Acinetobacter baumannii is an emerging pathogen, and over the last three decades it has proven to be particularly difficult to treat by healthcare services. It is now regarded as a formidable infectious agent with a genetic setup for prompt development of resistance to most of the available antimicrobial agents. we provide a comprehensive updated overview of the available data about A. baumannii, the multi-drug resistant (MDR) phenotype spread, carbapenem-resistance, and the associated genetic resistance determinants in low-income countries (LIICs) since the beginning of the 21st century.
Country | Study | Isolates ( | n | ) | MDR % * | CRAB% | Isolates Characterization | References | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sub-Saharan Africa | |||||||||||||||||||||||||
Ethiopia | Kempf et al., 2012 | 40 | NA | NA | rpoB | and | recA | sequencing for genotyping | [ | 10 | ] | ||||||||||||||
Lema et al., 2012 | 5 | ≥20% | NA | AST with KB | [ | 11 | ] | ||||||||||||||||||
Pritsch et al., 2017 | 3 | 100% | 100% | AST with KB and VITEK 2, CT102 Micro-Array, real-time PCR, WGS, MLST, and detection of the | blaNDM-1 | [ | 12 | ] | |||||||||||||||||
Solomon et al., 2017 | 43 | 81% | 37% | AST with KB and phenotypic detection of ESBLs and MBLs | [ | 13 | ] | ||||||||||||||||||
Bitew et al., 2017 | 2 | 100% | NA | Identification and AST with VITEK 2 | [ | 15 | ] | ||||||||||||||||||
Demoz et al., 2018 | 1 | 100% | 100% | AST with KB | [ | 18 | ] | ||||||||||||||||||
Gashaw et al., 2018 | 2 | 50% XDR and 50% PDR | 100% | AST with KB and phenotypic detection of ESBLs and AmpC | [ | 19 | ] | ||||||||||||||||||
Moges et al., 2019 | 15 | ≥63% | Yes | AST with KB and phenotypic detection of ESBLs and carbapenemases | [ | 14 | ] | ||||||||||||||||||
Admas et al., 2020 | 6 | 100% | NA | Identification and AST with VITEK 2 | [ | 16 | ] | ||||||||||||||||||
Motbainor et al., 2020 | 9 | 100% | 33% | Identification with VITEK 2 and AST with KB | [ | 17 | ] | ||||||||||||||||||
Madagascar | Randrianirina et al., 2010 | 50 | ≥44% | 44% | AST with KB and phenotypic detection of ESBLs | [ | 20 | ] | |||||||||||||||||
Andriamanantena et al., 2010 | 53 | 100% | 100% | AST with KB and MIC determination, phenotypic detection of carbapenemases, ReP-PCR for genotyping and PCR for detection of; | blaAmpC | , | blaoxa51 | , | blaoxa23 | , | blaoxa24 | , | blaVIM | , | blaIMP | , and | isAba-1 | [ | 21 | ] | |||||
Rasamiravaka et al., 2015 | 10 | ≥50% | 0% | AST with KB | [ | 22 | ] | ||||||||||||||||||
Tchuinte et al., 2019 | 15 | 100% | 100% | MALDI-TOF MS for identification, AST with KB and MIC determination, WGS, MLST for genotyping and WGS detecting; | blaoxa51 | , | blaoxa23 | , | blaoxa24 | , | blaoxa58 | , and | isAba-1 | [ | 23 | ] | |||||||||
Eremeeva et al., 2019 | 14 | NA | NA | TaqMan PCR of the | rpoB | for identification, and PCR for detecting: | blaoxa51-like | , | blaoxa23 | , | blaoxa24 | , | blaVIM | , and | blaIMP | [ | 24 | ] | |||||||
Uganda | Kateete et al., 2016 | 40 | 60% | 38% | AST with Phoenix Automated Microbiology System, PCR for: | blaoxa51-like | , | blaoxa51 | , | blaoxa23 | , | blaoxa24 | , | blaoxa58 | , | blaVIM | , | blaSPM | , and | blaIMP | [ | 26 | ] | ||
