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Published on: October 31, 2010
Development of a clinical algorithm to prioritise HIV testing of hospitalised paediatric patients in a low resource
Waridibo E Allison1, Mobumo Kiromat, John Vince
1NCHECR, University of New South Wales, Sydney, Australia. wallison@nchecr.unsw.edu.au
Insights
A new clinical algorithm identifies children with HIV infection using fever, swollen lymph nodes, oral thrush, or being underweight. This tool aids testing in resource-limited settings for better pediatric HIV management.
Area of Science:
- Pediatric Infectious Diseases
- Clinical Diagnostics
- Public Health
Background:
- Limited resources and moderate human immunodeficiency virus (HIV) prevalence necessitate targeted testing strategies for pediatric populations.
- Effective screening tools are crucial for early identification and management of HIV in children, especially in resource-constrained settings.
Purpose of the Study:
- To develop and validate a clinical algorithm for identifying hospitalized pediatric patients who would benefit from HIV testing.
- To improve the identification and management of HIV-infected children in Papua New Guinea.
Main Methods:
- A prospective cross-sectional study was conducted at Port Moresby General Hospital, Papua New Guinea.
- Clinical data and HIV status (antibody and DNA) were collected from 487 hospitalized children.
- Multivariate regression analysis identified independent predictors of HIV infection to develop a predictive algorithm.
Main Results:
- 11% (55/487) of children were HIV-infected, with a median age of 7 months.
- Independent predictors of HIV infection included persistent fever, lymphadenopathy, oral candidiasis, and being underweight.
- The algorithm, based on these predictors, achieved 96% sensitivity in detecting HIV infection.
Conclusions:
- A clinical algorithm incorporating specific symptoms can effectively screen hospitalized children for HIV infection in resource-limited settings.
- This tool can guide HIV testing practices, enhancing the early detection and management of pediatric HIV in Papua New Guinea and similar contexts.
Objective:
To develop a clinical algorithm to identify paediatric patients who should be offered HIV testing in a setting of moderate HIV prevalence and limited resources.
Methods:
In a prospective cross-sectional study at Port Moresby General Hospital, Papua New Guinea, carers of inpatients were offered HIV testing and counselling for their children. Recruited children were tested for HIV antibodies and DNA. Standardised clinical information was collected. Multivariate regression analysis was used to ascertain independent predictors of HIV infection and these were used to develop a predictive algorithm.
Results:
From September 2007 to October 2008, 487 children were enrolled. Overall, 55 (11%) with a median age of 7 months were found to be HIV-infected. In multivariate analysis, independent predictors of HIV infection were: persistent fever (OR = 2.05 (95% CI 1.11 to 4.68)), lymphadenopathy (OR = 2.29 (1.12 to 4.68)), oral candidiasis (OR = 3.94 (2.17 to 7.14)) and being underweight for age (OR = 2.03 (1.03 to 3.99)). The presence of any one of these conditions had a sensitivity of 96% in detecting a child with HIV infection. Using an algorithm based on the presence of at least one of these conditions would result in around 40% of hospitalised children being offered testing.
Conclusions:
This clinical algorithm may be a useful screening tool for HIV infection in hospitalised children in situations where it is not feasible to offer universal HIV testing, providing guidance for HIV testing practices for increased identification and management of HIV-infected children in Papua New Guinea.
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