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Do LUTS Predict Mortality? An Analysis Using Random Forest Algorithms.

Jonne Åkerla1,2, Jaakko Nevalainen3, Jori S Pesonen4

  • 1Department of Urology, Tampere University Hospital, Tampere, Finland.

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|February 19, 2024
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Summary
This summary is machine-generated.

Lower urinary tract symptoms (LUTS) can predict mortality in men. However, LUTS do not significantly improve mortality prediction when a patient's background information is already known.

Keywords:
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Area of Science:

  • Urology
  • Gerontology
  • Data Science

Background:

  • Lower urinary tract symptoms (LUTS) are common in aging men.
  • Predicting all-cause mortality is crucial for public health and clinical decision-making.
  • Machine learning algorithms offer novel approaches to mortality prediction.

Purpose of the Study:

  • To evaluate a random forest (RF) algorithm using LUTS to predict all-cause mortality.
  • To assess the added value of LUTS in mortality prediction compared to demographic and medical factors.
  • To explore the utility of RF in analyzing complex health data for mortality risk.

Main Methods:

  • A population-based cohort of 2663 men (born 1924, 1934, 1944) was followed until 2018.
  • Lower urinary tract symptoms (LUTS) were assessed using the Danish Prostatic Symptom Score (DAN-PSS-1).
  • Random forest (RF) algorithms were developed to predict five-year mortality using LUTS, demographic, medical, and behavioral factors.

Main Results:

  • The LUTS-based RF algorithm achieved an AUC of 0.60 for five-year mortality prediction.
  • An expanded RF algorithm including LUTS and other factors yielded an AUC of 0.73.
  • An algorithm excluding LUTS achieved an AUC of 0.71, indicating LUTS provided minimal additional predictive value.

Conclusions:

  • Random forest (RF) algorithms using LUTS can predict all-cause mortality at a group level.
  • LUTS are unlikely to improve mortality prediction accuracy in clinical practice when a patient's background is well-documented.
  • Further research may explore integrating LUTS into broader predictive models for specific populations.