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

NCT05534178 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 2500

Last updated 2022-09-09

No results posted yet for this study

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

Sponsors & Collaborators

  • Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau

    collaborator OTHER

  • Hospital del Mar

    collaborator OTHER

  • Vall d'Hebron Institute of Oncology

    lead OTHER

Principal Investigators

  • Oriol Mirallas, MD · Vall d'Hebron University Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-02-15
Primary Completion
2023-02-15
Completion
2024-03-15

Countries

  • Spain

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05534178 on ClinicalTrials.gov