Staphylococcus aureus Infections in Malaysia: A Review of Antimicrobial Resistance and Characteristics of the Clinical Isolates, 1990-2017.
Ainal Mardziah Che HamzahChew Chieng YeoSuat Moi PuahKek-Heng ChuaChing Hoong ChewPublished in: Antibiotics (Basel, Switzerland) (2019)
Staphylococcus aureus is an important nosocomial pathogen and its multidrug resistant strains, particularly methicillin-resistant S. aureus (MRSA), poses a serious threat to public health due to its limited therapeutic options. The increasing MRSA resistance towards vancomycin, which is the current drug of last resort, gives a great challenge to the treatment and management of MRSA infections. While vancomycin resistance among Malaysian MRSA isolates has yet to be documented, a case of vancomycin resistant S. aureus has been reported in our neighboring country, Indonesia. In this review, we present the antimicrobial resistance profiles of S. aureus clinical isolates in Malaysia with data obtained from the Malaysian National Surveillance on Antimicrobial Resistance (NSAR) reports as well as various peer-reviewed published records spanning a period of nearly three decades (1990-2017). We also review the clonal types and characteristics of Malaysian S. aureus isolates, where hospital-associated (HA) MRSA isolates tend to carry staphylococcal cassette chromosome mec (SCCmec) type III and were of sequence type (ST)239, whereas community-associated (CA) isolates are mostly SCCmec type IV/V and ST30. More comprehensive surveillance data that include molecular epidemiological data would enable further in-depth understanding of Malaysian S. aureus isolates.
Keyphrases
- methicillin resistant staphylococcus aureus
- antimicrobial resistance
- staphylococcus aureus
- public health
- genetic diversity
- multidrug resistant
- electronic health record
- biofilm formation
- type iii
- healthcare
- big data
- adverse drug
- escherichia coli
- drug resistant
- mental health
- acinetobacter baumannii
- randomized controlled trial
- emergency department
- quality improvement
- pseudomonas aeruginosa
- cystic fibrosis
- machine learning
- replacement therapy