Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Berjano P

PROVIDER: S-EPMC8705358 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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

Berjano Pedro P Langella Francesco F Ventriglia Luca L Compagnone Domenico D Barletta Paolo P Huber David D Mangili Francesca F Licandro Ginevra G Galbusera Fabio F Cina Andrea A Bassani Tito T Lamartina Claudio C Scaramuzzo Laura L Bassani Roberto R Brayda-Bruno Marco M Villafañe Jorge Hugo JH Monti Lorenzo L Azzimonti Laura L

Journal of personalized medicine 20211216 12

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 a ...[more]

PMID: 34945849

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Project description:Study designA post hoc analysis.ObjectiveAdvances in machine learning (ML) have led to tools offering individualized outcome predictions for adult spinal deformity (ASD). Our objective is to examine the properties of these ASD models in a cohort of adult symptomatic lumbar scoliosis (ASLS) patients.Summary of background dataML algorithms produce patient-specific probabilities of outcomes, including major complication (MC), reoperation (RO), and readmission (RA) in ASD. External validation of these models is needed.MethodsThirty-nine predictive factors (12 demographic, 9 radiographic, 4 health-related quality of life, 14 surgical) were retrieved and entered into web-based prediction models for MC, unplanned RO, and hospital RA. Calculated probabilities were compared with actual event rates. Discrimination and calibration were analyzed using receiver operative characteristic area under the curve (where 0.5=chance, 1=perfect) and calibration curves (Brier scores, where 0.25=chance, 0=perfect). Ninety-five percent confidence intervals are reported.ResultsA total of 169 of 187 (90%) surgical patients completed 2-year follow up. The observed rate of MCs was 41.4% with model predictions ranging from 13% to 68% (mean: 38.7%). RO was 20.7% with model predictions ranging from 9% to 54% (mean: 30.1%). Hospital RA was 17.2% with model predictions ranging from 13% to 50% (mean: 28.5%). Model classification for all three outcome measures was better than chance for all [area under the curve=MC 0.6 (0.5-0.7), RA 0.6 (0.5-0.7), RO 0.6 (0.5-0.7)]. Calibration was better than chance for all, though best for RA and RO (Brier Score=MC 0.22, RA 0.16, RO 0.17).ConclusionsASD prediction models for MC, RA, and RO performed better than chance in a cohort of adult lumbar scoliosis patients, though the homogeneity of ASLS affected calibration and accuracy. Optimization of models require samples with the breadth of outcomes (0%-100%), supporting the need for continued data collection as personalized prediction models may improve decision-making for the patient and surgeon alike.

Project description:BackgroundSeveral surgical options for degenerative lumbar spinal stenosis (LSS) are available, but current guidelines do not recommend which one should be prioritized. Although previous network meta-analyses (NMAs) have been performed on this topic, they have major methodological problems and could not provide the convincing evidence and clinical practical information required.MethodsRandomized controlled trials (RCTs) comparing at least two surgical interventions were included by searching AMED, CINAHL, EMBASE, the Cochrane Library, and MEDLINE (inception to August 2023). A frequentist random-effects NMA was performed for physical function and adverse events due to any reason. For physical function, three follow-up time points were included: short-term (< 6 months post-intervention), mid-term (≥ 6 months but < 12 months), and long-term (≥ 12 months). Laminectomy was the reference comparison intervention.ResultsA total of 43 RCTs involving 5017 participants were included in the systematic review and 28 RCTs encompassing 14 types of surgical interventions were included in the NMA. For improving physical function (scale 0-100), endoscopic-assisted laminotomy (mean difference: - 8.61, 95% confidence interval: - 10.52 to - 6.69; moderate-quality evidence), laminectomy combined with Coflex (- 8.41, - 13.21 to - 3.61; moderate quality evidence), and X-stop (- 6.65, - 8.60 to - 4.71; low-quality evidence) had small effects at short-term follow-up; no statistical difference was observed at mid-term follow-up (very low- to low-quality evidence); at long-term follow-up, endoscopic-assisted laminotomy (- 7.02, - 12.95 to - 1.08; very low-quality evidence) and X-stop (- 10.04, - 18.16 to - 1.93; very low-quality evidence) had a small and moderate effect, respectively. Compared with laminectomy, endoscopic-assisted laminotomy was associated with fewer adverse events due to any reason (odds ratio: 0.27, 0.09 to 0.86; low-quality evidence).ConclusionsFor adults with degenerative LSS, endoscopic-assisted laminotomy may be the safest and most effective intervention in improving physical function. However, the available data were insufficient to indicate whether the effect was sustainable after 6 months.Trial registrationPROSPERO (CRD42018094180).

Dataset Information

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

Publications

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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