Comparison of Hematological and Biochemical Biomarkers for Predicting Dengue Severity in Children

Dengue remains a global health concern, especially in tropical regions like Indonesia. As of July 2024, there were 149,866 reported cases and 844 deaths, with children aged 0–14 years accounting for 63% of fatalities. Dengue presents a wide clinical spectrum, ranging from asymptomatic infection to Severe Dengue. Early detection of Severe Dengue still heavily relies on clinical warning signs, which are often unreliable. This study aimed to identify potential biomarkers to predict Dengue severity in children. An observational study using secondary data from medical records at RSSA Malang (2014–2022) was conducted, involving 304 pediatric patients (161 Severe Dengue, 143 Non-Severe Dengue). Patient characteristics such as gender, age, weight, nutritional status, and hospital stay duration showed no significant correlation with severity. However, significant differences were found in several biomarkers, notably SGOT, SGPT, ΔHb, PT, albumin, platelets, monocytes, and NLR. SGPT (AUC 0.894; OR 24.3) and SGOT (AUC 0.884; OR 16.148) emerged as the most predictive biomarkers. SGPT showed the highest diagnostic performance with 73% sensitivity, 90% specificity, and a cut-off value ≥52 µ/dL, indicating a 24.3-fold increased risk for Severe Dengue. SGPT may serve as a reliable early indicator of severity in pediatric Dengue cases.

Authors

Institutions

Publication Details

Journal
Japanese Journal of Infectious Diseases
Published
2026-05-28
DOI
https://doi.org/10.7883/yoken.jjid.2025.093
Primary Topic
Mosquito-borne diseases and control
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Comparison of Hematological and Biochemical Biomarkers for Predicting Dengue Severity in Children

Irene Ratri Dewi, Agustin Iskandar, Jason Wiliam, Aurelia Bunanta et al.
Japanese Journal of Infectious Diseases
Mosquito-borne diseases and control
article

Comparison of Hematological and Biochemical Biomarkers for Predicting Dengue Severity in Children

Irene Ratri Dewi, Agustin Iskandar, Jason Wiliam, Aurelia Bunanta, Christopher Chang
article en

Abstract

Dengue remains a global health concern, especially in tropical regions like Indonesia. As of July 2024, there were 149,866 reported cases and 844 deaths, with children aged 0–14 years accounting for 63% of fatalities. Dengue presents a wide clinical spectrum, ranging from asymptomatic infection to Severe Dengue. Early detection of Severe Dengue still heavily relies on clinical warning signs, which are often unreliable. This study aimed to identify potential biomarkers to predict Dengue severity in children. An observational study using secondary data from medical records at RSSA Malang (2014–2022) was conducted, involving 304 pediatric patients (161 Severe Dengue, 143 Non-Severe Dengue). Patient characteristics such as gender, age, weight, nutritional status, and hospital stay duration showed no significant correlation with severity. However, significant differences were found in several biomarkers, notably SGOT, SGPT, ΔHb, PT, albumin, platelets, monocytes, and NLR. SGPT (AUC 0.894; OR 24.3) and SGOT (AUC 0.884; OR 16.148) emerged as the most predictive biomarkers. SGPT showed the highest diagnostic performance with 73% sensitivity, 90% specificity, and a cut-off value ≥52 µ/dL, indicating a 24.3-fold increased risk for Severe Dengue. SGPT may serve as a reliable early indicator of severity in pediatric Dengue cases.

Japanese Journal of Infectious Diseases
University of Brawijaya (ID), RSUD Prof. Dr. Saiful Anwar Malang (ID)
Universitas Brawijaya
Zero hunger
Openalex Percentile: Top 5%
Mosquito-borne diseases and control
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.