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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Identifying Risk Groups in 401,846 Osteoarthritis Patients Undergoing Total Hip Arthroplasty: A Machine Learning Clustering Analysis.

Journal of personalized medicine·2026
Same author

Management of Complex Peri-Prosthetic Joint Infection Following Total Knee Arthroplasty with Soft Tissue Defects: Case Series and Multidisciplinary Approach.

Journal of personalized medicine·2026
Same author

Socioeconomic Disparities in Outcomes Following Primary Total Hip Arthroplasty: A Large Database Analysis of 2,280,000 Procedures.

The Journal of arthroplasty·2026
Same author

Medial Opening-Wedge High Tibial Osteotomy.

JBJS essential surgical techniques·2026
Same author

Tibial Lengthening with a Motorized Intramedullary Lengthening Nail.

JBJS essential surgical techniques·2026
Same author

Evaluating the impact of COVID-19 on the HIV care continuum across global income levels: a mixed-methods systematic review.

AIDS research and therapy·2025
Same journal

Correction: Rao et al. Ensemble Deep-Learning-Based Prognostic and Prediction for Recurrence of Sporadic Odontogenic Keratocysts on Hematoxylin and Eosin Stained Pathological Images of Incisional Biopsies. <i>J. Pers. Med.</i> 2022, <i>12</i>, 1220.

Journal of personalized medicine·2026
Same journal

Three-Dimensional Bronchovascular Modelling in Sublobar Pulmonary Resection: A Tool for Personalised Thoracic Surgery.

Journal of personalized medicine·2026
Same journal

Serum Albumin, Globulin and Albumin-Globulin Ratios as Biomarkers of Clinical Outcomes in COVID-19 Pneumonia.

Journal of personalized medicine·2026
Same journal

New Advances and Perspectives in Ophthalmology: Progress and Modern Challenges Toward Personalized Eye Care.

Journal of personalized medicine·2026
Same journal

Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.

Journal of personalized medicine·2026
Same journal

Hormone-Driven Growth Signaling as a Therapeutic Target in Acute Myeloid Leukemia: Implications for Drug-Resistant Disease.

Journal of personalized medicine·2026
See all related articles

Related Experiment Video

Updated: Jan 10, 2026

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
07:22

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

695

Identifying Risk Groups in 73,000 Patients with Diabetes Receiving Total Hip Replacement: A Machine Learning

Alishah Ahmadi1, Anthony J Kaywood1, Alejandra Chavarria1

  • 1School of Medicine, New York Medical College, Valhalla, NY 10595, USA.

Journal of Personalized Medicine
|November 26, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning identified six patient groups with diabetes undergoing hip replacement surgery. Infection-related complications significantly increased risks for non-routine discharge and longer hospital stays in these diabetic patients.

Keywords:
clusteringdiabetes mellitusmachine learningnon-routine dischargetotal hip arthroplasty

More Related Videos

The Transition to an Anterior-Based Muscle Sparing Approach Improves Early Postoperative Function but is Associated with a Learning Curve
09:51

The Transition to an Anterior-Based Muscle Sparing Approach Improves Early Postoperative Function but is Associated with a Learning Curve

Published on: September 7, 2022

3.5K
The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
07:45

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report

Published on: August 4, 2022

3.8K

Related Experiment Videos

Last Updated: Jan 10, 2026

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
07:22

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

Published on: March 7, 2025

695
The Transition to an Anterior-Based Muscle Sparing Approach Improves Early Postoperative Function but is Associated with a Learning Curve
09:51

The Transition to an Anterior-Based Muscle Sparing Approach Improves Early Postoperative Function but is Associated with a Learning Curve

Published on: September 7, 2022

3.5K
The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report
07:45

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report

Published on: August 4, 2022

3.8K

Area of Science:

  • Orthopedic Surgery
  • Data Science
  • Public Health

Background:

  • Diabetes mellitus (DM) is common and impacts total hip arthroplasty (THA) outcomes.
  • Identifying specific comorbidity profiles in diabetic THA patients is crucial for improving care.

Purpose of the Study:

  • To apply machine learning clustering to define comorbidity profiles in diabetic THA patients.
  • To assess the association between identified clusters and postoperative outcomes like discharge disposition and length of stay.

Main Methods:

  • Utilized the 2015-2021 National Inpatient Sample database.
  • Included 73,606 diabetic patients undergoing THA, analyzing 49 comorbidities and covariates.
  • Employed clustering algorithms, logistic regression for non-routine discharge (NRD), and Kruskal-Wallis H testing for length-of-stay (LOS).

Main Results:

  • Six distinct patient clusters were identified based on comorbidity profiles.
  • A cluster characterized by urinary tract infection and sepsis showed significantly higher NRD risk (OR 7.83) and longest median LOS (9.0 days).
  • Other clusters demonstrated varied recovery patterns, with some achieving shorter LOS (2.0 days).

Conclusions:

  • Machine learning effectively stratified diabetic THA patients into six unique groups.
  • Infection-predominant clusters represent high-risk populations for adverse outcomes.
  • This clustering approach offers a novel method for risk stratification and personalized perioperative management in diabetic THA patients.