Evaluating the cost, cost-effectiveness and survival of an eHealth-facilitated integrated care model for allogeneic stem cell transplantation: Results of the German SMILe randomized, controlled implementation science trial
Notice bibliographique
Résumé
PURPOSE: eHealth-facilitated integrated care models (eICMs) have proved effective in improving outcomes for chronically ill patients. However, evidence on cost-effectiveness of eICMs is scarce so far. Allogeneic stem cell transplantation (alloSCT) recipients' post-discharge treatment costs and mortality are greatly influenced by complications. Within the international, multicentric SMILe implementation science project, the eHealth-facilitated SMILe integrated care model (SMILe-ICM) was developed to support patients minimize complications' effects within the first year post-alloSCT. Using initial effectiveness findings from the first center that implemented the SMILe-ICM, this study provides a cost and cost-effectiveness evaluation considering one-year and long-term survival effects, post-discharge costs, and patient-related factors. METHODS: A single-center hybrid effectiveness implementation randomized controlled trial was conducted at a German university hospital from 2/2020 to 8/2022. Eligible alloSCT patients were randomized to the SMILe-ICM or usual care, i.e., one pre-transplant educational nursing visit followed by a physician-led follow-up. The intervention group received usual care plus the SMILe-ICM's four intervention modules (i.e., monitoring of medical/symptom-related parameters, medication adherence, infection prevention, physical activity). All modules were delivered by Advanced Practice Nurses (APNs) in face-to-face visits, combined with continuous online support. Daily, patients entered seventeen medical and symptom-related parameters to the SMILe App, so that APNs could monitor for and investigate possible pre-complication signs. Healthcare utilization costs were assessed at eight time-points (d+30 post-alloSCT-d365) on fourteen self-reported cost indicators and validated against health records. To calculate costs, we applied German standardized unit costs. Cost- and cost-effectiveness were analyzed in five steps: 1.) Calculate total costs, including for the alloSCT inpatient stay and post-discharge follow-up. 2.) Determine life-years gained (survival) as a health benefit unit. 3.) Calculate overall and rehospitalization-free survival estimates. 4.) Calculate the intervention's long-term cost-effectiveness, including extended follow-up, rate of survival until day 1000, and restricted mean survival time. 5.) Contrast these long-term estimates to current post-discharge costs with comparable patient-related factors (age ≥ or < 65, living alone, gender). RESULTS: Seventy-two patients participated (n = 36/group). Total intergroup healthcare utilization and post-discharge costs differed, but non-significantly. Survival rates improved with the SMILe-ICM (88% vs. 80%) at least until day +1000. Rehospitalization-free survival showed improvement (38% vs. 30%); however, considering this sample size, both findings were nonsignificant. Cost-effectiveness analysis showed an overall post-discharge cost-effectiveness of 35,364.01€/patient and 6,742€/life year gained - a mean of 79.21 additional days of life for an intervention investment of 1.464€/patient in the first year post-alloSCT. One-year cost-effectiveness was highest for patients living alone. Younger age correlated with longer survival but higher costs. CONCLUSION: The SMILe-ICM appears to offer survival and rehospitalization benefits, particularly for vulnerable groups, e.g., patients living alone. Larger, adequately powered studies are needed to validate these findings.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».