Abstract 5489: The cost of delaying therapy for advanced non-small cell lung cancer (NSCLC): a population kinetics assessment
Notice bibliographique
Résumé
Abstract Background: Systemic therapy prolongs overall survival (OS) in advanced NSCLC. The best outcome requires the best therapy choice. To choose the best therapy requires baseline diagnostic tests, staging and molecular profiling, but patients are at risk of deteriorating and dying while awaiting testing prior to therapy initiation. OS follows first order kinetics. We used population kinetics assessments to estimate % of patients dying while awaiting therapy initiation. Method: For 1st line studies in advanced NSCLC that included a placebo or best supportive care (BSC) arm we digitized published OS curves, used GraphPad Prism 7 for exponential decay nonlinear regression analysis, calculated OS half-life (t1/2) and assessed data fit to 1 and 2 phase decay models. The proportion of patients “x” surviving after a time of interest tn was calculated by the Excel formula x =EXP(-tn*0.693/t1/2) where * indicates multiplication and 0.693 is the natural logarithm of 2. Results: We identified 7 trials and a meta-analysis. Across studies, the median OS t1/2 with 1st line placebo/BSC was 19.3 weeks. Hence, by 1, 2, 3 and 4 weeks after study entry 4%, 7%, 10% and 13% of patients, respectively, would have died (ie, 4% of the remaining patients with each passing week). This is in keeping with most OS curves showing rapid decline from the outset. OS curves fit 2 phase decay models in 5 studies, indicating a distinct short survival subgroup (on average, 89% of patients in these trials) and a longer surviving subgroup (potentially from having initiated systemic therapy when progression was detected). The short survival subgroup had a median OS t1/2 across studies of 11.3 weeks. The earliest deaths would be expected to occur predominantly in this short survival subgroup, in which 5%, 9%, 13% and 17% had died by 1, 2, 3 and 4 weeks respectively. Conclusions: Since OS follows first order kinetics, OS decline was probably following approximately the same rate prior to patient inclusion on these trials. In addition, since patients may deteriorate rapidly, others may have become too sick to consider therapy even if still alive. Rapid deterioration and short OS help explain why less than 25% of Ontario patients make it on to systemic therapy for advanced NSCLC despite the therapy being government funded. Since diagnostic, staging and molecular profiling procedures are needed before optimal therapy can start, these procedures must happen rapidly. It also illustrates why we must make screening procedures for clinical trial inclusion much faster. Otherwise patients are at risk of deteriorating rapidly or dying while awaiting eligibility assessment. It is also important to not delay initiation of systemic therapy for procedures such as radiotherapy for asymptomatic brain metastases. Any inefficiency that delays systemic therapy initiation may worsen patient outcome. Citation Format: David J. Stewart, Donna Maziak, Marcio Gomes, Michael Fung-Kee-Fung, Carole Dennie, Harman Sekhon, Bryan Lo, John-Peter Bradford, Sara Moore, Neil Reaume. The cost of delaying therapy for advanced non-small cell lung cancer (NSCLC): a population kinetics assessment [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5489.
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 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 | 0,031 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,016 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».