AI Article Synopsis

  • Targeted screening for HIV testing is essential, and the authors sought to create a risk assessment tool to better identify individuals at risk based on data from a Denver clinic.
  • Utilizing multivariable logistic regression, they developed a risk score from 48 variables, with a focus on newly diagnosed HIV infections, and validated it with a separate population in Cincinnati.
  • The final risk score takes into account factors like age, gender, sexual behavior, and drug use, effectively categorizing patients into distinct risk groups with varying probabilities of HIV infection, thus enhancing targeted screening efforts.

Article Abstract

Targeted screening remains an important approach to human immunodeficiency virus (HIV) testing. The authors aimed to derive and validate an instrument to accurately identify patients at risk for HIV infection, using patient data from a metropolitan sexually transmitted disease clinic in Denver, Colorado (1996-2008). With multivariable logistic regression, they developed a risk score from 48 candidate variables using newly identified HIV infection as the outcome. Validation was performed using an independent population from an urban emergency department in Cincinnati, Ohio. The derivation sample included 92,635 patients; 504 (0.54%) were diagnosed with HIV infection. The validation sample included 22,983 patients; 168 (0.73%) were diagnosed with HIV infection. The final score included age, gender, race/ethnicity, sex with a male, vaginal intercourse, receptive anal intercourse, injection drug use, and past HIV testing, and values ranged from -14 to +81. For persons with scores of <20, 20-29, 30-39, 40-49, and ≥50, HIV prevalences were 0.31% (95% confidence interval (CI): 0.20, 0.45) (n = 27/8,782), 0.41% (95% CI: 0.29, 0.57) (n = 36/8,677), 0.99% (95% CI: 0.63, 1.47) (n = 24/2,431), 1.59% (95% CI: 1.02, 2.36) (n = 24/1,505), and 3.59% (95% CI: 2.73, 4.63) (n = 57/1,588), respectively. The risk score accurately categorizes patients into groups with increasing probabilities of HIV infection.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3390011PMC
http://dx.doi.org/10.1093/aje/kwr389DOI Listing

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