The Canadian version of the National Institutes of Health minimum dataset for chronic low back pain research: reference values from the Quebec Low Back Pain Study

Estimating minimal important change of the National Institutes of health research task force impact score using computer adaptive measures: a secondary analysis of two randomized clinical trials in a military population with chronic pain

BACKGROUND

The National Institutes of Health (NIH) Research Task Force (RTF) on Research Standards for Chronic Low Back Pain impact score is a composite measure of Patient Reported Outcomes Measurement Information System (PROMIS) pain intensity, pain interference and physical function. PROMIS surveys are available in short-form and computer adaptive testing (CAT) formats. Minimal important change (MIC) can be estimated to determine if between-group differences are large enough to be important. To date, three anchor-based estimates of impact score MIC ranging from 3 to 7.5 have been published, and all were based on data collected using PROMIS short-form surveys. None used CAT versions of PROMIS surveys.

METHODS

Secondary analysis of data collected during the conduct of two randomized clinical trials of 6-week courses of nonpharmacological pain therapies. Research subjects were US active-duty service members referred to an interdisciplinary pain management center. Impact score was assessed at the beginning and end of treatment. The Patient Global Impression of Change (PGIC) questionnaire was administered at the end of treatment and asked respondents to report their status compared to the start of treatment using a 7-item categorical scale ranging from very much improved to very much worse. A PGIC response of "much" or "very much" improved defined important improvement. Receiver operating characteristic (ROC) curve analysis and predictive logistic regression models were used to estimate MIC for the full combined sample and stratified by study sample and baseline impact score. Measures of individual statistical change were also computed.

RESULTS

Overall, a decrease of 3 points in impact score was the estimated MIC (2.5 for ROC analysis and 3.4 for predictive modeling approach). Larger decreases in impact score were needed for participants with moderate and severe baseline pain impact to report important improvement. Thresholds for individual statistically significant change ranged from 6 to 14.

CONCLUSIONS

Using data collected with CAT surveys, we calculated an MIC of 3 points for the NIH RTF impact score, and estimates ranged from 1.3 to 7.2 depending on the baseline impact score and statistical approach used. These findings are consistent with previous MIC estimates that were based on non-adaptive short form surveys and have implications for improving the accuracy of pain treatment response assessment.

REGISTRY INFORMATION

Trial registration.

CLINICALTRIALS

gov. Registry numbers: NCT03297905 (registered 9/29/17) and NCT04656340 (registered 11/30/20). Link to full applications: https://classic.

CLINICALTRIALS

gov/ct2/show/NCT03297905?titles=Determinants+of+Optimal+Dosage%26cntry=US%26draw=2%26rank=1 ; https://classic.

CLINICALTRIALS

gov/ct2/show/results/NCT04656340?titles=Complementary+and+Integrative+pain+therapies+and+functional+restoration+%28IMPPPORT%29%26draw=2%26rank=1 . Patient enrollment dates: SMART: 17 March 2021, prospectively registered; IMPPPORT: 9 December 2015, retrospectively registered.

The Economic Analysis of the Overlooked Recurrent Low Back Pain: Three Years Retrospective Observational Study

Purpose

Recent redefinitions of pain emphasize the importance of the previously overlooked recurrent low back pain (LBP). Understanding the direct medical cost for recurrent LBP cases based on the cost per visit is crucial economically. We aimed to compare the cost per visit for LBP and recurrent LBP, including the impact of gender and type of medical service, estimating the approximate annual cost of recurrent LBP.

Patients and Methods

Data on LBP categorized according to ICD-10 codes (G54, G55, M45, M46, M47, M48, M49, M51, M53, and M54) from the Polish National Health Fund (NHF) and Opolskie Rehabilitation Center (OCR) were analyzed based on the recurrent state as outlined in the new chronic pain definition.

Results

In OCR, a recurrent LBP was confirmed for 22.78% of patients, of which 59.72% were female (p<0.001). The mean value of a single procedure for recurrent LBP was 110.56 EUR, it was significantly higher for males (135.35 EUR) than for females (92.94 EUR) (p=0.008). Recurrent LBP generated a higher cost per visit for medical services than LBP (p<0.001), except for physiotherapy. Notably, males had a higher cost per visit in inpatient admissions, while females had a significantly higher cost per visit in physiotherapy services for both LBP and recurrent LBP. Moreover, recurrent LBP generated a statistically higher cost per visit for medical services than non-recurrent cases, except for physiotherapy. The average annual cost of LBP-related medical services in Poland was €243,861,639.

Conclusion

Recurrent LBP accounts for 5% of total direct LBP costs and has a higher cost per visit than LBP, excluding physiotherapy services. Gender significantly affected per-visit costs, with males having more inpatient admissions and females utilizing more physiotherapy services for both LBP and recurrent LBP.

