SpineCloud: image analytics for predictive modeling of spine surgery outcomes

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

doi:10.1117/1.jmi.7.3.031502

Cited by 12 publications

(8 citation statements)

References 39 publications

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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

View full text Add to dashboard Cite

show abstract

“…Search results were manually evaluated and all studies that analyzed a perioperative SDS system with a machine learning (ML)-based component were included. Publication Subject Type of study Study size (# patients) Bahl et al (2017) ML-based prediction of pathological upgrade of high-risk breast lesions and reduction of unnecessary surgical excision based on data such as histologic results and text features from pathologic reports Retrospective cohort 986 Corey et al (2018) ML-based prediction of postoperative complication risk in surgical patients based on electronic health record data Prospective cohort 66,370 De Silva et al (2020) ML-based prediction models for postoperative outcomes of lumbar spine surgery based on image features and patient characteristics Retrospective cohort 64 Duke University (2016) ML-based clinical analytical platform for predicting risk of surgical complications and improving surgical outcomes based on patient care parameters Prospective cohort 200 Futoma et al (2017) ML-based sepsis prediction based on clinical patient data over time Prospective cohort 51,697 Hyland et al (2020) ML-based early prediction of circulatory failure in the intensive care unit based on physiological (clinical and laboratory) measurements from multiple organ systems Prospective cohort 36,098 Komorowski et al (2018) ML-based identification of optimal treatment strategies for sepsis in intensive care based on laboratory and clinical patient data Prospective cohort …”

Section: Table A1mentioning

confidence: 99%

Surgical data science – from concepts toward clinical translation

Maier-Hein

Eisenmann

Sarıkaya

et al. 2022

Medical Image Analysis

203

112

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“…100,101 A few models were designed for predicting outcome like, VAS, ODI, mJOA, and invasiveness score based on preoperative factors in lumbar disc herniation (LDH) or LBP patients. 14 , [102][103][104][105] Similarly, ML meaningful predicted survival outcomes of spinopelvic chondrosarcoma, ependymoma, malignant peripheral nerve sheath tumor and spinal metastases patients. [106][107][108][109][110] In spinal metastatic disease, SORG algorithms had been externally validated for survival prediction.…”

Section: Prognosismentioning

confidence: 97%

Current Applications of Machine Learning in Spine: From Clinical View

Ren

Xie

et al. 2021

Global Spine Journal

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Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

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SpineCloud: image analytics for predictive modeling of spine surgery outcomes

Cited by 12 publications

References 39 publications

Prediction Models in Degenerative Spine Surgery: A Systematic Review

Prediction Models in Degenerative Spine Surgery: A Systematic Review

Surgical data science – from concepts toward clinical translation

Current Applications of Machine Learning in Spine: From Clinical View

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