Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Machine learning using PROFUND components for 30-day readmission prediction in multimorbid patients: a prospective

Amaia Pikatza-Huerga1,2, Aitor Almeida3,4, Raúl Quirós4,5

  • 1Faculty of Engineering, University of Deusto, Av. de las Universidades, 24, Deusto, Bilbao, Bizkaia, E-48007, Spain. a.pikatza@deusto.es.

Scientific Reports
|May 13, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Beyond text: Using network centralities and AI models to detect suicide risk on Reddit.

International journal of clinical and health psychology : IJCHP·2026
Same author

Impact of diabetes mellitus on generic health-related quality of life measured by EQ-5D-5L, characterization and disutilities for adverse factors.

Journal of diabetes and metabolic disorders·2026
Same author

Ten-Year Outcomes in Colorectal Cancer-Competing Risks and Patient Vulnerability: A Prospective Multicenter Observational Study.

Journal of clinical medicine·2026
Same author

Developing an HL7 FHIR Implementation Guide for Rare Cancers: The IDEA4RC Project Experience.

Studies in health technology and informatics·2026
Same author

Impact of Visitor Restrictions Due to the COVID-19 Pandemic on the Occurrence of In-Hospital Delirium.

Journal of patient safety·2026
Same author

Joint probability framework for the development and validation of a prognostic model for the conditional outcome of quality of life: a retrospective study in historical European cohorts of survivors of head and neck cancer.

The Lancet. Digital health·2026

Predicting 30-day hospital readmissions for multimorbid patients is challenging. The PROFUND index, assessing frailty and social factors, showed only modest improvements in prediction, even with machine learning models.

Area of Science:

  • Geriatric Medicine
  • Health Services Research
  • Clinical Epidemiology

Background:

  • Early hospital readmission for multimorbid patients presents a significant clinical challenge.
  • Existing risk stratification tools often exhibit limited predictive performance.
  • The PROFUND index, encompassing frailty, functional dependence, and social vulnerability, has an unclear role in predicting 30-day readmissions.

Purpose of the Study:

  • To evaluate the predictive performance of the PROFUND index and its components for unplanned 30-day hospital readmissions in multimorbid patients.
  • To compare machine learning models incorporating PROFUND components against established scores like LACE and HOSPITAL.
  • To identify key predictors of 30-day readmission within the PROFUND framework.

Main Methods:

Related Experiment Videos

  • Prospective, multicentre cohort study of multimorbid patients admitted to Internal Medicine and Geriatrics.
  • Development of logistic regression and gradient boosting models (including ensemble) using PROFUND components.
  • External validation of developed models and comparison with LACE and HOSPITAL scores.
  • Utilized SHAP analysis to identify key predictive factors.

    • Main Results:

    • 14% of 435 patients experienced unplanned 30-day readmission.
    • External validation showed modest discrimination (AUC 0.52-0.59).
    • The ensemble XGBoost model achieved the highest AUC (0.59), outperforming LACE and HOSPITAL scores incrementally.
    • Key contributors included cognitive impairment, anemia, advanced age, heart failure severity, functional dependence, and limited caregiver support.

    Conclusions:

    • Incorporating frailty, functional, and social vulnerability domains via PROFUND components yielded only modest improvements in predicting 30-day readmissions.
    • Even advanced machine learning techniques demonstrated limited predictive discrimination.
    • The complexity of short-term readmission and potential constraints from sample size and predictors may limit performance.