Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
| Autor: | Gregory N. Kawchuk, Julie M. Fritz, Maliheh Hadizadeh, Narasimha Prasad |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Sports Medicine Likelihood ratios in diagnostic testing Stiffness 0302 clinical medicine Medicine and Health Sciences Back pain 030212 general & internal medicine Musculoskeletal System Measurement Multidisciplinary Middle Aged Prognosis Chiropractic Low back pain Sports Science Treatment Outcome Physical Sciences Engineering and Technology Medicine Female Anatomy medicine.symptom Manual therapy Research Article Adult Manipulation Spinal medicine.medical_specialty Adolescent Patients Science Materials Science Material Properties Lower Back Pain MEDLINE Pain Young Adult 03 medical and health sciences Signs and Symptoms Time frame Complementary and Alternative Medicine medicine Humans Mechanical Properties Sports and Exercise Medicine Skeleton business.industry Biology and Life Sciences Spine Health Care Multicollinearity Physical therapy Clinical Medicine business Low Back Pain 030217 neurology & neurosurgery |
| Zdroj: | PLoS ONE, Vol 15, Iss 11, p e0242831 (2020) PLoS ONE |
| ISSN: | 1932-6203 |
| DOI: | 10.1371/journal.pone.0242831 |
| Popis: | Background Spinal manipulative therapy (SMT) is among the nonpharmacologic interventions that has been recommended in clinical guidelines for patients with low back pain, however, some patients appear to benefit substantially more from SMT than others. Several investigations have examined potential factors to modify patients’ responses prior to SMT application. The objective of this study was to determine if the baseline prediction of SMT responders can be improved through the use of a restricted, non-pragmatic methodology, established variables of responder status, and newly developed physical measures observed to change with SMT. Materials and methods We conducted a secondary analysis of a prior study that provided two applications of standardized SMT over a period of 1 week. After initial exploratory analysis, principal component analysis and optimal scaling analysis were used to reduce multicollinearity among predictors. A multiple logistic regression model was built using a forward Wald procedure to explore those baseline variables that could predict response status at 1-week reassessment. Results Two hundred and thirty-eight participants completed the 1-week reassessment (age 40.0± 11.8 years; 59.7% female). Response to treatment was predicted by a model containing the following 8 variables: height, gender, neck or upper back pain, pain frequency in the past 6 months, the STarT Back Tool, patients’ expectations about medication and strengthening exercises, and extension status. Our model had a sensitivity of 72.2% (95% CI, 58.1–83.1), specificity of 84.2% (95% CI, 78.0–89.0), a positive likelihood ratio of 4.6 (CI, 3.2–6.7), a negative likelihood ratio of 0.3 (CI, 0.2–0.5), and area under ROC curve, 0.79. Conclusion It is possible to predict response to treatment before application of SMT in low back pain patients. Our model may benefit both patients and clinicians by reducing the time needed to re-evaluate an initial trial of care. |
| Databáze: | OpenAIRE |
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