Mild and Severe Obesity Reduce the Effectiveness of Lumbar Fusions: 1-Year Patient-Reported Outcomes in 8171 Patients

Mulvaney, Graham; Rice, Olivia M.; Rossi, Vincent J.; Peters, David R.; Patt, Joshua C.; Pfortmiller, Deborah; Asher, Anthony L.; Kim, Paul; Bernard, J; McGirt, Matthew J.

doi:10.1093/neuros/nyaa414

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Thus, despite several studies described good outcomes following spine fusion in obese patients, 5 , 57 the majority of spine surgeons seems to show a tendency toward favor for non-fusion procedures—a priori considered “less invasive”—since the historically reported higher rate of complications. 3 , 98 , 103 , 104 , 121 , 134 Recently, Onyekwelu et al conducted an interesting retrospective study aiming to establish differences in clinical outcomes and complication rates between obese and nonobese patients (1181 vs 1266) following decompression alone compared with decompression plus fusion for lumbar spinal stenosis.…”

Section: Discussionmentioning

confidence: 99%

Obesity and Spine Surgery: A Qualitative Review About Outcomes and Complications. Is It Time for New Perspectives on Future Researches?

Cofano

Perna

Bongiovanni

et al. 2021

Global Spine Journal

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Study Design: Literature review. Objectives: An increasing number of obese patients requires operative care for degenerative spinal disorders. The aim of this review is to analyze the available evidence regarding the role of obesity on outcomes after spine surgery. Peri-operative complications and clinical results are evaluated for both cervical and lumbar surgery. Furthermore, the contribution of MIS techniques for lumbar surgery to play a role in reducing risks has been analyzed. Methods: Only articles published in English in the last 10 years were reviewed. Inclusion criteria of the references were based on the scope of this review, according to PRISMA guidelines. Moreover, only paper analyzing obesity-related complications in spine surgery have been selected and thoroughly reviewed. Each article was classified according to its rating of evidence using the Sacket Grading System. Results: A total number of 1636 articles were found, but only 130 of them were considered to be relevant after thorough evaluation and according to PRISMA checklist. The majority of the included papers were classified according to the Sacket Grading System as Level 2 (Retrospective Studies). Conclusion: Evidence suggest that obese patients could benefit from spine surgery and outcomes be satisfactory. A higher rate of peri-operative complications is reported among obese patients, especially in posterior approaches. The use of MIS techniques plays a key role in order to reduce surgical risks. Further studies should evaluate the role of multidisciplinary counseling between spine surgeons, nutritionists and bariatric surgeons, in order to plan proper weight loss before elective spine surgery.

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Section: Discussionmentioning

confidence: 99%

Obesity and Spine Surgery: A Qualitative Review About Outcomes and Complications. Is It Time for New Perspectives on Future Researches?

Cofano

Perna

Bongiovanni

et al. 2021

Global Spine Journal

View full text Add to dashboard Cite

show abstract

“…According to Mulvanay et al [28], an increased BMI is associated with decreased effectiveness of one-to three-level elective lumbar fusion, despite the absence of surgical complications. A BMI value higher than 30 is considered a risk factor for surgical complications and poor spine surgery results.…”

Section: Predictive Models Of Surgical Improvement Based On Clinical ...mentioning

confidence: 99%

The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach

Berjano

Langella

Ventriglia

et al. 2021

JPM

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The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index—ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a “good early outcome”. A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an “excellent early outcome”. The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months’ follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

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“…Even if in the literature, its role it's still debated in the prediction of functional outcomes. According to Mulvanay et al [28], an increased BMI is associated with decreased effectiveness of 1to 3-level elective lumbar fusion, despite the absence of surgical complications. A BMI value higher than 30 is considered a risk factor for surgical complications and poor spine surgery results.…”

Section: Predictive Models Of Surgical Improvement Based On Clinical Datamentioning

confidence: 99%

The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach

Langella

Ventriglia

Compagnone

et al. 2021

Preprint

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Aims To create, using a machine learning (ML) approach, a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome. Materials and Methods A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineReg) was performed. The inclusion criteria were age > 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a "good early outcome". A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an "excellent early outcome". The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). Results A total of 1243 patients were included in this study. The model for predicting ODI at 6 months follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and a ROC AUC = 0.808. Conclusion The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

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Mild and Severe Obesity Reduce the Effectiveness of Lumbar Fusions: 1-Year Patient-Reported Outcomes in 8171 Patients

Cited by 3 publications

References 29 publications

Obesity and Spine Surgery: A Qualitative Review About Outcomes and Complications. Is It Time for New Perspectives on Future Researches?

Obesity and Spine Surgery: A Qualitative Review About Outcomes and Complications. Is It Time for New Perspectives on Future Researches?

The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach

The influence of baseline clinical status and surgical strategy on early good to excellent result in spinal lumbar arthrodesis: a machine learning approach

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