Validation and impact analysis of prognostic clinical prediction rules for low back pain is needed: 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.

Prognostic Models for Chronic Low Back Pain Outcomes in Primary Care Are at High Risk of Bias and Lack Validation-High-Quality Studies Are Needed: A Systematic Review

OBJECTIVE: To provide an updated overview of available prognostic models for people with chronic low back pain (LBP) in primary care. DESIGN: Prognosis systematic review LITERATURE SEARCH: We searched for relevant studies on MEDLINE, Embase, Web of Science, and CINAHL databases (up to July 13, 2022), and performed citation tracking in Web of Science. STUDY SELECTION CRITERIA: We included observational (cohort or nested case-control) studies and randomized controlled trials that developed or validated prognostic models for adults with chronic LBP in primary care. The outcomes of interest were physical functioning, pain intensity, and health-related quality of life at any follow-up time-point. DATA SYNTHESIS: Data were extracted using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and the Prediction model Risk of Bias Assessment Tool (PROBAST) tool was used to evaluate the risk of bias of the models. Due to the number of studies retrieved and the heterogeneity, we reported the results descriptively. RESULTS: Ten studies (out of 5593 hits screened) with 34 models met our inclusion criteria, of which six are development studies and four are external validation studies. Five studies reported the area under the curve of the models (ranging from 0.48 to 0.84), whereas no study reported calibration indices. The most promising model is the Örebro Musculoskeletal Pain Screening Questionnaire Short-Form. CONCLUSIONS: Given the high risk of bias and lack of external validation, we cannot recommend that clinicians use prognostic models for patients with chronic LBP in primary care settings. J Orthop Sports Phys Ther 2024;54(5):1-13. Epub 15 February 2024. doi:10.2519/jospt.2024.12081.

Non-Specific Low Back Pain: An Inductive Exploratory Analysis through Factor Analysis and Deep Learning for Better Clustering

Non-specific low back pain (NSLBP) is a significant and pervasive public health issue in contemporary society. Despite the widespread prevalence of NSLBP, our understanding of its underlying causes, as well as our capacity to provide effective treatments, remains limited due to the high diversity in the population that does not respond to generic treatments. Clustering the NSLBP population based on shared characteristics offers a potential solution for developing personalized interventions. However, the complexity of NSLBP and the reliance on subjective categorical data in previous attempts present challenges in achieving reliable and clinically meaningful clusters. This study aims to explore the influence and importance of objective, continuous variables related to NSLBP and how to use these variables effectively to facilitate the clustering of NSLBP patients into meaningful subgroups. Data were acquired from 46 subjects who performed six simple movement tasks (back extension, back flexion, lateral trunk flexion right, lateral trunk flexion left, trunk rotation right, and trunk rotation left) at two different speeds (maximum and preferred). High-density electromyography (HD EMG) data from the lower back region were acquired, jointly with motion capture data, using passive reflective markers on the subject's body and clusters of markers on the subject's spine. An exploratory analysis was conducted using a deep neural network and factor analysis. Based on selected variables, various models were trained to classify individuals as healthy or having NSLBP in order to assess the importance of different variables. The models were trained using different subsets of data, including all variables, only anthropometric data (e.g., age, BMI, height, weight, and sex), only biomechanical data (e.g., shoulder and lower back movement), only neuromuscular data (e.g., HD EMG activity), or only balance-related data. The models achieved high accuracy in categorizing individuals as healthy or having NSLBP (full model: 93.30%, anthropometric model: 94.40%, biomechanical model: 84.47%, neuromuscular model: 88.07%, and balance model: 74.73%). Factor analysis revealed that individuals with NSLBP exhibited different movement patterns to healthy individuals, characterized by slower and more rigid movements. Anthropometric variables (age, sex, and BMI) were significantly correlated with NSLBP components. In conclusion, different data types, such as body measurements, movement patterns, and neuromuscular activity, can provide valuable information for identifying individuals with NSLBP. To gain a comprehensive understanding of NSLBP, it is crucial to investigate the main domains influencing its prognosis as a cohesive unit rather than studying them in isolation. Simplifying the conditions for acquiring dynamic data is recommended to reduce data complexity, and using back flexion and trunk rotation as effective options should be further explored.

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.

