Personalized Prediction Models for Changes in Knee Pain among Patients with Osteoarthritis Participating in Supervised Exercise and Education

• URL: http://arxiv.org/abs/2410.12597v1
• Date: Wed, 16 Oct 2024 14:15:01 GMT
• Title: Personalized Prediction Models for Changes in Knee Pain among Patients with Osteoarthritis Participating in Supervised Exercise and Education
• Authors: M. Rafiei, S. Das, M. Bakhtiari, E. M. Roos, S. T. Skou, D. T. Grønne, J. Baumbach, L. Baumbach,
• Abstract summary: Knee osteoarthritis (OA) is a widespread chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing the OA symptoms pain and functional limitations, these strategies are often underutilized. To improve predictions, new variables beyond those in the GLA:D are required.
• Score: 0.0
• License:
• Abstract: Knee osteoarthritis (OA) is a widespread chronic condition that impairs mobility and diminishes quality of life. Despite the proven benefits of exercise therapy and patient education in managing the OA symptoms pain and functional limitations, these strategies are often underutilized. Personalized outcome prediction models can help motivate and engage patients, but the accuracy of existing models in predicting changes in knee pain remains insufficiently examined. To validate existing models and introduce a concise personalized model predicting changes in knee pain before to after participating in a supervised education and exercise therapy program (GLA:D) for knee OA patients. Our models use self-reported patient information and functional measures. To refine the number of variables, we evaluated the variable importance and applied clinical reasoning. We trained random forest regression models and compared the rate of true predictions of our models with those utilizing average values. We evaluated the performance of a full, continuous, and concise model including all 34, all 11 continuous, and the six most predictive variables respectively. All three models performed similarly and were comparable to the existing model, with R-squares of 0.31-0.32 and RMSEs of 18.65-18.85 - despite our increased sample size. Allowing a deviation of 15 VAS points from the true change in pain, our concise model and utilizing the average values estimated the change in pain at 58% and 51% correctly, respectively. Our supplementary analysis led to similar outcomes. Our concise personalized prediction model more accurately predicts changes in knee pain following the GLA:D program compared to average pain improvement values. Neither the increase in sample size nor the inclusion of additional variables improved previous models. To improve predictions, new variables beyond those in the GLA:D are required.

Related papers

• Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study [0.622895724042048]
  The pharmacological treatment of Major Depressive Disorder (MDD) relies on a trial-and-error approach. We introduce an artificial intelligence (AI) model aiming to personalize treatment outcomes.
  arXiv  Detail & Related papers  (2024-06-07T15:04:59Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments [1.1549572298362785]
  This study included subjects (1832 subjects, 3276 knees) from the baseline of the MOST study. PF joint regions-of-interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. Risk factors included age, sex, BMI and WOMAC score, and the radiographic osteoarthritis stage of the tibiofemoral joint (KL score)
  arXiv  Detail & Related papers  (2023-05-10T06:43:33Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• What Do You See in this Patient? Behavioral Testing of Clinical NLP Models [69.09570726777817]
  We introduce an extendable testing framework that evaluates the behavior of clinical outcome models regarding changes of the input. We show that model behavior varies drastically even when fine-tuned on the same data and that allegedly best-performing models have not always learned the most medically plausible patterns.
  arXiv  Detail & Related papers  (2021-11-30T15:52:04Z)
• Identifying and mitigating bias in algorithms used to manage patients in a pandemic [4.756860520861679]
  Logistic regression models were created to predict COVID-19 mortality, ventilator status and inpatient status using a real-world dataset. Models showed a 57% decrease in the number of biased trials. After calibration, the average sensitivity of the predictive models increased from 0.527 to 0.955.
  arXiv  Detail & Related papers  (2021-10-30T21:10:56Z)
• Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak [10.307715136465056]
  We present a comparative analysis of various machine learning (ML) approaches in predicting the COVID-19 outbreak in the epidemiological domain. The results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
  arXiv  Detail & Related papers  (2021-02-11T11:57:33Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs Using Convolutional Neural Networks [0.0]
  We propose a novel method using convolutional neural networks to automatically grade knee radiographs on the Kellgren-Lawrence (KL) scale. Our method works in two connected stages: in the first stage, an object detection model segments individual knees from the rest of the image; in the second stage, a regression model automatically grades each knee separately on the KL scale.
  arXiv  Detail & Related papers  (2020-04-18T09:46:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.