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Clinical signs predictive of severe illness in young Pakistani infants
Shahira Shahid1, Shiyam Sunder Tikmani2, Kanwal Nayani1
1Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan.
Insights
The seven-sign algorithm effectively identifies severe illness in young infants, aiding early detection to prevent infant mortality. This study evaluated its performance across three distinct age groups in Pakistani infants.
Area of Science:
- Pediatrics
- Infectious Disease Epidemiology
- Clinical Diagnostics
Background:
- Infant mortality remains a significant global health challenge, particularly in low-resource settings.
- Early detection of severe illness in neonates and young infants is critical for timely intervention and improved survival rates.
- The Young Infants Clinical Signs and Symptoms (YICSS) study previously identified key indicators of severe illness.
Purpose of the Study:
- To evaluate the performance of the seven-sign algorithm for predicting severe illness in Pakistani infants.
- To assess the algorithm's accuracy across three distinct age categories: 0-6 days, 7-27 days, and 28-59 days.
Main Methods:
- A prospective study involving 2950 infants aged 0-59 days in Pakistan.
- Data collection on clinical signs, symptoms, and diagnoses from September 2003 to November 2004.
- Analysis of the seven-sign algorithm's sensitivity and specificity in predicting severe infection/sepsis across different age groups.
Main Results:
- Severe infection/sepsis was the most common diagnosis requiring hospitalization across all age groups.
- The algorithm demonstrated good performance: sensitivity 85.9% (specificity 71.6%) in 0-6 days, sensitivity 72.4% (specificity 83.1%) in 7-27 days, and sensitivity 80.5% (specificity 80.2%) in 28-59 days.
- Common presenting symptoms varied by age, including umbilical redness/discharge in younger infants and cough in older infants.
Conclusions:
- The seven-sign algorithm is a valuable tool for the early detection of severe illness in young infants in Pakistan.
- The algorithm's performance is robust across different age strata, supporting its use in clinical settings for risk stratification.
- Further validation and implementation of this algorithm can contribute to reducing infant mortality.
Objective:
Early detection of specific signs and symptoms to predict severe illness is essential to prevent infant mortality. As a continuation of the results from the multicenter Young Infants Clinical Signs and Symptoms (YICSS) study, we present here the performance of the seven-sign algorithm in 3 age categories (0-6 days, 7-27 days and 28-59 days) in Pakistani infants aged 0-59 days.
Results:
From September 2003 to November 2004, 2950 infants were enrolled (age group 0-6 days = 1633, 7-27 days = 817, 28-59 days = 500). The common reason for seeking care was umbilical redness or discharge (29.2%) in the 0-6 days group. Older age groups presented with cough (16.9%) in the 7-27 age group and (26.9%) infants in the 28-59 days group. Severe infection/sepsis was the most common primary diagnoses in infants requiring hospitalization across all age groups. The algorithm performed well in every age group, with a sensitivity of 85.9% and specificity of 71.6% in the 0-6 days age group and a sensitivity of 80.5% and specificity of 80.2% in the 28-59 days group; the sensitivity was slightly lower in the 7-27 age group (72.4%) but the specificity remained high (83.1%).
