical pneumonia is 8.1% (95% CI 6.3% to 10.1%) In patients with severe pneumonia, the pooled estimated prevalence of Chlamydia, Mycoplasma and Legionella was 1.8% (95% CI 1.0% to 2.9%), 2.8% (95% CI 1.7% to 4.3%) and 4.0% (95% CI 2.8% to 5.3%), respectively. We noted significant heterogeneity in all pooled assessments. Meta-regression showed that the pneumonia category potentially influenced the prevalence rate of Chlamydia. The mean age and the diagnostic method of pathogens were likely moderators for the prevalence of Mycoplasma and Legionella, and contribute to the heterogeneity of their prevalence.
In severe pneumonia, atypical pathogens are notable causes, especially Legionella. The diagnostic method, regional difference, sample size and other factors contribute to the heterogeneity of prevalence. The estimated prevalence and relative heterogeneity factors can help with microbiological screening, clinical treatment and future research planning.
CRD42022373950.
摘要：
目的:总结重症肺炎患者非典型病原体的流行情况,了解非典型病原体所致重症肺炎的流行情况,改善临床决策并指导抗生素使用。
方法：系统评价和荟萃分析。
方法：PubMed，Embase,WebofScience和Cochrane图书馆在2022年11月进行了搜索。
方法：英语语言研究连续纳入诊断为重症肺炎的患者，完整的病因分析。
方法：我们在PubMed上进行了文献检索，Embase,WebofScience和Cochrane图书馆估计衣原体的患病率，重症肺炎患者的支原体和军团菌。对数据进行双反正弦变换后，使用随机效应模型进行荟萃分析,以计算每种病原体的合并患病率.还使用Meta回归分析来探索该地区是否，不同的诊断方法，研究人群,肺炎类别或样本量是异质性的潜在来源.
结果：我们纳入了75项符合条件的研究，其中有18379例重症肺炎病例。重症肺炎病人的非典型肺炎总患病率为8.1%(95%CI6.3%至10.1%),合并估计的衣原体患病率，支原体和军团菌为1.8％（95％CI1.0％至2.9％），2.8%（95%CI1.7%至4.3%）和4.0%（95%CI2.8%至5.3%），分别。我们注意到所有汇总评估中的显著异质性。Meta回归显示肺炎类别可能影响衣原体的患病率。平均年龄和病原体的诊断方法可能是支原体和军团菌流行的调节因素，并导致其患病率的异质性。
结论：在重症肺炎中，非典型病原体是值得注意的原因，尤其是军团菌.诊断方法,区域差异,样本量和其他因素导致患病率的异质性。估计的患病率和相对异质性因素可以帮助微生物筛查，临床治疗和未来研究规划。
■CRD42022373950。
方法：系统评价和荟萃分析。
方法：PubMed，Embase,WebofScience和Cochrane图书馆在2022年11月进行了搜索。
方法：英语语言研究连续纳入诊断为重症肺炎的患者，完整的病因分析。
方法：我们在PubMed上进行了文献检索，Embase,WebofScience和Cochrane图书馆估计衣原体的患病率，重症肺炎患者的支原体和军团菌。对数据进行双反正弦变换后，使用随机效应模型进行荟萃分析,以计算每种病原体的合并患病率.还使用Meta回归分析来探索该地区是否，不同的诊断方法，研究人群,肺炎类别或样本量是异质性的潜在来源.
结果：我们纳入了75项符合条件的研究，其中有18379例重症肺炎病例。重症肺炎病人的非典型肺炎总患病率为8.1%(95%CI6.3%至10.1%),合并估计的衣原体患病率，支原体和军团菌为1.8％（95％CI1.0％至2.9％），2.8%（95%CI1.7%至4.3%）和4.0%（95%CI2.8%至5.3%），分别。我们注意到所有汇总评估中的显著异质性。Meta回归显示肺炎类别可能影响衣原体的患病率。平均年龄和病原体的诊断方法可能是支原体和军团菌流行的调节因素，并导致其患病率的异质性。
结论：在重症肺炎中，非典型病原体是值得注意的原因，尤其是军团菌.诊断方法,区域差异,样本量和其他因素导致患病率的异质性。估计的患病率和相对异质性因素可以帮助微生物筛查，临床治疗和未来研究规划。
■CRD42022373950。
