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Assembling a global database of child pneumonia studies to inform WHO pneumonia management algorithm: Methodology and
Helena Martin1, Jennifer Falconer1, Emmanuel Addo-Yobo2
1Centre for Global Health, Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom.
Insights
This study created a large dataset of childhood pneumonia cases to improve diagnostic accuracy. The findings will help refine the World Health Organization
Area of Science:
- Pediatric infectious diseases
- Global child health
- Clinical epidemiology
Background:
- Current World Health Organization (WHO) pneumonia guidelines for children under 5 rely on clinical signs.
- There is a need for improved diagnostic and prognostic tools for childhood pneumonia.
Purpose of the Study:
- To create a comprehensive individual patient-level data set from high-quality studies on childhood pneumonia.
- To identify more valid signs and symptoms for diagnosing, predicting, and treating pneumonia in children.
- To improve existing WHO pneumonia case management guidelines.
Main Methods:
- Data from a systematic review and expert knowledge were used to identify eligible studies.
- Investigators shared individual patient data, including demographics, medical history, clinical presentation, and outcomes.
- Data were collected from both hospital-based and community-based cases, followed by narrative synthesis.
Main Results:
- The Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) database includes 285,839 children with pneumonia from 41 datasets.
- A significant number of deaths occurred in infants under 2 years old.
- Detailed clinical data were collected on presentation, progression, and outcomes.
Conclusions:
- The PREPARE dataset can identify improved criteria for diagnosing clinical pneumonia.
- It can aid in identifying children requiring higher care or treatment changes.
- The PREPARE methodology offers a model for disease database assembly and future field studies.
Background:
The existing World Health Organization (WHO) pneumonia case management guidelines rely on clinical symptoms and signs for identifying, classifying, and treating pneumonia in children up to 5 years old. We aimed to collate an individual patient-level data set from large, high-quality pre-existing studies on pneumonia in children to identify a set of signs and symptoms with greater validity in the diagnosis, prognosis, and possible treatment of childhood pneumonia for the improvement of current pneumonia case management guidelines.
Methods:
Using data from a published systematic review and expert knowledge, we identified studies meeting our eligibility criteria and invited investigators to share individual-level patient data. We collected data on demographic information, general medical history, and current illness episode, including history, clinical presentation, chest radiograph findings when available, treatment, and outcome. Data were gathered separately from hospital-based and community-based cases. We performed a narrative synthesis to describe the final data set.
Results:
Forty-one separate data sets were included in the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) database, 26 of which were hospital-based and 15 were community-based. The PREPARE database includes 285 839 children with pneumonia (244 323 in the hospital and 41 516 in the community), with detailed descriptions of clinical presentation, clinical progression, and outcome. Of 9185 pneumonia-related deaths, 6836 (74%) occurred in children <1 year of age and 1317 (14%) in children aged 1-2 years. Of the 285 839 episodes, 280 998 occurred in children 0-59 months old, of which 129 584 (46%) were 2-11 months of age and 152 730 (54%) were males.
Conclusions:
This data set could identify an improved specific, sensitive set of criteria for diagnosing clinical pneumonia and help identify sick children in need of referral to a higher level of care or a change of therapy. Field studies could be designed based on insights from PREPARE analyses to validate a potential revised pneumonia algorithm. The PREPARE methodology can also act as a model for disease database assembly.
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