scholarly journals Risk Factors of Carbapenem-Resistant Klebsiella pneumoniae Infection: A Serious Threat in ICUs

2015 ◽  
Vol 21 ◽  
pp. 219-224 ◽  
Author(s):  
Yeşim Taşova
Keyword(s):  
Risk Factors ◽  
Klebsiella Pneumoniae ◽  
Carbapenem Resistant

Disease Severity Is an Independent Risk Factor of Mortality and Outcomes in Critically Ill Patients With Carbapenem-resistant Klebsiella Pneumoniae Bloodstream Infections: a 5-year Retrospective Analysis

2021 ◽  
Author(s):  
Yuzhen Qiu ◽  
Wen Xu ◽  
Yunqi Dai ◽  
Ruoming Tan ◽  
Jialin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Septic Shock ◽  
Klebsiella Pneumoniae ◽  
Critically Ill Patients ◽  
Bloodstream Infections ◽  
Polymyxin B ◽  
Mortality Rates ◽  
Carbapenem Resistant ◽  
All Cause Mortality ◽  
Independent Risk Factors

Abstract Background: Carbapenem-resistant Klebsiella pneumoniae bloodstream infections (CRKP-BSIs) are associated with high morbidity and mortality rates, especially in critically ill patients. Comprehensive mortality risk analyses and therapeutic assessment in real-world practice are beneficial to guide individual treatment.Methods: We retrospectively analyzed 87 patients with CRKP-BSIs (between July 2016 and June 2020) to identify the independent risk factors for 28-day all-cause mortality. The therapeutic efficacies of tigecycline-and polymyxin B-based therapies were analyzed.Results: The 28-day all-cause mortality and in-hospital mortality rates were 52.87% and 67.82%, respectively, arising predominantly from intra-abdominal (56.32%) and respiratory tract infections (21.84%). A multivariate analysis showed that 28-day all-cause mortality was independently associated with the patient’s APACHE II score (p = 0.002) and presence of septic shock at BSI onset (p = 0.006). All-cause mortality was not significantly different between patients receiving tigecycline- or polymyxin B-based therapy (55.81% vs. 53.85%, p = 0.873), and between subgroups mortality rates were also similar. Conclusions: Critical illness indicators (APACHE II scores and presence of septic shock at BSI onset) were independent risk factors for 28-day all-cause mortality. There was no significant difference between tigecycline- and polymyxin B-based therapy outcomes. Prompt and appropriate infection control should be implemented to prevent CRKP infections.

Machine Learning Algorithms to Predict the Mortality of Carbapenem Resistant Klebsiella Pneumoniae bacteremia

2019 ◽  
Author(s):  
Qiqiang Liang ◽  
Fang Qian ◽  
Yibing Chen ◽  
Zhijun Xu ◽  
Zhijiang Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Septic Shock ◽  
High Risk ◽  
Klebsiella Pneumoniae ◽  
Severe Thrombocytopenia ◽  
Acute Renal Injury ◽  
High Risk Factors ◽  
Carbapenem Resistant ◽  
Logical Regression

Abstract Purpose To establish mortality prediction models in 14 days of Carbapenem-Resistant Klebsiella Pneumoniae bacteremia using Machine learning.Materials and Methods It is a single-center retrospective study. We collect the relevant clinical information of all patients with Carbapenem-Resistant Klebsiella Pneumoniae (CRKP) bacteremia in the past 5 years using the local database. Data analysis and verification are carried out by multiple logical regression, decision tree, random forest, support vector machine (SVM), and XGBoost.Result This study includes 187 patients with 40 related variables. In multiple logical regression, acute renal injury (P=0.003), Apache II score (P=0.036), immunodeficiency (P=0.025), severe thrombocytopenia (P=0.025) and septic shock (P=0.044) are the high-risk factors for 14 days mortality of CRKP bloodstream infections. According to the importance of those parameters, risk scoring is established to predict the survival rate of CRKP bacteremia. The analysis of the five models, with 70% training set and 30% test set, show the comprehensive performance of random forest (AUROC=0.953, precision=91.85%) is slightly better than that of XGBoost (AUROC=0.912, precision=86.41%) and SVM (AUROC=0.936, precision=79.89%) in predicting 14-day mortality of CRKP bacteremia. The multiple logical regression model (AUROC=0.825, precision=81.52%) is the second, and the decision tree model (AUROC=0.712, precision=79.89%) is not very ideal.Conclusion Machine learning has good performances in predicting 14-day mortality of CRKP bacteremia than multiple logical regression. Acute renal injury, severe thrombocytopenia, and septic shock are the high-risk factors of CRKP bacteremia mortality.

