Malik Simran, Shirvankar Chetan Mahadev, Jacob Rahul Kurian, Adhya Debashree Guha, Sinha Subir, Bhattacharya Sanjay, Walia Kamini, Bhattacharya Sangeeta Das
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal, India; Department of Microbiology, Tata Medical Center, Kolkata, West Bengal, India.
School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, West Bengal, India.
Indian J Med Microbiol. 2024 Nov-Dec;52:100732. doi: 10.1016/j.ijmmb.2024.100732. Epub 2024 Sep 25.
Determining regional patterns of antimicrobial resistance in bacterial infections in the healthcare setting (AMR) identifies surveillance gaps and informs policies for mitigation. We estimated the prevalence of AMR for six WHO priority pathogens in diagnostic and surveillance samples in the twelve east and north-east Indian states from 2011 to 2022 (PROSPERO ID: CRD42021278961).
Studies were searched on Medline, Scopus, and Web of Science. Observational, descriptive, and cross-sectional studies, reporting AMR based on laboratory diagnostics, in individuals from east and north-east India from 2011 to 2022 were included. Four reviewers in pairs conducted abstract, full-text screening, and data extraction. We estimated the prevalence of resistance in fifty-four pathogen-antibiotic combinations, and six antibiotic resistance patterns. Pooled estimates of prevalence (Ɵ), heterogeneity (I), and 95 % confidence intervals were calculated using the random effects model.
Fifty-five studies were included. Information was available for nine states, none from Arunachal Pradesh, Mizoram, and Nagaland. E. coli was most frequently isolated (59.2 %, 95 % CI: 48.8-69.6 %), followed by S. aureus (36.2 %, 95 % CI: 20.2-52.2 %), Enterococcus (27.5 %, 95 % CI: 11.2-43.7 %), Klebsiella (25 %, 95 % CI: 15-35 %), Acinetobacter (15.7 %, 95 % CI: 2.3-29.1 %), and Pseudomonas aeruginosa (15.7 %, 95 % CI: 4.1-27.3 %). There was high prevalence of ESBL (45 %, 95 % CI: 35-55 %) and carbapenem resistance (30 %, 95 % CI: 22-38 %). AmpC (23 %, 95 % CI: 9-37 %) and colistin resistance was lower (10 %, 95 % CI: 0-22 %) but supporting data was limited. Overall prevalence of MRSA was 26 % (95 % CI: 14-39 %), and VRE was 9 % (95 % CI: 0-17 %).
High prevalence of resistance was seen to all first-line antibiotics. Gram positive bacteria had high resistance to penicillins, and Gram negatives to third-generation cephalosporins, beta-lactam/beta-lactamase inhibitors, and carbapenems. Aminoglycoside, fluoroquinolone, and trimethoprim-sulphamethoxazole resistance was common across all genera. Critical regional AMR information gaps exist.
确定医疗机构中细菌感染的抗菌药物耐药性(AMR)区域模式,有助于发现监测漏洞,并为缓解策略提供政策依据。我们估计了2011年至2022年印度东部和东北部12个邦诊断和监测样本中六种世界卫生组织重点病原体的AMR流行率(PROSPERO编号:CRD42021278961)。
在Medline、Scopus和Web of Science上检索研究。纳入2011年至2022年期间印度东部和东北部地区基于实验室诊断报告AMR的观察性、描述性和横断面研究。四名评审员两两进行摘要、全文筛选和数据提取。我们估计了54种病原体-抗生素组合和六种抗生素耐药模式的耐药流行率。使用随机效应模型计算流行率(Ɵ)、异质性(I)和95%置信区间的合并估计值。
纳入了55项研究。有九个邦的信息可用,阿鲁纳恰尔邦、米佐拉姆邦和那加兰邦没有相关信息。大肠埃希菌最常被分离出来(59.2%,95%置信区间:48.8-69.6%),其次是金黄色葡萄球菌(36.2%,95%置信区间:20.2-52.2%)、肠球菌(27.5%,95%置信区间:11.2-43.7%)、克雷伯菌(25%,95%置信区间:15-35%)、不动杆菌(15.7%,95%置信区间:2.3-29.1%)和铜绿假单胞菌(15.7%,95%置信区间:4.1-27.3%)。超广谱β-内酰胺酶(ESBL)耐药率较高(45%,95%置信区间:35-55%),碳青霉烯类耐药率为30%(95%置信区间:22-38%)。AmpC耐药率(23%,95%置信区间:9-37%)和黏菌素耐药率较低(10%,95%置信区间:0-22%),但支持数据有限。耐甲氧西林金黄色葡萄球菌(MRSA)的总体流行率为26%(95%置信区间:14-39%),耐万古霉素肠球菌(VRE)为9%(95%置信区间:0-17%)。
所有一线抗生素的耐药率都很高。革兰氏阳性菌对青霉素耐药性高,革兰氏阴性菌对第三代头孢菌素、β-内酰胺/β-内酰胺酶抑制剂和碳青霉烯类耐药。氨基糖苷类、氟喹诺酮类和甲氧苄啶-磺胺甲恶唑耐药在所有菌属中都很常见。存在关键的区域AMR信息空白。