{"title":"[The relationship between comorbidity factors and in-hospital mortality in patients with carbapenem-resistant Klebsiella pneumoniae pneumonia].","authors":"Y Wang, J Cui, D D Wang, C Y Ge, Y J Hu, X M Ai","doi":"10.3760/cma.j.cn112150-20240612-00462","DOIUrl":null,"url":null,"abstract":"<p><p>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 (<i>P</i>=0.003), comorbidities such as heart failure (<i>P</i><0.001), arrhythmia (<i>P</i>=0.002), chronic liver disease (<i>P</i>=0.003), chronic kidney disease (<i>P</i>=0.002), CCI score (<i>P</i>=0.007), total number of comorbidities (<i>P</i><0.001), and comorbidity patterns (respiratory/immune/psychiatric disease patterns and cardiovascular/tumor/metabolic disease patterns, <i>P</i>=0.003) between the survival and death groups of CRKP pneumonia patients. The multiple logistic regression showed that cardiovascular/tumor/metabolic disease patterns (<i>P</i>=0.030), CCI score (<i>P</i>=0.040), concomitant heart failure (<i>P</i>=0.011), and concomitant arrhythmia (<i>P</i>=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.</p>","PeriodicalId":24033,"journal":{"name":"中华预防医学杂志","volume":"58 11","pages":"1705-1710"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中华预防医学杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3760/cma.j.cn112150-20240612-00462","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Chinese Journal of Preventive Medicine (CJPM), the successor to Chinese Health Journal , was initiated on October 1, 1953. In 1960, it was amalgamated with the Chinese Medical Journal and the Journal of Medical History and Health Care , and thereafter, was renamed as People’s Care . On November 25, 1978, the publication was denominated as Chinese Journal of Preventive Medicine . The contents of CJPM deal with a wide range of disciplines and technologies including epidemiology, environmental health, nutrition and food hygiene, occupational health, hygiene for children and adolescents, radiological health, toxicology, biostatistics, social medicine, pathogenic and epidemiological research in malignant tumor, surveillance and immunization.