Prediction of residual time to AIDS and death based on markers and cofactors

被引:16
作者
Geskus, RB
Miedema, FA
Goudsmit, J
Reiss, P
Schuitemaker, H
Coutinho, RA
机构
[1] Municipal Hlth Serv, NL-1018 WT Amsterdam, Netherlands
[2] CLB, Sanquin Div, Amsterdam, Netherlands
[3] Univ Amsterdam, Acad Med Ctr, NL-1105 AZ Amsterdam, Netherlands
[4] Crucell, Leiden, Netherlands
[5] Leiden Univ, Med Ctr, Leiden, Netherlands
关键词
markers; predictive value; prognosis; AIDS progression; therapy guidelines; baseline model;
D O I
10.1097/00126334-200304150-00008
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
A model was constructed that estimates the probability of an HIV-infected individual developing AIDS or dying within a certain time span if left untreated, based on the most recent CD4 lymphocyte count, HIV-1 RNA load, and HIV-1 phenotype, together with age, time since seroconversion, and two genetic cofactors. The model helps clinicians in deciding when to start highly active antiretroviral treatment (HAART). Data from the Amsterdam Cohort Study among homosexual men restricted to individuals with an estimated date of seroconversion (N = 280) were used. Individual predictions based on several combinations of marker and cofactor values were obtained, and their accuracy was measured using two indices of predictive value. CD4 lymphocyte count and HIV-1 RNA load have the highest predictive value and act independently. The predictive value of the HIV-1 phenotype is only slightly lower and greatly enhances predictions at high CD4 counts. The CCR5-Delta32 and CCR2-64I alleles have no additional predictive value. Some predictive value is lost by not knowing time since seroconversion, and some effect of calendar period is present. In summary, for prognosis, the markers CD4 count, HIV-1 RNA load, and HIV-1 phenotype (at a high CD4 count) are equally important, and the genetic cofactors considered are of no use.
引用
收藏
页码:514 / 521
页数:8
相关论文
共 48 条
[1]  
Altman DG, 2000, STAT MED, V19, P453, DOI 10.1002/(SICI)1097-0258(20000229)19:4<453::AID-SIM350>3.3.CO
[2]  
2-X
[3]  
Babiker A, 2000, LANCET, V355, P1131, DOI 10.1016/S0140-6736(00)02061-4
[4]   A unified approach for modeling longitudinal and failure time data, with application in medical monitoring [J].
Berzuini, C ;
Larizza, C .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1996, 18 (02) :109-123
[5]  
Bjorndal A, 1997, J VIROL, V71, P7478
[6]   Antiretroviral therapy in adults - Updated recommendations of the International AIDS Society-USA Panel [J].
Carpenter, CCJ ;
Cooper, DA ;
Fischl, MA ;
Gatell, JM ;
Gazzard, BG ;
Hammer, SM ;
Hirsch, MS ;
Jacobsen, DM ;
Katzenstein, DA ;
Montaner, JSG ;
Richman, DD ;
Saag, MS ;
Schechter, M ;
Schooley, RT ;
Vella, S ;
Yeni, PG ;
Volberding, PA .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2000, 283 (03) :381-390
[7]   AIDS prognosis based on HIV-1 RNA, CD4+ T-cell count and function: Markers with reciprocal predictive value over time after seroconversion [J].
deWolf, F ;
Spijkerman, I ;
Schellekens, PT ;
Langendam, M ;
Kuiken, C ;
Bakker, M ;
Roos, M ;
Coutinho, R ;
Miedema, F ;
Goudsmit, J .
AIDS, 1997, 11 (15) :1799-1806
[8]   Survival from early, intermediate, and late stages of HIV infection [J].
Enger, C ;
Graham, N ;
Peng, Y ;
Chmiel, JS ;
Kingsley, LA ;
Detels, R ;
Munoz, A .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 1996, 275 (17) :1329-1334
[9]  
Faucett CL, 1996, STAT MED, V15, P1663, DOI 10.1002/(SICI)1097-0258(19960815)15:15<1663::AID-SIM294>3.0.CO
[10]  
2-1