Deep-Learning-Based Radiomics to Predict Surgical Risk Factors for Lumbar Disc Herniation in Young Patients: A Multicenter Study

Objective

The aim of this study is to develop and validate a deep-learning radiomics model for predicting surgical risk factors for lumbar disc herniation (LDH) in young patients to assist clinicians in identifying surgical candidates, alleviating symptoms, and improving prognosis.

Methods

A retrospective analysis of patients from two medical centers was conducted. From sagittal and axial MR images, the regions of interest were handcrafted to extract radiomics features. Various machine-learning algorithms were employed and combined with clinical features, resulting in the development of a deep-learning radiomics nomogram (DLRN) to predict surgical risk factors for LDH in young adults. The efficacy of the different models and the clinical benefits of the model were compared.

Results

We derived six sets of features, including clinical features, radiomics features (Rad_SAG and Rad_AXI) and deep learning features (DL_SAG and DL_AXI) from sagittal and axial MR images, as well as fused deep-learning radiomics (DLR) features. The support vector machine(SVM) algorithm exhibited the best performance. The area under the curve (AUC) of DLR in the training and testing cohorts of 0.991 and 0.939, respectively, were significantly better than those of the models developed with radiomics(Rad_SAG=0.914 and 0.863, Rad_AXI=0.927 and 0.85) and deep-learning features(DL_SAG=0.959 and 0.818, DL_AXI=0.960 and 0.811). The AUC of DLRN coupled with clinical features(ODI, Pfirrmann grade, SLRT, MMFI, and MSU classification) were 0.994 and 0.941 in the training and testing cohorts, respectively. Analysis of the calibration and decision curves demonstrated good agreement between the predicted and observed outcomes, and the use of the DLRN to predict the need for surgical treatment of LDH demonstrated significant clinical benefits.

Conclusion

The DLRN established based on clinical and DLR features effectively predicts surgical risk factors for LDH in young adults, offering valuable insights for diagnosis and treatment.

Trajectories of pain and depressive symptoms among people living with low back pain during the COVID-19 pandemic: a 24-month longitudinal study

Introduction: We explored trajectories of pain intensity and depressive symptoms over the first 24 months of the pandemic in people with low back pain. Methods: This longitudinal study was conducted alongside the Quebec Low Back Pain Study. Starting in April 2020 and every 3 months until July 2022, 291 participants completed an online survey. Group-based trajectory modeling was used to identify patterns of pain intensity and depressive symptoms. Onset outbreak characteristics were then put in relation with trajectory groups using multivariate logistic regression. Results: The analysis revealed 5 trajectories of pain intensity and depressive symptoms, respectively. The pain trajectories were stable mild (n = 17, 5.8%); stable moderate (n = 103, 35.4%); stable severe (n = 81, 27.8%); U-shape (n = 24, 8.3%), and inverted U-shape (n = 66, 22.7%). The trajectories of depressive symptoms were stable none (n = 58, 19.9%); stable very mild (n = 61, 21.0%); stable mild (n = 85, 29.2%); stable moderate (n = 59, 21.7%); and severe slightly improving (n = 24, 8.3%). Pre-COVID everyday/nearly everyday pain, average pain intensity, and widespread bodily pain were predictive of pain trajectory groups. Higher pre-COVID depression, acute stress disorder, and lockdown measures-related stress were associated with moderate/severe depressive trajectories. Discussion: Our findings indicated relative stability of pain and depressive symptoms among participants during the COVID-19 pandemic but also highlighted subgroups of people who experienced temporary deterioration or improvement over the first months of the pandemic that then reverted back to baseline levels. Modifiable risk factors were identified before the onset of the pandemic, which could give preventive measures in targeted populations.

Longitudinal multisite study of the chronobiological control of chronic pain: the CircaHealth CircaPain study protocol

INTRODUCTION

One in five Canadians lives with chronic pain. Evidence shows that some individuals experience pain that fluctuates in intensity following a circadian (24-hour) rhythm. Endogenous molecular rhythms regulate the function of physiological processes that govern pain mechanisms. Addressing chronic pain rhythmicity on a molecular and biopsychosocial level can advance understanding of the disease and identify new treatment/management strategies. Our CircaHealth CircaPain study uses an online survey combined with ecological momentary assessments and biosample collection to investigate the circadian control of chronic pain and identify potential biomarkers. Our primary objective is to understand interindividual variability in pain rhythmicity, by collecting biopsychosocial measures. The secondary objective accounts for seasonal variability and the effect of latitude on rhythmicity.

METHODS AND ANALYSIS

Following completion of a baseline questionnaire, participants complete a series of electronic symptom-tracking diaries to rate their pain intensity, negative affect, fatigue and stress on a 0-10 scale at 8:00, 14:00 and 20:00 daily over 10 days. These measures are repeated at 6 and 12 months postenrolment to account for potential seasonal changes. We aim to recruit ≥2500 adults with chronic pain within Canada. Infrastructure is being developed to facilitate the collection of blood samples from subgroups of participants (~800) two times per day over 24-48 hours to identify rhythmic expression of circulating genes and/or proteins.

