Development and validation of a diabetes-specific health state classification system and valuation function based on the multi-attribute theory
Notice bibliographique
Résumé
Preference-Based Measures of Health (PBMH) provide 'preference' or 'utility' weights that enable the calculation of quality-adjusted life years for the economic evaluations of interventions. The Diabetes Utility Index (DUI) was developed as a two-page, self-administered diabetes-specific PBMH that can replace expensive time-consuming interviews with patients to estimate their health state utilities. Inputs from theory, an existing diabetes-specific measure of quality of life, and statistical analyses were submitted to a clinical expert panel. After three rounds of pilot surveys (n1=52, n2=65, n3=111) at primary care clinics in Morgantown, WV, five attributes and severity categories for each attribute were finalized on the basis of the results of Rasch Analysis and consultations with the panel. The final attributes were: 'Physical Ability & Energy', 'Relationships', 'Mood & Feelings', 'Enjoyment of Diet', and 'Satisfaction with Management of diabetes'. The next step involved obtaining preferences for health states based on combinations of DUI attributes and severity levels from 100 individuals with diabetes, recruited from primary care and community settings in and around Morgantown, WV, in hour-long one-on-one interviews. These health states were anchor states, single-attribute level states including corner states, and marker states. The interviews provided data to calculate a Multi-Attribute Utility Function (MAUF) that calculates utilities for any of the 768 health states that can be defined by the DUI, on a scale where 1.00=Perfect Health and 0.00=the all worse 'Pits' state, from respondents' answers to its five questions. In addition to an overall index score, attribute-level preference scores were also calculable by the function. Finally, a validation survey was conducted in collaboration with the West Virginia University (WVU) Diabetes Institute. For concurrent and construct validation purposes, the DUI was mailed to individuals with diabetes along with generic PBMH like the EuroQol EQ-5D, the SF-6D and other patient-reported outcomes measures like the Diabetes Symptoms Checklist-Revised, the Short Form 12 (SF-12) and the Well-Being Questionnaire (W-BQ12), and their surveys responses (n=396) were merged with a clinical database consisting of ICD-9 diagnosis codes. The DUI utilities were found to be largely free of socio-demographic effects and its scores were well distributed between 0.00 and 1.00. The DUI moderately correlated with generic PBMH and distinguished between severity groups based on diabetes symptoms and complications. The scoring function of the DUI calculated utilities favorably compared against cardinal Standard Gamble utilities obtained directly from patients for three DUI health states. These results show evidence of the feasibility and validity of
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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 | 0,028 | 0,057 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».