Outcomes of Carbapenem-Resistant Klebsiella pneumoniae Infection and the Impact of Antimicrobial and Adjunctive Therapies

2008 ◽  
Vol 29 (12) ◽  
pp. 1099-1106 ◽  
Author(s):  
Gopi Patel ◽  
Shirish Huprikar ◽  
Stephanie H. Factor ◽  
Stephen G. Jenkins ◽  
David P. Calfee
Keyword(s):  
Risk Factors ◽  
Klebsiella Pneumoniae ◽  
Case Control Study ◽  
Patient Survival ◽  
Case Control ◽  
Factors Associated ◽  
Carbapenem Resistant ◽  
Matched Case ◽  
The Impact ◽  
Control Study

Background.Carbapenem-resistant Klebsiella pneumoniae is an emerging healthcare-associated pathogen.Objective.To describe the epidemiology of and clinical outcomes associated with carbapenem-resistant K. pneumoniae infection and to identify risk factors associated with mortality among patients with this type of infection.Setting.Mount Sinai Hospital, a 1,171-bed tertiary care teaching hospital in New York City.Design.Two matched case-control studies.Methods.In the first matched case-control study, case patients with carbapenem-resistant K. pneumoniae infection were compared with control patients with carbapenem-susceptible K. pneumoniae infection. In the second case-control study, patients who survived carbapenem-resistant K. pneumoniae infection were compared with those who did not survive, to identify risk factors associated with mortality among patients with carbapenem-resistant K. pneumoniae infection.Results.There were 99 case patients and 99 control patients identified. Carbapenem-resistant K. pneumoniae infection was independently associated with recent organ or stem-cell transplantation (P = .008), receipt of mechanical ventilation (P = .04), longer length of stay before infection (P = .01), and exposure to cephalosporins (P = .02) and carbapenems (P < .001). Case patients were more likely than control patients to die during hospitalization (48% vs 20%; P < .001) and to die from infection (38% vs 12%; P < .001). Removal of the focus of infection (ie, debridement) was independently associated with patient survival (P = .002). The timely administration of antibiotics with in vitro activity against carbapenem-resistant K. pneumoniae was not associated with patient survival.Conclusions.Carbapenem-resistant K. pneumoniae infection is associated with numerous healthcare-related risk factors and with high mortality. The mortality rate associated with carbapenem-resistant K. pneumoniae infection and the limited antimicrobial options for treatment of carbapenem-resistant K. pneumoniae infection highlight the need for improved detection of carbapenem-resistant K. pneumoniae infection, identification of effective preventive measures, and development of novel agents with reliable clinical efficacy against carbapenem-resistant K. pneumoniae.

Carbapenem-Resistant Klebsiella pneumoniae Outbreak in a Neonatal Intensive Care Unit: Risk Factors for Mortality

2020 ◽  
Author(s):  
Meltem Bor ◽  
Ozkan Ilhan
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Klebsiella Pneumoniae ◽  
Congenital Anomalies ◽  
Neonatal Intensive Care ◽  
Carbapenem Resistant ◽  
Group 2 ◽  
Antifungal Use ◽  
Group 1

Abstract Aim The aim of our study was to determine the factors associated with mortality in neonates with carbapenem-resistant Klebsiella pneumoniae (CRKP). Material and methods This retrospective, single-center study was conducted in the Neonatal Intensive Care Unit of Harran University Faculty of Medicine between January 2017 and July 2018 who had CRKP growth in their blood, urine or cerebrospinal fluid cultures. The discharged group was designated as the control group (Group 1), whereas the group that faced mortality was classified as the case group (Group 2). The demographic data, clinical findings and laboratory and microbiological results of the two groups were compared to identify risk factors. Results A total of 58 patients (36 in Group 1 and 22 in Group 2) exhibited CRKP growth during the study period. Low birth weight (p = 0.039), previous antifungal (p = 0.002) or amikacin use (p = 0.040), congenital anomalies (p = 0.002), total parenteral nutrition (TPN) administration (p = 0.002), surgery (p = 0.035), thrombocytopenia (p = 0.007), low platelet mass index (p = 0.011), elevated C-reactive protein (p = 0.004), high carbapenem minimum inhibitory concentration (MIC) (p = 0.029) and high amikacin MIC (p = 0.019) were associated with mortality. In a multivariate regression analysis, previous antifungal use (p = 0.028), congenital anomalies (p = 0.032) and TPN use (p = 0.013) were independent factors in predicting mortality. Conclusion Previous antifungal use, congenital anomalies and TPN use were found to be independent risk factors for mortality in neonates with CRKP infection.

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