ETHICS AND DISSEMINATION

Ethical approval for this study was obtained by the Queen's University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board (File No. 6038114). Participants provide informed consent to participate, and their data will not be identifiable in any publication or report. Findings will be published in a relevant scientific journal and disseminated at scientific meetings and online webinars. We maintain a website to post updated resources and engage with the community. We employ knowledge mobilisation in the form of direct data sharing with participants.

A confirmatory factor analysis of an electronic format painDETECT questionnaire for patients with low back pain

BACKGROUND

The substantial burden of low back pain on patients and healthcare systems is exacerbated by unclear pathology and ineffective diagnostic methods, hindering effective management. The painDETECT questionnaire (PD-Q) has been used to facilitate the evaluation and categorization of low back pain. While preliminary validation and translations of the paper-based format of PD-Q into languages such as Spanish and Dutch have been accomplished, the underlying factor model inherent to the electronic format of the PD-Q remains to be established.

OBJECTIVE

The objective of this study was to utilise confirmatory factor analysis (CFA) to investigate the factor structure of an electronic format PD-Q among patients with neuropathic low back pain.

METHODS

This cross-sectional study was conducted at a Spinal Clinic in Sydney between November 2020 and October 2022. Eligible participants were adults over 18 with low back pain and no history of lumbar surgery or systemic co-morbidities. Participants completed the electronic format of the PD-Q, and CFA was employed to assess the validity of the suggested two-factor, nine-item structure. Recommended cut-offs for goodness-of-fit indices were used to evaluate the model fit.

RESULTS

Of the 236 patients that visited the clinic during the data collection period, 142 (71, 50% female, mean age 51.26 ± 15.28 years) participated in the study. Median pain severity was 9/10 over 4 weeks. CFA indicated strong model fit, with goodness-of-fit and comparative fit indices over 0.9, and overall internal consistency was 0.77. Construct validity analysis demonstrated the PD-Q's effectiveness in distinguishing neuropathic, mixed, and nociceptive LBP, aiding neuropathic pain evaluation in low back pain patients.

CONCLUSION

This study confirms the reliability and two-factor structure of the electronic PD-Q for neuropathic pain assessment in low back pain patients. To enhance comprehension of the clinical applicability of the electronic format PD-Q, future research should conduct clinimetric evaluations.

Risk Factors in Patients with Low Back Pain Under 40 Years Old: Quantitative Analysis Based on Computed Tomography and Magnetic Resonance Imaging mDIXON-Quant

Purpose

While low back pain (LBP) constitutes a global life disorder cause, the contribution of paraspinal muscles to its pathogenicity remains elusive. We characterized the paraspinal muscles of patients with LBP using lumbar three-dimensional computed tomography (CT) and magnetic resonance imaging (MRI) mDIXON-Quant, and evaluated the risk factors combined with clinical data.

Methods

A retrospective study involving 181 patients (10-40 years) who underwent lumbar 3D-CT and MRI mDIXON from January 1, 2021 to December 31, 2022, and divided into normal, non-chronic LBP [non-CLBP], and CLBP groups. Clinical data, paraspinal muscle cross-sectional area, Hounsfield unit for CT values, and fat fraction derived from mDIXON-Quant were compared. Three readers analyzed the images independently; intra- and interobserver agreement was measured. Spearman analysis and multiple logistic regression were used to analyze the correlation between clinical data, radiologic and paraspinal muscle parameters. A nomogram was constructed for individualized prediction.

Results

Correlation analysis revealed that body mass index, visual analog scale score, Pfirrmann grade, annulus fibrosus tear, lumbar lordosis (LL), and Modic changes correlated with LBP (all P<0.05). The Pfirrmann grade and annulus fibrosus tear showed positive correlation (r=0.673, 0.559), whereas LL was negatively correlated (r=-0.469). The multifidus CT values were negatively correlated with LBP at L4-5/L5-S1; the multifidus fat fraction was positively correlated at L4-5/L5-S1 (r=0.734, r=0.584, P<0.001). The multiple logistic regression showed that L4-5 multifidus fat fraction (P=0.046, OR=1.167), Pfirrmann grade (P=0.017, OR=0.063), LL (P=0.002, OR=0.828) and annulus fibrosus tear (P=0.005, OR=0.024) were risk factors for predicting LBP in the non-CLBP group; in the CLBP group, BMI (P=0.048 OR=1.225), L4-5 multifidus fat fraction (P=0.001 OR=1.299), LL (P=0.003, OR=0.841) and Pfirrmann classification (P=0.009, OR=0.046) were risk factors.

Conclusion

BMI, L4-5 multifidus fat fraction, LL, and Pfirrmann grade are risk factors for CLBP in patients under 40; whereas annulus fibrosus tear is an independent risk factor for non-CLBP, nomograms derived from these parameters can help predict LBP and MRI mDIXON-Quant is recommended for quantitatively analyzing paraspinal muscle fat infiltration.