Clinical course of pain and disability following primary lumbar discectomy: systematic review and meta-analysis

© 2020, The Author(s). Purpose: To conduct a meta-analysis to describe clinical course of pain and disability in adult patients post-lumbar discectomy (PROSPERO: CRD42015020806). Methods: Sensitive topic-based search strategy designed for individual databases was conducted. Patients (> 16 years) following first-time lumbar discectomy for sciatica/radiculopathy with no complications, investigated in inception (point of surgery) prospective cohort studies, were included. Studies including revision surgery or not published in English were excluded. Two reviewers independently searched information sources, assessed eligibility at title/abstract and full-text stages, extracted data, assessed risk of bias (modified QUIPs) and assessed GRADE. Authors were contacted to request raw data where data/variance data were missing. Meta-analyses evaluated outcomes at all available time points using the variance-weighted mean in random-effect meta-analyses. Means and 95% CIs were plotted over time for measurements reported on outcomes of leg pain, back pain and disability. Results: A total of 87 studies (n = 31,034) at risk of bias (49 moderate, 38 high) were included. Clinically relevant improvements immediately following surgery (> MCID) for leg pain (0–10, mean before surgery 7.04, 50 studies, n = 14,910 participants) and disability were identified (0–100, mean before surgery 53.33, 48 studies, n = 15,037). Back pain also improved (0–10, mean before surgery 4.72, 53 studies, n = 14,877). Improvement in all outcomes was maintained (to 7 years). Meta-regression analyses to assess the relationship between outcome data and a priori potential covariates found preoperative back pain and disability predictive for outcome. Conclusion: Moderate-level evidence supports clinically relevant immediate improvement in leg pain and disability following lumbar discectomy with accompanying improvements in back pain. Graphic abstract: These slides can be retrieved under Electronic Supplementary Material. [Figure not available: see fulltext.]

Predicting chronic low-back pain based on pain trajectories in patients in an occupational setting: an exploratory analysis.

OBJECTIVE: This study aimed to (i) identify subpopulations of patients in an occupational setting who will still have or develop chronic low-back pain (LBP) and (ii) evaluate a previously developed prediction model based on the determined subpopulations. METHOD: In this prospective cohort, study data were analyzed from three merged randomized controlled trials, conducted in an occupational setting (N=622). Latent class growth analysis (LCGA) was used to distinguish patients with a different course of pain intensity measured over 12 months. The determined subpopulations were used to derive a definition for chronic LBP and evaluate an existing model to predict chronic LBP. RESULTS: The LCGA model identified three subpopulations of LBP patients. These were used to define recovering (353) and chronic (269) patients. None of the interventions showed a relevant treatment effect over another but the rate of decline in symptoms during the first months of the intervention seems to predict recovery. The prediction model, based on this dichotomous outcome, with the variables pain intensity, kinesiophobia and a clinically relevant change in pain intensity and functional status in the first three months, showed a bootstrap-corrected performance with an area under the operating characteristic curve (AUC) of 0.75 and explained variance of 0.26. CONCLUSION: In an occupational setting, different subpopulations of chronic LBP patients could be identified using LCGA. The prediction model based on these subpopulations showed a promising predictive performance

External validation of prognostic models for recovery in patients with neck pain

BackgroundNeck pain is one of the leading causes of disability in most countries and it is likely to increase further. Numerous prognostic models for people with neck pain have been developed, few have been validated. In a recent systematic review, external validation of three promising models was advised before they can be used in clinical practice.ObjectiveThe purpose of this study was to externally validate three promising models that predict neck pain recovery in primary care.MethodsThis validation cohort consisted of 1311 patients with neck pain of any duration who were prospectively recruited and treated by 345 manual therapists in the Netherlands. Outcome measures were disability (Neck Disability Index) and recovery (Global Perceived Effect Scale) post-treatment and at 1-year follow-up. The assessed models were an Australian Whiplash-Associated Disorders (WAD) model (Amodel), a multicenter WAD model (Mmodel), and a Dutch non-specific neck pain model (Dmodel). Models' discrimination and calibration were evaluated.ResultsThe Dmodel and Amodel discriminative performance (AUC ConclusionsExternal validation of promising prognostic models for neck pain recovery was not successful and their clinical use cannot be recommended. We advise clinicians to underpin their current clinical reasoning process with evidence-based individual prognostic factors for recovery. Further research on finding new prognostic factors and developing and validating models with up-to-date methodology is needed for recovery in patients with neck pain in primary care