Kateete et al., 2017 | 20 | 40% | 35% | AST with MIC determination, PAMS, Rep-PCR for genotyping and phenotypic detection of ESBLs and AmpC | [ | 27 | ] | ||||||||||||||||||
Moore et al., 2019 | 3 | NA | NA | qPCR TAC | [ | 25 | ] | ||||||||||||||||||
Aruhomukama et al., 2019 | 1077 | 3% | 3% | AST with KB, PCR for detecting: | blaoxa23 | , | blaoxa24 | , | blaoxa58 | , | blaVIM | , | blaSPM | , | blaKPC | , and | blaIMP | , phenotypic detection of carbapenemases, and conjugation to show transferability of | blaVIM | . | [ | 28 | ] | ||
Burkina Faso | Kaboré et al., 2016 | 3 | 100% | NA | AST with KB and phenotypic detection of ESBLs | [ | 29 | ] | |||||||||||||||||
Sanou et al., 2021 | # | 5 | 100% | 60% | MALDI-TOF MS for identification, AST with KB and MIC determination, phenotypic detection of ESBLs, PCR and sequencing of multiple resistance genes including; | blaoxa1-like | , | blaoxa48-like | , | blaNDM | , | blaVIM | , | blaSPM | , | blaKPC | , | blaCTX-M | , and | blaIMP | , and MLST for genotyping. | [ | 30 | ] | |
DR of the Congo | Lukuke et al., 2017 | 2 | 0% | NA | API for identification and AST with KB | [ | 32 | ] | |||||||||||||||||
Koyo et al., 2019 | 15 | NA | NA | qPCR and phylogenetic analysis using the | rpoB | gene | [ | 33 | ] | ||||||||||||||||
Malawi | Bedell et al., 2012 | 1 | NA | NA | Identification with standard diagnostic techniques | [ | 34 | ] | |||||||||||||||||
Iroh Tam et al., 2019 | 84 | ≥44% | NA | API for identification, AST with KB, and phenotypic detection of ESBLs | [ | 35 | ] | ||||||||||||||||||
Mozambique | Martínez et al., 2016 | 1 | NA | NA | 16S rRNA PCR and MALDI-TOF MS for identification | [ | 37 | ] | |||||||||||||||||
Hurtado et al., 2019 | 1 | 100% | 0% | 16S rRNA for identification and AST with KB | [ | 36 | ] | ||||||||||||||||||
Sudan | Mohamed et al., 2019 | 1 | NA | NA | API for identification followed by WGS | [ | 38 | ] | |||||||||||||||||
Dirar et al., 2020 | 12 | ≥83% | 89% | Identification with PAMS, AST with KB and phenotypic detection of ESBLs and carbapenemases. | [ | 39 | ] | ||||||||||||||||||
Rwanda | La Scola and Raoult 2004 | 10 | NA | NA | API for identification and | recA | genotyping | [ | 40 | ] | |||||||||||||||
Heiden et al., 2020 | 1 | 100% | 0% | MALDI-TOF MS for identification, AST with VITEK 2, phenotypic detection of ESBLs and carbapenemases, and WGS | [ | 41 | ] | ||||||||||||||||||
Burundi | La Scola and Raoult 2004 | 3 | NA | NA | API for identification and | recA | genotyping | [ | 40 | ] | |||||||||||||||
Mali | Doumbia-Singare et al., 2014 | 1 | NA | NA | Not mentioned | [ | 42 | ] | |||||||||||||||||
Sierra Leone | Lakoh et al., 2020 | 14 | ≥40% | 10% | Identification and AST with VITEK 2 | [ | 43 | ] | |||||||||||||||||
Somalia | Mohamed et al., 2020 | 7 | 100% | 100% | AST with KB | [ | 44 | ] | |||||||||||||||||
Niger | Louni et al., 2018 | 29 | NA | NA | qPCR and | rpoB | PCR for identification and phylogenetic analysis | [ | 45 | ] | |||||||||||||||
Central African Republic | No Reports | ||||||||||||||||||||||||
Chad | No Reports | ||||||||||||||||||||||||
Eritrea | No Reports | ||||||||||||||||||||||||
Gambia | No Reports | ||||||||||||||||||||||||
Guinea | No Reports | ||||||||||||||||||||||||
Guinea-Bissau | No Reports | ||||||||||||||||||||||||
Liberia | No Reports | ||||||||||||||||||||||||
