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Updated: Jan 27, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

Paul T Ogink1, Aditya V Karhade2, Quirina C B S Thio2

  • 1Orthopaedic Spine Service, Massachusetts General Hospital - Harvard Medical School, 3.946, Yawkey Building, 55 Fruit Street, Boston, MA, 02114, USA. ptogink@gmail.com.

European Spine Journal : Official Publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
|March 29, 2019
PubMed
Summary

A machine learning algorithm can accurately predict discharge placement for patients undergoing elective surgery for degenerative spondylolisthesis. This predictive model can be applied to other conditions and treatments.

Keywords:
Degenerative spondylolisthesisDischargeMachine learning

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Area of Science:

  • Spine surgery outcomes research
  • Machine learning in healthcare
  • Predictive analytics in patient care

Background:

  • Degenerative spondylolisthesis requires surgical intervention, with discharge placement being a critical factor in patient recovery and healthcare resource utilization.
  • Accurate prediction of discharge destination is essential for optimizing patient management and resource allocation following elective spine surgery.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for predicting non-home discharge in patients undergoing elective surgery for degenerative spondylolisthesis.
  • To assess the accuracy and calibration of machine learning models in forecasting patient discharge disposition.

Main Methods:

  • Utilized the National Surgical Quality Improvement Program (NSQIP) database (2009-2016) for patient selection.
  • Developed four machine learning algorithms to predict non-home discharge, employing stepwise backward logistic regression and Akaike information criterion for model selection.
  • Assessed model performance using discrimination (AUC), calibration (slope, intercept), and overall performance (Brier score).

Main Results:

  • Included 9,338 patients, with a non-home discharge rate of 18.6%.
  • Key predictors identified were age, sex, diabetes, elective surgery, BMI, procedure type, number of levels fused, ASA class, and preoperative lab values.
  • The Bayes point machine demonstrated the best performance with an AUC of 0.753.

Conclusions:

  • Machine learning algorithms can be developed with high accuracy and calibration to predict discharge placement after degenerative spondylolisthesis surgery.
  • The methodology is adaptable for developing predictive models for various other medical conditions and elective treatments.
  • This approach offers a valuable tool for improving patient care planning and resource management in spine surgery.