Neurosurg Focus
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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. ⋯ 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.
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OBJECTIVEPatient-reported outcome measures (PROMs) are standard of care for the assessment of functional impairment. Subjective outcome measures are increasingly complemented by objective ones, such as the "Timed Up and Go" (TUG) test. Currently, only a few studies report pre- and postoperative TUG test assessments in patients with lumbar spinal stenosis (LSS). ⋯ CONCLUSIONSThe TUG test is a quick and easily applicable tool that reliably measures OFI in patients with LSS. Objective tests incorporating longer walking time should be considered if OFI is suspected but fails to be proven by the TUG test, taking into account that neurogenic claudication may not clinically manifest during the brief TUG examination. Objective tests do not replace the subjective PROM-based assessment, but add valuable information to a comprehensive patient evaluation.
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OBJECTIVESince the enactment of the Affordable Care Act in 2010, providers and hospitals have increasingly prioritized patient-centered outcomes such as patient satisfaction in an effort to adapt the "value"-based healthcare model. In the current study, the authors queried a prospectively maintained multiinstitutional spine registry to construct a predictive model for long-term patient satisfaction among patients undergoing surgery for Meyerding grade I lumbar spondylolisthesis. METHODSThe authors queried the Quality Outcomes Database for patients undergoing surgery for grade I lumbar spondylolisthesis between July 1, 2014, and June 30, 2016. ⋯ Multivariable proportional odds logistic regression revealed that older age (OR 1.57, 95% CI 1.09-2.76; p = 0.009), preoperative active employment (OR 2.06, 95% CI 1.27-3.67; p = 0.015), and fusion surgery (OR 2.3, 95% CI 1.30-4.06; p = 0.002) were the most important predictors of achieving satisfaction with surgical outcome. CONCLUSIONSCurrent findings from a large multiinstitutional study indicate that most patients undergoing surgery for grade I lumbar spondylolisthesis achieved long-term satisfaction. Moreover, the authors found that older age, preoperative active employment, and fusion surgery are associated with higher odds of achieving satisfaction.