Objective To investigate the distribution characteristics of nasal cavity and maxillary sinus microbiota in aged patients with chronic maxillary sinusitis.Methods A total of 15 aged patients with chronic unilateral maxillary sinusitis who received surgical treatment between January 2017 to June 2018 in Beijing Hospital were enrolled and analyzed retrospectively.Their lavage samples from nasal cavity(N)and maxillary sinus(M)were collected and the samples were labeled according to the location(N and M groups,n=15 each).The high-throughput sequencing was used for sequencing all bacterial 16S rRNA genes in the samples.The composition of nasal cavity and maxillary sinus microbial communities was obtained,and the distribution features of nasal cavity and maxillary sinus microbiota were analyzed.Results A total of 8 bacterial phyla and 34 bacterial genera were found in nasal cavity and maxillary sinus microbiota.The most widely distributed phyla in nasal and sinus groups were Bacteroidetes,Fusobacteria,Frimicutes,Spirochaetes and Proteobacteria.The abundance of Bacteroidetes was higher in group M(60.0 ％,51 762/86 301)than in group N(42.9 ％,37 999/88 576)with statistically significant difference(P ＜0.05).The most widely distributed bacteria genera were Prevotella,Fusobacterium,Alloprevotella,Treponema,Parvimonas,Streptococcus,Filifactor,Phocaeicola,Campylobacter,Prevotella-7 and Lentimicrobiaceae.The abundance of Prevotella was higher in group M(47.7％,41 252/86 414)than in group N(33.5％,29 680/88 598) with statistically significant difference(P ＜ 0.05).Conclusions In the aged patients with chronic maxillary sinusitis,the distribution of bacteria in the nasal cavity and maxillary sinus is partly consistent.The abundance of the anaerobes distribution is higher in maxillary sinus than in nasal cavity in aged patients with chronic maxillary sinusitis.

This study aimed to explore the relationship between comorbidity factors and in-hospital mortality related to factors in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) pneumonia. This study collected clinical data from 218 patients with CRKP pneumonia in Beijing hospital from November 2011 to December 2023, analyzed the number of comorbidities carried by CRKP pneumonia patients, comorbidity patterns, Charlson Comorbidity Index (CCI) scores, and comorbidity of underlying diseases, and explored the relationship between various indicators and comorbidity factors and in-hospital mortality in CRKP pneumonia patients. The Ward.D cluster analysis was performed on the comorbidities of patients and used to draw heatmaps. Using a multiple logistic regression model, a nomogram model was constructed to predict in-hospital mortality in patients with CRKP pneumonia. This study included 218 patients with CRKP pneumonia. The results showed that there were significant differences in the age ( P=0.003), comorbidities such as heart failure ( P<0.001), arrhythmia ( P=0.002), chronic liver disease ( P=0.003), chronic kidney disease ( P=0.002), CCI score ( P=0.007), total number of comorbidities ( P<0.001), and comorbidity patterns (respiratory/immune/psychiatric disease patterns and cardiovascular/tumor/metabolic disease patterns, P=0.003) between the survival and death groups of CRKP pneumonia patients. The multiple logistic regression showed that cardiovascular/tumor/metabolic disease patterns ( P=0.030), CCI score ( P=0.040), concomitant heart failure ( P=0.011), and concomitant arrhythmia ( P=0.025) were independent risk factors for in-hospital mortality in patients with CRKP pneumonia. The nomogram model for predicting the risk of in-hospital mortality in patients with CRKP pneumonia, constructed based on the identified risk factors, had an area under the ROC curve of 0.758. Both the ROC curve and validation curve indicated that the nomogram model had stable performance in predicting in-hospital mortality in patients with CRKP pneumonia. In summary, comorbidity factors are risk factors for predicting in-hospital mortality in patients with CRKP pneumonia, and the role of comorbidity factors in in-hospital mortality in patients with CRKP pneumonia should be taken seriously.

This study aimed to explore the relationship between comorbidity factors and in-hospital mortality related to factors in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) pneumonia. This study collected clinical data from 218 patients with CRKP pneumonia in Beijing hospital from November 2011 to December 2023, analyzed the number of comorbidities carried by CRKP pneumonia patients, comorbidity patterns, Charlson Comorbidity Index (CCI) scores, and comorbidity of underlying diseases, and explored the relationship between various indicators and comorbidity factors and in-hospital mortality in CRKP pneumonia patients. The Ward.D cluster analysis was performed on the comorbidities of patients and used to draw heatmaps. Using a multiple logistic regression model, a nomogram model was constructed to predict in-hospital mortality in patients with CRKP pneumonia. This study included 218 patients with CRKP pneumonia. The results showed that there were significant differences in the age ( P=0.003), comorbidities such as heart failure ( P<0.001), arrhythmia ( P=0.002), chronic liver disease ( P=0.003), chronic kidney disease ( P=0.002), CCI score ( P=0.007), total number of comorbidities ( P<0.001), and comorbidity patterns (respiratory/immune/psychiatric disease patterns and cardiovascular/tumor/metabolic disease patterns, P=0.003) between the survival and death groups of CRKP pneumonia patients. The multiple logistic regression showed that cardiovascular/tumor/metabolic disease patterns ( P=0.030), CCI score ( P=0.040), concomitant heart failure ( P=0.011), and concomitant arrhythmia ( P=0.025) were independent risk factors for in-hospital mortality in patients with CRKP pneumonia. The nomogram model for predicting the risk of in-hospital mortality in patients with CRKP pneumonia, constructed based on the identified risk factors, had an area under the ROC curve of 0.758. Both the ROC curve and validation curve indicated that the nomogram model had stable performance in predicting in-hospital mortality in patients with CRKP pneumonia. In summary, comorbidity factors are risk factors for predicting in-hospital mortality in patients with CRKP pneumonia, and the role of comorbidity factors in in-hospital mortality in patients with CRKP pneumonia should be taken seriously.