Description of Clinical Characteristics of VAP Patients in MIMIC Database

Association between anion gap and the 30-day mortality of patients with ventilator-associated pneumonia: a study of the MIMIC-III database

Background

Serum anion gap (AG) can potentially be applied to the diagnosis of various metabolic acidosis, and a recent study has reported the association of AG with the mortality of patients with coronavirus disease 2019 (COVID-19). However, the relationship of AG with the short-term mortality of patients with ventilator-associated pneumonia (VAP) is still unclear. Herein, we aimed to investigate the association between AG and the 30-day mortality of VAP patients, and construct and assess a multivariate predictive model for the 30-day mortality risk of VAP.

Methods

This retrospective cohort study extracted data of 477 patients with VAP from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Data of patients were divided into a training set and a testing set with a ratio of 7:3. In the training set, variables significantly associated with the 30-day mortality of VAP patients were included in the multivariate predictive model through univariate Cox regression and stepwise regression analyses. Then, the predictive performance of the multivariate predictive model was assessed in both training set and testing set, and compared with the single AG and other scoring systems including the Sequential Organ Failure Assessment (SOFA) score, the confusion, urea, respiratory rate (RR), blood pressure, and age (≥65 years old) (CURB-65) score, and the blood urea nitrogen (BUN), altered mental status, pulse, and age (>65 years old) (BAP-65) score. In addition, the association of AG with the 30-day mortality of VAP patients was explored in subgroups of gender, age, and infection status. The evaluation indexes were hazard ratios (HRs), C-index, and 95% confidence intervals (CIs).

Results

A total of 70 patients died within 30 days. The multivariate predictive model consisted of AG (HR =1.052, 95% CI: 1.008-1.098), age (HR =1.037, 95% CI: 1.019-1.055), duration of mechanical ventilation (HR =0.998, 95% CI: 0.996-0.999), and vasopressors use (HR =1.795, 95% CI: 1.066-3.023). In both training set (C-index =0.725, 95% CI: 0.670-0.780) and testing set (C-index =0.717, 95% CI: 0.637-0.797), the multivariate model had a relatively superior predictive performance to the single AG value. Moreover, the association of AG with the 30-day mortality was also found in patients who were male (HR =1.088, 95% CI: 1.029-1.150), and whatever the pathogens they infected (bacterial infection: HR =1.059, 95% CI: 1.011-1.109; fungal infection: HR =1.057, 95% CI: 1.002-1.115).

Conclusions

The AG-related multivariate model had a potential predictive value for the 30-day mortality of patients with VAP. These findings may provide some references for further exploration on simple and robust predictors of the short-term mortality risk of VAP, which may further help clinicians to identify patients with high risk of mortality in an early stage in the intensive care units (ICUs).

The footprint of gut microbiota in gallbladder cancer: a mechanistic review

Gallbladder cancer (GBC) is the most common malignant tumor of the biliary system with the worst prognosis. Even after radical surgery, the majority of patients with GBC have difficulty achieving a clinical cure. The risk of tumor recurrence remains more than 65%, and the overall 5-year survival rate is less than 5%. The gut microbiota refers to a variety of microorganisms living in the human intestine, including bacteria, viruses and fungi, which profoundly affect the host state of general health, disease and even cancer. Over the past few decades, substantial evidence has supported that gut microbiota plays a critical role in promoting the progression of GBC. In this review, we summarize the functions, molecular mechanisms and recent advances of the intestinal microbiota in GBC. We focus on the driving role of bacteria in pivotal pathways, such as virulence factors, metabolites derived from intestinal bacteria, chronic inflammatory responses and ecological niche remodeling. Additionally, we emphasize the high level of correlation between viruses and fungi, especially EBV and Candida spp., with GBC. In general, this review not only provides a solid theoretical basis for the close relationship between gut microbiota and GBC but also highlights more potential research directions for further research in the future.

Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study

Background: While Enterobacteriaceae bacteria are commonly found in the healthy human gut, their colonization of other body parts can potentially evolve into serious infections and health threats. We investigate a graph-based machine learning model to predict risks of inpatient colonization by multidrug-resistant (MDR) Enterobacteriaceae. Methods: Colonization prediction was defined as a binary task, where the goal is to predict whether a patient is colonized by MDR Enterobacteriaceae in an undesirable body part during their hospital stay. To capture topological features, interactions among patients and healthcare workers were modeled using a graph structure, where patients are described by nodes and their interactions are described by edges. Then, a graph neural network (GNN) model was trained to learn colonization patterns from the patient network enriched with clinical and spatiotemporal features. Results: The GNN model achieves performance between 0.91 and 0.96 area under the receiver operating characteristic curve (AUROC) when trained in inductive and transductive settings, respectively, up to 8% above a logistic regression baseline (0.88). Comparing network topologies, the configuration considering ward-related edges (0.91 inductive, 0.96 transductive) outperforms the configurations considering caregiver-related edges (0.88, 0.89) and both types of edges (0.90, 0.94). For the top 3 most prevalent MDR Enterobacteriaceae, the AUROC varies from 0.94 for Citrobacter freundii up to 0.98 for Enterobacter cloacae using the best-performing GNN model. Conclusion: Topological features via graph modeling improve the performance of machine learning models for Enterobacteriaceae colonization prediction. GNNs could be used to support infection prevention and control programs to detect patients at risk of colonization by MDR Enterobacteriaceae and other bacteria families.

