Existing validated clinical prediction rules for predicting response to physiotherapy interventions for musculoskeletal conditions have limited clinical value: A systematic review

Beliefs and practice patterns of spinal thrust manipulation for mechanical low back pain of physical therapists in the state of Minnesota

INTRODUCTION

The primary purpose of this study was to examine the perceptions and utilization of spinal thrust manipulation (STM) techniques of physical therapists who treat patients with low back pain (LBP) in the State of Minnesota. A secondary purpose was to investigate differences between physical therapists who perform STM and those who do not.

METHODS

A cross-sectional design was utilized through the completion of an electronic survey. 74 respondents completed the survey. Descriptive measures were recorded as frequencies for categorical data or mean ± standard deviation for continuous data. For between-group comparisons, chi-square analyses were used for categorical items of nominal or ordinal data and t-tests were utilized for continuous data. The alpha level was set at p < 0.05.

RESULT

60.2% of respondents reported using STM when treating patients with LBP. 69.9% of respondents utilize a classification system. 76.7% of individuals answered correctly regarding the Minnesota State practice act. Of those who use STM, 81.8% utilize a Clinical Prediction Rule. Respondents who use STM were more likely to have a specialist certification (chi-square = 6.471, p = 0.011) and to have completed continuing education courses on manual therapy (chi-square = 4.736, p = 0.030).

DISCUSSION/CONCLUSIONS

Physical therapists who perform STM are more likely to have a better understanding of their state practice act, be board certified, and have completed continuing education in manual therapy.

External validation of a clinical prediction tool for the use of manual therapy for patients with temporomandibular disorders: a protocol for a prospective observational study

INTRODUCTION

Clinical guidelines recommend conservative treatment for the management of temporomandibular disorders (TMD), and manual therapy directed to temporomandibular structures is commonly applied to reduce pain and improve function. In a recent prospective study, we developed a clinical prediction tool based on an array of predictors to identify people with TMD who are likely to experience significant pain relief and functional improvements following a programme of manual therapies (MTP) applied to temporomandibular structures. The purpose of this study is to externally validate in a different sample (temporal validation) the prediction model obtained in the initial study.

METHODS/ANALYSIS

This observational prospective study will recruit a cohort of 120 adults with TMD from a Dental Hospital in Italy. The intervention will be an MTP consisting of four sessions (once per week) of manual therapy applied to temporomandibular structures. Candidate predictors included in the predictive model will be pain intensity during mouth opening, treatment expectations, number of pain locations, central sensitisation, TMD pain duration and maximal mouth opening. Outcome measures (i.e., pain intensity, functional improvement) will be collected before starting the MTP, after the last session and after 1 month (2 months from baseline). A reduction of pain intensity by at least 30% will be considered a good outcome. External validity of the prediction model will be evaluated after the last session by measuring its calibration, discrimination and overall fit. Additionally, the performance of the model will be evaluated considering the clinical outcomes collected 1 month after the last MTP session.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the Ethics Committee of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Italy. The results will be submitted for publication in a peer-reviewed journal, and the prediction model will be implemented in a web-based calculator to facilitate its use by clinicians.

TRIAL REGISTRATION NUMBER

NCT03990662.

Prognostic models of quality of life after total knee replacement: A systematic review

OBJECTIVE

To systematically review and critically appraise prognostic models for quality of life (QOL) in patients with total knee replacement (TKA).

METHODS

Subjects were TKA recipients recruited from inpatient postoperative settings. Searches were made on June 2022 and updated on April 2023. Databases included PubMed.gov, CINAHL, The Cochrane Library, Web of Science. Two authors performed all review stages independently. Risk of bias assessments on participants, predictors, outcomes and analysis methods followed the Prediction study Risk Of Bias ASsessment Tool (PROBAST).

RESULTS

After screening 2204 studies, 9 were eligible for inclusion. Twelve prognostic models were reported, of which 10 models were developed from data without validation and 2 were both developed and validated. The most frequently applied predictor was the pre-TKA QOL score. Discriminatory measures were reported for 9 (75.0%) models with areas under the curve values of 0.66-0.95. All models showed a high risk of bias, mostly due to limitations in statistical methods and outcome assessments.

