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Optimal Single or Combined Parameters for Dyssynergic Defecation on Anorectal Manometry: A Proof-of-Concept Machine

John A Damianos1, Saam Dilmaghani, Ayah Matar

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Summary

Diagnosing dyssynergic defecation (DD) can be improved using machine learning models analyzing anorectal manometry (ARM) and balloon expulsion testing (BET) data. Abnormal BET or combined manometry parameters offer high predictive value for DD diagnosis.

Keywords:
constipationrectal evacuation disorder

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Area of Science:

  • Gastroenterology
  • Medical Diagnostics
  • Machine Learning

Background:

  • Dyssynergic defecation (DD) is a common cause of chronic constipation.
  • Accurate diagnosis of DD is crucial for effective treatment.
  • Current diagnostic methods may have limitations.

Purpose of the Study:

  • To evaluate the diagnostic performance of individual and combined anorectal manometry (ARM) and balloon expulsion testing (BET) parameters for dyssynergic defecation (DD).
  • To utilize machine learning (ML) for optimizing DD diagnosis.
  • To establish a proof-of-concept for an ML-driven diagnostic approach.

Main Methods:

  • A machine learning model was developed using ARM and BET data from 307 patients.
  • Data preprocessing and imputation were performed.
  • Five common ML models were trained and compared using ROC AUC and accuracy, with the best models fine-tuned and tested.

Main Results:

  • Patients with DD showed significantly lower rectoanal pressure gradient (RAG) and more abnormal BET compared to controls.
  • Key predictors for DD included abnormal BET, greater resting anal pressure (RAP), and more negative RAG.
  • An optimized logistic regression model achieved an AUC of 0.878, with abnormal BET alone or combined RAP and RAG yielding >80% positive predictive value.

Conclusions:

  • Abnormal balloon expulsion testing (BET) is a significant predictor of dyssynergic defecation (DD).
  • Combining resting anal pressure (RAP) and rectoanal pressure gradient (RAG) manometry parameters offers high positive predictive value for DD diagnosis.
  • Machine learning models show promise in enhancing the diagnosis of DD in patients with chronic constipation.