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Predicting nosocomial lower respiratory tract infections by a risk index based system.

ABSTRACT: Although belonging to one of the most common type of nosocomial infection, there was currently no simple prediction model for lower respiratory tract infections (LRTIs). This study aims to develop a risk index based system for predicting nosocomial LRTIs based on data from a large point-prevalence survey. Among the 49328 patients included, the prevalence of nosocomial LRTIs was 1.70% (95% confidence interval [CI], 1.64% to 1.76%). The areas under the receiver operating characteristic (ROC) curve for logistic regression and fisher discriminant analysis were 0.907 (95% CI, 0.897 to 0.917) and 0.902 (95% CI, 0.892 to 0.912), respectively. The constructed risk index based system also displayed excellent discrimination (area under the ROC curve: 0.905 [95% CI, 0.895 to 0.915]) to identify LRTI in internal validation. Six risk levels were generated according to the risk score distribution of study population, ranging from 0 to 5, the corresponding prevalence of nosocomial LRTIs were 0.00%, 0.39%, 3.86%, 12.38%, 28.79% and 44.83%, respectively. The sensitivity and specificity of prediction were 0.87 and 0.79, respectively, when the best cut-off point of risk score was set to 14. Our study suggested that this newly constructed risk index based system might be applied to boost more rational infection control programs in clinical settings.

SUBMITTER: Chen Y

PROVIDER: S-EPMC5698311 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Predicting nosocomial lower respiratory tract infections by a risk index based system.

Chen Yong Y Shan Xue X Zhao Jingya J Han Xuelin X Tian Shuguang S Chen Fangyan F Su Xueting X Sun Yansong Y Huang Liuyu L Grundmann Hajo H Wang Hongyuan H Han Li L

Scientific reports 20171121 1

Although belonging to one of the most common type of nosocomial infection, there was currently no simple prediction model for lower respiratory tract infections (LRTIs). This study aims to develop a risk index based system for predicting nosocomial LRTIs based on data from a large point-prevalence survey. Among the 49328 patients included, the prevalence of nosocomial LRTIs was 1.70% (95% confidence interval [CI], 1.64% to 1.76%). The areas under the receiver operating characteristic (ROC) curve ...[more]

PMID: 29162852

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Project description:IntroductionObservational studies have reported the association between gut microbiota and the risk of lower respiratory tract infections (LRTIs). However, whether the association reflects a causal relationship remains obscure.MethodsA bidirectional twosample Mendelian randomization (MR) analysis was conducted by assessing genome-wide association study (GWAS) summary statistics for gut microbiota taxa and five common LRTIs. MR methods including inverse-variance-weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode were used to analyze the causality. Gene pleiotropy was tested using MR-Egger regression and MR-PRESSO methods. Cochran's Q test was used to check for heterogeneity. Leave-one-out analysis was used to assess the stability of effect sizes. Detected significant associations were validated by using an independent LRTI GWAS summary statistics dataset. An optional MR method of causal analysis using summary effect estimates (CAUSE) was further performed as a validation to avoid potential false-positive results.ResultsAccording to the MR-Egger estimates in forward MR analysis, a causal effect of gut Blautia on increased odds of bronchiectasis and pneumonia was suggested. MR-Egger regression pleiotropy intercept methods detected no significant horizontal pleiotropy between the instrumental variables of these associations. MR-PRESSO global test examined no potential horizontal pleiotropy. Cochran's Q test showed that no heterogeneity biased the results. The leave-one-out sensitivity analyses suggested robust causality results. These associations with consistent effect direction were successfully replicated in IVW analysis by using the validation GWAS dataset. However, these evidence of causality did not survive after applying strict Bonferroni correction or CAUSE analysis. The reverse MR analysis failed to achieve consistent results in the effect of LRTIs on gut microbiota through comprehensive discovery and validation processes.DiscussionThis study established no strong causality between genetically predicted gut microbiome and the risk of lower respiratory tract infections. However, specific subtypes of microbial genera, such as Blautia, were identified as potential influencers and require further investigation, particularly at the species or strain levels.

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Predicting nosocomial lower respiratory tract infections by a risk index based system.

Publications

Predicting nosocomial lower respiratory tract infections by a risk index based system.

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