CONCLUSION

Several prognostic models have been developed for QOL in patients with TKA, but all models show a high risk of bias and are unreliable in clinical practice. Future, prognostic models overcoming the risk of bias identified in this study are needed.

Six Externally Validated Prognostic Models Have Potential Clinical Value to Predict Patient Health Outcomes in the Rehabilitation of Musculoskeletal Conditions: A Systematic Review

OBJECTIVE

The purpose of this systematic review was to identify and appraise externally validated prognostic models to predict a patient's health outcomes relevant to physical rehabilitation of musculoskeletal (MSK) conditions.

METHODS

We systematically reviewed 8 databases and reported our findings according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020. An information specialist designed a search strategy to identify externally validated prognostic models for MSK conditions. Paired reviewers independently screened the title, abstract, and full text and conducted data extraction. We extracted characteristics of included studies (eg, country and study design), prognostic models (eg, performance measures and type of model) and predicted clinical outcomes (eg, pain and disability). We assessed the risk of bias and concerns of applicability using the prediction model risk of bias assessment tool. We proposed and used a 5-step method to determine which prognostic models were clinically valuable.

RESULTS

We found 4896 citations, read 300 full-text articles, and included 46 papers (37 distinct models). Prognostic models were externally validated for the spine, upper limb, lower limb conditions, and MSK trauma, injuries, and pain. All studies presented a high risk of bias. Half of the models showed low concerns for applicability. Reporting of calibration and discrimination performance measures was often lacking. We found 6 externally validated models with adequate measures, which could be deemed clinically valuable [ie, (1) STart Back Screening Tool, (2) Wallis Occupational Rehabilitation RisK model, (3) Da Silva model, (4) PICKUP model, (5) Schellingerhout rule, and (6) Keene model]. Despite having a high risk of bias, which is mostly explained by the very conservative properties of the PROBAST tool, the 6 models remain clinically relevant.

CONCLUSION

We found 6 externally validated prognostic models developed to predict patients' health outcomes that were clinically relevant to the physical rehabilitation of MSK conditions.

IMPACT

Our results provide clinicians with externally validated prognostic models to help them better predict patients' clinical outcomes and facilitate personalized treatment plans. Incorporating clinically valuable prognostic models could inherently improve the value of care provided by physical therapists.

Development of a Clinical Prediction Rule for Treatment Success with Transcranial Direct Current Stimulation for Knee Osteoarthritis Pain: A Secondary Analysis of a Double-Blind Randomized Controlled Trial

The study’s objective was to develop a clinical prediction rule that predicts a clinically significant analgesic effect on chronic knee osteoarthritis pain after transcranial direct current stimulation treatment. This is a secondary analysis from a double-blind randomized controlled trial. Data from 51 individuals with chronic knee osteoarthritis pain and an impaired descending pain inhibitory system were used. The intervention comprised a 15-session protocol of anodal primary motor cortex transcranial direct current stimulation. Treatment success was defined by the Western Ontario and McMaster Universities’ Osteoarthritis Index pain subscale. Accuracy statistics were calculated for each potential predictor and for the final model. The final logistic regression model was statistically significant (p < 0.01) and comprised five physical and psychosocial predictor variables that together yielded a positive likelihood ratio of 14.40 (95% CI: 3.66−56.69) and an 85% (95%CI: 60−96%) post-test probability of success. This is the first clinical prediction rule proposed for transcranial direct current stimulation in patients with chronic pain. The model underscores the importance of both physical and psychosocial factors as predictors of the analgesic response to transcranial direct current stimulation treatment. Validation of the proposed clinical prediction rule should be performed in other datasets.

