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Development of a prognostic model to predict 90-day mortality in hospitalised cancer patients (PROMISE tool): a prospective observational studyResearch in context

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Title: Development of a prognostic model to predict 90-day mortality in hospitalised cancer patients (PROMISE tool): a prospective observational studyResearch in context
Authors: Oriol Mirallas, Berta Martin-Cullell, Víctor Navarro, Kreina Sharela Vega, Jordi Recuero-Borau, Diego Gómez-Puerto, Daniel López-Valbuena, Clara Salva de Torres, Laura Andurell, Anna Pedrola, Roger Berché, Fiorella Palmas, José María Ucha, Guillermo Villacampa, Alejandra Rezqallah, Judit Sanz-Beltran, Rafael Bach, Sergio Bueno, Cristina Viaplana, Gaspar Molina, Alberto Hernando-Calvo, Juan Aguilar-Company, María Roca, Eva Muñoz-Couselo, Alex Martínez-Martí, Ada Alonso, Simeon Eremiev, Teresa Macarulla, Ana Oaknin, Cristina Saura, Elena Élez, Enriqueta Felip, Ángeles Peñuelas, Rosa Burgos, Patricia Gómez Pardo, Elena Garralda, Josep Tabernero, Sònia Serradell, Sònia Servitja, David Paez, Rodrigo Dienstmann, Joan Carles
Source: The Lancet Regional Health. Europe, Vol 46, Iss , Pp 101063- (2024)
Publisher Information: Elsevier, 2024.
Publication Year: 2024
Collection: LCC:Public aspects of medicine
Subject Terms: Prognostic factors, Hospital oncology service, 90-day mortality, PROMISE tool, LASSO method, Public aspects of medicine, RA1-1270
More Details: Summary: Background: Prognostic factors for ambulatory oncology patients have been described, including Eastern Cooperative Oncology Group (ECOG), tumor stage and malnutrition. However, there is no firm evidence on which variables best predict mortality in hospitalized patients receiving active systemic treatment. Our main goal was to develop a predictive model for 90-day mortality upon admission. Methods: Between 2020 and 2022, we prospectively collected data from three sites for cancer patients with hospitalizations. Those with metastatic disease receiving systemic therapy in the 6 months before unplanned admission were eligible to this study. The least absolute shrinkage and selection operator (LASSO) method was used to select the most relevant factors to predict 90-day mortality at admission. A multivariable logistic regression was fitted to create the PROgnostic Score for Hospitalized Cancer Patients (PROMISE) score. The score was developed in a single-center training cohort and externally validated. Findings: Of 1658 hospitalized patients, 1009 met eligibility criteria. Baseline demographics, patient and disease characteristics were similar across cohorts. Lung cancer was the most common tumor type in both cohorts. Factors associated with higher 90-day mortality included worse ECOG, stable/progressive disease, low levels of albumin, increased absolute neutrophil count, and high lactate dehydrogenase. The c-index after bootstrap correction was 0.79 (95% CI, 0.75–0.82) and 0.74 (95% CI, 0.68–0.80) in the training and validation cohorts, respectively. A web tool (https://promise.vhio.net/) was developed to facilitate the clinical deployment of the model. Interpretation: The PROMISE tool demonstrated high performance for identifying metastatic cancer patients who are alive 90 days after an unplanned hospitalization. This will facilitate healthcare providers with rational clinical decisions and care planning after discharge. Funding: Merck S.L.U., Spain.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2666-7762
Relation: http://www.sciencedirect.com/science/article/pii/S2666776224002308; https://doaj.org/toc/2666-7762
DOI: 10.1016/j.lanepe.2024.101063
Access URL: https://doaj.org/article/510c740dd59f414d89fc0806c8cb60c4
Accession Number: edsdoj.510c740dd59f414d89fc0806c8cb60c4
Database: Directory of Open Access Journals
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  Data: Summary: Background: Prognostic factors for ambulatory oncology patients have been described, including Eastern Cooperative Oncology Group (ECOG), tumor stage and malnutrition. However, there is no firm evidence on which variables best predict mortality in hospitalized patients receiving active systemic treatment. Our main goal was to develop a predictive model for 90-day mortality upon admission. Methods: Between 2020 and 2022, we prospectively collected data from three sites for cancer patients with hospitalizations. Those with metastatic disease receiving systemic therapy in the 6 months before unplanned admission were eligible to this study. The least absolute shrinkage and selection operator (LASSO) method was used to select the most relevant factors to predict 90-day mortality at admission. A multivariable logistic regression was fitted to create the PROgnostic Score for Hospitalized Cancer Patients (PROMISE) score. The score was developed in a single-center training cohort and externally validated. Findings: Of 1658 hospitalized patients, 1009 met eligibility criteria. Baseline demographics, patient and disease characteristics were similar across cohorts. Lung cancer was the most common tumor type in both cohorts. Factors associated with higher 90-day mortality included worse ECOG, stable/progressive disease, low levels of albumin, increased absolute neutrophil count, and high lactate dehydrogenase. The c-index after bootstrap correction was 0.79 (95% CI, 0.75–0.82) and 0.74 (95% CI, 0.68–0.80) in the training and validation cohorts, respectively. A web tool (https://promise.vhio.net/) was developed to facilitate the clinical deployment of the model. Interpretation: The PROMISE tool demonstrated high performance for identifying metastatic cancer patients who are alive 90 days after an unplanned hospitalization. This will facilitate healthcare providers with rational clinical decisions and care planning after discharge. Funding: Merck S.L.U., Spain.
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