Machine learning–based preoperative predictive analytics for lumbar spinal stenosis

Abstract

<jats:sec><jats:title>OBJECTIVE</jats:title><jats:p>Patient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The authors aim to evaluate the feasibility of predicting short- and long-term PROMs, reoperations, and perioperative parameters by machine learning (ML) methods.</jats:p></jats:sec><jats:sec><jats:title>METHODS</jats:title><jats:p>Data were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. The prediction models were trained using various ML-based algorithms to predict the endpoints of interest. Models were selected by area under the receiver operating characteristic curve (AUC). The endpoints were dichotomized by minimum clinically important difference (MCID) and included 6-week and 12-month numeric rating scales for back pain (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), as well as prolonged surgery (&gt; 45 minutes), extended length of hospital stay (&gt; 28 hours), and reoperations.</jats:p></jats:sec><jats:sec><jats:title>RESULTS</jats:title><jats:p>A total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85%, and with AUCs of 0.75, 0.79, and 0.92. At 12 months, 66%, 63%, and 51% of patients reported MCID; the observed accuracies were 62%, 74%, and 66%, with AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) occurred at the index level. Overall and index-level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61. In 15%, a length of surgery greater than 45 minutes was observed and predicted with 78% accuracy and AUC of 0.54. Only 15% of patients were admitted to the hospital for longer than 28 hours. The developed ML-based model enabled prediction of extended hospital stay with an accuracy of 77% and AUC of 0.58.</jats:p></jats:sec><jats:sec><jats:title>CONCLUSIONS</jats:title><jats:p>Preoperative prediction of a range of clinically relevant endpoints in decompression surgery for LSS using ML is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with LSS.</jats:p></jats:sec>

Development of a Machine-Learning Model of Short-Term Prognostic Prediction for Spinal Stenosis Surgery in Korean Patients

“…Although there are both advantages and disadvantages associated with ML technology, the proposed study is in line with those reported by Siccoli [20], Azimi [21], and Guha [22] that claim the technological possibility of various, clinically relevant prognosis predictions for the decompression surgery of spinal stenosis and the possibility of shared decision-making between the patient and the surgeon based on the predicted information. The approach to a prognosis predictive analysis prior to surgery based on the individual patient characteristics is anticipated to push forward the advancement of surgical treatment for patients with spinal stenosis.…”

Machine learning in neurosurgery: a global survey

“…In fact, ML has already been broadly applied to several subspecialties in neurosurgery spanning from cranial [1,7,39], vascular [15,32], spinal [5,11,13,25,31,36] and radiosurgery, among others [23,41]. Several examples of how ML outperforms traditional statistics and prognostic indexes commonly applied in the clinical practice are already available in the medical literature.…”

“…With the exponential growth of data in the era of big data, it is increasingly important to provide clinicians with tools for integrating this individual patient data into reliable prediction models. The latter primarily aims to enhance the surgical decision-making processes and potentially improve outcomes, but predictive analytics furthermore harbour the potential to reduce unnecessary health-care costs [21,29,31,34,36,37,41].…”

“…At present, the neurosurgical literature is increasingly focusing on substituting traditional statistical models with more complex ML models with the aim of improving predictive power [29,31]. For example, ML has been used in neurosurgery to predict post-operative satisfaction [2], early postoperative complications [41] or cerebrospinal fluid leaks [37].…”

Predicting recovery after lumbar spinal stenosis surgery: A protocol for a historical cohort study using data from the Canadian Spine Outcomes Research Network (CSORN)

“…[7][8][9] More specifically, there is evidence to suggest that approximately 30% of patients do not reach minimal clinical important change in disability, pain or quality of life after 1 year post-surgery. [10][11][12] Further, a recently published large population-based study identified that more than 40% of patients undergoing fusion for SLSS remain long-term opioid users. 13 14 There have been a number of studies evaluating predictors of post-surgical outcomes including a systematic review published in 2006.…”