scholarly journals Artificial Intelligence to Get Insights of Multi-Drug Resistance Risk Factors during the First 48 Hours from ICU Admission

Antibiotics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 239
Author(s):  
Inmaculada Mora-Jiménez ◽  
Jorge Tarancón-Rey ◽  
Joaquín Álvarez-Rodríguez ◽  
Cristina Soguero-Ruiz
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Drug Resistance ◽  
Multi Drug Resistance ◽  
Time Interval ◽  
Health Records ◽  
Resistance Risk ◽  
Icu Admission ◽  
Selection Strategies ◽  
Patient Will

Multi-drug resistance (MDR) is one of the most current and greatest threats to the global health system nowadays. This situation is especially relevant in Intensive Care Units (ICUs), where the critical health status of these patients makes them more vulnerable. Since MDR confirmation by the microbiology laboratory usually takes 48 h, we propose several artificial intelligence approaches to get insights of MDR risk factors during the first 48 h from the ICU admission. We considered clinical and demographic features, mechanical ventilation and the antibiotics taken by the patients during this time interval. Three feature selection strategies were applied to identify statistically significant differences between MDR and non-MDR patient episodes, ending up in 24 selected features. Among them, SAPS III and Apache II scores, the age and the department of origin were identified. Considering these features, we analyzed the potential of machine learning methods for predicting whether a patient will develop a MDR germ during the first 48 h from the ICU admission. Though the results presented here are just a first incursion into this problem, artificial intelligence approaches have a great impact in this scenario, especially when enriching the set of features from the electronic health records.

scholarly journals Molecular epidemiology and survival analysis of nosocomial meningitis induced by multi-drug resistance Enterobacteriaceae

2020 ◽  
Author(s):  
Guanghui Zheng ◽  
Yanfei Cao ◽  
Lingye Qian ◽  
Yumeng Cai ◽  
Hong Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Drug Resistance ◽  
Molecular Epidemiology ◽  
Mortality Risk ◽  
Multiple Drug Resistance ◽  
Carbapenem Resistance ◽  
Multi Drug Resistance ◽  
Multiple Drug ◽  
Nosocomial Meningitis ◽  
Cox Analysis

Objectives: To evaluate the molecular epidemiology and mortality risk factors of nosocomial meningitis (NM) induced by multi-drug resistance Enterobacteriaceae (MDRE) in China. Methods: We performed a multi-center study of MDRE NM patients in 2 neurosurgical centers in China from 2014 to 2019. Molecular and phenotype microbiology epidemiology of each MDRE were reviewed and tested, and 21 clinical variables on mortality risk factors were extracted and evaluated by multivariate Cox analysis for NM. Results: In total, 90 MDRE NM patients were included in this study. Klebsiella (K.) pneumoniae occupied the highest proportion (51.11%, 46/90), 44 (44.44%) were meropenem-resistant, ceftriaxone resistance in target MDRE was relatively high (92.22%, 83/90), blaKPC (67.50%, 27/40) was the predominant carbapenem resistance gene, and blaCTX-M-1, blaTEM and blaCTM-M-9 were the three most popular extended spectrum β-lactamases (ESBLs) producing genes of the MDRE. Multivariate Cox analysis showed that external ventricular drainage (EVD, odds ratio (OR) 2.524, 95% confidence interval (CI) 1.101-5.787, P = 0.029) and Glasgow Coma Scale (GCS) ≤8 (OR 4.033, 95% CI 1.526-10.645, P = 0.005) were mortality risk factors of MDRE NM. Conclusions: NM caused by MDRE is an important sign of the failure of neurosurgery, and MDRE has multiple drug resistance genotypes, and EVD and GCS≤8 are independent mortality risk factors of MDRE NM, which deserves the attention of microbiologist and neurosurgical clinicians.

scholarly journals Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multi-drug resistance

2014 ◽  
Author(s):  
Stefany Moreno-Gamez ◽  
Alison L Hill ◽  
Daniel I.S. Rosenbloom ◽  
Dmitri A. Petrov ◽  
Martin A Nowak ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Rapid Evolution ◽  
Public Health Problem ◽  
Antimicrobial Treatment ◽  
The Body ◽  
Drug Coverage ◽  
Multi Drug Resistance ◽  
Resistance Risk ◽  
Combination Treatments ◽  
Microbial Infections

Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goals of combination therapy is to reduce the risk of drug resistance emerging during a patient's treatment. While this strategy generally has significant benefits over monotherapy, it may also select for multi-drug resistant strains, which present an important clinical and public health problem. For many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multi-drug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way, and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria and parasites.

Bloodstream infections caused by multi-drug resistantProteus mirabilis: Epidemiology, risk factors and impact of multi-drug resistance

2016 ◽  
Vol 48 (6) ◽  
pp. 428-431 ◽  
Author(s):  
Alexander Korytny ◽  
Klaris Riesenberg ◽  
Lisa Saidel-Odes ◽  
Fransisc Schlaeffer ◽  
Abraham Borer
Keyword(s):  
Risk Factors ◽  
Drug Resistance ◽  
Bloodstream Infections ◽  
Multi Drug Resistance

scholarly journals Risk factor analysis of equine strongyle resistance to anthelmintics

2017 ◽  
Author(s):  
G. Sallé ◽  
J. Cortet ◽  
I. Bois ◽  
C. Dubès ◽  
Q. Guyot-Sionest ◽  
...  
Keyword(s):  
Risk Factors ◽  
Drug Resistance ◽  
Factor Analysis ◽  
Risk Factor ◽  
Anthelmintic Resistance ◽  
Wide Distribution ◽  
Risk Factor Analysis ◽  
Resistance Risk ◽  
Treatment Regimens ◽  
Anthelmintic Efficacy

AbstractIntestinal strongyles are the most problematic endoparasites of equids as a result of their wide distribution and the spread of resistant isolates throughout the world. While abundant literature can be found on the extent of anthelmintic resistance across continents, empirical knowledge about associated risk factors is missing. This study brought together results from anthelmintic efficacy testing and risk factor analysis to provide evidence-based guidelines in the field. It involved 688 horses from 39 French horse farms and riding schools to both estimate Faecal Egg Count Reduction (FECR) after anthelmintic treatment and to interview farm and riding school managers about their practices. Risk factors associated with reduced anthelmintic efficacy in equine strongyles were estimated across drugs using a marginal modelling approach. Results demonstrated ivermectin efficacy (96.3% FECR), the inefficacy of fenbendazole (42.8% FECR) and an intermediate profile for pyrantel (90.3% FECR). Risk factor analysis provided support to advocate for FEC-based treatment regimens combined with individual anthelmintic dosage and the enforcement of tighter biosecurity around horse introduction that contributed to lower drug resistance risk by 1.75. Premises falling under this typology also relied more on their veterinarians suggesting they play an important role in the sustainability of anthelmintic usage. Similarly, drug resistance risk was halved in premises with frequent pasture rotation and with stocking rate below five horses/ha. This is the first empirical risk factor analysis for anthelmintic resistance in equids, whose findings should guide the implementation of more sustained strongyle management in the field.

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