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Frailty and Frequent Hospitalizations in Older Adults: Risk, Management, and...
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Frailty and Frequent Hospitalizations in Older Adults: Risk, Management, and Interventions

A special issue of Diseases (ISSN 2079-9721).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1152

Special Issue Editors

Dr. Francesco Ragusa

E-Mail Website
Guest Editor
Geriatric Unit, Department of Internal Medicine and Geriatrics, University of Palermo, 90127 Palermo, Italy
Interests: geriatrics; dementia; sarcopenia; neurodegenerative diseases; public health; aging; clinical nutrition; global health; vaccines
Dr. Nicola Veronese

E-Mail Website
Guest Editor
Geriatrics, Internal Medicine Sector, UniCamillus—International Medical University in Rome, Via di Sant’Alessandro, 8, 00131 Rome, Italy
Interests: aging; health; diseases; nutrition; magnesium
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Frailty is a complex clinical syndrome characterized by a decline in physiological reserves and reduced homeostatic capacity, which limits the body’s ability to respond effectively to internal or external stressors. As the global population ages, frailty is becoming a major public health concern, frequently associated with repeated hospital admissions, prolonged hospital stays, reduced quality of life, and increased healthcare costs. Frequent hospitalizations among older adults often signal the progression of frailty and are predictive of disability and mortality.

We are pleased to invite you to contribute to this Special Issue of Diseases, which aims to explore the multifaceted relationship between frailty and frequent hospitalizations in older populations. This topic lies at the intersection of geriatrics, internal medicine, public health, and healthcare policy, aligning closely with the journal’s multidisciplinary scope. Our goal is to gather high-quality, evidence-based insights into risk assessment, early detection, and targeted interventions that may reduce hospital readmissions and improve the care of frail elderly patients.

In this Special Issue, original research articles and systematic or narrative reviews are welcome. Research areas may include (but are not limited to) the following:

  • Epidemiology of frailty and hospital readmissions;
  • Clinical risk factors and biomarkers of frequent hospitalizations;
  • Multidisciplinary approaches to frailty management;
  • Nutrition, lifestyle, and functional interventions;
  • Transitional care and hospital-at-home models;
  • Pharmacological and non-pharmacological strategies;
  • Policy and health system innovations in elderly care.

We look forward to hearing from you.

Dr. Francesco Ragusa
Dr. Nicola Veronese
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diseases is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • frailty
  • hospital readmissions
  • aging
  • geriatric care
  • interventions
  • risk factors
  • transitional care
  • older adults
  • healthcare utilization
  • multimorbidity

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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21 pages, 5439 KB  
Article
Multi-Task Deep Learning Model for Automated Detection and Severity Grading of Lumbar Spinal Stenosis on MRI: Multi-Center External Validation
by Phatcharapon Udomluck, Watcharaporn Cholamjiak, Jakkaphong Inpun and Waragunt Waratamrongpatai
Diseases 2026, 14(1), 32; https://doi.org/10.3390/diseases14010032 - 14 Jan 2026
Viewed by 165
Abstract
Background/Objectives: Accurate and reproducible grading of lumbar spinal stenosis (LSS) is clinically critical for guiding treatment decisions and patient management, yet manual assessment remains challenging due to imaging variability and inter-observer subjectivity. To address these limitations, this study aimed to evaluate the [...] Read more.
Background/Objectives: Accurate and reproducible grading of lumbar spinal stenosis (LSS) is clinically critical for guiding treatment decisions and patient management, yet manual assessment remains challenging due to imaging variability and inter-observer subjectivity. To address these limitations, this study aimed to evaluate the generalizability of deep learning–based feature extraction methods—VGG19, ConvNeXt-Tiny, and DINOv2—combined with classical machine learning classifiers for automated multi-grade LSS assessment. Automated grading enables objective, reproducible, and scalable assessment of lumbar spinal stenosis severity, addressing key limitations of manual interpretation. Methods: Axial MRI images were processed using pretrained VGG19, ConvNeXt-Tiny, and DINOv2 models to extract deep features. Logistic Regression, Support Vector Machine (SVM), and LightGBM were trained on internal datasets and externally validated using MRI data from the University of Phayao Hospital. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrices, and multi-class ROC curves. Results: VGG19-based features yielded the strongest external performance, with Logistic Regression achieving the highest accuracy (0.9556) and F1-score (0.9558). External validation further demonstrated excellent discrimination, with AUC values ranging from 0.994 to 1.000 across all severity grades. SVM (0.9333 accuracy) and LightGBM (0.9222 accuracy) also performed well. ConvNeXt-Tiny showed stable cross-model performance, while DINOv2 features exhibited reduced generalizability, especially with LightGBM (accuracy 0.6222). Most classification errors occurred between adjacent grades. Conclusions: Deep convolutional features—particularly VGG19—combined with classical machine learning classifiers provide robust and generalizable LSS grading across external MRI data. Despite advances in modern architectures, CNN-based feature extraction remains highly effective for spinal imaging and represents a practical pathway for clinical decision support. Full article
(This article belongs to the Special Issue Frailty and Frequent Hospitalizations in Older Adults: Risk, Management, and Interventions)
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11 pages, 706 KB  
Article
Revolving Door in Older Patients: An Observational Study of Risk Assessment of Rehospitalization Using the BRASS Scale
by Francesco Saverio Ragusa, Anna La Vattiata, Antonio Terranova, Giuseppina Pesco, Davide Mariani, Ligia J. Dominguez, Nicola Veronese, Pasquale Mansueto and Mario Barbagallo
Diseases 2025, 13(10), 325; https://doi.org/10.3390/diseases13100325 - 1 Oct 2025
Viewed by 611
Abstract
Introduction: The “revolving” door is a phenomenon that refers to the rehospitalization of older patients who, after being discharged, soon require specialized hospital care again. Unfortunately, the use of tools able to predict this phenomenon is still limited. The aim of this [...] Read more.
Introduction: The “revolving” door is a phenomenon that refers to the rehospitalization of older patients who, after being discharged, soon require specialized hospital care again. Unfortunately, the use of tools able to predict this phenomenon is still limited. The aim of this study was to highlight the validity of the Blaylock Risk Assessment Screening (BRASS) Scale in objectively assessing the risk of rehospitalization and mortality among older patients. Methods: Patients were classified as low, medium, or high risk using the BRASS scale. Adverse events (rehospitalization or death) were recorded at baseline and at 12 months. Kaplan–Meier curves evaluated survival and rehospitalization across risk groups, and ROC analysis assessed the BRASS Scale’s predictive value for mortality. Results: Out of 179 enrolled older adults (mean age 67.7 years), 54.2% were classified as low risk, 29.5% as medium, and 16.8% as high risk based on the BRASS Scale. High-risk patients had significantly higher mortality (HR: 4.40; 95% CI: 1.60–12.19, p = 0.004) and lower survival rates, while intermediate-risk patients had increased rehospitalization (HR: 2.11; 95% CI: 1.09–4.08, p = 0.02). The BRASS scale showed good predictive value for mortality (AUC 0.76). Conclusion: The BRASS Scale has a good predictive value for negative outcomes, and it confirms that a substantial proportion of older patients are at risk of future hospital readmissions and complex discharges. These findings underscore the importance of early post-discharge care planning and the implementation of protected discharge programs. Full article
(This article belongs to the Special Issue Frailty and Frequent Hospitalizations in Older Adults: Risk, Management, and Interventions)
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