Knowledge, beliefs, and attitudes of spinal manipulation: a cross-sectional survey of Italian physiotherapists

Background and Objective

High-velocity low-amplitude thrust spinal manipulation (SM) is a recommended and commonly used manual therapy intervention in physiotherapy. Beliefs surrounding the safety and effectiveness of SM have challenged its use, and even advocated for its abandonment. Our study aimed to investigate the knowledge and beliefs surrounding SM by Italian physiotherapists compared with similar practitioners in other countries.

Methods

An online survey with 41 questions was adapted from previous surveys and was distributed via a mailing list of the Italian Physiotherapists Association (March 22–26, 2020). The questionnaire was divided into 4 sections to capture information on participant demographics, utilization, potential barriers, and knowledge about SM. Questions were differentiated between spinal regions. Attitudes towards different spinal regions, attributes associated with beliefs, and the influence of previous educational background were each evaluated.

Results

Of the 7398 registered physiotherapists, 575 (7.8%) completed the survey and were included for analysis. The majority of respondents perceived SM as safe and effective when applied to the thoracic (74.1%) and lumbar (72.2%) spines; whereas, a smaller proportion viewed SM to the upper cervical spine (56.8%) as safe and effective. Respondents reported they were less likely to provide and feel comfortable with upper cervical SM (respectively, 27.5% and 48.5%) compared to the thoracic (respectively, 52.2% and 74.8%) and lumbar spines (respectively, 46.3% and 74.3%). Most physiotherapists (70.4%) agreed they would perform additional screening prior to upper cervical SM compared to other spinal regions. Respondents who were aware of clinical prediction rules were more likely to report being comfortable with SM (OR 2.38–3.69) and to perceive it as safe (OR 1.75–3.12). Finally, physiotherapists without musculoskeletal specialization, especially those with a traditional manual therapy background, were more likely to perform additional screening prior to SM, use SM less frequently, report being less comfortable performing SM, and report upper cervical SM as less safe (p < 0.001).

Discussion

The beliefs and attitudes of physiotherapists surrounding the use of SM are significantly different when comparing the upper cervical spine to other spinal regions. An educational background in traditional manual therapy significantly influences beliefs and attitudes. We propose an updated framework on evidence-based SM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12998-022-00449-x.

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.

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

OBJECTIVE

To systematically review clinical prediction rules (CPRs) that have undergone validation testing for predicting response to physiotherapy-related interventions for musculoskeletal conditions.

STUDY DESIGN AND SETTING

PubMed, EMBASE, CINAHL and Cochrane Library were systematically searched to September 2020. Search terms included musculoskeletal (MSK) conditions, physiotherapy interventions and clinical prediction rules. Controlled studies that validated a prescriptive CPR for physiotherapy treatment response in musculoskeletal conditions were included. Two independent reviewers assessed eligibility. Original derivation studies of each CPR were identified. Risk of bias was assessed with the PROBAST tool (derivation studies) and the Cochrane Effective Practice and Organisation of Care group criteria (validation studies).

RESULTS

Nine studies aimed to validate seven prescriptive CPRs for treatment response for MSK conditions including back pain, neck pain, shoulder pain and carpal tunnel syndrome. Treatments included manipulation, traction and exercise. Seven studies failed to demonstrate an association between CPR prediction and outcome. Methodological quality of derivation studies was poor and for validation studies was good overall.

CONCLUSION

Results do not support the use of any CPRs identified to aid physiotherapy treatment selection for common musculoskeletal conditions, due to methodological shortcomings in the derivation studies and lack of association between CPR and outcome in validation studies.

Predicting pain recovery in patients with acute low back pain: a study protocol for a broad validation of a prognosis prediction model

BACKGROUND

The clinical course of acute low back pain (LBP) is generally favourable; however, there is significant variability in the prognosis of these patients. A clinical prediction model to predict the likelihood of pain recovery at three time points for patients with acute LBP has recently been developed. The aim of this study is to conduct a broad validation test of this clinical prediction model, by testing its performance in a new sample of patients and a different setting.

METHODS

The validation study with a prospective cohort design will recruit 420 patients with recent onset non-specific acute LBP, with moderate pain intensity, seeking care in the emergency departments of hospitals in São Paulo, Brazil. The primary outcome measure will be days to recovery from pain. The predicted probability of pain recovery for each individual will be computed based on predictions of the development model and this will be used to test the performance (calibration and discrimination) in the validation dataset.

