Browsing by Subject "neck pain"
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Item Open Access Clinical Outcomes, Utilization, and Charges in Persons With Neck Pain Receiving Guideline Adherent Physical Therapy.(Eval Health Prof, 2016-12) Horn, Maggie E; Brennan, Gerard P; George, Steven Z; Harman, Jeffrey S; Bishop, Mark DIn efforts to decrease practice variation, clinical practice guidelines for neck pain have been published. The purpose of this study was to determine the effect of receiving guideline adherent physical therapy (PT) on clinical outcomes, health care utilization, and charges for health care services in patients with neck pain. A retrospective review of 298 patients with neck pain receiving PT from 2008 to 2011 was performed. Clinical outcomes, utilization, and charges were compared between patients who received guideline adherent care and nonadherent care. Patients in the adherent care group experienced a lower percentage improvement in pain score compared to nonadherent care group (p = .01), but groups did not significantly differ on percentage improvement in disability (p = .32). However, patients receiving adherent care had an average 3.6 fewer PT visits (p < .001) and less charges for PT (p < .001). Additionally, patients receiving adherent care had 7.3 fewer visits to other health care providers (p < .001), one less prescription medication (p = .02) and 43% fewer diagnostic images (p = .02) but did not differ in their charges to other health care providers (p = .68) during the calendar year of undergoing PT. Although receiving guideline adherent care demonstrated positive effects on health care utilization and financial outcomes, there appears to be a trade-off with clinical outcomes.Item Open Access High-impact chronic pain transition in surgical recipients with cervical spondylotic myelopathy.(Journal of neurosurgery. Spine, 2022-01) Cook, Chad E; George, Steven Z; Asher, Anthony L; Bisson, Erica F; Buchholz, Avery L; Bydon, Mohamad; Chan, Andrew K; Haid, Regis W; Mummaneni, Praveen V; Park, Paul; Shaffrey, Christopher I; Than, Khoi D; Tumialan, Luis M; Wang, Michael Y; Gottfried, Oren NObjective
High-impact chronic pain (HICP) is a recently proposed metric that indicates the presence of a severe and troubling pain-related condition. Surgery for cervical spondylotic myelopathy (CSM) is designed to halt disease transition independent of chronic pain status. To date, the prevalence of HICP in individuals with CSM and their HICP transition from presurgery is unexplored. The authors sought to define HICP prevalence, transition, and outcomes in patients with CSM who underwent surgery and identify predictors of these HICP transition groups.Methods
CSM surgical recipients were categorized as HICP at presurgery and 3 months if they exhibited pain that lasted 6-12 months or longer with at least one major activity restriction. HICP transition groups were categorized and evaluated for outcomes. Multivariate multinomial modeling was used to predict HICP transition categorization.Results
A majority (56.1%) of individuals exhibited HICP preoperatively; this value declined to 15.9% at 3 months (71.6% reduction). The presence of HICP was also reflective of other self-reported outcomes at 3 and 12 months, as most demonstrated notable improvement. Higher severity in all categories of self-reported outcomes was related to a continued HICP condition at 3 months. Both social and biological factors predicted HICP translation, with social factors being predominant in transitioning to HICP (from none preoperatively).Conclusions
Many individuals who received CSM surgery changed HICP status at 3 months. In a surgical population where decisions are based on disease progression, most of the changed status went from HICP preoperatively to none at 3 months. Both social and biological risk factors predicted HICP transition assignment.Item Open Access Validation of the recently developed Total Disability Index: a single measure of disability in neck and back pain patients.(Journal of neurosurgery. Spine, 2019-12) Cruz, Dana L; Ayres, Ethan W; Spiegel, Matthew A; Day, Louis M; Hart, Robert A; Ames, Christopher P; Burton, Douglas C; Smith, Justin S; Shaffrey, Christopher I; Schwab, Frank J; Errico, Thomas J; Bess, Shay; Lafage, Virginie; Protopsaltis, Themistocles SOBJECTIVE:Neck and back pain are highly prevalent conditions that account for major disability. The Neck Disability Index (NDI) and Oswestry Disability Index (ODI) are the two most common functional status measures for neck and back pain. However, no single instrument exists to evaluate patients with concurrent neck and back pain. The recently developed Total Disability Index (TDI) combines overlapping elements from the ODI and NDI with the unique items from each. This study aimed to prospectively validate the TDI in patients with spinal deformity, back