AIDS (London, England) · 2023
Association of CD4 + cell count and HIV viral load with risk of non-AIDS-defining cancers.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Ma Yunqing, Zhang Jiajia, Yang Xueying, Chen Shujie, Weissman Sharon, Olatosi Bankole, Alberg Anthony, Li Xiaoming
Аннотация
OBJECTIVES: HIV-induced immunodeficiency contributes to an increased risk of non-AIDS-defining cancers (NADC). This study aims to identify the most predictive viral load (VL) or CD4 + measures of NADC risk among people with HIV (PWH).
DESIGN: Extracted from South Carolina electronic HIV reporting system, we studied adult PWH who were cancer-free at baseline and had at least 6 months of follow-up since HIV diagnosis between January 2005 and December 2020.
METHODS: Using multiple proportional hazards models, risk of NADC was investigated in relation to 12 measures of VL and CD4 + cell count at three different time intervals before NADC diagnosis. The best VL/CD4 + predictor(s) and final model were determined using Akaike's information criterion.
RESULTS: Among 10 413 eligible PWH, 449 (4.31%) developed at least one type of NADC. After adjusting for potential confounders, the best predictors of NADC were the proportion of days with viral suppression (hazard ratio [HR]: 0.47 (>25% and ≤50% vs. 0), 95% confidence interval [CI]: [0.28, 0.79]) and proportion of days with low CD4 + cell count (AIC = 7201.35) (HR: 12.28 (>75% vs. = 0), 95% CI: [9.29, 16.23]).
CONCLUSIONS: VL and CD4 + measures are strongly associated with risk of NADC. In analyses examining three time windows, proportion of days with low CD4 + cell count was the best CD4 + predictor for each time window. However, the best VL predictor varied across time windows. Thus, using the best combination of VL and CD4 + measures for a specific time window should be considered when predicting NADC risk.
DESIGN: Extracted from South Carolina electronic HIV reporting system, we studied adult PWH who were cancer-free at baseline and had at least 6 months of follow-up since HIV diagnosis between January 2005 and December 2020.
METHODS: Using multiple proportional hazards models, risk of NADC was investigated in relation to 12 measures of VL and CD4 + cell count at three different time intervals before NADC diagnosis. The best VL/CD4 + predictor(s) and final model were determined using Akaike's information criterion.
RESULTS: Among 10 413 eligible PWH, 449 (4.31%) developed at least one type of NADC. After adjusting for potential confounders, the best predictors of NADC were the proportion of days with viral suppression (hazard ratio [HR]: 0.47 (>25% and ≤50% vs. 0), 95% confidence interval [CI]: [0.28, 0.79]) and proportion of days with low CD4 + cell count (AIC = 7201.35) (HR: 12.28 (>75% vs. = 0), 95% CI: [9.29, 16.23]).
CONCLUSIONS: VL and CD4 + measures are strongly associated with risk of NADC. In analyses examining three time windows, proportion of days with low CD4 + cell count was the best CD4 + predictor for each time window. However, the best VL predictor varied across time windows. Thus, using the best combination of VL and CD4 + measures for a specific time window should be considered when predicting NADC risk.