DISCUSSION

The findings of this study will better inform about the performance of the clinical prediction model, helping both clinicians and patients. If the model's performance is acceptable, then future research should evaluate the impact of the prediction model, assessing whether it produces a change in clinicians' behaviour and/or an improvement in patient outcomes.

ETHICS AND DISSEMINATION

Ethics were granted by the Research Ethics Committee of the Universidade Cidade de São Paulo, #20310419.4.0000.0064. Study findings will be disseminated widely through peer-reviewed publications and conference presentations.

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.

Development and temporal validation of a prognostic model for 1-year clinical outcome after decompression surgery for lumbar disc herniation

PURPOSE

Surgeons need tools to provide individualised estimates of surgical outcomes and the uncertainty surrounding these, to convey realistic expectations to the patient. This study developed and validated prognostic models for patients undergoing surgical treatment of lumbar disc herniation, to predict outcomes 1 year after surgery, and implemented these models in an online prediction tool.

METHODS

Using the data of 1244 patients from a large spine unit, LASSO and linear regression models were fitted with 90% upper prediction limits, to predict scores on the Core Outcome Measures Index, and back and leg pain. Candidate predictors included sociodemographic factors, baseline symptoms, medical history, and surgeon characteristics. Temporal validation was conducted on 364 more recent patients at the same unit, by examining the proportion of observed outcomes exceeding the threshold of the 90% upper prediction limit (UPL), and by calculating mean bias and other calibration measures.

RESULTS

Poorer outcome was predicted by obesity, previous spine surgery, and having basic obligatory (rather than private) insurance. In the validation data, fewer than 12% of outcomes were above the 90% UPL. Calibration plots for the model validation showed values for mean bias < 0.5 score points and regression slopes close to 1.

CONCLUSION

While the model accuracy was good overall, the prediction intervals indicated considerable predictive uncertainty on the individual level. Implementation studies will assess the clinical usefulness of the online tool. Updating the models with additional predictors may improve the accuracy and precision of outcome predictions. These slides can be retrieved under Electronic Supplementary Material.

Exploring conceptual preprocessing for developing prognostic models: a case study in low back pain patients

OBJECTIVES

A conceptually oriented preprocessing of a large number of potential prognostic factors may improve the development of a prognostic model. This study investigated whether various forms of conceptually oriented preprocessing or the preselection of established factors was superior to using all factors as input.

STUDY DESIGN AND SETTING

We made use of an existing project that developed two conceptually oriented subgroupings of low back pain patients. Based on the prediction of six outcome variables by seven statistical methods, this type of preprocessing was compared with medical experts' preselection of established factors, as well as using all 112 available baseline factors.

RESULTS

Subgrouping of patients was associated with low prognostic capacity. Applying a Lasso-based variable selection to all factors or to domain-specific principal component scores performed best. The preselection of established factors showed a good compromise between model complexity and prognostic capacity.

CONCLUSION

The prognostic capacity is hard to improve by means of a conceptually oriented preprocessing when compared to purely statistical approaches. However, a careful selection of already established factors combined in a simple linear model should be considered as an option when constructing a new prognostic rule based on a large number of potential prognostic factors.

Predictors of clinical success with stabilization exercise are associated with lower levels of lumbar multifidus intramuscular adipose tissue in patients with low back pain

