The development and utility of a clinical algorithm to predict early HIV-1 infection

J Acquir Immune Defic Syndr. 2005 Dec 1;40(4):472-8. doi: 10.1097/01.qai.0000164246.49098.47.

Abstract

The association between self-reported clinical factors and recent HIV-1 seroconversion was evaluated in a prospective cohort of 4652 high-risk participants in the HIV Network for Prevention Trials (HIVNET) Vaccine Preparedness Study. Eighty-six individuals seroconverted, with an overall annual seroconversion rate of 1.3 per 100 person-years. Four self-reported clinical factors were significantly associated with HIV-1 seroconversion in multivariate analyses: recent history of chlamydia infection or gonorrhea, recent fever or night sweats, belief of recent HIV exposure, and recent illness lasting > or =3 days. Two scoring systems, based on the presence of either 4 or 11 clinical factors, were developed. Sensitivity ranged from 2.3% (with a positive predictive value of 12.5%) to 72.1% (with a positive predictive value of 1%). Seroconversion rates were directly associated with the number of these clinical factors. The use of scoring systems comprised of clinical factors may aid in detecting early and acute HIV-1 infection in vaccine and microbicide trials. Organizers can educate high-risk trial participants to return for testing during interim visits if they develop these clinical factors. Studying individuals during early and acute HIV-1 infection would allow scientists to investigate the impact of the intervention being studied on early transmission or pathogenesis of HIV-1 infection.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Algorithms*
  • Chlamydia Infections
  • Female
  • Fever
  • Forecasting
  • Gonorrhea
  • HIV Infections / physiopathology*
  • HIV Seropositivity*
  • Humans
  • Male
  • Multivariate Analysis
  • Predictive Value of Tests
  • Prospective Studies
  • Risk Factors
  • Sensitivity and Specificity
  • Statistics as Topic
  • Sweating