Jayesh Vallabh

Study Design: Retrospective cohort. Objective: To compare trends, healthcare utilization, and costs in surgical sacroiliac (SI) fusion versus radiofrequency ablation (RFA). Summary of Background Data: SI joint RFA and surgical SI joint fusion (SI fusion) are 2 possible options to treat refractory SI pain. There is no evidence on practice trends or comparisons of healthcare utilization after these procedures. Methods: The PearlDiver database was used to identify RFA and SI fusion cohorts from 2020 to Q1 2023. A sex-based subgroup analysis was also conducted. Outcome measures were quarterly trends, reimbursements, and healthcare utilization [physical therapy, office visits, emergency department (ED) visits, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, opioid prescriptions, and nonsteroidal anti-inflammatory drugs (NSAIDs)]. Results: This study included 20,448 patients: 12,581 (62.8%) in the RFA cohort and 7597 (37.2%) in the SI fusion cohort. Of the patients, 69.8% were female. The RFA cohort was older, had diabetes, a high Elixhauser–Comorbidity Index (ECI), and lower family income. The total number of RFA procedures per quarter has consistently exceeded that of SI fusions, though the gap has narrowed. From Q1 2020 to Q1 2023, the number of RFAs decreased by an average of 155 procedures per quarter, while SI fusions increased by an average of 331 procedures per quarter. Healthcare utilization was higher after SI fusion in physical therapy (36.4% vs. 18.4%, P P P P P <0.001) as compared with RFA. Conclusion: Overall, RFA was performed more frequently with lower healthcare utilization after the procedure than SI fusion. Although the gap in procedure frequency narrowed and average reimbursement for RFA decreased over time. Level of Evidence: Level III.

Purpose: Low back pain (LBP) is a debilitating condition with highly variable treatment responses due to its complex biopsychosocial nature. This study utilized standardized in-clinic sensor-based spinal motion assessments to identify distinct motion profile subgroups in LBP patients and examine their association with biopsychosocial measures and longitudinal outcomes after usual care. Methods: In this prospective observational study, 607 LBP patients underwent standardized sensor-based spinal motion assessments to quantify extent of low back impairment. Machine learning clustering of patient’s spinal motion signatures identified patient subgroups, which were compared across demographic, clinical, and biopsychosocial measures. Finally, we used patient’s global impression of change (PGIC) as our primary outcome to compare variation in 3-month responses between clusters after usual care treatments. Results: We identified three baseline motion-based clusters: Poor function ( n = 179), Moderate function ( n = 281), and High function ( n = 147), reflecting low, moderate, and high spinal mobility. Demographic and clinical characteristics including age, body mass index, employment and comorbidity differed significantly across clusters. Notably, although clusters were derived using motion features only, we found significant variation in pain outcomes including pain intensity and interference. Moreover, cluster membership was independently associated with key biopsychosocial measures, including pain intensity, pain interference, physical function, social role, and self-efficacy, indicating that these phenotypes capture clinically relevant differences in pain experience and related biopsychosocial domains. At 3 months, PGIC responses differed significantly by cluster, with the Poor function group showing a higher proportion of negative outcomes. Conclusions: Three distinct motion-based clusters were identified that demonstrated variability across demographic, clinical, and biopsychosocial measures. Although derived solely from spinal motion, these clusters were associated with key patient-reported outcomes, suggesting that motion-based clustering may offer complementary insight into patient heterogeneity and identifying subgroups at risk of suboptimal outcomes. Clinical Trials Registration ID: NCT05776771. Key findings: Question Can sensor-enabled motion assessments be utilized for patient phenotyping to identify unique low back pain subgroups and predict treatment responders? Findings: Three distinct functional clusters were identified using wearable sensor-enabled motion assessments and cluster membership was significantly associated with 3-month responses. Meaning: The use of sensor-enabled motion assessments can inform patient heterogeneity and identify clusters associated with poor outcomes.

[This corrects the article DOI: 10.2196/39863.].

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Introduction Low back pain is a significant burden to society and the lack of reliable outcome measures, combined with a prevailing inability to quantify the biopsychosocial elements implicated in the disease, impedes clinical decision-making and distorts treatment efficacy. This paper aims to validate the utility of a biopsychosocial spine platform to provide standardized wearable sensor-derived functional motion assessments to assess spine function and differentiate between healthy controls and patients. Secondarily, we explored the correlation between these motion features and subjective biopsychosocial measures. Methods An observational study was conducted on healthy controls (n=50) and patients with low back pain (n=50) to validate platform utility. The platform was used to conduct functional assessments along with patient-reported outcome assessments to holistically document cohort differences. Our primary outcomes were motion features; and our secondary outcomes were biopsychosocial measures (pain, function, etc). Results Our results demonstrated statistically significant differences in motion features between healthy and patient cohorts across anatomical planes. Importantly, we found velocity and acceleration in the axial plane showed the largest difference, with healthy controls having 49.7% and 55.7% higher values, respectively, than patients. In addition, we found significant correlations between motion features and biopsychosocial measures for pain, physical function and social role only. Conclusions Our study validated the use of wearable sensor-derived functional motion metrics in differentiating healthy controls and patients. Collectively, this technology has the potential to facilitate holistic biopsychosocial evaluations to enhance spine care and improve patient outcomes. Trial registration number NCT05776771.

Peripheral nerve stimulation (PNS) is rapidly increasing in use. This interventional pain treatment modality involves modulating peripheral nerves for a variety of chronic pain conditions. This review evaluated its use specifically in the context of chronic lower extremity pain. Studies continue to elucidate the utility of PNS and better define indications, contraindications, as well as short- and long-term benefits of the procedure for the lower extremity. While large, prospective evidence is still lacking, the best available evidence suggests that improvements may be seen in pain scores, functionality, and opioid consumption. Overall, evidence synthesis suggests that PNS for the lower extremities may be a viable option for patients with chronic lower extremity pain.