Purpose: Investigate the construct validity of prognostic factors purported to predict clinical success with stabilization exercise for low back pain by exploring their associations with lumbar multifidus composition.Methods: Patients with low back pain were recruited from a hospital imaging department. The presence of fivepredictors (age <40 years, positive prone instability test, aberrant trunk flexion movements, straight leg raise range of motion >91°, spinal hypermobility) were identified by standardized physical examination. Predictors were grouped by total positive findings and status on a clinical prediction rule. The proportion of lower lumbar multifidus intramuscular adipose tissue was measured with 3.0 T magnetic resonance imaging. Univariate and multivariate associations were examined with linear regression and reported with standardized beta coefficients (β) and 95% confidence intervals.Results: Data from 62 patients (11 female) with mean (SD) age of 45.2 (11.8) years were included. Total number of predictors (β[95% CI] = -0.37[-0.61,-0.12]; R² = 0.12), positive prediction rule status (β[95% CI] = -0.57[-0.79,-0.35]; R² = 0.30), and age <40 years were associated with lower intramuscular adipose tissue (β[95% CI] = -0.55[-0.77,-0.33]; R² = 0.27). No other individual factors were associated with lumbar multifidus intramuscular adipose tissue.Conclusions: These findings support the construct validity of the grouped prognostic criteria. Future research should examine the clinical utility of these criteria. Implications for RehabilitationLow back pain is the single largest cause of disability worldwide and exercise therapy is recommended by international low back pain treatment guidelines.Lower levels of lumbar multifidus intramuscular adipose tissue were associated with predictors of clinical success with stabilization exercise.Higher proportions of lumbar multifidus intramuscular adipose tissue may help identify patients who require longer duration exercise training, or those who are unlikely to respond to stabilization exercise.

Can patient-reported profiles avoid unnecessary referral to a spine surgeon? An observational study to further develop the Nijmegen Decision Tool for Chronic Low Back Pain

INTRODUCTION

Chronic Low Back Pain (CLBP) is a heterogeneous condition with lack of diagnostic clarity. Therapeutic interventions show small effects. To improve outcomes by targeting interventions it is recommended to develop a triage system to surgical and non-surgical treatments based on treatment outcomes. The objective of the current study was to develop and internally validate prognostic models based on pre-treatment patient-reported profiles that identify patients who either respond or do not respond to two frequently performed treatments (lumbar spine surgery and multidisciplinary pain management program).

METHODS

A consecutive cohort study in a secondary referral spine center was performed. The study followed the recommendations of the PROGRESS framework and was registered in the Dutch Trial Register (NTR5946). Data of forty-seven potential pre-consultation (baseline) indicators predicting 'response' or 'non-response' at one-year follow-up for the two treatments were obtained to develop and validate four multivariable logistic regression models. The source population consisted of 3,410 referred CLBP-patients. Two treatment cohorts were defined: elective 'spine surgery' (n = 217 [6.4%]) and multidisciplinary bio-psychosocial 'pain management program' (n = 171 [5.0%]). Main inclusion criteria were age ≥18, CLBP (≥6 months), and not responding to primary care treatment. The primary outcome was functional ability: 'response' (Oswestry Disability Index [ODI] ≤22) and 'non-response' (ODI ≥41).

RESULTS

Baseline indicators predictive of treatment outcome were: degree of disability (all models), ≥2 previous spine surgeries, psychosocial complaints, age (onset <20 or >50), and patient expectations of treatment outcomes. The explained variances were low for the models predicting response and non-response to pain management program (R2 respectively 23% and 26%) and modest for surgery (R2 30% and 39%). The overall performance was acceptable (c-index; 0.72-0.83), the model predicting non-response to surgery performed best (R2 = 39%; c-index = 0.83).

CONCLUSION

This study was the first to identify different patient-reported profiles that predict response to different treatments for CLBP. The model predicting 'non-response' to elective lumbar spine surgery performed remarkably well, suggesting that referrals of these patients to a spine surgeon could be avoided. After external validation, the patient-reported profiles could potentially enhance timely patient triage to the right secondary care specialist and improve decision-making between clinican and patient. This could lead to improved treatment outcomes, which results in a more efficient use of healthcare resources.

Predicting pain recovery in patients with acute low back pain: Updating and validation of a clinical prediction model

BACKGROUND

The prognosis of acute low back pain (LBP) is typically good; however, there is substantial variation in individual patient's outcomes. We recently developed a prediction model that was able to predict the likelihood of pain recovery in patients with acute LBP who continue to have pain approximately 1 week after initially seeking care. The aims of the current study were to (a) re-categorize the variables in the developmental dataset to be able to validate the model in the validation dataset; (b) refit the existing model in the developmental dataset; and (c) validate the model in the validation dataset.

METHODS

The validation study sample comprised 737 patients with acute LBP, with a pain score of ≥2/10, 1 week after initially seeking care and with duration of current episode of ≤4 weeks. The primary outcome measure was days to pain recovery. Some of the variables from the development dataset were re-categorized prior to refitting the existing model in the developmental dataset using Cox regression. The performance (calibration and discrimination) of the prediction model was then tested in the validation dataset.

