Trajectory of symptoms reported in remote symptom monitoring over the course of oncology treatment for lung cancer.
Notice bibliographique
Résumé
250 Background: Electronic patient reported outcomes (ePROs) enable patients to report symptoms from treatment in real time using their mobile device. This analysis sought to better understand the trajectory of reported symptoms via remote symptom monitoring (RSM) during treatment for patients with lung cancer. Methods: We approached patients with lung cancer initiating treatment at the Mitchell Cancer Institute (MCI) between March 2022-October 2023 to participate in an RSM program. Patients were eligible if they were initiating treatment (chemotherapy, targeted therapy, or immunotherapy) for the first time at MCI. Patients seeking a second opinion were excluded. Enrolled patients received a symptom survey (PRO-CTCAE questions) once a week via text or email. Alerts were forwarded to the clinical care team for symptom management. Patients completed symptom assessments for 24 weeks or until withdrawal. At 24 weeks, patients were given the ability to continue symptom assessments if they were continuing treatment. Patient demographics including age at enrollment, race, sex, cancer type, cancer stage, and PRO data were collected from electronic health records and the PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Results: Of 80 patients approached, a total of 57 (71%) patients with lung cancer were enrolled in RSM; 20% were Black or African American and 80% were White; median age was 66 (IQR 61-73). Over 24 weeks, 732 symptom alerts were reported; 75% considered moderate and 25% considered severe. Overall, the most frequently reported symptom was pain (29%), followed by dyspnea/cough (25%) and constipation (13%). At baseline (week 0), 78 moderate symptoms and 39 severe symptoms alerts were reported. At week 24, 10 moderate symptoms and 4 severe symptoms alerts were reported. Overall, there was a decrease in symptom alerts over time for both moderate and severe alerts. Specific symptom trajectories followed similar patterns. Conclusions: In our sample, most symptoms were reported during the initial three months of treatment with a subsequent decrease in symptom alerts over time, indicating effective monitoring and management by clinical teams engaged in RSM. Future research is needed to understand if symptom improvement correlates with enhanced quality of life, reduced hospitalizations, and prolonged survival, as well as a lessened burden of call volume on the clinical team. Future analyses should also be done to compare similar results of different disease types.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».