Seeking predictors for paroxysmal atrial fibrillation in stroke with an online clinical database

OBJECTIVE:

A considerable fraction of ischemic stroke cases remain cryptogenic and there is increasing data suggesting the role of missed paroxysmal atrial fibrillations (pAF) in at least a number of these cases. Since electrophysiological identification of pAFs can be challenging, there has been an accumulation of proposed predictors and biomarkers for pAFs. The predictive values of these is varying and sometimes conflicting among studies. Therefore, we aimed to verify a fraction of previously reported parameters for pAF detection by investigating an independent clinical sample.

METHODS:

Using a publicly available data downloaded from the MIMIC-3 intensive care unit database, we tested the predictive role of particular risk factors and biomarkers for pAF detection after ischemic stroke in 124 patients with ischemic stroke admitted within 24 hours of stroke onset.

RESULTS:

Our evaluation revealed a strong association of older age in women, as well as admission National Institutes of Health Stroke Scale (NIHSS) and discharge modified Rankin Scores (mRS) in both sexes for pAFs, in patients that were in sinus rhythm on admission. We also detected a trend for lower gender-adjusted hemoglobin in patients with pAF, although the difference was insignificant. On the other hand, we did not find any significant association of pAF detection with some other previously reported biomarkers: serum magnesium level, leukocyte count, neutrophil/lymphocyte ratio or left atrial dilatation.

CONCLUSION:

Even though our analysis did not reveal a strong and specific biomarker to predict pAFs after stroke, it identified key risk factors. It may be necessary to consider the possibility of pAFs and perform rigorous evaluation to prevent further events of embolic stroke in female patients older than 75 years, with more severe neurological deficits on admission, higher disability on discharge and also with relatively lower hemoglobin level. This first study from Turkey using clinical data from the MIMIC-3 database also demonstrates the value of publicized clinical data for confirmatory studies on various medical fields across the World.

Brief introduction of medical database and data mining technology in big data era

Data mining technology can search for potentially valuable knowledge from a large amount of data, mainly divided into data preparation and data mining, and expression and analysis of results. It is a mature information processing technology and applies database technology. Database technology is a software science that researches manages, and applies databases. The data in the database are processed and analyzed by studying the underlying theory and implementation methods of the structure, storage, design, management, and application of the database. We have introduced several databases and data mining techniques to help a wide range of clinical researchers better understand and apply database technology.

The Effects of Open and Closed Suction Methods on Occurrence of Ventilator Associated Pneumonia; a Comparative Study

INTRODUCTION

Endotracheal suctioning is a method commonly used to clean airway secretions in patients under mechanical ventilation (MV). This study aimed to compare the effects of open and closed suction methods on the occurrence of ventilator associated pneumonia (VAP).

METHODS

This comparative study was carried out on adult intensive care unit (ICU) patients in need of MV for more than 48 hours, from October 2018 to January 2019. Patients were randomly allocated to either closed tracheal suction system (CTSS) group or open tracheal suction system (OTSS) group. Patients were monitored for developing VAP within 72 hours of intubation and the findings were compared between groups.

RESULTS

120 cases with the mean age of 57.91±19.9 years were randomly divided into two groups (56.7% male). The two groups were similar regarding age (p = 0.492) and sex (p = 0.713) distribution. 22 (18.3%) cases developed VAP (12 (20%) in OSST group and 10 (16.7%) in CSST; p = 0.637). The most prevalent bacterial causes of VAP were Acinetobacter_Baumannii (72.7%), Klebsiella pneumoniae (18.2%), and Methicillin-Resistant Staphylococcus aureus (9.1%), respectively. There was not any significant difference between groups regarding the mean duration of remaining under MV (p = 0.623), mean duration of hospitalization (p = 0.219), frequency of VAP (p = 0.637), and mortality (p = 0.99).

CONCLUSION

It seems that type of endotracheal suction system (OSST vs. CSST) had no effect on occurrence of VAP and other outcomes such as duration of need for MV and ICU stay as well as mortality.