Symptom alert by type and severity among cancer patients using electronic patient reported outcomes for remote symptom monitoring.
Notice bibliographique
Résumé
340 Background: Remote symptom monitoring (RSM) by electronic patient-reported outcomes (ePRO) data can elicit actionable symptoms from patients with cancer. However, patients with different cancer diagnoses are likely to have differing symptom profiles and variability in symptom alerts. To understand potential workflow needs, this analysis was conducted to determine which types of symptoms and severity of alerts can be expected based on cancer type. Methods: Cancer patients initiating chemotherapy, immunotherapy, or targeted therapy at 2 academic cancer centers in Alabama, UAB O’Neal Comprehensive Cancer Center and USA Health Mitchell Cancer Institute (MCI), were enrolled in ePRO-based RSM. Site rollouts were differential: UAB enrolled by disease group starting May 2021, MCI by provider starting July 2021. Patients received weekly symptom surveys of selected PRO-CTCAE questions through the Carevive ePRO mobile platform (PROmpt), triggering alerts to clinical teams if reported symptoms were determined to be moderate or severe. Demographics, cancer diagnosis, and ePRO data were extracted from electronic health records and Carevive. Descriptive statistics of categorical variables were calculated by frequencies and percentages; Cramer’s V and Cohen’s d were used for associations and effect size. Results: UAB enrolled 598 patients and MCI enrolled 274 patients by April 2023, consistent with the patient volume difference of the centers. 68% of enrollees were White; MCI saw a moderately higher % of Black patients (V: 0.21). 67% of enrolled patients were female. Median age was 61 years (Interquartile range: 51-69); UAB patients were slightly younger (d: 0.15). Among 872 enrolled patients, 9765 symptom alerts were generated. There was a small effect of cancer type on the overall type of symptoms and symptom severity reported (V: 0.11 and 0.09 respectively). Of the total number of symptom alerts, 28.0% of the alerts generated were for pain, followed by nausea/vomiting (14.9%), and constipation (11.7%). When broken down by cancer type, pain was the symptom most frequently reported for each type. The next most frequently reported symptoms differed but were as expected by cancer type: coughing/dyspnea by lung cancer patients (20.6%); urinary complaints in genitourinary cancers (14.1%); and nausea/vomiting in gastrointestinal cancers (18.0%). The frequency of moderate alerts was 62.1%, varying from 34.0% in sarcoma to 66.6% in gastrointestinal cancers. 31.1% of the alerts were severe; sarcoma had the most severe alerts (56.0%) and hematologic had the least (27.1%). Conclusions: Across patients with differing cancer types, pain and gastrointestinal issues were over half the reported symptoms. However, variability by cancer diagnosis in both symptom type and severity was observed, suggesting the remote symptom management workload for providers may vary by cancer type.
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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 | 0,005 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».