Dataset Information

Rising prevalence of multidrug-resistant uropathogenic bacteria from urinary tract infections in pregnant women.

ABSTRACT:

Objectives

This study aimed to determine the prevalence of urinary tract infections (UTI) in pregnant women and characterise the uropathogenic bacterial strains associated with symptomatic and asymptomatic bacteriuria in Lahore, Pakistan.

Methods

Between December 2018 and June 2019, we analysed the uropathogenic bacterial strains from midstream urine samples in 80 pregnant women. The age of the pregnant women ranged from 19 to 45 years, and they resided in urban and rural areas. We also recorded socioeconomic factors in this cohort. The isolated strains were phenotypically identified and evaluated for multiple drug resistance (MDR) patterns against recommended antimicrobial drugs.

Results

Of the 80 pregnant women, 65 had UTI, reflecting an 81% prevalence of UTI in women during pregnancy. The majority of participants aged 24-35 years, were multipara, and were in their third trimester. Results showed that 67 uropathogenic bacterial strains belonged to Escherichia (31%), Klebsiella (23%), Pseudomonas (16%), Streptococcus (4%), Enterococcus (4%), Staphylococcus (4%), and Proteus (3%) genera, as identified using biochemical characterisation. The highest overall resistance of Escherichia was seen against amoxicillin, pipemidic acid, and ampicillin; for Klebsiella against pipemidic acid, ampicillin, and cefotaxime; and for Pseudomonas against ciprofloxacin and cefotaxime. The three strains with the highest MDR were identified using 16S rRNA as Pseudomonas aeruginosa strain UA17, Escherichia coli strain UA32, and Klebsiella pneumoniae strain UA47.

Conclusion

In this study, the MDR uropathogenic strains showed the highest resistance pattern. The alarming signs of MDR uropathogenic infections are infrequently addressed and thus, urgent attention to this matter is essential.

SUBMITTER: Asmat U

PROVIDER: S-EPMC7858016 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS:We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS:the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.

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