RESULTS

Three variables of the development dataset were re-categorized. The performance of the prediction model with re-categorized variables in the development dataset was good (C-statistic = 0.76, 95% CI 0.70-0.82). The discrimination of the model using the validation dataset resulted in a C-statistic of 0.71 (95% CI 0.63-0.78). The calibration for the validation sample was acceptable at 1 month. However, at 1 week the predicted proportions within quintiles tended to overestimate the observed recovery proportions, and at 3 months, the predicted proportions tended to underestimate the observed recovery proportions.

CONCLUSIONS

The updated prediction model demonstrated reasonably good external validity and may be useful in practice, but further validation and impact studies in relevant populations should be conducted.

SIGNIFICANCE

A clinical prediction model based on five easily collected variables demonstrated reasonable external validity. The prediction model has the potential to inform patients and clinicians of the likely prognosis of individuals with acute LBP but requires impact studies to assess its clinical usefulness.

Prognostic factors of a favorable outcome following a supervised exercise program for soldiers with sub-acute and chronic low back pain

Background

Low back pain (LBP) encompasses heterogeneous patients unlikely to respond to a unique treatment. Identifying sub-groups of LBP may help to improve treatment outcomes. This is a hypothesis-setting study designed to create a clinical prediction rule (CPR) that will predict favorable outcomes in soldiers with sub-acute and chronic LBP participating in a multi-station exercise program.

Methods

Military members with LBP participated in a supervised program comprising 7 stations each consisting of exercises of increasing difficulty. Demographic, impairment and disability data were collected at baseline. The modified Oswestry Disability Index (ODI) was administered at baseline and following the 6-week program. An improvement of 50% in the initial ODI score was considered the reference standard to determine a favorable outcome. Univariate associations with favorable outcome were tested using chi-square or paired t-tests. Variables that showed between-group (favorable/unfavorable) differences were entered into a logistic regression after determining the sampling adequacy. Finally, continuous variables were dichotomized and the sensitivity, specificity and positive and negative likelihood ratios were determined for the model and for each variable.

Results

A sample of 85 participants was included in analyses. Five variables contributed to prediction of a favorable outcome: no pain in lying down (p = 0.017), no use of antidepressants (p = 0.061), FABQ work score < 22.5 (p = 0.061), fewer than 5 physiotherapy sessions before entering the program (p = 0.144) and less than 6 months’ work restriction (p = 0.161). This model yielded a sensitivity of 0.78, specificity of 0.80, LR+ of 3.88, and LR- of 0.28. A 77.5% probability of favorable outcome can be predicted by the presence of more than three of the five variables, while an 80% probability of unfavorable outcome can be expected if only three or fewer variables are present.

Conclusion

The use of prognostic factors may guide clinicians in identifying soldiers with LBP most likely to have a favorable outcome. Further validation studies are needed to determine if the variables identified in our study are treatment effect modifiers that can predict success following participation in the multi-station exercise program.

Trial registration

ClinicalTrials.gov Identifier: NCT03464877 registered retrospectively on 14 March 2018.

Electronic supplementary material

The online version of this article (10.1186/s12891-018-2022-x) contains supplementary material, which is available to authorized users.

Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: a systematic review

QUESTION

Which multivariable prognostic model(s) for recovery in people with neck pain can be used in primary care?

DESIGN

Systematic review of studies evaluating multivariable prognostic models.

PARTICIPANTS

People with non-specific neck pain presenting at primary care.

DETERMINANTS

Baseline characteristics of the participants.

OUTCOME MEASURES

Recovery measured as pain reduction, reduced disability, or perceived recovery at short-term and long-term follow-up.

RESULTS

Fifty-three publications were included, of which 46 were derivation studies, four were validation studies, and three concerned combined studies. The derivation studies presented 99 multivariate models, all of which were at high risk of bias. Three externally validated models generated usable models in low risk of bias studies. One predicted recovery in non-specific neck pain, while two concerned participants with whiplash-associated disorders (WAD). Discriminative ability of the non-specific neck pain model was area under the curve (AUC) 0.65 (95% CI 0.59 to 0.71). For the first WAD model, discriminative ability was AUC 0.85 (95% CI 0.79 to 0.91). For the second WAD model, specificity was 99% (95% CI 93 to 100) and sensitivity was 44% (95% CI 23 to 65) for prediction of non-recovery, and 86% (95% CI 73 to 94) and 55% (95% CI 41 to 69) for prediction of recovery, respectively. Initial Neck Disability Index scores and age were identified as consistent prognostic factors in these three models.

