Pre-operative prediction of post-operative urinary retention in lumbar surgery: a prospective validation of machine learning model

• Published in: European spine journal Vol. 32; no. 11; pp. 3868 - 3874
• Main Authors: Porche, Ken, Maciel, Carolina B., Lucke-Wold, Brandon, Mehkri, Yusuf, Murtaza, Yasmeen, Goutnik, Michael, Robicsek, Steven A., Busl, Katharina M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2023; Springer Nature B.V
• Subjects: Aged; Complications; Humans; Learning algorithms; Machine Learning; Male; Medicine; Medicine & Public Health; Middle Aged; Neural networks; Neurosurgery; Original Article; Postoperative; Postoperative Complications - diagnosis; Postoperative Complications - etiology; Prediction models; Prospective Studies; Reproducibility of Results; Retrospective Studies; Surgical Orthopedics; Urinary retention; Urinary Retention - diagnosis; Urinary Retention - etiology; Urinary retention; Lumbar spine surgery; Prediction tool; Machine learning

Purpose Predicting urinary retention is difficult. The aim of this study is to prospectively validate a previously developed model using machine learning techniques. Methods Patients were recruited from pre-operative clinic. Prediction of urinary retention was completed pre-operatively by 4 individu...