Literature DB >> 29391206

Long-term outcomes of a large, prospective observational cohort of older adults with back pain.

Jeffrey G Jarvik1, Laura S Gold2, Katherine Tan3, Janna L Friedly4, Srdjan S Nedeljkovic5, Bryan A Comstock3, Richard A Deyo6, Judith A Turner7, Brian W Bresnahan8, Sean D Rundell4, Kathryn T James2, David R Nerenz9, Andrew L Avins10, Zoya Bauer2, Larry Kessler11, Patrick J Heagerty3.   

Abstract

BACKGROUND CONTEXT: Although back pain is common among older adults, there is relatively little research on the course of back pain in this age group.
PURPOSE: Our primary goals were to report 2-year outcomes of older adults initiating primary care for back pain and to examine the relative importance of patient factors versus medical interventions in predicting 2-year disability and pain. STUDY DESIGN/
SETTING: This study used a predictive model using data from a prospective, observational cohort from a primary care setting. PATIENT SAMPLE: The study included patients aged ≥65 years at the time of new primary care visits for back pain. OUTCOME MEASURES: Self-reported 2-year disability (Roland-Morris Disability Questionnaire [RDQ]) and back pain (0-10 numerical rating scale [NRS]).
METHODS: We developed our models using a machine learning least absolute shrinkage and selection operator approach. We evaluated the predictive value of baseline characteristics and the incremental value of interventions that occurred between 0 and 90 days, and the change in patient disability and pain from 0 to 90 days. Limitations included confounding by indication and unmeasured confounding.
RESULTS: Of 4,665 patients (89%) with follow-up, both RDQ (from mean 9.6 [95% confidence interval {CI} 9.4-9.7] to mean 8.3 [95% CI 8.0-8.5]) and back pain NRS (from mean 5.0 [95% CI 4.9-5.1] to mean 3.5 [95% CI 3.4-3.6]) scores improved slightly. Only 16% (15%-18%) reported no back pain-related disability or back pain at 2 years after initial visits. Regression model parameters explained 40% of the variation (R2) in 2-year RDQ scores, and the addition of 0- to 3-month change in RDQ score and pain improved prediction (R2=51%). The most consistent predictors of 2-year RDQ scores and back pain NRS scores were 0- to 90-day change in each respective outcome and patient confidence in improvement. Patients experienced 50% and 43% improvement in back pain and disability, respectively, 2 years after their initial visit. However, fewer than 20% of patients had complete resolution of their back pain and disability at that time.
CONCLUSIONS: Baseline patient factors were more important than early interventions in explaining disability and pain after 2 years.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Back pain; Disability; Functional status; Older adults; Predictive modeling; Prognosis

Mesh:

Year:  2018        PMID: 29391206     DOI: 10.1016/j.spinee.2018.01.018

Source DB:  PubMed          Journal:  Spine J        ISSN: 1529-9430            Impact factor:   4.166


  6 in total

Review 1.  Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.

Authors:  Scott D Tagliaferri; Maia Angelova; Xiaohui Zhao; Patrick J Owen; Clint T Miller; Tim Wilkin; Daniel L Belavy
Journal:  NPJ Digit Med       Date:  2020-07-09

2.  The Manual Therapy and Strengthening for the Hip (MASH) Trial: Protocol for a Multisite Randomized Trial of a Subgroup of Older Adults With Chronic Back and Hip Pain.

Authors:  Jenifer M Pugliese; Peter C Coyle; Patrick J Knox; J Megan Sions; Charity G Patterson; Ryan T Pohlig; Corey B Simon; Debra K Weiner; Steven Z George; Sara Piva; Gregory E Hicks
Journal:  Phys Ther       Date:  2022-01-01

Review 3.  Artificial intelligence in spine care: current applications and future utility.

Authors:  Alexander L Hornung; Christopher M Hornung; G Michael Mallow; J Nicolás Barajas; Augustus Rush; Arash J Sayari; Fabio Galbusera; Hans-Joachim Wilke; Matthew Colman; Frank M Phillips; Howard S An; Dino Samartzis
Journal:  Eur Spine J       Date:  2022-03-27       Impact factor: 2.721

Review 4.  Artificial intelligence to improve back pain outcomes and lessons learnt from clinical classification approaches: three systematic reviews.

Authors:  Scott D Tagliaferri; Maia Angelova; Xiaohui Zhao; Patrick J Owen; Clint T Miller; Tim Wilkin; Daniel L Belavy
Journal:  NPJ Digit Med       Date:  2020-07-09

5.  Spinal manipulative therapy and exercise for older adults with chronic low back pain: a randomized clinical trial.

Authors:  Craig Schulz; Roni Evans; Michele Maiers; Karen Schulz; Brent Leininger; Gert Bronfort
Journal:  Chiropr Man Therap       Date:  2019-05-15

6.  Characteristics of older adults with back pain associated with choice of first primary care provider: a cross-sectional analysis from the BACE-N cohort study.

Authors:  Ørjan Nesse Vigdal; Kjersti Storheim; Rikke Munk Killingmo; Milada Cvancarova Småstuen; Margreth Grotle
Journal:  BMJ Open       Date:  2021-09-17       Impact factor: 2.692

  6 in total

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