CONCLUSION

Three externally validated models were found to be usable and to have low risk of bias, of which two showed acceptable discriminative properties for predicting recovery in people with neck pain. These three models need further validation and evaluation of their clinical impact before their broad clinical use can be advocated.

REGISTRATION

PROSPERO CRD42016042204. [Wingbermühle RW, van Trijffel E, Nelissen PM, Koes B, Verhagen AP (2018) Few promising multivariable prognostic models exist for recovery of people with non-specific neck pain in musculoskeletal primary care: a systematic review. Journal of Physiotherapy 64: 16-23].

Latent class analysis derived subgroups of low back pain patients - do they have prognostic capacity?

BACKGROUND

Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables').

METHODS

This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above.

RESULTS

The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up.

CONCLUSIONS

Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients.

Decision support tools in low back pain

Information from individual classification systems or clinical prediction rules that aim to facilitate stratified care in low back pain is important but often not comprehensive enough to be used to support clinical decision-making. The development and implementation of a clinically useful decision support tool (DST) that considering all key features is a challenging enterprise, requiring a multidisciplinary approach. Key features are inclusion of all relevant treatment options, patient characteristics, and benefits and harms and presentation as an accessible and easy to use toolkit. To be of clinical value, a DST should (1) be based on large numbers of high-quality data, allowing robust estimation of benefits and harms; (2) be presented using visually attractive and easy-to-use software; (3) be externally validated with a clinical beneficial impact established; and (4) include a procedure for regular updating and monitoring. As an illustration, we describe the development; presentation; and plans for further validation, implementation, and updating of the Nijmegen Decision Tool for Chronic Low Back Pain (NDT-CLBP).

Reconceptualising manual therapy skills in contemporary practice

With conflicting evidence regarding the effectiveness of manual therapy calls have arisen within some quarters of the physiotherapy profession challenging the continued use of manual skills for assessment and treatment. A reconceptualisation of the importance of manual examination findings is put forward, based upon a contemporary understanding of pain science, rather than considering these skills only in terms of how they should "guide" manual therapy interventions. The place for manual examination findings within complex, multidimensional presentations is considered using vignettes describing the presentations of five people with low back pain. As part of multidimensional, individualised management, the balance of evidence relating to the effectiveness, mechanisms of action and rationale for manual skills is discussed. It is concluded that if manual examination and therapeutic skills are used in a manner consistent with a contemporary understanding of pain science, multidimensional patient profiles and a person-centred approach, their selective and judicious use still has an important role.

Clinical prediction rules for prognosis and treatment prescription in neck pain: A systematic review

Clinical prediction rules (CPRs) developed to identify sub-groups of people with neck pain for different prognoses (i.e. prognostic) or response to treatments (i.e. prescriptive) have been recommended as a research priority to improve health outcomes for these conditions. A systematic review was undertaken to identify prognostic and prescriptive CPRs relevant to the conservative management of adults with neck pain and to appraise stage of development, quality and readiness for clinical application. Six databases were systematically searched from inception until 4th July 2016. Two independent reviewers assessed eligibility, risk of bias (PEDro and QUIPS), methodological quality and stage of development. 9840 records were retrieved and screened for eligibility. Thirty-two studies reporting on 26 CPRs were included in this review. Methodological quality of included studies varied considerably. Most prognostic CPR development studies employed appropriate designs. However, many prescriptive CPR studies (n = 12/13) used single group designs and/or analysed controlled trials using methods that were inadequate for identifying treatment effect moderators. Most prognostic (n = 11/15) and all prescriptive (n = 11) CPRs have not progressed beyond the derivation stage of development. Four prognostic CPRs relating to acute whiplash (n = 3) or non-traumatic neck pain (n = 1) have undergone preliminary validation. No CPRs have undergone impact analysis. Most prognostic and prescriptive CPRs for neck pain are at the initial stage of development and therefore routine clinical use is not yet supported. Further validation and impact analyses of all CPRs are required before confident conclusions can be made regarding clinical utility.

