The Hammersmith Score optimises patient selection and predicts for overall survival in early-phase cancer trial participants independent of tumour burden.


Journal

Chemotherapy
ISSN: 1421-9794
Titre abrégé: Chemotherapy
Pays: Switzerland
ID NLM: 0144731

Informations de publication

Date de publication:
27 Apr 2024
Historique:
received: 21 01 2024
accepted: 13 04 2024
medline: 29 4 2024
pubmed: 29 4 2024
entrez: 28 4 2024
Statut: aheadofprint

Résumé

As tumour response rates are increasingly demonstrated in early-phase cancer trials (EPCT), optimal patient selection and accurate prognostication is paramount. Hammersmith Score (HS), a simple prognostic index derived on routine biochemical measures (Albumin <35g/L, Lactate Dehydrogenase (LDH) >450 IU/L, Sodium <135mmol/L) is a validated predictor of response and survival in EPCT participants. HS has not been validated in the cancer immunotherapy era. We retrospectively analysed characteristics and outcomes of unselected referrals to our early-phase unit (12/2019-12/2022). Independent predictors for overall survival (OS) were identified from univariable and multivariable models. HS was calculated for 66 eligible trial participants and compared with the Royal Marsden Score (RMS) to predict OS. Multivariable logistic regression and c-index was used to compare predictive ability of prognostic models. Of 212 referrals, 147 patients were screened and 82 patients treated in EPCT. Prognostic stratification by HS identifies significant difference in median OS and HS was confirmed as a multivariable predictor for OS (HR: HS 1 vs. 0 2.51, 95%CI: 1.01-6.24, p=0.049; HS 2/3 vs. 0: 10.32, 95%CI: 2.15-49.62, p=0.004; C-index 0.771) with superior multivariable predictive ability than RMS (HR: RMS 2 vs. 0/1 5.46, 95%CI: 1.12-26.57, p=0.036; RMS 3 vs. 0/1 6.83, 95%CI: 1.15-40.53, p<0.001; C-index 0.743). HS is a validated prognostic index for patients with advanced cancer treated in the context of modern EPCTs, independent of tumour burden. HS is a simple, inexpensive prognostic tool to optimise referral for EPCT.

Identifiants

pubmed: 38679017
pii: 000539109
doi: 10.1159/000539109
doi:

Types de publication

News

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

The Author(s). Published by S. Karger AG, Basel.

Auteurs

Classifications MeSH