pain, and/or neck pain. METHODS:This study is a retrospective review of prospectively collected data from a single center. The 14-item TDI, derived from ODI and NDI domains, was administered to consecutive patients presenting to a spine practice. Patients were assessed using the ODI, NDI, and EQ-5D. Validation of internal consistency, test-retest reproducibility, and validity of reconstructed NDI and ODI scores derived from TDI were assessed. RESULTS:A total of 252 patients (mean age 55 years, 56% female) completed initial assessments (back pain, n = 115; neck pain, n = 52; back and neck pain, n = 55; spinal deformity, n = 55; and no pain/deformity, n = 29). Of these patients, 155 completed retests within 14 days. Patients represented a wide range of disability (mean ODI score: 36.3 ± 21.6; NDI score: 30.8 ± 21.8; and TDI score: 34.1 ± 20.0). TDI demonstrated excellent internal consistency (Cronbach's alpha = 0.922) and test-retest reliability (intraclass correlation coefficient = 0.96). Differences between actual and reconstructed scores were not clinically significant. Subanalyses demonstrated TDI's ability to quantify the degree of disability due to back or neck pain in patients complaining of pain in both regions. CONCLUSIONS:The TDI is a valid and reliable disability measure in patients with back and/or neck pain and can capture each spine region's contribution to total disability. The TDI could be a valuable method for total spine assessment in a clinical setting, and its completion is less time consuming than that for both the ODI and NDI.Item Open Access Which supervised machine learning algorithm can best predict achievement of minimum clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study.(Neurosurgical focus, 2023-06) Park, Christine; Mummaneni, Praveen V; Gottfried, Oren N; Shaffrey, Christopher I; Tang, Anthony J; Bisson, Erica F; Asher, Anthony L; Coric, Domagoj; Potts, Eric A; Foley, Kevin T; Wang, Michael Y; Fu, Kai-Ming; Virk, Michael S; Knightly, John J; Meyer, Scott; Park, Paul; Upadhyaya, Cheerag; Shaffrey, Mark E; Buchholz, Avery L; Tumialán, Luis M; Turner, Jay D; Sherrod, Brandon A; Agarwal, Nitin; Chou, Dean; Haid, Regis W; Bydon, Mohamad; Chan, Andrew KObjective
The purpose of this study was to evaluate the performance of different supervised machine learning algorithms to predict achievement of minimum clinically important difference (MCID) in neck pain after surgery in patients with cervical spondylotic myelopathy (CSM).Methods
This was a retrospective analysis of the prospective Quality Outcomes Database CSM cohort. The data set was divided into an 80% training and a 20% test set. Various supervised learning algorithms (including logistic regression, support vector machine, decision tree, random forest, extra trees, gaussian naïve Bayes, k-nearest neighbors, multilayer perceptron, and extreme gradient boosted trees) were evaluated on their performance to predict achievement of MCID in neck pain at 3 and 24 months after surgery, given a set of predicting baseline features. Model performance was assessed with accuracy, F1 score, area under the receiver operating characteristic curve, precision, recall/sensitivity, and specificity.Results
In total, 535 patients (46.9%) achieved MCID for neck pain at 3 months and 569 patients (49.9%) achieved it at 24 months. In each follow-up cohort, 501 patients (93.6%) were satisfied at 3 months after surgery and 569 patients (100%) were satisfied at 24 months after surgery. Of the supervised machine learning algorithms tested, logistic regression demonstrated the best accuracy (3 months: 0.76 ± 0.031, 24 months: 0.773 ± 0.044), followed by F1 score (3 months: 0.759 ± 0.019, 24 months: 0.777 ± 0.039) and area under the receiver operating characteristic curve (3 months: 0.762 ± 0.027, 24 months: 0.773 ± 0.043) at predicting achievement of MCID for neck pain at both follow-up time points, with fair performance. The best precision was also demonstrated by logistic regression at 3 (0.724 ± 0.058) and 24 (0.780 ± 0.097) months. The best recall/sensitivity was demonstrated by multilayer perceptron at 3 months (0.841 ± 0.094) and by extra trees at 24 months (0.817 ± 0.115). Highest specificity was shown by support vector machine at 3 months (0.952 ± 0.013) and by logistic regression at 24 months (0.747 ± 0.18).Conclusions
Appropriate selection of models for studies should be based on the strengths of each model and the aims of the studies. For maximally predicting true achievement of MCID in neck pain, of all the predictions in this balanced data set the appropriate metric for the authors' study was precision. For both short- and long-term follow-ups, logistic regression demonstrated the highest precision of all models tested. Logistic regression performed consistently the best of all models tested and remains a powerful model for clinical classification tasks.