Accuracy of physical therapists' prognosis of low back pain from the clinical examination: a prospective cohort study

What factors do physical therapists consider when determining patient prognosis: A mixed methods study

INTRODUCTION

A prognosis provides valuable information to expected progress and anticipated outcome over the course of care. Although it is known that physical therapists can accurately prognose, it is unknown what factors are utilised in clinical practice.

OBJECTIVE

The purpose of this study was to determine the prognostic domains and factors that influenced a PT's clinical reasoning processes.

DESIGN

Mixed Methods Design, affirming the prognostic ability of the physical therapists and the qualitative exploration of the prognostic factors considered by physical therapists.

METHODS

Twenty-nine physical therapists participated in this study. Participants underwent semi-structured qualitative interviews that were coded to populate a prognostic framework. In addition, de-identified patient data was used to determine the ability of the PT to form a prognosis. Linear regression was used to determine if an initial prognostic score was related to function at discharge.

RESULTS

There were significant relationships (p = <0.05) between the prognosis score and Focus on Therapeutic Outcomes (B = 2.25), Numeric Pain Rating Scale (B = 0.257), and GROC (B = 0.289) upon patient discharge. Qualitative factors were categorised into prognostic domains (prevalence): Mood, Motivation, Pain Behaviours (100%), Disease Severity (93.1%), Health Status (86.2%), Social, Occupation, Environmental (67.0%), and Genetics, Biology, Biomarkers (44.8%). Factors that did not fit established domains were reported and categorised as Other (86.2%).

CONCLUSION

Our findings support the relationship between PT prognosis of patients with musculoskeletal pain and patient outcomes. In addition, the domains and factors PTs use to formulate prognosis during evaluation present a complex biopsychosocial framework, suggesting that PTs consider factors from multiple domains when forming a prognosis.

Predicting treatment adherence and outcome to outpatient multimodal rehabilitation in chronic low back pain

BACKGROUND

There is a growing need to identify patient pre-treatment characteristics that could predict adherence and outcome following specific interventions.

OBJECTIVE

To identify predictors of adherence and outcome to outpatient multimodal rehabilitation in chronic low back pain (CLBP).

METHODS

A total of 273 CLBP patients participated in an exercise-based rehabilitation program. Patients who completed ⩾ 70% of the treatment course were classified as adherent. Patients showing a post-treatment reduction of ⩾ 30% in Oswestry Disability Index (ODI) and Visual Analogue Scale (VAS) back pain intensity scores were assigned to the favorable outcome group.

RESULTS

Multivariate logistic regression revealed that higher age, higher ability to perform low-load activities, and higher degrees of kinesiophobia increased the odds to complete the rehabilitation program. By contrast, lower levels of education and back pain unrelated to poor posture increased the odds for non-adherence. Furthermore, a favorable outcome was predicted in case the cause for LBP was known, shorter symptom duration, no pain in the lower legs, no difficulties falling asleep, and short-term work absenteeism.

CONCLUSIONS

Assessment and consideration of patient pre-treatment characteristics is of great importance as they may enable therapists to identify patients with a good prognosis or at risk for non-responding to outpatient multimodal rehabilitation.

Explanatory multivariate modeling for disability, pain, and claims in patients with spine pain via a physical therapy direct access model of care

BACKGROUND

Direct access physical therapy (DAPT) may result in improved patient outcomes and reduced healthcare costs. Prognostic factors associated with spine-related outcomes and insurance claims with DAPT are needed.

OBJECTIVE

To identify factors that predict variations in outcomes for spine pain and insurance claims using DAPT.

METHODS

Individuals (N = 250) with spine pain were analyzed. Outcomes were classified into High, Low, or Did Not Meet minimal clinically important difference (MCID) scores. Claims were categorized into low, medium, or high tertiles. Prognostic variables were identified from patient information.

RESULTS

Females were more likely to meet High MCID (odds ratio [OR] 2.84 (95% CI = 1.32, 6.11) and Low MCID (OR 2.86, 95% CI = 1.34, 6.10). Higher initial ODI/NDI scores were associated with High MCID (OR 1.04, 95% CI = 1.07, 1.22) and Low MCID (OR 0.91, 95% CI = 0.77, 1.07). Odds of a high claim were lowered by the absence of imaging (OR 0.04, 95% CI = 0.02, 0.09) and an active versus passive treatment (OR 0.38, 95% CI = 0.18, 0.80).

CONCLUSION

Females and higher initial disability predicted favorable outcomes. The novel introduction of claims into the prognostic modeling supports that active interventions and avoiding imaging may reduce claims.

Agreement is very low between a clinical prediction rule and physiotherapist assessment for classifying the risk of poor recovery of individuals with acute whiplash injury

BACKGROUND

A prognostic clinical prediction rule (whiplash CPR) has been validated for use in individuals with acute whiplash associated disorders (WAD). The clinical utility of this tool is unknown.

