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.

The burden of preoperative fear-avoidance beliefs in workers after thoracic and lumbar spine surgery: a 2-year follow-up study

“…According to several research studies, the preoperative mental condition influences the postoperative outcomes in spine surgery. 8,13,34,40 The most relevant predictor of residual lower back pain and leg pain, as well as the possibility of still being sick 1 year after the surgery, were predicted by questions that assessed the patient's expectations and thoughts. Indeed, being classified at high risk of FAB preoperatively and being a woman significantly increased the risk of having low quality of life 1 year after the surgery.…”

“…20 These facts make identifying patient characteristics related to good or poor surgical outcomes essential for better decision making. 8 In this sense, psychosocial factors have been identified as key players in the chronicity process of pain and disability, 25,32 which can contribute to long-lasting functional brain changes and may partially explain the perpetuation of symptoms after suppressing the nociceptive source. 14 In the context of spine surgery, psychosocial factors, such as distress, depression, or anxiety, 2,13,15,42 are considered risk factors that may contribute to the variability of surgical outcomes.…”

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

“…A list of these spine surgery outcome prediction algorithms published to date is shown in Table 1. Seventeen studies predicted PROMs [68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84], fifteen predicted complications [85][86][87][88][89][90][91][92][93][94][95][96][97][98], seven examined the discharge disposition after spine surgery [99][100][101][102][103][104][105], seven predicted the length of hospital stay [81,87,92,102,[105][106][107], six predicted readmission or re-herniation after spine surgery [100,[108]…”

An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases

“…The transformation from a diagnosis-based decision to a combined decision, based on diagnosis and individual patient data, will only result in optimized care if machine learning can successfully and verifiably deliver the right results. In the hands of a physician as an assistance tool, patients could then benefit from faster, customized care [23,[62][63][64]. As a result, it may be possible to avoid costly, still unsuccessful, treatments, as well as the chronicity of spinal conditions, by integrating AI applications into clinical practice.…”