Predicting Clinical Outcomes Following Surgical Correction of Adult Spinal Deformity

Sharma, Akshay; Tanenbaum, Joseph E.; Hogue, Olivia; Mehdi, Syed K.; Vallabh, Sagar; Hu, Emily; Benzel, Edward C.; Steinmetz, Michael P.; Savage, Jason W.

doi:10.1093/neuros/nyy190

Cited by 14 publications

(15 citation statements)

References 43 publications

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“…In addition to complications and risk factors related to gender, Gulbrandsen et al ( 30 ) indicated that females reported slightly more pain and worse function than males at the time of surgery. Finally, Sharma et al also showed that in spine surgeries gender interacted with obesity at 1 year of follow-up: obese males with at higher odds of improvement when compared to nonobese males; among females, obesity did not affect the odds of improvement ( 58 ).…”

Section: Resultsmentioning

confidence: 99%

“…In this study, there were substantial differences between the patient's series and those in the comparison groups. When degenerative diseases and deformities were considered and analyzed together it was also shown that gender interacted with obesity at 1 year of follow-up; obese males showed a higher odd of improvement when compared to nonobese males while among females, obesity did not affect the odds of improvement ( 58 ). Nevertheless, it should be noted that numerous studies present in the literature did not investigate and analyze spinal fusion outcomes dichotomizing by gender, thus it is difficult to establish a real conclusion and draw definitive results.…”

Section: Discussionmentioning

confidence: 99%

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Sex and gender determinants following spinal fusion surgery: A systematic review of clinical data

et al. 2022

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In the last decade, numerous studies analyzed and described the surgical outcomes in male and female patients submitted to orthopedic surgery. Although this, the impact of sex/gender on spinal fusion surgery clinical outcomes is still poorly defined. This review systematically maps and synthesizes the scientific literature on sex/gender differences in postoperative outcomes for patients undergoing spinal fusion surgery. The search was performed in PubMed, Scopus, and Web of Science in the last 22 years. Clinical studies evaluating potential sex/gender differences in postoperative outcomes and/or complications, as primary or secondary aim, were included and analyzed. Out of the 1,885 records screened, 47 studies were included. These studies comprised a total of 1,158,555 patients (51.31% female; 48.69% male). About 77% of the analyzed studies reported sex/gender-related differences in postoperative outcomes. Most studies treated patients for lumbar degenerative diseases and more than 55% of them reported a worse postoperative outcome in female patients in terms of pain, disability, health-related quality of life questionnaires, and complications. Differently, a significant heterogeneity across studies on patients treated for cervical and sacral degenerative diseases as well as for spinal deformity and traumatic spinal fracture prevented the understanding of specific sex/gender differences after spinal fusion surgery. Despite this, the present review highlighted those female patients treated for lumbar degenerative spine diseases could require more clinical awareness during postoperative care. The understanding of how sex/gender differences can really affect clinical outcomes after spinal fusion surgeries is mandatory for all spinal pathological conditions to drive clinical research toward oriented and personalized protocols.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Sex and gender determinants following spinal fusion surgery: A systematic review of clinical data

et al. 2022

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“…We included studies in patients undergoing cervical spine (n = 3), thoracolumbar spine (n = 2), and lumbar spine (n = 8) surgery [3••, 5••, 6••, [23][24][25][26][27][28][29][30][31][32]. These studies reported predictive models (n = total number of models) to estimate post-surgical patient-reported outcomes of the following: return to work (n = 3), pain (n = 9), physical functioning and disability (n = 5), quality of life (QOL) (n = 6), and psychosocial disposition (n = 2) reported in Table 4.…”

Section: Predicted Patient-reported Outcomes By Spine Regionmentioning

confidence: 99%

“…Patient data for these risk assessment tools was derived from EMR data, QOD registry, and Multi-center AOSpine CSM North America or International study-specific registries for patients undergoing elective cervical and lumbar surgeries [3••, 6••, 26••,30,31]. The predictive accuracy of these tools ranged from fair to good, with Sharma et al exhibiting the greatest predictive accuracy with a c-index value of 0.764[30].…”

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confidence: 99%

Predicting Patient-Centered Outcomes from Spine Surgery Using Risk Assessment Tools: a Systematic Review

White

Bradley

Hadgis

et al. 2020

Curr Rev Musculoskelet Med

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Purpose of Review The purpose of this systematic review is to evaluate the current literature in patients undergoing spine surgery in the cervical, thoracic, and lumbar spine to determine the available risk assessment tools to predict the patient-centered outcomes of pain, disability, physical function, quality of life, psychological disposition, and return to work after surgery. Recent Findings Risk assessment tools can assist surgeons and other healthcare providers in identifying the benefit-risk ratio of surgical candidates. These tools gather demographic, medical history, and other pertinent patient-reported measures to calculate a probability utilizing regression or machine learning statistical foundations. Currently, much is still unknown about the use of these tools to predict quality of life, disability, and other factors following spine surgery. A systematic review was conducted using PRISMA guidelines that identified risk assessment tools that utilized patient-reported outcome measures as part of the calculation. From 8128 identified studies, 13 articles met inclusion criteria and were accepted into this review. Summary The range of c-index values reported in the studies was between 0.63 and 0.84, indicating fair to excellent model performance. Post-surgical patient-reported outcomes were identified in the following categories (n = total number of predictive models): return to work (n = 3), pain (n = 9), physical functioning and disability (n = 5), quality of life (QOL) (n = 6), and psychosocial disposition (n = 2). Our review has synthesized the available evidence on risk assessment tools for predicting patient-centered outcomes in patients undergoing spine surgery and described their findings and clinical utility.

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“…However, there is room for improvement. 7,8 Machine learning (ML) shows promise to improve clinical decision-making and patient outcomes. It is a collection of statistical techniques that uses large quantities of data to develop a model to determine nuanced patterns and predicting outcomes.…”

Section: Introductionmentioning

confidence: 99%

Emerging Technologies in the Treatment of Adult Spinal Deformity

et al. 2021

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Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patientspecific rods are tools that are being used to improve preoperative planning and patient satisfaction. Machine learning can be used to predict complications, readmissions, and generate postoperative radiographs which can be shown to patients to guide discussions about surgery. Robot-guided spinal surgery is a rapidly growing field showing signs of greater accuracy in screw placement during surgery. Patient-specific rods offer improved outcomes through higher correction rates and decreased rates of rod breakage while decreasing operative time. The objective of this review is to evaluate trends in the literature about machine learning, robot-guided spinal surgery, and patient-specific rods in the treatment of adult spinal deformity.

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Predicting Clinical Outcomes Following Surgical Correction of Adult Spinal Deformity

Abstract: The predictive nomogram that we developed using these data can improve preoperative risk counseling and patient selection for deformity correction surgery.

Cited by 14 publications

References 43 publications

Sex and gender determinants following spinal fusion surgery: A systematic review of clinical data

Sex and gender determinants following spinal fusion surgery: A systematic review of clinical data

Predicting Patient-Centered Outcomes from Spine Surgery Using Risk Assessment Tools: a Systematic Review

Emerging Technologies in the Treatment of Adult Spinal Deformity

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