Machine Learning Model to Predict HOLS and Mortality After Discharge in Hospitalized Oncologic Patients

NCT05534178 | Unknown DMC

• 1 other identifier: VHIO1601
• Study Type: observational
• Target: 2,500
• Locations: 1 country
• Sites: 3

Brief Summary

The study aims to understand which are the most relevant parameters at admission which may allow to predict the hospital length of stay (HOLS) and mortality after discharge of oncologic hospitalized patients. This is the first multicentric prospective observational study that tries to understand the complexity of the hospitalized oncologic patients. A comprehensive analysis will be performed with the help of the nutrition, nursery, internal medicine and oncology teams.

Conditions

Solid Tumor; Nutrition Related Neoplasm/Cancer; Comorbidities and Coexisting Conditions; Mental Status Change; Artificial Intelligence; Oncology; Tumor; Quality of Life

Keywords

Hospitalization; Oncologic patients

Outcome Measures

Primary Outcomes (2)

• Predict Mortality

  Mortality at 30-day after discharge

  30 days after discharge

• Predict hospital length of stay

  Number of days hospitalized

  Through study completion, an average of 3 years

Secondary Outcomes (8)

• Measure the impact of Anxiety and Depression

  Within 24 hours of admission

• Measure the impact of Quality of life (QoL)

  Within 24 hours of admission

• Validate standardized test HOSPITAL score: Risk of readmission

  Evaluated at discharge through study completion, an average of 3 years. The outcome is the probability of readmission within the first 30 days after discharge.

• Sarcopenia Assessment

  Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years

• Sarcopenia Test

  Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All oncologic patients that require hospitalization.

You may qualify if:

• ≥18 years-old.
• Histological cancer confirmation.
• Hospitalization in oncology ward.

You may not qualify if:

• \<18 years-old.
• Not histological malignancy confirmed.
• Less than 24 hours in the hospital.

Sponsors & Collaborators

• Vall d'Hebron Institute of Oncology: lead
• Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau: collaborator
• Hospital del Mar: collaborator

Study Sites (3)

Hospital del Mar

Barcelona, 08003, Spain

RECRUITING

Hospital Universitari Vall d'Hebron

Barcelona, 08035, Spain

RECRUITING

Hospital de la Santa Creu i Sant Pau

Barcelona, 08041, Spain

RECRUITING

Related Publications (5)

• Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.

  PMID: 33538338 RESULT

• Brooks GA, Cronin AM, Uno H, Schrag D, Keating NL, Mack JW. Intensity of Medical Interventions between Diagnosis and Death in Patients with Advanced Lung and Colorectal Cancer: A CanCORS Analysis. J Palliat Med. 2016 Jan;19(1):42-50. doi: 10.1089/jpm.2015.0190. Epub 2015 Nov 24.

  PMID: 26600474 RESULT

• Manzano JG, Luo R, Elting LS, George M, Suarez-Almazor ME. Patterns and predictors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer. J Clin Oncol. 2014 Nov 1;32(31):3527-33. doi: 10.1200/JCO.2014.55.3131. Epub 2014 Oct 6.

  PMID: 25287830 RESULT

• Earle CC, Park ER, Lai B, Weeks JC, Ayanian JZ, Block S. Identifying potential indicators of the quality of end-of-life cancer care from administrative data. J Clin Oncol. 2003 Mar 15;21(6):1133-8. doi: 10.1200/JCO.2003.03.059.

  PMID: 12637481 RESULT

• Whitney RL, Bell JF, Tancredi DJ, Romano PS, Bold RJ, Joseph JG. Hospitalization Rates and Predictors of Rehospitalization Among Individuals With Advanced Cancer in the Year After Diagnosis. J Clin Oncol. 2017 Nov 1;35(31):3610-3617. doi: 10.1200/JCO.2017.72.4963. Epub 2017 Aug 29.

  PMID: 28850290 RESULT

MeSH Terms

Conditions

Neoplasms

Study Officials

• Oriol Mirallas, MD

  Vall d'Hebron University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Oriol Mirallas, MD

CONTACT

+34934 89 30 00; omirallas@vhebron.net

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 4 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 27, 2022
• First Posted: September 9, 2022
• Study Start: February 15, 2020
• Primary Completion: February 15, 2023
• Study Completion: March 15, 2024
• Last Updated: September 9, 2022

Record last verified: 2022-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF, CSR
• Time Frame: 1 year
• Access Criteria: Principal investigators and sub-investigators

Locations

ES (3)