Development of a clinical algorithm to prioritise HIV testing of hospitalised paediatric patients in a low resource moderate prevalence setting.
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.
NCHECR, University of New South Wales, Sydney, Australia. wallison@nchecr.unsw.edu.au
Waridibo E
WE
Allison
Mobumo
M
Kiromat
John
J
Vince
Handan
H
Wand
Philip
P
Cunningham
Stephen M
SM
Graham
John
J
Kaldor
D015492:AIDS Serodiagnosis / Q000379:methods*; D017088:AIDS-Related Opportunistic Infections / Q000150:complications; D000465:Algorithms; D002180:Candidiasis, Oral / Q000150:complications; D002648:Child; D002675:Child, Preschool; D003661:Decision Support Techniques; D003906:Developing Countries; D004812:Epidemiologic Methods; D005260:Female; D005334:Fever / Q000821:virology; D015658:HIV Infections / Q000150:complications / Q000175:diagnosis*; D015276:Health Care Rationing / Q000379:methods*; D006760:Hospitalization; D006801:Humans; D007223:Infant; D008206:Lymphatic Diseases / Q000821:virology; D008297:Male; D008507:Medically Underserved Area; D010219:Papua New Guinea; D010808:Physical Examination; D013851:Thinness / Q000821:virology