Personal clinical history predicts antibiotic resistance of urinary tract infections

Yelin, Idan; Snitser, Olga; Novich, Gal; Katz, Rachel; Tal, Ofir; Parizade, Miriam; Chodick, Gabriel; Koren, Gideon; Shalev, Varda; Kishony, Roy

doi:10.1038/s41591-019-0503-6

Cited by 139 publications

(150 citation statements)

References 54 publications

Supporting

Mentioning

144

Contrasting

Unclassified

Order By: Relevance

“…In the space of bacterial infection, retrospective analyses of etiology and descriptive studies have been reported in regular intervals [31][32][33] . However, collective and methodical applications of predictive modeling is yet to be extensively implemented in the field of empiric treatment via antimicrobial sensitivity analysis barring a few interesting attempts 34,35 . Also the data used in the existing studies were not publicly available.…”

Section: Discussionmentioning

confidence: 99%

Machine learning based prediction of antibiotic sensitivity in patients with critical illness

Ghosh¹,

Sharma²,

Hasan³

et al. 2019

Preprint

View full text Add to dashboard Cite

Rising antibiotic resistance inflicts a heavy burden on healthcare, both clinically and economically. Owing to the time required to obtain culture and sensitivity test results, quite often the clinicians rely on their experience and static clinical guidelines to prescribe antibiotics. Such empirical treatment often fails to account for patient-specific attributes and changes in the antibiotic resistance patterns with time and location. The aim of this study was to analyze the patient and hospital specific features regarding their prognostic relevance to treat bacterial infections of patients in the intensive care units (ICUs). We performed a single-center retrospective cohort analysis across 25526 positive cultures recorded in MIMIC-III critical care database. We retrieved a number of clinically relevant relationships from association analysis between patient factors and bacterial strains. For instance, higher elapsed time from patient admission to sample collection for culture showed strong association with blood stream infection caused by Enterococcus faecium, Pseudomonas aeruginosa, and Staphylococcus, indicating that these infections are possibly hospital acquired. To predict antibiotic sensitivity at the level of individual patients we developed an ensemble of machine learning algorithms. The model provided superior prediction accuracy (about 87%) and area under the ROC curve (around 0.91 on an average) for the four most common sample types as compared to a number of off-the-shelf techniques. We demonstrate the predictive power of commonly recorded patient attributes in personalised prediction of antibiotic efficacy.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning based prediction of antibiotic sensitivity in patients with critical illness

Ghosh¹,

Sharma²,

Hasan³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Resistance to beta-lactam antibiotics, mediated primarily through beta-lactamases, is growing as a major threat to public health. Beta-lactams are commonly used for the treatment of a range of bacterial pathogens, constituting the most widely prescribed antibiotic class 1,2 . These antibiotics are characterized by a core of a beta-lactam ring and are classified by their moieties into four major families: penicillins, cephalosporins, carbapenems, and monobactams 3 .…”

Section: Main Textmentioning

confidence: 99%

Escape mutations circumvent a tradeoff between resistance to a beta-lactam and a beta-lactamase inhibitor

Russ

Glaser

Tamar

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Beta-lactamase inhibitors are increasingly used to counteract microbial resistance to beta-lactam antibiotics mediated by beta-lactamase enzymes. These inhibitors compete with the beta-lactam drug for the same binding site of the beta-lactamase, thereby generating an inherent evolutionary tradeoff: enzyme mutations that increase its activity against the beta-lactam drug also increase its susceptibility towards the inhibitor. It is unclear how common and accessible are mutants that escape this adaptive tradeoff. Here, systematically constructing and phenotyping a deep mutant library of the ampC beta-lactamase gene of Escherichia coli , we identified escape mutations, which even in the presence of the enzyme inhibitor allow growth at beta-lactam concentrations far exceeding the native inhibitory levels of the wildtype strain.Importantly, while such escape mutations appear for combinations of avibactam with some beta-lactam drugs, for other drugs escape phenotypes are completely restricted. Amplicon sequencing of the selected mutant pool identified these escape mutations and showed that they are rare and drug specific. For the combination of avibactam with aztreonam, an escape phenotype was conferred via multiple substitutions in a single conserved amino acid (Tyr 150).In contrast, a different set of mutations showed an escape phenotype for cefepime, and no escape mutants appeared for piperacillin. The differential adaptive potential of ampC to combinations of avibactam and different beta-lactam drugs can help guide drug treatments that are more resilient to evolution of resistance.

show abstract

“…Pharmaceutical residues in wastewater are a severe threat to the ecological environment. Misuse and overuse of antibiotics can lead to high antibiotic resistance and multi-resistant strains of microorganisms [1][2][3][4]. As a broad-spectrum antibiotic, tetracycline (TC) is widely used for disease treatment in humans, and it is frequently detected in various bodies of water owing to its high solubility.…”

Section: Introductionmentioning

confidence: 99%

In Situ Construction of a MgSn(OH)6 Perovskite/SnO2 Type-II Heterojunction: A Highly Efficient Photocatalyst towards Photodegradation of Tetracycline

Tian

Wang

et al. 2019

Nanomaterials

View full text Add to dashboard Cite

Using solar energy to remove antibiotics from aqueous environments via photocatalysis is highly desirable. In this work, a novel type-II heterojunction photocatalyst, MgSn(OH)6/SnO2, was successfully prepared via a facile one-pot in situ hydrothermal method at 220 °C for 24 h. The obtained heterojunctions were characterized via powder X-ray diffraction, Fourier-transform infrared spectroscopy, transmission electron microscopy, and ultraviolet-visible diffuse reflectance spectroscopy. The photocatalytic performance was evaluated for photodegradation of tetracycline solution under ultraviolet irradiation. The initial concentration of tetracycline solution was set to be 20 mg/L. The prepared heterojunctions exhibited superior photocatalytic activity compared with the parent MgSn(OH)6 and SnO2 compounds. Among them, the obtained MgSn(OH)6/SnO2 heterojunction with MgCl2·6H2O:SnCl4·5H2O = 4:5.2 (mmol) displayed the highest photocatalytic performance and the photodegradation efficiency conversion of 91% could be reached after 60 min under ultraviolet irradiation. The prepared heterojunction maintained its performance after four successive cycles of use. Active species trapping experiments demonstrated that holes were the dominant active species. Hydroxyl radicals and superoxide ions had minor effects on the photocatalytic oxidation of tetracycline. Photoelectrochemical measurements were used to investigate the photocatalytic mechanism. The enhancement of photocatalytic activity could be assigned to the formation of a type-II junction photocatalytic system, which was beneficial for efficient transfer and separation of photogenerated electrons and holes. This research provides an in situ growth strategy for the design of highly efficient photocatalysts for environmental restoration.

show abstract

Personal clinical history predicts antibiotic resistance of urinary tract infections

Cited by 139 publications

References 54 publications

Machine learning based prediction of antibiotic sensitivity in patients with critical illness

Machine learning based prediction of antibiotic sensitivity in patients with critical illness

Escape mutations circumvent a tradeoff between resistance to a beta-lactam and a beta-lactamase inhibitor

In Situ Construction of a MgSn(OH)6 Perovskite/SnO2 Type-II Heterojunction: A Highly Efficient Photocatalyst towards Photodegradation of Tetracycline

Contact Info

Product

Resources

About