A STUDY ON OUTCOMES OF VARIOUS TREATMENT REGIMENS IN DRUG- RESISTANT TUBERCULOSIS

Background: Drug-resistant tuberculosis (DR-TB) poses a significant challenge to global public health due to its complexity in treatment and the emergence of extensively drug-resistant strains. Conventional treatment protocols have shown varying success rates, necessitating exploration into alternative regimens. Understanding the outcomes of these diverse treatment approaches is crucial for optimizing patient care and combating the spread of resistant strains. Aims & Objectives: This study aims to investigate the outcomes of different treatment regimens employed for DR-TB. Methodology: This is a record-based study in a Government Medical college in Maharashtra during the year 2020 to 2022. All adult drug resistant TB cases registered were included, giving a sample size of 258. The outcomes of all regimens were examined. Results: Out of the total 258cases, 136(52.71%) were males and 122(47.28%) were females. Majority of them 90 (34.88%) belonged to age group of 21 to 30 years. The treatment outcomes were as follows: Out of the total 258 cases, 107(41.47%) were cured , 55(21.31%) completed treatment, 3(1.16%) were treatment failed, 6(2.32%) were lost to follow up, 45(17.44%) expired before treatment completion, 41(15.89%) required change of regimen. Conclusion: Optimizing treatment strategies for DR-TB is essential for improving patient outcomes, reducing transmission, and addressing the global burden of tuberculosis.

Authors

Publication Details

Published
2024-12-07
DOI
https://doi.org/10.53555/xszjay52
Citations
1
Primary Topic
Tuberculosis Research and Epidemiology
Type
article
Field-Weighted Citation Impact
0.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A STUDY ON OUTCOMES OF VARIOUS TREATMENT REGIMENS IN DRUG- RESISTANT TUBERCULOSIS

Abhijeet Yadav
1 citations
Tuberculosis Research and Epidemiology
0.39
article

A STUDY ON OUTCOMES OF VARIOUS TREATMENT REGIMENS IN DRUG- RESISTANT TUBERCULOSIS

Abhijeet Yadav
article en
1 citations

Abstract

Background: Drug-resistant tuberculosis (DR-TB) poses a significant challenge to global public health due to its complexity in treatment and the emergence of extensively drug-resistant strains. Conventional treatment protocols have shown varying success rates, necessitating exploration into alternative regimens. Understanding the outcomes of these diverse treatment approaches is crucial for optimizing patient care and combating the spread of resistant strains. Aims & Objectives: This study aims to investigate the outcomes of different treatment regimens employed for DR-TB. Methodology: This is a record-based study in a Government Medical college in Maharashtra during the year 2020 to 2022. All adult drug resistant TB cases registered were included, giving a sample size of 258. The outcomes of all regimens were examined. Results: Out of the total 258cases, 136(52.71%) were males and 122(47.28%) were females. Majority of them 90 (34.88%) belonged to age group of 21 to 30 years. The treatment outcomes were as follows: Out of the total 258 cases, 107(41.47%) were cured , 55(21.31%) completed treatment, 3(1.16%) were treatment failed, 6(2.32%) were lost to follow up, 45(17.44%) expired before treatment completion, 41(15.89%) required change of regimen. Conclusion: Optimizing treatment strategies for DR-TB is essential for improving patient outcomes, reducing transmission, and addressing the global burden of tuberculosis.

Good health and well-being
Openalex Percentile: Top 34%
Tuberculosis Research and Epidemiology
0.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.