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

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This article discusses a study that aimed to validate a previously developed model using machine learning techniques to predict urinary retention. The study recruited patients from a pre-operative clinic and compared the predictions made by four individuals to the actual outcomes. The results showed that the model’s performance was moderate, achieving a specificity of 90.6% and a sensitivity of 22.7%. The study confirmed the correlation between the prediction model and the development of postoperative complications such as urinary retention, supporting the use of machine learning in predicting these complications

Summarised by Mr Mo Akmal – Lead Spinal Surgeon
The London Spine Unit : most established treatment hospital in London

Published article

S: 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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Eur Spine J. 2023 Sep 28. doi: 10.1007/s00586-023-07954-4. Online ahead of print.ABSTRACTPURPOSE: 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,

Eur Spine J. 2023 Sep 28. doi: 10.1007/s00586-023-07954-4. Online ahead of print.

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/m2American 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.

S: 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.

PMID:37768336 | DOI:10.1007/s00586-023-07954-4

The London Spine Unit : most established treatment hospital in London

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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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Eur Spine J. 2023 Sep 28. doi: 10.1007/s00586-023-07954-4. Online ahead of print.ABSTRACTPURPOSE: 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
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