May 1, 2019Open Access

Machine learning–based preoperative predictive analytics for lumbar spinal stenosis

Q: What is the clinical evidence from this study?

Study design: Observational. Population: Lumbar spinal stenosis (n=635). Intervention: Machine learning-based prediction models. Primary outcome: Area under the receiver operating characteristic curve (AUC) for predicting minimum clinically important difference (MCID) in patient-reported outcome measures, reoperations, and perioperative parameters.

Key Result

Machine learning models preoperatively predicted minimum clinically important differences in 6-week back pain severity with an AUC of 0.92, and 12-month functional disability with an AUC of 0.68.

Study Design

Type

Observational (n=635)

Multicenter

PICO

Population

635 adult patients (mean age 62 years, 47.6% female) who underwent single- or multilevel decompression surgery for lumbar spinal stenosis, evaluated to develop machine learning prediction models for surgical outcomes.

Exposure / Comparator

Machine learning-based prediction models

Primary Outcome

Area under the receiver operating characteristic curve (AUC) for predicting minimum clinically important difference (MCID) in patient-reported outcome measures, reoperations, and perioperative parameters

Limitations

Selection bias cannot effectively be ruled out due to retrospective analysis.
Methodology was not prospectively decided upon.
Variable importance analysis may be biased by confounders.
Models did not include measures of quality of life, objective functional impairment, anxiety, depression, severity of stenosis, comorbidities, or neurological deficits.
Single-center data limits generalizability to other cohorts or settings.
Relatively small amount of data for training machine learning algorithms.

Abstract

OBJECTIVEPatient-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.METHODSData 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 (> 45 minutes), extended length of hospital stay (> 28 hours), and reoperations.RESULTSA 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.CONCLUSIONSPreoperative 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.

Bookmark

View Full Paper