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Enregistrement W2319277361 · doi:10.1097/00002030-200303070-00022

The impact on health-related quality of life of treatment interruptions in HIV-1-infected patients

2003· article· en· W2319277361 sur OpenAlexaffabout
Hartmut B. Krentz, M. John Gill

Notice bibliographique

RevueAIDS · 2003
Typearticle
Langueen
DomaineMedicine
ThématiqueHIV/AIDS Research and Interventions
Établissements canadiensUniversity of CalgaryCanadian Institute for Health Information
Organismes subventionnairesnon disponible
Mots-clésHuman immunodeficiency virus (HIV)MedicineQuality of life (healthcare)SidaViral diseaseIntensive care medicineVirologyImmunologyNursing

Résumé

récupéré en direct d'OpenAlex

To examine health-related quality of life (HRQOL) before, during and after treatment interruptions (TI) in antiretroviral therapy, we analysed results from Medical Outcomes Study HIV health surveys on 50 HIV-1-infected patients. HRQOL scores decreased during a TI but increased after re-initiating treatment, although scores remained lower than those preceding the TI. The reasons for a TI differentially affected HRQOL. The findings suggest that TI should be based on medical decisions and not used to increase HRQOL. Treatment interruptions (TI) in highly active antiretroviral therapy (HAART) are used for a wide range of legitimate clinical reasons [1–8]. The demands of adhering rigorously to a HAART regimen over many years as well as the toxicities associated with almost every antiretroviral drug suggests that there might be a substantial benefit in health-related quality of life (HRQOL) from stopping treatment [9–11]. The well-described deterioration in laboratory markers such as the declining CD4 cell count and a burst in viral replication after a TI might be balanced by a short-term improvement in the quality of life [12–14]. We investigated this relationship by using a well-used and reliable instrument, the Medical Outcomes Study HIV Health Survey (MOS-HIV) to look at the HRQOL data in both the general and the TI subset of a large cohort of patients being followed, prospectively, over many years in a health economics study. HRQOL is a multidimensional concept that includes general health perceptions, physical and social functional status, psychological and mental health, and cognitive perceptions. It is a measure used to evaluate self-perceived health status. HRQOL has been measured in a variety of HIV-positive populations [15]. The impact a TI has on HRQOL has not yet been well described. We analysed this impact before, during and after a TI and compared any changes in HRQOL with patients remaining on continuous therapy. From October 1999 to April 2002, 294 patients at the Southern Alberta Clinic were administered a MOS-HIV health survey as part of an ongoing economic study involving 80% of eligible patients attending our clinic. The study is independent of any drug regimen or subsequent changes in their drugs. HRQOL measures were collected at the initial visit after the patient consented to participate in the study, and at subsequent visits at approximately 3–4 month intervals. HRQOL was evaluated using the MOS-HIV [16], which is a 35-item general HRQOL measure containing 11 subscales that are scored independently. Subscale dimensions include: general overall perception of health, quality of life, mental health, physical functioning, pain, levels of energy and fatigue, social functioning, role functioning, health distress, cognitive functioning, and health transition. Higher scores on all of the scales (range 0–100) indicate better functioning. Univariate and bivariate analysis was used to compare populations experiencing a TI with the population remaining on treatment. Non-parametric tests (sign test; Wilcoxon matched-pairs signed-rank test) compared subpopulations of patients who experienced TI. Of the 294 patients enrolled in the ongoing economic study, 50 had experienced a TI of 2 or more months, and completed the MOS-HIV survey before, during or after their TI. Patients experiencing a TI were younger than those remaining on therapy (P = 0.10), but otherwise did not exhibit significant sociodemographic differences. They had, however, lower mean CD4 cell counts, slightly longer times since HIV diagnosis, and were more likely to have AIDS (P = 0.05). The most frequent reasons for undergoing TI included virological failure and drug resistance (41%), adverse effects or toxicity to antiretroviral drugs (36%), or patient decision (14%). Mental health issues and inadequate adherence accounted for the remaining 10% of TI. The mean length of a TI was 5.5 months (range 2–22 months). CD4 cell counts declined from a mean of 443 cells/mm3 before the TI to 328 cells/mm3 during the TI. The mean decline in the CD4 cell count per month during the TI was 65 cells/mm3. The mean HRQOL scores at baseline between patients remaining on therapy and those experiencing a TI are shown in Table 1. Patients starting a TI, however, had a lower mean baseline scores in 10 of the 11 HRQOL dimensions than those remaining on therapy, with only the mental health score higher in the TI population. Scores for pain, levels of energy and fatigue, social functioning, role functioning, and health transition showed statistically significant differences (P < 0.05) at baseline. The mean MOS-HIV scores for the 210 non-TI patients did not show significant trends over 96 weeks, and remained essentially stable.Table 1: Mean health-related quality of life subscale dimension scores between non-treatment interruptions (i.e. patients remaining on therapy) and treatment interruption patients at baseline, and for treatment interruption patients, the mean health-related quality of life subscale scores preceding, during and after a treatment interruption of 2 or more months.For patients experiencing a TI, mean HRQOL scores decreased in nine out of 11 dimensions, with five dimensions declining significantly. When HAART treatment was re-initiated, the mean HRQOL scores increased in seven dimensions but decreased in four dimensions. In only one dimension, health transition, did the mean scores reach or exceed the pre-TI scores. The reason for a TI may significantly impact on HRQOL. Patients starting a TI as a result of viral failure showed significant decreases in HRQOL scores during the TI but rebounded to near pre-TI levels after re-initiating therapy. For patients stopping treatment because of adverse effects HRQOL scores showed more variability. When these patients re-initiated treatment, HRQOL declined for most dimensions, indicating that there was a negative impact on their perception of health. HRQOL scores for patients choosing to stop treatment increased during the TI and decreased when therapy was re-initiated. Small sample sizes preclude overinterpretation of the data; however, the trends seen here indicate important differences in HRQOL between these subsets when the reason for the TI is taken into consideration. Our on-going study continues to analyse these differences. TI are indicated for a variety of reasons. Whereas the virological, immunological, economic, and clinical merits of a TI remain debatable, our study has shown that a TI does not necessarily increase HRQOL, but we did see an improvement in HRQOL on restarting therapy. Overall, we found that TI negatively affects HRQOL both during and after a TI. The findings of this study suggest that healthcare providers and patients should view a TI as a medical decision and not as a means to increase HRQOL. Acknowledgements The authors would like to thank Cailen Henry for her assistance in data collection and analysis, and to acknowledge the support received from the HIV Economic Study Group.

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)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,059
Tête enseignante GPT0,414
Écart entre enseignants0,355 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations19
Publié2003
Routes d'admission2
Résumé présentoui

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