Treatment-Decision Algorithm of Child TB: Evaluation of WHO Algorithm and Development of Indonesia Algorithm.

Area of Science:

• Pediatrics
• Infectious Diseases
• Public Health

Background:

• Clinical algorithms are crucial for guiding child tuberculosis (TB) treatment initiation.
• Existing Indonesian diagnostic algorithms require evaluation against global standards like the WHO algorithm.
• Variability in diagnostic capabilities and clinical presentations impacts algorithm implementation.

Purpose of the Study:

• To evaluate the agreement between Indonesia's pediatric TB diagnostic algorithms and the 2022 WHO treatment decision algorithm.
• To develop a new, improved Indonesian algorithm for child TB diagnosis based on expert consensus and study findings.
• To identify challenges and limitations in implementing algorithmic approaches for pediatric TB diagnosis.

Main Methods:

• Retrospective study involving 523 children (0-10 years) diagnosed with TB in 10 Indonesian hospitals during 2022.
• Expert panel diagnosis using the 2022 WHO and 2016 Indonesian algorithms, compared against attending physician diagnoses.
• Assessment of diagnostic agreement using Cohen's Kappa statistic.

Main Results:

• Attending doctors diagnosed TB in 70.9% of cases, the WHO algorithm in 56.4%, and the Indonesian algorithm in 47%.
• Cohen's Kappa values indicated poor to moderate agreement: attending doctor vs. WHO (0.27), attending doctor vs. Indonesia (0.45), WHO vs. Indonesia (0.42).
• Significant challenges were identified for the practical implementation of both evaluated algorithms.

Conclusions:

• Current Indonesian and WHO algorithms show limited agreement and face implementation hurdles in pediatric TB diagnosis.
• The developed Indonesian guideline aims to improve diagnosis but faces limitations due to resource variability and diverse clinical presentations.
• An algorithmic approach for child TB diagnosis requires adaptation to local contexts for effective implementation.