SpineCloud: image analytics for predictive modeling of spine surgery outcomes

Silva, T. De; Vedula, Satyanarayana S; Perdomo‐Pantoja, Alexander; Vijayan, Rohan C.; Doerr, Sophia A.; Uneri, Ali; Han, Runze; Ketcha, Michael D.; Skolasky, Richard L.; Caffo, Brian; Hager, Greg; Witham, Timothy F.; Theodore, Nicholas; Siewerdsen, J. H.

doi:10.1117/12.2566372

Cited by 7 publications

(9 citation statements)

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“…Eighteen articles examined functional outcomes (Table 4), which included quality-of-life measures (n = 11), opioid dependence (n = 3), returning to work (n = 2), patient satisfaction (n = 2), and persistent postsurgical pain (n = 1). 11,16,17,26 -40 Quality-of-life outcome measures included scores on the following validated inventories: ODI, visual analog scale for leg and lower back pain, EuroQol 5-dimensions (EQ-5D), Patient Health Questionnaire-9 (PHQ-9), Pain and Disability Questionnaire (PDQ), Short Form 6-dimensions (SF-6D), and the modified Japanese Orthopedic Association (mJOA). Seven were single institution studies, 5 used the QOD database, and 6 were multi-institutional.…”

Section: Resultsmentioning

confidence: 99%

Prediction Models in Degenerative Spine Surgery: A Systematic Review

Lubelski

Hersh

Azad

et al. 2021

Global Spine Journal

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Study Design: Systematic review. Objectives: To review the existing literature of prediction models in degenerative spinal surgery. Methods: Review of PubMed/Medline and Embase databases was conducted to identify articles between January 1, 2000 and March 1, 2020 that reported prediction model performance for outcomes following elective degenerative spine surgery. Results: Thirty-one articles were included. Twenty studies were of thoracolumbar, 5 were of cervical, and 6 included all spine patients. Five studies were externally validated. Prediction models were developed using machine learning (42%) and logistic regression (42%) as well as other techniques. Web-based calculators were included in 45% of published articles. Various outcomes were investigated, including complications, infection, length of stay, discharge disposition, reoperation, readmission, disability score, back pain, leg pain, return to work, and opioid dependence. Conclusions: Significant heterogeneity exists in methods used to develop prediction models of postoperative outcomes after degenerative spine surgery. Most internally validate their scores, but a few have been externally validated. Areas under the curve for most models range from 0.6 to 0.9. Techniques for development are becoming increasingly sophisticated with different machine learning tools. With further external validation, these models can be deployed online for patient, physician, and administrative use, and have the potential to optimize outcomes and maximize value in spine surgery.

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Section: Resultsmentioning

confidence: 99%

Prediction Models in Degenerative Spine Surgery: A Systematic Review

Lubelski

Hersh

Azad

et al. 2021

Global Spine Journal

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show abstract

“…The DWI scan can be added to the spinal imaging work-up obtained in preparation for surgery and could serve as a quantitative risk assessment to help clinicians and patients make an informed decision about the procedure's outcome. This of course will depend on completing large, and preferably multi-center, clinical trials where DWI brain measures are used to build and validate predictive models of prognosis (e.g., back-pain intensity, or disability) after spine surgery ( 179 ).…”

Section: Brain Biomarkers Of Chronic Painmentioning

confidence: 99%

Brain Imaging Biomarkers for Chronic Pain

2022

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The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.

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“…For further understanding, here is an example of a structured abstract from a recently accepted JMI manuscript: 4 Purpose: Data-intensive modeling could provide insight on the broad variability in outcomes in spine surgery. Previous studies were limited to analysis of demographic and clinical characteristics.…”

Section: Maryellen Gigermentioning

confidence: 99%

Integrating structured abstracts in the Journal of Medical Imaging

Giger¹

2020

J. Med. Imag.

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SpineCloud: image analytics for predictive modeling of spine surgery outcomes

Cited by 7 publications

References 0 publications

Prediction Models in Degenerative Spine Surgery: A Systematic Review

Prediction Models in Degenerative Spine Surgery: A Systematic Review

Brain Imaging Biomarkers for Chronic Pain

Integrating structured abstracts in the Journal of Medical Imaging

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