Impact of real-world remote symptom monitoring program on hospitalizations and ICU admissions.
Notice bibliographique
Résumé
377 Background: Previous randomized controlled trials have demonstrated benefits to patients from remote symptom monitoring (RSM) with electronic patient-reported outcomes (ePROs) including healthcare utilization. However, less is known about the impact of RSM in diverse, real-world populations. Methods: This cross-sectional analysis from a hybrid, type 2 implementation-effectiveness trial evaluated the impact of RSM on healthcare utilization amongst patients with cancer receiving chemotherapy, immunotherapy, monoclonal antibody, or targeted therapy at two academiccancer centers in the Southeastern United States. Modified Poisson regression models with robust standard error and 95% confidence interval (CI) was used to calculate the relative risk (RR) of any hospital or ICU utilization between patients receiving RSM and controls for 3 and 6 months after index date. Models were controlled for age at index, race, sex, cancer type, cancer stage, insurance, prior treatment, comorbidities, RUCA, and follow-up during COVID-19 pandemic. Additional logistic regression models were used to estimate odds ratios (OR) for subset analysis stratified by race (Black or African American, Other, or White), rurality using Rural-Urban Commuting Area Codes, and neighborhood disadvantage using Area Deprivation Index (ADI). Results: From 5/2021-2/2024, 1215 patients were enrolled in RSM; 27% were Black, 16% lived in a rural area, and 25% lived in an area with high neighborhood disadvantage. The populations receiving RSM were similar to the control population (n = 4559); 26% were Black, 22% lived in a rural area, and 28% lived in area with high neighborhood disadvantage. The unadjusted relative risk of hospitalization for patients receiving RSM and control patients were 0.70 (95% CI, 0.63-0.70) and 0.77 (95% CI, 0.71-0.85), respectively. In adjusted analyses, hospitalizations were lower amongst patients receiving RSM compared to control patients with a RR of 0.82 (95% CI 0.73-0.92). Similar patterns were observed for ICU admissions (RR 0.59; 95% CI,0.40-0.88). Analysis by patient subgroup was similar to the overall analysis. A lower odd of hospitalizations and ICU admissions at 6 months was observed across all subset analyses: Black vs. White patients (OR 0.80; OR 0.48); rural vs. urban patients (OR 0.78; OR 0.68); and patients living in areas of high vs. lower neighborhood disadvantage (OR 0.59; OR 0.33). Conclusions: The use of RSM amongst patients receiving treatment for cancer is associated with reductions in hospitalizations and ICU admissions in real-world, diverse settings. Further work to expand this intervention nationally is needed. Clinical trial information: NCT04809740 .
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».