51. Phase I prognostic online (PIPO): A web tool to improve patient selection for oncology early phase clinical trials
- Author
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Teresa Macarulla, A. Pedrola, O. Saavedra, Javier Ros, Cristina Saura, Cristina Viaplana, Elena Garralda, R. Berché, Elena Elez, Enriqueta Felip, I. Gardeazabal, Maria Ochoa de Olza, Irene Brana, Rodrigo Dienstmann, Joan Carles, Juan Martin-Liberal, Guzman Alonso, Guillermo Villacampa, Jordi Rodon, Cinta Hierro, Eva Muñoz-Couselo, A. Hernando-Calvo, Analia Azaro, Maria Vieito, Vladimir Galvao, Josep Tabernero, Ignacio Matos, Ana Oaknin, Institut Català de la Salut, [Matos I, Carles J] Servei d’Oncologia Mèdica, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain. [Villacampa G, Berché R, Pedrola A, Viaplana C, Dienstmann R] Oncology Data Science (OdysSey) Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Hierro C, Martin-Liberal J, Braña I, Azaro A, Vieito M, Saavedra O, Gardeazabal I, Hernando-Calvo A, Alonso G, Galvao V, Ochoa de Olza M, Ros J, Muñoz-Couselo E, Elez E, Rodon J, Saura C, Macarulla T, Oaknin A, Felip E, Garralda E] Servei d’Oncologia Mèdica, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Tabernero J] Servei d’Oncologia Mèdica, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Department of Medicine, UVic-UCC, Spain, and Vall d'Hebron Barcelona Hospital Campus
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Male ,Oncology ,Information Science::Informatics::Medical Informatics::Medical Informatics Applications [INFORMATION SCIENCE] ,Cancer Research ,medicine.medical_specialty ,Prognostic variable ,Oncologia ,Phases of clinical research ,Ciencias de la información::informática::informática médica::aplicaciones de la informática médica [CIENCIA DE LA INFORMACIÓN] ,Medicina - Informàtica ,Medical Oncology ,Web tool ,neoplasias [ENFERMEDADES] ,Patient Portals ,Internal medicine ,medicine ,Humans ,Selection (genetic algorithm) ,técnicas de investigación::métodos::diseño de la investigación::selección de los pacientes [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS] ,Aged ,Clinical Trials as Topic ,business.industry ,Patient Selection ,Middle Aged ,Prognosis ,Clinical trial ,Neoplasms [DISEASES] ,Cohort ,Prognostic model ,Investigative Techniques::Methods::Research Design::Patient Selection [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT] ,Female ,Early phase ,business ,Internet-Based Intervention ,Assaigs clínics - Abstract
Immunotherapy; Phase 1 trials; Prognostic model Inmunoterapia; Ensayos de fase 1; Modelo pronóstico Immunoteràpia; Assajos de fase 1; Model pronòstic Purpose Patient selection in phase 1 clinical trials (Ph1t) continues to be a challenge. The aim of this study was to develop a user-friendly prognostic calculator for predicting overall survival (OS) outcomes in patients to be included in Ph1t with immune checkpoint inhibitors (ICIs) or targeted agents (TAs) based on clinical parameters assessed at baseline. Methods Using a training cohort with consecutive patients from the VHIO phase 1 unit, we constructed a prognostic model to predict median OS (mOS) as a primary endpoint and 3-month (3m) OS rate as a secondary endpoint. The model was validated in an internal cohort after temporal data splitting and represented as a web application. Results We recruited 799 patients (training and validation sets, 558 and 241, respectively). Median follow-up was 21.2 months (m), mOS was 10.2 m (95% CI, 9.3–12.7) for ICIs cohort and 7.7 m (95% CI, 6.6–8.6) for TAs cohort. In the multivariable analysis, six prognostic variables were independently associated with OS – ECOG, number of metastatic sites, presence of liver metastases, derived neutrophils/(leukocytes minus neutrophils) ratio [dNLR], albumin and lactate dehydrogenase (LDH) levels. The phase 1 prognostic online (PIPO) calculator showed adequate discrimination and calibration performance for OS, with C-statistics of 0.71 (95% CI 0.64–0.78) in the validation set. The overall accuracy of the model for 3m OS prediction was 87.2% (95% CI 85%–90%). Conclusions PIPO is a user-friendly objective and interactive tool to calculate specific survival probabilities for each patient before enrolment in a Ph1t. The tool is available at https://pipo.vhio.net/. The research leading to these results has received funding from “la Caixa” Foundation (LCF/PR/CE07/50610001). Cellex Foundation for providing research facilities and equipment. This work was supported by the Accelerator Award (UpSMART) from Fundacion Científica – Asociacion Espanola Contra el Cancer (FC -AECC)/ Associazione Italiana per la Ricerca sul Cancro (AIRC) /Cancer Research United Kingdom (CRUK).
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- 2021