Analysis of Prognostic Factors for Mortality in Patients With Gastrointestinal Bleeding: Application of Machine Learning Tools

Introduction : Treatment of upper gastrointestinal bleeding (UGIB) is a complex challenge due to the wide range of causes and factors affecting hospitalization outcomes. Objective : To study the impact of various factors on 30-day hospital outcomes using machine learning (ML) tools. Materials and me...

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Bibliographic Details
Published in:	Innovacionnaâ medicina Kubani (Online) no. 4; pp. 68 - 76
Main Authors:	Ismati, A. O., Anosov, V. D., Mamarajabov, S. E.
Format:	Journal Article
Language:	English Russian
Published:	Scientific Research Institute, Ochapovsky Regional Clinical Hospital no. 1 15-11-2024
Subjects:	gastrointestinal bleeding mortality outcomes predictor prognostic significance
Online Access:	Get full text
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Summary:	Introduction : Treatment of upper gastrointestinal bleeding (UGIB) is a complex challenge due to the wide range of causes and factors affecting hospitalization outcomes. Objective : To study the impact of various factors on 30-day hospital outcomes using machine learning (ML) tools. Materials and methods : We compiled a retrospective data set that includes clinical, laboratory, and imaging data of 101 patients. The database was divided into 2 groups by UGIB etiology: ulcer and variceal bleedings. Both etiological groups were processed using ML tools in 2 steps: imputation by the MICE (multiple imputation by chained equations) model and factor importance analysis using the Random Forest model. Results : Analysis revealed that the most prognostically valuable parameters in both groups were well-known mortality predictors and emerging predictive factors, such as creatinine, blood pressure, activated partial thromboplastin time, level of consciousness, urea, lactate, comorbidity status, procalcitonin, ferritin, and total protein. Conclusions : The application of advanced tools confirmed the significance of popular and validated mortality predictors and contributed to the development of predictors, both explored and unexplored ones.
ISSN:	2541-9897 2541-9897
DOI:	10.35401/2541-9897-2024-9-4-68-76