Development and internal validation of prognostic models for recovery in patients with non-specific neck pain presenting in primary care

Objectives: Development and internal validation of prognostic models for post-treatment and 1-year recovery in patients with neck pain in primary care. Design: Prospective cohort study. Setting: Primary care manual therapy practices. Participants: Patients with non-specific neck pain of any duration (n = 1193). Intervention: Usual care manual therapy. Outcome measures: Recovery defined in terms of pain intensity, disability, and global perceived improvement directly post-treatment and at 1-year follow-up. Results: All post-treatment models exhibited acceptable discriminative performance after derivation (AUC ≥ 0.7). The developed post-treatment disability model exhibited the best overall performance (R2 = 0.24; IQR, 0.22–0.26), discrimination (AUC = 0.75; 95% CI, 0.63–0.84), and calibration (slope 0.92; IQR, 0.91–0.93). After internal validation and penalization, this model retained acceptable discriminative performance (AUC = 0.74). The five other models, including those predicting 1-year recovery, did not reach acceptable discriminative performance after internal validation. Baseline pain duration, disability, and pain intensity were consistent predictors across models. Conclusion: A post-treatment prognostic model for disability was successfully developed and internally validated. This model has potential to inform primary care clinicians about a patient’s individual prognosis after treatment, but external validation is required before clinical use can be recommended

Psychosocial interventions to improve mental health in adults with vision impairment: systematic review and meta-analysis

Purpose To systematically assess the literature on psychosocial interventions to improve mental health (i.e. depression, anxiety, mental fatigue, loneliness, psychological stress and psychological well-being) in visually impaired adults (≥18 years). Methods The databases Medline, Embase and Psychinfo were searched for relevant studies, which were categorised into randomised controlled trials (RCTs), non-RCTs and before and after comparisons (BA). The Cochrane Collaboration Risk of Bias Tool was used to assess study quality. Standardised mean differences (SMD) were calculated to quantitatively summarise the outcomes of the RCTs and non-RCTs in a meta-analysis. Meta-regression was used to explore sources of heterogeneity in the data. Results The search identified 27 papers (published between 1981 and 2015), describing the outcomes of 22 different studies (14 RCTs, four non-RCTs, and four BAs). Pooled analyses showed that interventions significantly reduced depressive symptoms (SMD −0.30, 95% confidence interval (CI) −0.60 to −0.01), while effects on anxiety symptoms, mental fatigue, psychological stress and psychological well-being were non-significant. Meta-regression analyses showed homogeneity in effect sizes across a range of intervention, population, and study characteristics. Only a higher age of participants was associated with less effective results on depressive symptoms (b = 0.03, 95% CI 0.01 to 0.05), psychological stress (b = 0.07, 95% CI 0.01 to 0.13) and psychological well-being (b = −0.03, 95% CI −0.05 to 0.01). However, after removing a clear outlier the overall effect on depressive symptoms and the influence of age on depressive symptoms and psychological stress were no longer significant, while the influence of age on psychological well-being remained. Conclusions There is currently only limited evidence for the effectiveness of psychosocial interventions in the field of low vision. More well-designed trials are needed with specific attention for interventions tailored to the needs of elderly patients

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Behavioral determinants as predictors of return to work after long-term sickness absence: an application of the theory of planned behavior

