The Patient's or Society's: Whose Quality of Life is it Anyway?
Notice bibliographique
Résumé
The authors have indicated no financial conflicts of interest. In healthcare systems around the world, patients, providers, and governments are faced with escalating costs and tightening budgets. In this environment of scarcity, how are policy-makers to make rational choices about whether the benefits afforded by programs and technologies justify the costs? Consider newly diagnosed patients with obstructive sleep apnea syndrome (OSAS). The use of continuous positive airway pressure (CPAP) is a relatively inexpensive technology that improves daytime sleepiness and is associated with a reduced rate of heart attacks, strokes, and motor vehicle crashes.1,2 Despite these potential benefits, there are side effects associated with CPAP, including dry nose and mouth, general discomfort, claustrophobia, inconvenience, and embarrassment.3,4 It can be challenging to determine if the benefits provided outweigh the harms and inconveniences. Furthermore, how this “net benefit” compares to those provided by other, often very different interventions, is important to understand, so the relative value of the intervention can be defined. In this issue of SLEEP, Rizzi and colleagues5 describe changes in health related quality of life (HRQoL) and quality-adjusted-life-years (QALYs) in patients during the year following diagnosis and treatment with CPAP. They measured HRQoL using a utility approach, where the utility represents the desirability or preference that individuals have for the health state experienced—be it the positive benefit of a treatment or the negative consequence of a side effect.6 These utilities can either be valued at the individual level, where they represent the preference of the person experiencing the impaired health state, or the societal level, where they represent the average preferences of a representative sample of the population. Broadly speaking, utilities are measured on a scale from 0 to 1, where 0 represents a state equivalent to dead, and 1, perfect health. This enables researchers to “quality adjust” the life years an intervention might provide and so enable the comparison of the net benefits between treatments. Rizzi et al.5 used the Short Form 6-Dimension (SF-6D) to measure HRQoL from a societal perspective, and found that patients who were offered CPAP treatment experienced an increase in HRQoL from 0.61 at baseline to 0.71 at one year. The SF-6D, along with the EuroQol 5-Dimension (EQ-5D), is one of the most commonly used tools to measure societal utilities in health care. These instruments provide a descriptive system that enables patients to report their health status, and a scoring function based on pre-determined societal preferences (in this example, a sample of the Brazilian population) for each health state than enables the derivation of a utility value.7 The HRQoL gain found by Rizzi et al. were preserved over a year of follow-up, providing an additional 0.093 QALYs (since all patients lived for the year). These improvements were associated with a decrease in sleepiness (as measured by the Epworth Sleepiness Scale), systolic and diastolic blood pressure, and blood pressure normalization. The results provide validation of QALY gains provided by CPAP treatment, and when integrated with costs, they enable the cost-effectiveness of treatment to guide resource allocation decisions in Brazil. Interestingly, Rizzi and colleagues found that there was no statistical difference in HRQoL and thus QALYs between those patients classified as compliant and noncompliant with treatment. The authors suggest, reasonably, that this could be because the noncompliant group, despite not reaching the defined threshold for compliance (4 hours per night on 70% of nights), still benefited from CPAP enough to improve their HRQoL. This could also reflect the fact that benefits afforded by being compliant to CPAP (compared to noncompliant) are small and not discernible by the SF-6D. An alternative explanation lies with the disparity between HRQoL as measured using societal utilities, as done by Rizzi et al., and individual utilities, which reflect the individual patient's personal preferences and priorities. Societal utilities are advocated for informing policy making, such as how to allocate and prioritize resources across the healthcare system in a manner that best serves the interests of society. However, it is well known that individual preferences vary widely, and this means that some members of society place dramatically different values on the same outcomes. As such, understanding how patients value individual health states is critical to ensure that treatment decisions match with the patient's preferred outcomes. Rizzi et al. found that approximately 40% of patients were noncompliant, a similar finding to other studies.8 Whether or not an individual chooses to be compliant can be represented by the HRQoL valuations that they place on the two health states (compliant vs. noncompliant). In the context of OSA, it is conceivable, and indeed likely, that the net benefit provided by CPAP could be very different in two patients who are otherwise clinically identical. For one patient, the reduced risk of cardiovascular disease and daytime sleepiness may outweigh negative side effects, meaning that they choose to be compliant with CPAP. Alternatively, an otherwise clinically identical patient may deem that the embarrassment and inconvenience of CPAP outweighs its benefits and chooses to be noncompliant. Studies such Rizzi et al. provide important evidence to inform societal decision-making around resource allocation and reimbursement. The future of this research area is to move toward individual HRQoL metrics, to ensure that clinical decision-making matches with patients' values, and to incorporate these values into QALYs. Understanding how patients value these health states is critical to inform clinical decision-making and improve the patient-centeredness of care, thereby ensuring that decision-making at the policy level is in line with decision-making between the patient and their health care provider.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,048 | 0,049 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,003 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».