Abstract PO-111: Postoperative lung cancer pain over 4 days: Changes in painful body surface area
Notice bibliographique
Résumé
Abstract Introduction: It is unknown if the body surface area measure is sensitive to the change in pain over the first four days after lung cancer surgery. Known for cancer health disparities, improving pain outcomes for patients with lung cancer could have implications for reducing disparities. The purpose of this study was to compare body surface area over the first four days post thoracotomy for lung cancer. Methodology: In a repeated measures observational study, 41 lung cancer patients (mean age 60±12 years, 80% male, 100% Turkish) who had thoracotomy surgery reported pain postoperatively for four days. Patients were asked to mark on a body outline the location of the area where they had pain each day using PAINReportIt, a valid and reliable electronic version of the McGill Pain Questionnaire, on an Internet-enabled Surface Pro tablet. The number of sites that the patient marked was counted automatically by PAINReportIt. Then the body surface area percentage (BSA%) was calculated using ImageJ software. Each day patients were asked to rate on a 0-10 scale the pain intensity they were currently having and the least and worst that the pain was in the past 24 hours. These 3 values were used to create the average of pain intensity (API) the patient experienced for each of the four days. Analyses included scatter plots, descriptive statistics, longitudinal analysis of BSA% using linear mixed effects model, and Pearson correlations of BSA% and API. Results: The mean number of pain sites recorded by PAINReportIt for each postop day 1 to 4 was 2.1±0.7, 2.2±0.7, 2.1±0.9, and 1.9±0.7, respectively (p=.152). The mean BSA% on postop days 1 to 4 was 2.1%±1.9%, 2.4%±2.2%, 2.5%±2.7%, and 2.2%±2.4%, respectively. The correlations between postop day 1-4 values were high (r=.68 to r=.86). Longitudinal analysis shows that the time trend was not statistically significant: b=0.023, Std Err=0.094, 5=0.242, p=.81. The API significantly decreased over the 4 postop days: 7.3±1.8, 5.6±1.8, 4.2±1.9, and 3±2.1, respectively (p=.000). The correlations between BSA% and API on each of the 4 postop days were: r=-.56, p<.001, r=-.15, p=.36, r=-.03, p=.85, and r=-.05, p=.78. The negative relationship between BSA% and API was unexpected and scatter plots show that at very high pain and very low pain, BSA%s were low. High BSA% values were mostly observed at moderate pain. This inverse U shape relationship explains the correlations between BSA% and API at different time points. Conclusions: In a sample of patients who had thoracotomy for lung cancer, the BSA% was not sensitive to recovery over the first 4 days after surgery, but the API was. This finding is likely due to the few pain sites and small areas marked on the body outline to represent the pain of the surgical incision and chest tubes. In the post thoracotomy lung cancer population, BSA% is not likely to be a measure that will help reduce cancer health disparities. Citation Format: Mecca M. James, Sevgi D. Dogan, Seda K. Yedir, Rishabh Garg, Yingwei Yao, Sevily Erden, Diana J. Wilkie. Postoperative lung cancer pain over 4 days: Changes in painful body surface area [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-111.
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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 | 0,002 | 0,001 |
| 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 | 0,000 |
| 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) | 0,002 | 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 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 ».