Potential of predictive computer models for preoperative patient selection to enhance overall quality-adjusted life years gained at 2-year follow-up: a simulation in 234 patients with adult spinal deformity

Abstract

<jats:sec><jats:title>OBJECTIVE</jats:title><jats:p>Patients with adult spinal deformity (ASD) experience significant quality of life improvements after surgery. Treatment, however, is expensive and complication rates are high. Predictive analytics has the potential to use many variables to make accurate predictions in large data sets. A validated minimum clinically important difference (MCID) model has the potential to assist in patient selection, thereby improving outcomes and, potentially, cost-effectiveness.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>The present study was a retrospective analysis of a multiinstitutional database of patients with ASD. Inclusion criteria were as follows: age ≥ 18 years, radiographic evidence of ASD, 2-year follow-up, and preoperative Oswestry Disability Index (ODI) &gt; 15. Forty-six variables were used for model training: demographic data, radiographic parameters, surgical variables, and results on the health-related quality of life questionnaire. Patients were grouped as reaching a 2-year ODI MCID (+MCID) or not (−MCID). An ensemble of 5 different bootstrapped decision trees was constructed using the C5.0 algorithm. Internal validation was performed via 70:30 data split for training/testing. Model accuracy and area under the curve (AUC) were calculated. The mean quality-adjusted life years (QALYs) and QALYs gained at 2 years were calculated and discounted at 3.5% per year. The QALYs were compared between patients in the +MCID and –MCID groups.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>A total of 234 patients met inclusion criteria (+MCID 129, −MCID 105). Sixty-nine patients (29.5%) were included for model testing. Predicted versus actual results were 50 versus 40 for +MCID and 19 versus 29 for −MCID (i.e., 10 patients were misclassified). Model accuracy was 85.5%, with 0.96 AUC. Predicted results showed that patients in the +MCID group had significantly greater 2-year mean QALYs (p = 0.0057) and QALYs gained (p = 0.0002).</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS</jats:title><jats:p>A successful model with 85.5% accuracy and 0.96 AUC was constructed to predict which patients would reach ODI MCID. The patients in the +MCID group had significantly higher mean 2-year QALYs and QALYs gained. This study provides proof of concept for using predictive modeling techniques to optimize patient selection in complex spine surgery.</jats:p></jats:sec>

Artificial intelligence and machine learning in spine research

“…CARTs and random forests have been used for several applications in spine research. As a clinical decision support system, decision trees have been used for the management of low back pain, and for the preoperative selection of patients with adult spinal deformity . Other applications include the evaluation of the primary fixation strength of pedicle screws (Figure ), and the prediction of proximal junctional failure …”

Artificial Intelligence for Adult Spinal Deformity

“…Predictive analytics has now been applied across a wide variety of topics within ASD surgery, including predicting intraoperative, 15 perioperative, 16,17 and postoperative complications and outcomes. [18][19][20][21][22][23][24][25] The majority of studies published on this topic share similar principles and methodologies in the development of their respective predictive models. The most common technique employed across the studies mentioned in this article relies on decision tree-based machine learning, where either classification or regression trees are built based on the target variable (output).…”

“…Importantly, numerous studies have also been published predicting QoL measures [23][24][25] and cervical alignment 22 following thoracolumbar ASD surgery. Passias et al 22 used predictive analytics to produce a model for predicting reciprocal changes, specifically cervical alignment, following thoracolumbar spinal deformity surgery in a cohort of 225 ASD patients.…”

“…The results of this study showed that patients with increased preoperative C2-T3 cobb angle at baseline (odds ratio [OR], 1.048; p = 0.005), and the number of Smith-Peterson osteotomies used in surgery (OR, 1.336; p = 0.017), were both significantly associated with developing poor cervical alignment postoperatively. With respect to QoL outcomes in ASD patients, Oh et al 23 were among the first to apply predictive analytics to determine how patients would fare postoperatively using patient-derived metrics. Similar to previous studies, they used an ensemble of 5 different bootstrapped decision trees in a cohort of 234 ASD patients with 2-year follow-up, with a total of 46 variables for model development.…”

Predictive Analytics in Spine Oncology Research: First Steps, Limitations, and Future Directions

“…Great advanc-es have been made in other areas of spine surgery, such as spinal deformity surgery, to develop predictive algorithms that can help surgeons and patients alike in the decision-making process. 4,5 For example, predictive algorithms that can help calculate potential complications, readmission, and the invasiveness of surgery may help patients decide on whether to proceed with surgery. Such information would provide a true risk assessment.…”