Prediction models for the risk of gestational diabetes: a systematic review

BACKGROUND

Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality.

METHODS

MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible.Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias.

RESULTS

Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis.

CONCLUSIONS

Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.

Estimating the Risk of Chronic Pain: Development and Validation of a Prognostic Model (PICKUP) for Patients with Acute Low Back Pain

BACKGROUND

Low back pain (LBP) is a major health problem. Globally it is responsible for the most years lived with disability. The most problematic type of LBP is chronic LBP (pain lasting longer than 3 mo); it has a poor prognosis and is costly, and interventions are only moderately effective. Targeting interventions according to risk profile is a promising approach to prevent the onset of chronic LBP. Developing accurate prognostic models is the first step. No validated prognostic models are available to accurately predict the onset of chronic LBP. The primary aim of this study was to develop and validate a prognostic model to estimate the risk of chronic LBP.

METHODS AND FINDINGS

We used the PROGRESS framework to specify a priori methods, which we published in a study protocol. Data from 2,758 patients with acute LBP attending primary care in Australia between 5 November 2003 and 15 July 2005 (development sample, n = 1,230) and between 10 November 2009 and 5 February 2013 (external validation sample, n = 1,528) were used to develop and externally validate the model. The primary outcome was chronic LBP (ongoing pain at 3 mo). In all, 30% of the development sample and 19% of the external validation sample developed chronic LBP. In the external validation sample, the primary model (PICKUP) discriminated between those who did and did not develop chronic LBP with acceptable performance (area under the receiver operating characteristic curve 0.66 [95% CI 0.63 to 0.69]). Although model calibration was also acceptable in the external validation sample (intercept = -0.55, slope = 0.89), some miscalibration was observed for high-risk groups. The decision curve analysis estimated that, if decisions to recommend further intervention were based on risk scores, screening could lead to a net reduction of 40 unnecessary interventions for every 100 patients presenting to primary care compared to a "treat all" approach. Limitations of the method include the model being restricted to using prognostic factors measured in existing studies and using stepwise methods to specify the model. Limitations of the model include modest discrimination performance. The model also requires recalibration for local settings.

CONCLUSIONS

Based on its performance in these cohorts, this five-item prognostic model for patients with acute LBP may be a useful tool for estimating risk of chronic LBP. Further validation is required to determine whether screening with this model leads to a net reduction in unnecessary interventions provided to low-risk patients.

Evidence base and future research directions in the management of low back pain

Low back pain (LBP) is a prevalent and costly condition. Awareness of valid and reliable patient history taking, physical examination and clinical testing is important for diagnostic accuracy. Stratified care which targets treatment to patient subgroups based on key characteristics is reliant upon accurate diagnostics. Models of stratified care that can potentially improve treatment effects include prognostic risk profiling for persistent LBP, likely response to specific treatment based on clinical prediction models or suspected underlying causal mechanisms. The focus of this editorial is to highlight current research status and future directions for LBP diagnostics and stratified care.

Barriers and facilitators perceived by physicians when using prediction models in practice

OBJECTIVES

Prediction models may facilitate risk-based management of health care conditions. In a large cluster-randomized trial, presenting calculated risks of postoperative nausea and vomiting (PONV) to physicians (assistive approach) increased risk-based management of PONV. This increase did not improve patient outcome-that is, PONV incidence. This prompted us to explore how prediction tools guide the decision-making process of physicians.

STUDY DESIGN AND SETTING

Using mixed methods, we interviewed eight physicians to understand how predicted risks were perceived by the physicians and how they influenced decision making. Subsequently, all 57 physicians of the trial were surveyed for how the presented risks influenced their perceptions.

RESULTS

Although the prediction tool made physicians more aware of PONV prevention, the physicians reported three barriers to use predicted risks in their decision making. PONV was not considered an outcome of utmost importance; decision making on PONV prophylaxis was mostly intuitive rather than risk based; prediction models do not weigh benefits and risks of prophylactic drugs.

CONCLUSION

Combining probabilistic output of the model with their clinical experience may be difficult for physicians, especially when their decision-making process is mostly intuitive. Adding recommendations to predicted risks (directive approach) was considered an important step to facilitate the uptake of a prediction tool.