OBJECTIVES

To investigate: 1) the level of agreement between physiotherapist- and whiplash CPR-determined prognostic risk classification of people with acute WAD; 2) which clinical findings are used by physiotherapists to classify prognostic risk; and 3) whether physiotherapists plan to differ the number of treatment sessions provided based on prognostic risk classification.

DESIGN

Pragmatic, observational.

METHOD

38 adults with acute WAD were classified as low, medium, or high risk of poor recovery by their treating physiotherapist (n = 24) at the conclusion of the initial consultation. A weighted Cohen's kappa examined the agreement between physiotherapist estimated risk classification and the whiplash CPR. Physiotherapists' reasons for classification were provided and summarised descriptively. Kruskal-Wallis and post-hoc Dunn's tests compared projected number of treatment sessions between risk subgroups.

RESULTS

Physiotherapist agreement with the whiplash CPR occurred in 29% of cases (n = 11/38), which was less than what is expected by chance (K = -0.03; 95%CI -0.17 to 0.12). Physiotherapists most frequently considered range of movement (n = 23/38, 61%), a premorbid pain condition (n = 14/38, 37%), response to initial physiotherapy treatment (n = 12/38, 32%), and pain intensity (n = 12/38, 32%) when classifying prognostic risk. The projected number of treatment sessions was not different between risk groups using classifications provided by the physiotherapists (χ²(2) = 2.69, p = 0.26).

CONCLUSIONS

Physiotherapists should consider incorporating the whiplash CPR into current assessment processes to enhance accuracy in prognostic decision-making.

Understanding Monitoring Technologies for Adults With Pain: Systematic Literature Review

BACKGROUND

Monitoring of patients may decrease treatment costs and improve quality of care. Pain is the most common health problem that people seek help for in hospitals. Therefore, monitoring patients with pain may have significant impact in improving treatment. Several studies have studied factors affecting pain; however, no previous study has reviewed the contextual information that a monitoring system may capture to characterize a patient's situation.

OBJECTIVE

The objective of this study was to conduct a systematic review to (1) determine what types of technologies have been used to monitor adults with pain, and (2) construct a model of the context information that may be used to implement apps and devices aimed at monitoring adults with pain.

METHODS

A literature search (2005-2015) was conducted in electronic databases pertaining to medical and computer science literature (PubMed, Science Direct, ACM Digital Library, and IEEE Xplore) using a defined search string. Article selection was done through a process of removing duplicates, analyzing title and abstract, and then reviewing the full text of the article.

RESULTS

In the final analysis, 87 articles were included and 53 of them (61%) used technologies to collect contextual information. A total of 49 types of context information were found and a five-dimension (activity, identity, wellness, environment, physiological) model of context information to monitor adults with pain was proposed, expanding on a previous model. Most technological interfaces for pain monitoring were wearable, possibly because they can be used in more realistic contexts. Few studies focused on older adults, creating a relevant avenue of research on how to create devices for users that may have impaired cognitive skills or low digital literacy.

CONCLUSIONS

The design of monitoring devices and interfaces for adults with pain must deal with the challenge of selecting relevant contextual information to understand the user's situation, and not overburdening or inconveniencing users with information requests. A model of contextual information may be used by researchers to choose possible contextual information that may be monitored during studies on adults with pain.

Can experienced physiotherapists identify which patients are likely to succeed with physical therapy treatment?

Background

The purpose of the study was to determine if clinician predicted prognosis is associated with patient outcomes.

Methods

The study was a secondary analysis of data that were collected in 8 physiotherapy outpatient clinics. Nine physiotherapists with post-graduate training in manual therapy (mean 20.3 years of experience) were asked at baseline to project the outcome of the patients evaluated. In total, 112 patients with low back (74 %) or neck (26 %) pain were treated pragmatically with interventions consisting of manual therapy, strengthening, and patient-specific education. Outcomes measures consisted of percent change in disability (Oswestry or Neck Disability Index), self-reported rate of recovery (0–100 %), and percent change in pain (numerical pain rating scale). Hierarchical logistic regression determined potential factors (clinician predicted prognosis score (1–10) at baseline, dichotomised as poor (1–6) and good (7–10); symptom duration categorised as acute, subacute or chronic; same previous injury (yes/no); baseline pain and disability scores; within-session improvement at initial visit (yes/no); and presence of ≥ one psychological factor) associated with meaningful changes in each of the three outcomes at discharge (disability and pain > 50 % improvement, rate of recovery ≥82.5 % improvement).

Results

Clinician predicted prognosis (OR 4.15, 95%CI = 1.31, 13.19, p = 0.02) and duration of symptoms (OR subacute 0.24, 95%CI = 0.07, 0.89, p = 0.03; chronic 0.21, 95%CI = 0.05, 0.90, p = 0.04) were associated with rate of recovery, whereas only clinician predicted prognosis was associated with disability improvement (OR 4.28, 95 % CI 1.37, 13.37, p = 0.01). No variables were associated with pain improvement.

Conclusions

Clinician predicted prognosis is potentially valuable for patients, as a good predicted prognosis is associated with improvements in disability and rate of recovery.