Mastering Prognostic Tools: An Opportunity to Enhance Personalized Care and to Optimize Clinical Outcomes in Physical Therapy

In health care, clinical decision making is typically based on diagnostic findings. Rehabilitation clinicians commonly rely on pathoanatomical diagnoses to guide treatment and define prognosis. Targeting prognostic factors is a promising way for rehabilitation clinicians to enhance treatment decision-making processes, personalize rehabilitation approaches, and ultimately improve patient outcomes. This can be achieved by using prognostic tools that provide accurate estimates of the probability of future outcomes for a patient in clinical practice. Most literature reviews of prognostic tools in rehabilitation have focused on prescriptive clinical prediction rules. These studies highlight notable methodological issues and conclude that these tools are neither valid nor useful for clinical practice. This has raised the need to open the scope of research to understand what makes a quality prognostic tool that can be used in clinical practice. Methodological guidance in prognosis research has emerged in the last decade, encompassing exploratory studies on the development of prognosis and prognostic models. Methodological rigor is essential to develop prognostic tools, because only prognostic models developed and validated through a rigorous methodological process should guide clinical decision making. This Perspective argues that rehabilitation clinicians need to master the identification and use of prognostic tools to enhance their capacity to provide personalized rehabilitation. It is time for prognosis research to look for prognostic models that were developed and validated following a comprehensive process before being simplified into suitable tools for clinical practice. New models, or rigorous validation of current models, are needed. The approach discussed in this Perspective offers a promising way to overcome the limitations of most models and provide clinicians with quality tools for personalized rehabilitation approaches.

IMPACT

Prognostic research can be applied to clinical rehabilitation; this Perspective proposes solutions to develop high-quality prognostic models to optimize patient outcomes.

Neck Pain Clinical Prediction Rule to Prescribe Combined Aerobic and Neck-Specific Exercises: Secondary Analysis of a Randomized Controlled Trial

OBJECTIVE

A previous randomized controlled trial revealed that combined aerobic and neck-specific exercises yielded greater improvement than neck-specific exercises alone after a 6-month intervention in outpatients with nonspecific neck pain (NP). The aim of this secondary analysis was to identify subgroups of patients in the combined exercises group most likely to benefit from the intervention.

METHODS

Sixty-nine patients were included. The original trial was conducted in multiple physical therapy outpatient clinics twice a week for 6 weeks; follow-up was 6 months after assignment. The primary outcome was the therapeutic success rate (Global Rating of Change Score ≥ +5, "quite a bit better") after 6 weeks of training and at the 6-month follow-up. Candidate predictors from patients' medical history and physical examination were selected for univariable regression analysis to determine their association with treatment response status. Multivariable logistic regression analysis was used to derive preliminary clinical prediction rules.

RESULTS

The clinical prediction rule contained 3 predictor variables: (1) symptom duration ≤6 months, (2) neck flexor endurance ≥18 seconds, and (3) absence of referred pain (Nagelkerke R2 = .40 and -2 log likelihood = 60.30). The pre-test probability of success was 61.0% in the short term and 77.0% in the long term. The post-test probability of success for patients with at least 2 of the 3 predictor variables was 84.0% in the short term and 87.0% in the long term; such patients will likely benefit from this program.

CONCLUSION

A simple 3-item assessment, derived from easily obtainable baseline data, can identify patients with NP who may respond best to combined aerobic and neck-specific exercises. Validation is required before clinical recommendation.

IMPACT

Patients experiencing NP symptoms ≤6 months who have no referred pain and exhibit neck flexor endurance ≥18 seconds may benefit from a simple self-training program of combined aerobic and neck-specific exercises.

Choosing what works for whom: towards a better use of mechanistic knowledge in clinical practice

BACKGROUND

Clinicians commonly try to use mechanism-based knowledge to make sense of the complexity and uncertainty of chronic pain treatments to create a rationale for their clinical decision-making. Although this seems intuitive, there are some problems with this approach.

DISCUSSION

The widespread use of mechanism-based knowledge in clinical practice can be a source of confusion for clinicians, especially when complex interventions with different proposed mechanisms of action are equally effective. Although the available mechanistic evidence is still of very poor quality, in choosing from various treatment options for people with chronic pain, an approach that correctly incorporates mechanistic reasoning might aid clinical thinking and practice.

CONCLUSION

By explaining that not all evidence of mechanism is the same and by making a proposal to start using mechanism-based knowledge in clinical practice properly, we hope to help clinicians to incorporate mechanistic reasoning to prioritize and start choosing what may best work for whom.