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

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Abstract

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 individuals and compared to ground truth POUR outcomes. Inter-rater reliability was calculated with intercorrelation coefficient (2,1).

Results

171 patients were included with age 63 ± 14 years, 58.5% (100/171) male, BMI 30.4 ± 5.9 kg/m2, American Society of Anesthesiologists class 2.6 ± 0.5, 1.7 ± 1.0 levels, 56% (96/171) fusions. The observed rate of POUR was 25.7%. The model’s performance was found to be 0.663 (0.567–0.759). With a regression model probability cutoff of 0.24 and a neural network cutoff of 0.23, the following predictive power was achieved: specificity 90.6%, sensitivity 22.7%, negative predictive value 77.2%, positive predictive value 45.5%, and accuracy 73.1%. Intercorrelation coefficient for the regression aspect of the model was found to be 0.889 and intercorrelation coefficient for the neural network aspect of the model was found to be 0.874.

Conclusions

This prospective study confirms performance of the prediction model for POUR developed with retrospective data, showing great correlation. This supports the use of machine learning techniques in the prediction of postoperative complications such as urinary retention.

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Abbreviations

ASA:

American society of anesthesiologists

AUC:

Area under the curve

BMI:

Body mass index

LOS:

Length of stay

NPV:

Negative predictive value

POUR:

Postoperative urinary retention

PPV:

Positive predictive value

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Acknowledgements

None.

Funding

Research reported in this publication was supported by the University of Florida Clinical and Translational Science Institute, which is supported in part by the NIH National Center for Advancing Translational Sciences under award number UL1TR001427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Department of Neurosurgery, University of Florida, PO Box 100265, Gainesville, FL, 32610, USA

    Ken Porche, Carolina B. Maciel, Brandon Lucke-Wold & Katharina M. Busl

  2. College of Medicine, University of Florida, Gainesville, FL, USA

    Yusuf Mehkri, Yasmeen Murtaza & Michael Goutnik

  3. Department of Anesthesiology, University of Florida, Gainesville, FL, USA

    Steven A. Robicsek

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  1. Ken Porche
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  2. Carolina B. Maciel
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Correspondence to Ken Porche.

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Porche, K., Maciel, C.B., Lucke-Wold, B. et al. Pre-operative prediction of post-operative urinary retention in lumbar surgery: a prospective validation of machine learning model. Eur Spine J 32, 3868–3874 (2023). https://doi.org/10.1007/s00586-023-07954-4

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  • DOI: https://doi.org/10.1007/s00586-023-07954-4

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