Application of center for disease control and prevention standardized antimicrobial administration ratio to an Indian hospital.

Background: Rigorous antibiotic stewardship is advised by international societies to combat rising antibiotic resistance. A major component of these programs is the metric used for antibiotic consumption measurement. A method for standardized antimicrobial administration ratio (SAAR) is suggested by the Centre for Disease Control & Prevention-National Healthcare Safety Network (NHSN). Objectives: We applied the SAAR method to calculate antibiotic consumption in a tertiary care hospital in India. We also validated a limited sampling approach to calculate SAAR. Method: The prospective study was conducted in three medical intensive care units over a period of 12 months. Monthly antibiotic consumption was measured by the hospital electronic records. Limited sampling was performed by weekly bedside review of the antibiotic orders. Formulae for SAAR calculation were derived from the NHSN guide. SAAR obtained by electronic records and limited sampling were compared to validate this approach. Results: SAAR was calculated as >1 for an Indian hospital (1.49 by electronic records and 1.43 by limited sampling approach). The difference between the two ratios was not statistically significant (P = .47). Conclusions: SAAR in our setting is 1.49, which is slightly higher than the NHSN benchmark. Antibiotic usage (AU) risk adjustment based on data from the NHSN might not be adequate for calculating SAAR for Indian hospitals. There is a need to perform AU risk factor analysis for Indian settings for better defining SAAR in Indian context. The limited sampling approach can be adapted for calculation of SAAR in settings with limited resources.

Drug resistance pattern and mutation pattern in pediatric tuberculosis: Study from north India.

BACKGROUND: The emergence of multidrug-resistant MDR-TB and extensively drug-resistant XDR-TB are serious threats to global TB control. Molecular tests like GenoType MTBDRplus has revolutionized MDR-TB diagnosis by rapid detection of resistance, leading to early and appropriate management of DR-TB. Information about common mutations imparting resistance to RIF and INH, helps in understanding the disease epidemiology in various regions. The study was conducted to determine the genetic mutation in drug resistant tuberculosis in children less than 12 years with pulmonary or extrapulmonary tuberculosis. MATERIALS/METHODS: Retrospective analysis was done over a period of 54 months from January 2015 to June 2019 to study the resistance pattern and mutations present in DR-TB in children less than 12 years with suspected pulmonary or extrapulmonary tuberculosis using Hain's GenoType MTBDRplus VER 2.0. RESULTS: Over a period of 54 months, samples from 3461 patients with suspected TB were received for MGIT culture, out of which, 347 were positive for Mycobacterium tuberculosis. 250 of these 347 isolated were tested for drug resistance by Hain's GenoType MTBDRplus VER 2.0.61.1% were sensitive to isoniazid and rifampicin while 15.2% were DR-TB (38 out of 250). Out of these 38, 22 were MDR TB, 13 were isoniazid monoresistant (34.2%) and 3 were rifampicin monoresistant. The most common genotypic resistance for rifampicin was absence of rpoB WT8 band and presence of rpoB MUT 3 band (88%). 84.6% of the INH monoresistant isolates showed high level isoniazid resistant. All these isolates showed presence of katG MUT 1 band. On comparing Hain's GenoType MTBDRplus VER 2.0 with Xpert MTB/Rif Assay, most common mutation for rifampicin resistance at S531L which can be detected by Xpert MTB/Rif Assay (probe E). However, two cases with rifampicin resistance had mutation in codon region 509-513 and 513-519 which could be missed by Xpert MTB/Rif Assay. CONCLUSIONS: We cannot solely rely on Xpert MTB/Rif Assay for detection of drug resistance due to the risk of missing the isoniazid monoresistance. GenoType MTBDRplus has revolutionized MDR-TB diagnosis by substantially reducing turn around time and leading to early management of DR-TB cases.

Occurrence of blaTEM and blaROB in Haemophilus species Causing Respiratory Tract Infections.

BACKGROUND: Emergence of resistance to some antibiotics in Haemophilus influenzae, a respiratory pathogen is a cause of concern. The aim is to study the antibiotic susceptibility pattern of Haemophilus isolates from respiratory infections with reference to beta-lactam resistance. METHODS: This is a laboratory based prospective study done in the department of microbiology in a tertiary care center after institutional ethics committee clearance. Haemophilus influenzae isolates from respiratory tract specimens over a period of one year were subjected to antibiotic susceptibility tests. Beta-lactamase production was detected by nitrocefin disc. hpd gene, blaTEM and blaROB genes were detected by PCR. The data was analysed using SPSS 11.5 version. RESULTS: Of the 162 isolates, 89.5% were from sputum specimens. Ampicillin resistance was seen in 5 (3.09%) isolates. The ampicillin resistant strains were positive for beta-lactamase enzyme and blaTEM gene. BLNAR and isolates with blaROB gene were not found. CONCLUSION: In case of Haemophilus influenzae respiratory tract infection empirical treatment with amoxicillin clavulanate or third generation cephalosporin may be the drugs of choice in our geographic area.

Matrix factorization from non-linear projections: application in estimating T2 maps from few echoes.

This work addresses the problem of estimating T2 maps from very few (two) echoes. Existing multi-parametric non-linear curve fitting techniques require a large number (16 or 32) of echoes to estimate T2 values. We show that our method yields very accurate and robust results from only two echoes, where as the curve-fitting techniques require about 16 echoes to achieve the same level of accuracy. We model T2 maps as a rank-deficient matrix. Since the relationship between T2 values and intensity values/K-space samples is not linear, estimating the T2 values requires recovering a low-rank matrix from non-linear projections. We solve this as a non-linear matrix factorization problem. Since the said problem has not been solved before, we propose a simple algorithm for the same.