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Enregistrement W2058782464 · doi:10.1097/qai.0b013e31804d684b

When To Initiate HIV Antiretroviral Therapy

2007· letter· en· W2058782464 sur OpenAlexaffabout
Evan Wood, Julio Montaner

Notice bibliographique

RevueJAIDS Journal of Acquired Immune Deficiency Syndromes · 2007
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV/AIDS Research and Interventions
Établissements canadiensSt. Paul's HospitalUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésAntiretroviral therapyMedicineViral loadRegimenImmunologyHuman immunodeficiency virus (HIV)DiseaseLentivirusVirologyInternal medicineViral disease

Résumé

récupéré en direct d'OpenAlex

Seminal work by Mellors et al1 clearly demonstrated that CD4 cell counts and plasma HIV RNA levels are key prognostic indicators among untreated individuals infected with HIV. CD4 cell counts and plasma HIV-1 RNA levels are widely accepted as markers of vulnerability to opportunistic diseases and subsequent mortality in this setting.1-3 Coffin4 compared the contribution of these parameters to the risk of disease progression related to HIV/AIDS to a steam engine careening toward an impending wreck, with the plasma HIV RNA level representing the speed of the train and the CD4 cell count representing the length of track remaining. Highly active antiretroviral therapy (HAART) first emerged at the International AIDS Society-sponsored Vancouver AIDS Conference in 1996. Since then, HAART has been shown to suppress the circulating levels of plasma HIV RNA dramatically, thereby bringing the metaphoric steam engine to a halt.5 Suppression of plasma HIV RNA leads to CD4 cell count rebound6 and, in turn, dramatic reductions in HIV-related morbidity and mortality.7-9 The impressive effects of antiretroviral therapy initially led to a “hit early/hit hard”10 approach in which HIV treatment guidelines recommended initiation of antiretroviral therapy when the CD4 count decreased to less than 500 cells/μL or the plasma HIV/RNA level rose to greater than 30,000 copies/mL.11 Because eradication of HIV infection is not considered possible with presently available therapeutic agents,12 those undergoing treatment for HIV disease must take a daily regimen of at least 3 antiretroviral agents.13 Near-perfect levels of adherence to the regimen are required to suppress the plasma HIV RNA level in a sustained fashion.14,15 Furthermore, incomplete adherence has been associated with rapid emergence of antiretroviral resistance.16,17 Although simpler and more tolerable HAART regimens are being developed, the long-term side effects of HAART may be substantial.18-21 These challenges have led to a recent shift away from the hit early/hit hard approach to a strategy in which patients are encouraged to delay the initiation of HAART until the CD4 count falls to or less than 350 cells/μL but before the CD4 count reaches 200 cells/μL.22 These thresholds have been derived from prospectively monitored cohorts of HIV-infected individuals initiating HAART that clearly demonstrate a survival advantage when HAART is initiated with CD4 cell counts greater than 200 cells/μL, whereas no such advantage is seen if HAART is initiated with CD4 cell counts greater than 350 cells/μL. Most of these studies have focused on the impact of HAART, initiated at various CD4 cell counts and plasma HIV RNA levels, on survival, whereas issues other than survival have received little attention.22 In this issue, Gras and colleagues23 use data from the AIDS Therapy Evaluation Project, Netherlands (ATHENA) to demonstrate elegantly that many patients on long-term HAART regain CD4 counts greater than 800 cells/μL. More importantly, they demonstrate a strong association between pretreatment CD4 cell count and enhanced CD4 cell count recovery among patients initiating HAART with higher CD4 cell counts. Among patients with a baseline CD4 count of 200 to 350 cells/mm3, 46% reach 800 cells/mm3, whereas this jumped to 73% among those with baseline CD4 cell counts of 350 to 500 cells/mm3. The clinical implications of these findings have yet to be determined, because most HIV-related complications are not observed until the CD4 cell count declines to less than 200 cells/mm3.24 Reports from the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) study have indicated that elevated risk of mortality from liver disease and certain cancers is seen at lower CD4 cell counts.25 Of interest, this study noted differences in these specific endpoints even between those in the 200 to 350 cells/mm3 and >350 cells/mm3 categories. Similarly, the recently reported results from the Strategies for Management of Antiretroviral Therapy (SMART) trial have indicated increased risk of disease progression in the CD4 cell count-guided episodic antiretroviral therapy arm in comparison to the continuous antiretroviral therapy arm despite the fact that almost all participants in the trial had CD4 counts greater than 200 cells/mm3 during follow-up.26 Interestingly, although HAART is associated with the range of side effects described previously, the SMART trial indicated that patients in the CD4 cell count-guided interrupted antiretroviral therapy arm had increased risk of certain health outcomes (combined endpoint: myocardial infarction, stroke, liver cirrhosis, and renal failure) compared with those on continuous HAART treatment. As HAART evolves over time, newer regimens tend to be simpler and safer. This progressively opens the door for a broader re-evaluation of the ideal time to start therapy, incorporating outcomes other than survival, such as the level of immune reconstitution demonstrated by Gras et al.23 In addition, there is growing evidence that HAART-treated patients are less likely to transmit HIV infection to others.27 Recently, mathematic modeling has suggested that expansion of HAART programs could play a substantial role in decreasing HIV incidence.28 So far, the added preventive value of HAART has not been incorporated into the equation when evaluating the ideal time to start therapy. Given the potential effect that HAART can have in various domains other than survival, however, we must broaden our focus to incorporate a variety of appropriately weighted patient outcomes, and their public health consequences. ACKNOWLEDGMENTS The authors thank Deborah Graham, Kelly Hsu, and Peter Vann for their research and administrative assistance.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,105
Score d'incertitude au seuil0,352

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0040,003
Science ouverte0,0010,001
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,1050,052

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,043
Tête enseignante GPT0,332
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2007
Routes d'admission2
Résumé présentoui

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