Background The aim of this prospective, longitudinal cohort study was to analyze the association between the three behavioral determinants of the theory of planned behavior (TPB) model-attitude, subjective norm and self-efficacy-and the time to return-to-work (RTW) in employees on long-term sick leave. Methods The study was based on a sample of 926 employees on sickness absence (maximum duration of 12 weeks). The employees filled out a baseline questionnaire and were subsequently followed until the tenth month after listing sick. The TPB-determinants were measured at baseline. Work attitude was measured with a Dutch language version of the Work Involvement Scale. Subjective norm was measured with a self-structured scale reflecting a person's perception of social support and social pressure. Self-efficacy was measured with the three subscales of a standardised Dutch version of the general self-efficacy scale (ALCOS): willingness to expend effort in completing the behavior, persistence in the face of adversity, and willingness to initiate behavior. Cox proportional hazards regression analyses were used to identify behavioral determinants of the time to RTW. Results Median time to RTW was 160 days. In the univariate analysis, all potential prognostic factors were significantly associated (P < 0.15) with time to RTW: work attitude, social support, and the three subscales of self-efficacy. The final multivariate model with time to RTW as the predicted outcome included work attitude, social support and willingness to expend effort in completing the behavior as significant predictive factors. Conclusions This prospective, longitudinal cohort-study showed that work attitude, social support and willingness to expend effort in completing the behavior are significantly associated with a shorter time to RTW in employees on long-term sickness absence. This provides suggestive evidence for the relevance of behavioral characteristics in the prediction of duration of sickness absence. It may be a promising approach to address the behavioral determinants in the development of interventions focusing on RTW in employees on long-term sick leave

Prognostic factors for disability claim duration due to musculoskeletal symptoms among self-employed persons

<p>Abstract</p> <p>Background</p> <p>Employees and self-employed persons have, among others, different personal characteristics and different working conditions, which may influence the prognosis of sick leave and the duration of a disability claim. The purpose of the current study is to identify prognostic factors for the duration of a disability claim due to non-specific musculoskeletal disorders (MSD) among self-employed persons in the Netherlands.</p> <p>Methods</p> <p>The study population consisted of 276 self-employed persons, who all had a disability claim episode due to MSD with at least 75% work disability. The study was a cohort study with a follow-up period of 12 months. At baseline, participants filled in a questionnaire with possible individual, work-related and disease-related prognostic factors.</p> <p>Results</p> <p>The following prognostic factors significantly increased claim duration: age > 40 years (Hazard Ratio 0.54), no similar symptoms in the past (HR 0.46), having long-lasting symptoms of more than six months (HR 0.60), self-predicted return to work within more than one month or never (HR 0.24) and job dissatisfaction (HR 0.54).</p> <p>Conclusions</p> <p>The prognostic factors we found indicate that for self-employed persons, the duration of a disability claim not only depends on the (history of) impairment of the insured, but also on age, self-predicted return to work and job satisfaction.</p

Methodology of a novel risk stratification algorithm for patients with multiple myeloma in the relapsed setting

Introduction Risk stratification tools provide valuable information to inform treatment decisions. Existing algorithms for patients with multiple myeloma (MM) were based on patients with newly diagnosed disease, and these have not been validated in the relapsed setting or in routine clinical practice. We developed a risk stratification algorithm (RSA) for patients with MM at initiation of second-line (2L) treatment, based on data from the Czech Registry of Monoclonal Gammopathies. Methods Predictors of overall survival (OS) at 2L treatment were identified using Cox proportional hazards models and backward selection. Risk scores were obtained by multiplying the hazard ratios for each predictor. The K-adaptive partitioning for survival (KAPS) algorithm defined four groups of stratification based on individual risk scores. Results Performance of the RSA was assessed using Nagelkerke’s R2 test and Harrell’s concordance index through Kaplan–Meier analysis of OS data. Prognostic groups were successfully defined based on real-world data. Use of a multiplicative score based on Cox modeling and KAPS to define cut-off values was effective. Conclusion Through innovative methods of risk assessment and collaboration between physicians and statisticians, the RSA was capable of stratifying patients at 2L treatment by survival expectations. This approach can be used to develop clinical decision-making tools in other disease areas to improve patient management

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p