Resistance of gram negative bacteria in hospital acquired pneumonia: a prospective study

Dehghan, Farzaneh; Zolghadri, Nader; Boostani, Vahid; Shafii, Afsaneh; Eftekhaari, Tasnim Eghbal

doi:10.1016/j.bjid.2015.11.006

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…We revealed that the mean mortality rate in this study was 13.0%, consistent with the HAP mortality reported by other researchers [8, 14]. In the ICUs, the mortality was the highest, similar to that seen in the previous studies [8, 15] because the patients often had multiple organ dysfunction or underwent bronchoscopy [15].…”

Section: Discussionsupporting

confidence: 91%

Elevated Blood Urea Nitrogen-to-Serum Albumin Ratio as a Factor That Negatively Affects the Mortality of Patients with Hospital-Acquired Pneumonia

Feng

Zhou

Zou

et al. 2019

Canadian Journal of Infectious Diseases and Medical Microbiolog

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This study aimed to evaluate the factors that affect 30-day mortality of patients with HAP. The data used in this study were collected from all HAP occurred in our hospital between January 2014 and December 2017. A total of 1158 cases were included. 150 (13.0%) of whom died within 30 days. This reported mortality identified by the univariate Cox regression analysis is found to have been affected by the following factors: age greater than 70 years, presence of diabetes mellitus and chronic obstructive pulmonary disease, gastric tube intubation, administration of proton-pump inhibitor, blood albumin level less than 30 g/l, elevated neutrophil-to-lymphocyte ratio, antibiotics therapy in the preceding 90 days, intensive care unit (ICU) admission, blood lymphocyte count less than 0.8 × 109/L, elevated blood urea nitrogen/albumin (BUN/ALB) level, and presence of multidrug-resistant (MDR) pathogens. In the second multivariate analysis, administration of proton-pump inhibitor, administration of antibiotics in the preceding 90 days, ICU admission, blood lymphocyte count less than 0.8 × 109/L, elevated BUN/ALB level, and presence of MDR pathogens were still associated with 30-day mortality. The area under the receiver operating characteristic curves in the BUN/ALB predicting 30-day mortality due to HAP was 0.685. A high BUN/ALB was significantly associated with a worse survival than a low BUN/ALB P<0.001. Therefore, an elevated BUN/ALB level is a risk factor for mortality on patients with HAP.

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Section: Discussionsupporting

confidence: 91%

Elevated Blood Urea Nitrogen-to-Serum Albumin Ratio as a Factor That Negatively Affects the Mortality of Patients with Hospital-Acquired Pneumonia

Feng

Zhou

Zou

et al. 2019

Canadian Journal of Infectious Diseases and Medical Microbiolog

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“…However, nosocomial pneumonia remains a major concern owing to its associated high mortality rate, [ 1 ] long hospitalization time and increased hospitalization costs. [ 2 ] Nosocomial pneumonia is the most common nosocomial infection in the intensive care unit (ICU). [ 3 ] Nosocomial pneumonia in the ICU can be divided into ventilator-acquired pneumonia (VAP) and hospital-acquired pneumonia (HAP).…”

Section: Introductionmentioning

confidence: 99%

Intensive care unit-acquired pneumonia caused by Klebsiella pneumoniae in China: Risk factors and prediction model of mortality

Huang

Yan

et al. 2023

Medicine

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Intensive care unit (ICU)-acquired pneumonia (ICUAP) is a major concern owing to its associated high mortality rate. Few studies have focused on ICUAP caused by Klebsiella pneumoniae (KP). This study aimed to investigate the risk factors for ICUAP-associated death due to KP and to develop a mortality prediction model. Patients with KP-associated ICUAP at Renji Hospital were enrolled from January 2012 to December 2017. The patients were registered from the ICU units of the Surgery, Gynecology and Obstetrics, Neurosurgery, Emergency and Geriatric Departments, and were followed for 30 days. A multivariate analysis was performed to analyze the differences between 30-day survivors and nonsurvivors, and to determine the independent risk factors. Receiver operator characteristic (ROC) curves were also used to determine the predictive power of the model. Among the 285 patients with KP-associated ICUAP, the median age was 70.55 years, and 61.6% were men. Fifty patients died. The nonsurvivors had a lower Glasgow coma score (GCS), platelet count, and albumin concentrations, but higher lactate concentrations, than the survivors. The nonsurvivors were also more likely to be admitted to the ICU for respiratory failure and surgery, and they received less appropriate empirical antimicrobial therapy than the survivors. A lower GCS (odds ratio [OR] = 0.836, 95% confidence interval [CI]: 0.770–0.907), lower albumin concentrations (OR = 0.836, 95% CI: 0.770–0.907), higher lactate concentrations (OR = 1.167, 95% CI: 1.0013–1.344) and inappropriate empirical treatment (OR = 2.559, 95% CI: 1.080–6.065) were independent risk factors for mortality in patients with KP-associated ICUAP. ROC curve analysis showed that the risk of death was higher in patients with 2 or more independent risk factors. The predictive model was effective, with an area under the ROC curve of 0.823 (95% CI: 0.773–0.865). The number of independent risk factors is positively correlated with the risk of death. Our model shows excellent predictive performance.

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“…Gram-negative pneumonia is a lung infection caused by Gram-negative bacteria [2]. The use of the word Gram comes from the name of its inventor Hans Christian Gram [3].…”

Section: Introductionmentioning

confidence: 99%

An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning

2020

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This paper proposes an image processing approach to identify Gram-negative bacteria. Gram-negative bacteria are one of the bacteria that cause lung lobe damage-bacterial samples obtained through smears of the patient's sputum. The first step bacterium should pass the pathogen test process. After that, it bred using Mc Conkey's media. The problem faced is that the process of identifying bacterial objects is still done manually under a fluorescence microscope. The contributions offered from this research are focused on observing bacterial morphology for the operation of selecting shape features. The proposed method is a convolutional neural network with fine-tuning. In the stages of the process, a convolutional neural network of the VGG-16 architecture used dropout, data augmentation, and fine-tuning stages. The main goal of the current research was to determine the method selection is to get a high degree of accuracy. This research uses a total sample of 2520 images from 2 different classes. The amount of data used at each stage of training, testing, and validation is 840 images with dimensions of 256x256 pixels, a resolution of 96 points per inch, and a depth of 24 bits. The accuracy of the results obtained at the training stage is 99.20%.

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Resistance of gram negative bacteria in hospital acquired pneumonia: a prospective study

Cited by 6 publications

References 5 publications

Elevated Blood Urea Nitrogen-to-Serum Albumin Ratio as a Factor That Negatively Affects the Mortality of Patients with Hospital-Acquired Pneumonia

Elevated Blood Urea Nitrogen-to-Serum Albumin Ratio as a Factor That Negatively Affects the Mortality of Patients with Hospital-Acquired Pneumonia

Intensive care unit-acquired pneumonia caused by Klebsiella pneumoniae in China: Risk factors and prediction model of mortality

An improvement of Gram-negative bacteria identification using convolutional neural network with fine tuning

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