Evaluation of a modified clinical prediction rule for use with spinal manipulative therapy in patients with chronic low back pain: a randomized clinical trial

Dougherty, Paul; Karuza, Jurgis; Savino, Dorian; Katz, Paul R.

doi:10.1186/preaccept-2691944861131143

Cited by 8 publications

(8 citation statements)

References 25 publications

(27 reference statements)

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“…The better model performance of the FABQ-W is in line with the emerging evidence that the FABQ-W is a stronger moderator of treatment efficacy in chronic LBP compared to the FABQ-PA, although this might be dependent on the patient population George et al, 2008;Waddell et al, 1993;Wertli et al, 2014a). In support of this, the FABQ-W scale qualified for a clinical prediction rule regarding improvement after spinal manipulation, whereas the FABQ-PA scale did not (Dougherty et al, 2014;Flynn et al, 2002).…”

Section: Fabq and Tsksupporting

confidence: 71%

Pain-related fear – Dissociable neural sources of different fear constructs

Meier

Humphreys

Vrana

et al. 2018

Preprint

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The ability to infer emotional states through self-reports is often limited. Their measurement becomes even more challenging when considering emotional phenomena such as pain-related fear where different associated fear constructs have been proposed. Demonstrating significant predictive value regarding disability in patients with persistent musculoskeletal pain, pain-related fear is often assessed by questionnaires focusing on either fear of movement/(re)injury/kinesiophobia, fear avoidance beliefs or pain anxiety. Furthermore, the relationship of general anxiety measures such as trait anxiety to pain-related fear remains ambiguous. Advances in neuroimaging might help to support potential commonalities or differences across psychological constructs using appropriate machine learning techniques with the ability to reveal predictive relationships between neural information and questionnaire scores.Here, we applied a pattern regression approach using functional magnetic resonance imaging data of 20 non-specific chronic low back pain (LBP) patients. More specifically, we applied a novel approach using Multiple Kernel Learning that allows investigating the contribution of experimental conditions and regional neural information to a prediction model. We hypothesized to find evidence for or against a common fear construct by computing and comparing the prediction model of each questionnaire according to the contribution of fear-related neural information and conditions. The current results underpin the diversity of fear constructs among self-report measures of pain-related fear by demonstrating evidence of non-overlapping and differentially contributing neural sources within fear processing regions. Thus, the current approach might ultimately help to further understand and dissect the fear constructs captured by the various pain-related fear questionnaires. Significance. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/251751 doi: bioRxiv preprint first posted online Jan. 25, 2018; Pain-related fear, often assessed through self-reports such as questionnaires, has shown prognostic value and clinical utility for a variety of musculoskeletal pain disorders. However, it remains difficult to determine a common underlying fear construct of pain-related fear due to several proposed constructs among questionnaires. The current study describes a novel neuroscientific approach using machine learning of neural patterns within the fear network of chronic LBP patients that might have the potential to identify neural commonalities or differences among the various fear constructs. Ultimately, this approach might afford a deeper understanding of the suggested fear constructs of pain-related fear and might be also applied to other domains where ambiguity exists between emotional phenomena and underlying psychological constructs.

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Section: Fabq and Tsksupporting

confidence: 71%

Pain-related fear – Dissociable neural sources of different fear constructs

Meier

Humphreys

Vrana

et al. 2018

Preprint

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“…Our data suggest that isostretching is an effective resource for the treatment of pain, since there was a significant reduction in pain during the treatment, as can be seen in Figure 1. Similar results were obtained by Doughert and collaborators [22], where an intervention with exercises was also effective in reducing the pain as assessed by the VAS. These satisfactory results regarding pain reduction are caused by the practice of physical activity to which the elderly were submitted.…”

Section: Discussionsupporting

confidence: 86%

Effect of the isostretching method on lumbar pain and flexibility of the lumbar spine in elderly women.

Moreira¹,

Sene²,

Dourado³

et al. 2016

Int Arch Med

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Introduction: Lower back pain is defined as the presence of pain, with or without stiffness, located in the lower region of the spine, between the last costal arch and the gluteal fold. In the elderly, inherent factors of aging, such as inactivity, postural instability and a decrease in neuromuscular response, contribute to its occurrence. Although studies have pointed to a reduction in prevalence in this population, when the severe forms of lower back pain are analyzed, there is a significant increase. Objectives: The aim of this study was to evaluate the effect of the Isostretching method on the pain and flexibility of the lumbar spine in the elderly. Methods: The sample was composed of 14 women with lower back pain and a mean age of 70 years. 24 isostretching sessions were performed and assessed regarding the pain and flexibility of the lumbar spine. The elderly were evaluated before the start and after the 12th and 24th session, through a specific evaluation that included data from anamnesis, history, a specific test (Schober), goniometry of the lumbar spine, and the Visual Analogue Scale (VAS). Results: The results reveal that there was a reduction in the sensation of pain and a gain in flexibility after the isostretching sessions. Conclusions: It was concluded that the method the method was effective in the improvement of the variables under study.

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“…Predictive analytics relevant to chiropractic practice has been limited to clinical prediction rules associated with response to spinal manipulation [22,23]. Prior studies have examined components of service utilization as "dose" and "frequency" effects of spinal manipulation, however definitions of these terms vary considerably across studies [24].…”

Section: Rationalementioning

confidence: 99%

Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization

et al. 2020

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Background: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. Methods: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. Results: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each oneversus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification.

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Evaluation of a modified clinical prediction rule for use with spinal manipulative therapy in patients with chronic low back pain: a randomized clinical trial

Cited by 8 publications

References 25 publications

Pain-related fear – Dissociable neural sources of different fear constructs

Pain-related fear – Dissociable neural sources of different fear constructs

Effect of the isostretching method on lumbar pain and flexibility of the lumbar spine in elderly women.

Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization

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