Real-world impact of patient-reported outcome measurement on overall survival, healthcare use and treatment discontinuation in cancer patients
Notice bibliographique
Résumé
Aim: The purpose of this retrospective, population-based, observational cohort analysis was to assess whether routine patient-reported outcomes (PRO) monitoring alone has an impact on real-world overall survival (OS) and hospitalizations among individuals diagnosed with lung, breast or colorectal cancer. The importance of follow-up care in post-PRO data collection was also discussed. Patients & methods: Administrative databases covering 17 cancer centers from Alberta, Canada were queried and individuals ≥18 years old and diagnosed with lung, breast or colorectal cancer from 1 January 2016 to 31 December 2019 were included and followed until 31 December 2020. Patients were stratified by whether they received routine PRO monitoring initiated within 120 days of diagnosis and matched 1:1 with use of propensity scores based on baseline characteristics. OS was assessed from the index date to death, and the respective Kaplan–Meier curves were estimated along with hazard ratios from Cox Proportional Hazard Models. Linear and logistic regression models were used to estimate mean differences and odds ratios (OR) respectively for healthcare resource utilization events including cancer physician visits, emergency department visits and outpatient ambulatory care encounters. Results: 4800 patients were included in each matched cohort. There was no statistically significant difference between PRO monitoring and non-monitoring cohorts in OS (HR = 1.01; 95% CI: 0.93–1.09; p = 0.836) and treatment discontinuation (OR = 0.98; 95% CI: 0.85–1.12; p = 0.75). Median OS was 51.5 months for unmonitored cohort (95% CI: 47.5–NA) versus 50.6 months for monitored cohort (95% CI: 47.6–55.7). Compared with PRO-monitored patients, unmonitored patients were associated with lower hospitalization risks (OR = 1.12; 95% CI: 1.03–1.22; p = 0.01). However, PRO-monitored patients experienced significantly fewer physician visits in comparison to unmonitored patients (MD = -1.036; 95% CI: -1.288 to -0.784, p < 0.001). Conclusion: Our results show that capturing patient-reported symptoms alone reduced the number of physician visits but neither reduced hospitalizations nor improved OS in this real-world cancer population. To drive more meaningful clinical impact, PRO monitoring programs must be met with rigorous follow-up response to the identified symptoms.
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 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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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 ».