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A data mining-based cross-industry process for predicting major bleeding in mechanical circulatory support.

Susanne E A Felix¹, Ayoub Bagheri^1,2, Faiz R Ramjankhan³

¹Department of Cardiology, Division Heart & Lungs, University Medical Centre Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands.

European Heart Journal. Digital Health

|January 30, 2023

Summary

This summary is machine-generated.

Predicting major bleeding in mechanical circulatory support (MCS) patients is challenging. A new data mining tool, Auto-Crisp, shows acceptable performance in predicting bleeding risk in MCS patients.

Keywords:

Bleeding Cross-industry standard process for data mining (CRISP-DM)Data mining Mechanical circulatory support Prediction

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Area of Science:

Cardiology
Medical Informatics
Data Mining

Background:

Mechanical circulatory support (MCS) is used for end-stage heart failure.
Major bleeding affects over a third of MCS patients.
Predicting bleeding risk in MCS patients is currently difficult.

Purpose of the Study:

To develop an easily applicable data mining tool to predict major bleeding in MCS patients.
To assess the predictive performance of the developed tool.

Main Methods:

Electronic health records of 273 MCS patients were analyzed.
The Auto-Crisp application, based on CRISP-DM methodology, was developed.
Predictive models for major bleeding within 3, 7, and 30 days post-operatively were evaluated using AUC.

Main Results:

142 major bleedings occurred in 25.6% of patients.
The best predictive models achieved AUC values of 0.792 (3 days), 0.788 (7 days), and 0.776 (30 days).

Conclusions:

The Auto-Crisp tool demonstrates acceptable performance in predicting near-future major bleeding in MCS patients.
Further validation of Auto-Crisp in diverse research settings is recommended.