Timothy C Lin1, Maartje Dijkstra2,3, Godelieve J De Bree4, Maarten F Schim van der Loeff2,3, Martin Hoenigl5,6. 1. UC San Diego School of Medicine, La Jolla, CA. 2. Department of Infectious Diseases, Research and Prevention, Public Health Service of Amsterdam, Amsterdam, the Netherlands. 3. Department of Infectious Diseases, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands. 4. Amsterdam Institute for Global Health and Development, University of Amsterdam, Amsterdam, the Netherlands. 5. Section of Infectious Diseases and Tropical Medicine, Division of Pulmonology, Medical University of Graz, Auenbruggerplatz, Graz, Austria. 6. Division of Infectious Diseases, Department of Medicine, UC San Diego School of Medicine, San Diego, CA.
Abstract
OBJECTIVE: Dijkstra et al recently described a risk- and symptom-based score moderately predictive for HIV seroconversion in the preceding 6-12 months in men who have sex with men (MSM) in Amsterdam. Our objective was to determine whether this "Amsterdam Score" could also predict for acute HIV infection (AHI) in MSM. DESIGN AND SETTING: This study is a case-control analysis of a prospectively enrolled cohort of MSM who voluntarily presented for HIV testing in San Diego. The study sample was composed of MSM who screened HIV antibody-negative and then either tested positive with AHI [HIV nucleic acid test (NAT)-positive] or tested HIV NAT-negative. METHODS: The Amsterdam Score was calculated for each participant in the study sample. Score performance was assessed using receiver operating characteristic curves and their area under the curve (AUC). An optimal cutoff was determined using the Youden index. RESULTS: Seven hundred fifty-seven MSM (110 AHI and 647 HIV NAT-negative) were included in the analysis. AHI and HIV-negative cases were similar in age [median 32 years (interquartile range 26-42) vs 33 (27-45), respectively, P = 0.082]. The Amsterdam Score yielded a receiver operating characteristic curve with an AUC of 0.88 (95% confidence interval: 0.84 to 0.91). An optimal cutoff of ≥1.6 was 78.2% sensitive and 81.0% specific. CONCLUSIONS: The risk- and symptom-based Amsterdam Score was highly predictive (AUC of 0.88) of AHI in MSM in San Diego. The Amsterdam Score could be used to target NAT utilization in resource-poor settings among MSM who test HIV antibody-negative, although the potential cost-savings must be balanced with the risk of missing AHI diagnoses.
OBJECTIVE: Dijkstra et al recently described a risk- and symptom-based score moderately predictive for HIV seroconversion in the preceding 6-12 months in men who have sex with men (MSM) in Amsterdam. Our objective was to determine whether this "Amsterdam Score" could also predict for acute HIV infection (AHI) in MSM. DESIGN AND SETTING: This study is a case-control analysis of a prospectively enrolled cohort of MSM who voluntarily presented for HIV testing in San Diego. The study sample was composed of MSM who screened HIV antibody-negative and then either tested positive with AHI [HIV nucleic acid test (NAT)-positive] or tested HIV NAT-negative. METHODS: The Amsterdam Score was calculated for each participant in the study sample. Score performance was assessed using receiver operating characteristic curves and their area under the curve (AUC). An optimal cutoff was determined using the Youden index. RESULTS: Seven hundred fifty-seven MSM (110 AHI and 647 HIV NAT-negative) were included in the analysis. AHI and HIV-negative cases were similar in age [median 32 years (interquartile range 26-42) vs 33 (27-45), respectively, P = 0.082]. The Amsterdam Score yielded a receiver operating characteristic curve with an AUC of 0.88 (95% confidence interval: 0.84 to 0.91). An optimal cutoff of ≥1.6 was 78.2% sensitive and 81.0% specific. CONCLUSIONS: The risk- and symptom-based Amsterdam Score was highly predictive (AUC of 0.88) of AHI in MSM in San Diego. The Amsterdam Score could be used to target NAT utilization in resource-poor settings among MSM who test HIV antibody-negative, although the potential cost-savings must be balanced with the risk of missing AHI diagnoses.
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