South Sudan | No Reports | ||||||||||||||||||||||||
Togo | No Reports | ||||||||||||||||||||||||
Middle East and North Africa | |||||||||||||||||||||||||
Syria | Hamzeh et al., 2012 | 260 | ≥65% | 65% | Identification and AST with PAMS | [ | 46 | ] | |||||||||||||||||
Teicher et al., 2014 | 6 | 100% | 80% | API for identification and AST with MicroScan Walk-Away System | [ | 47 | ] | ||||||||||||||||||
Peretz et al., 2014 | 5 | 100% | NA | Not mentioned | [ | 50 | ] | ||||||||||||||||||
Rafei et al., 2014 | 4 | 100% | 100% | Identification with | rpoB | sequencing and | blaoxa51 | , PCR, AST with KB and Etest, PCR for: | blaoxa23-like | , | blaoxa24-like | , | blaoxa58-like | , and | blaNDM | , and PFGE for genotyping | [ | 53 | ] | ||||||
Heydari et al., 2015 | 1 | 100% | 100% | Identification and AST with VITEK 2, phenotypic detection of ESBLs and carbapenemases, PCR for the | blaNDM | , PFGE and MLST for typing | [ | 51 | ] | ||||||||||||||||
Rafei et al., 2015 | 59 | Yes | 74% | Identification with MALDI-TOF MS, | rpoB | sequencing and | blaoxa51 | PCR, AST with KB and Etest, PCR for detecting: | blaoxa23 | , | blaoxa24 | , | blaoxa58 | , | blaNDM-1 | , | blaVIM | , | blaoxa143 | , and | blaIMP | , and MLST for typing | [ | 54 | ] |
Herard and Fakhri 2017 | 38 | NA | NA | Not mentioned | [ | 48 | ] | ||||||||||||||||||
Salloum et al., 2018 | 2 | 100% | 100% | AST with KB and Etest, PCR for | blaoxa58 | and | blaNDM | , plasmid typing with PBRT, MLST, and WGS | [ | 55 | ] | ||||||||||||||
Fily et al., 2019 | 6 | NA | 67% | AST with KB | [ | 49 | ] | ||||||||||||||||||
Hasde et al., 2019 | 5 | NA | NA | Not mentioned | [ | 52 | ] | ||||||||||||||||||
Yemen | Bakour et al., 2014 | 3 | 100% | 100% | API and MALDI-TOF MS for identification, AST with KB and E-test, phenotypic detection of carbapenemases, PCR detection of: | blaoxa23 | , | blaoxa24 | , | blaoxa58 | , | blaNDM | , | blaVIM | , | blaSIM | , | blaoxa48-like | , | blaIMP | and others, and MLST | [ | 56 | ] | |
Fily et al., 2019 | 1 | NA | 100% | AST with KB | [ | 49 | ] | ||||||||||||||||||
South Asia | |||||||||||||||||||||||||
Afghanistan | Sutter et al., 2011 | 57 | ¥ | ≥75% | 76% | Identification and AST with MicroScan autoSCAN-4 | [ | 57 | ] | ||||||||||||||||
Latin America and The Caribbean | |||||||||||||||||||||||||
Haiti | Potron et al., 2011 | 3 | 66.7% | 0% | API and 16sRNA for identification, AST with KB and E-test, phenotypic detection of ESBLs, PCR for detection of: | blaTEM | , | blaSHV | , | blaPER-1 | , | blaVEB-1 | , | blaGES-1 | , and | blaCTX-M | [ | 58 | ] | ||||||
Marra et al., 2012 | 1 | 100% | 0% | Identification and AST with VITEK 2 | [ | 59 | ] | ||||||||||||||||||
Murphy et al., 2016 | 4 | ≥25% | 25% | AST but the method was not indicated | [ | 60 | ] | ||||||||||||||||||
Chaintarli et al., 2018 | 2 | 0% | 0% | Identification and AST with VITEK 2 and phenotypic detection of ESBLs | [ | 61 | ] | ||||||||||||||||||
Roy et al., 2018 | 0 | ϕ | NA | NA | Metagenomic analyses of water samples | [ | 62 | ] | |||||||||||||||||
Europe and Central Asia | |||||||||||||||||||||||||
Tajikistan | No Reports | ||||||||||||||||||||||||
East Asia and Pacific | |||||||||||||||||||||||||
Democratic People’s Republic of Korea | No Reports |