scholarly journals Prevalence, susceptibility testing and multi drug resistance risk factors to methicillin resistant Staphylococcus aureus in nasal carriage of hospitalized patients and medical staff in selected hospitals in Cameroon

2020 ◽  
Vol 12 (2) ◽  
pp. 42-51
Author(s):  
Kouemou Sinda Leontine ◽  
Nyhalah Dinga Jerome ◽  
Wanji Samuel ◽  
Shiro Koulla Sinata
Keyword(s):  
Risk Factors ◽  
Staphylococcus Aureus ◽  
Drug Resistance ◽  
Medical Staff ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Susceptibility Testing ◽  
Nasal Carriage ◽  
Hospitalized Patients ◽  
Multi Drug Resistance ◽  
Resistance Risk

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 Multi-drug resistance traits of methicillin-resistant Staphylococcus aureus and other Staphylococcal species from clinical and environmental sources

2019 ◽  
Vol 17 (6) ◽  
pp. 930-943 ◽  
Author(s):  
Adegboyega O. Oladipo ◽  
Oluwatosin G. Oladipo ◽  
Cornelius C. Bezuidenhout
Keyword(s):  
Staphylococcus Aureus ◽  
Drug Resistance ◽  
Antibiotic Resistance ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Animal Health ◽  
Antibiotic Resistance Genes ◽  
Multi Drug Resistance ◽  
Methicillin Resistant ◽  
Hospital Effluent ◽  
Resistance Traits

Abstract Multi-drug resistance traits of Staphylococcus species especially methicillin-resistant Staphylococcus aureus (MRSA) in the clinical settings are well established. Of environmental concern is hospital effluents discharging into wastewaters. This article investigated the prevalence and detection of antibiotic resistance genes in Staphylococcus species from clinical and environmental sources in Ile-Ife, Nigeria. Standard culture-based and molecular protocols were used. Seventy-six (27 clinical, 14 hospital effluent and 35 environmental) Staphylococcus isolates were recovered: 56.58% were coagulase-negative and 43.42% coagulase-positive (S. aureus). For the clinical isolates, 10, 6, 4, 4 and 1 were isolated from urine, skin, wounds, blood and pus, respectively. Isolates were resistant to methicillin and amoxycillin (91.7%), cloxacillin (88.0%), ciprofloxacin (84.0%), ofloxacin (83.3%), azithromycin (78.0%), ceftazidime (76.0%), gentamycin (75.0%), cefuroxime (75.0%) and erythromycin (72.0%). Nearly, all isolates (90.8%) had multiple antibiotic resistance (MAR) index >0.2. Overall MAR indices for Staphylococcus species isolated from the clinical, hospital effluent and environmental wastewaters were relatively similar (0.482; 0.500; 0.435). mecA, nuc and luk-pvl genes were detected in S. aureus, while mecA was detected in S. arlettae, S. sciuri, S. cohnii, S. epidermidis and S. saprophyticus. This study informs on the potential contamination of environmental waters downstream from hospitals and possible impacts that this could have on human and animal health.

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