Retrospective Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Crit Care Med. Sep 9, 2022; 11(5): 317-329
Published online Sep 9, 2022. doi: 10.5492/wjccm.v11.i5.317
Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach
Elena Caires Silveira, Soraya Mattos Pretti, Bruna Almeida Santos, Caio Fellipe Santos Corrêa, Leonardo Madureira Silva, Fabrício Freire de Melo
Elena Caires Silveira, Soraya Mattos Pretti, Bruna Almeida Santos, Caio Fellipe Santos Corrêa, Leonardo Madureira Silva, Fabrício Freire de Melo, Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45-029094, Brazil
Author contributions: Caires Silveira E collected and entered the data, performed the data analysis/statistics and interpretation, and participated in preparation and review of manuscript; Mattos Pretti S and Santos BA participated in the preparation of manuscript and wrote the literature analysis/search; Santos Corrêa CF and Madureira Silva L participated in review of manuscript; Freire de Melo F designed the research and participated in review of manuscript.
Institutional review board statement: For this study, there was no need for an appraisal by an ethics committee, since only publicly available anonymized data were used.
Informed consent statement: This manuscript does not involve “Signed Informed Consent Form”, as it was produced from previously anonymized, publicly available and free of charge data, obeying the norms of medical bioethics. Thus, there was no direct or even indirect contact between researchers and patients, with no necessity for "Signed Informed Consent Form" to carry out our study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Fabrício Freire de Melo, PhD, Professor, Multidisciplinary Institute of Health, Federal University of Bahia, Rua Hormindo Barros, 58, Quadra 17, Lote 58, Candeias, Vitória da Conquista 45-029094, Brazil. freiremeloufba@gmail.com
Received: June 22, 2021
Peer-review started: June 22, 2021
First decision: July 31, 2021
Revised: August 13, 2021
Accepted: July 5, 2022
Article in press: July 5, 2022
Published online: September 9, 2022
Processing time: 441 Days and 4.9 Hours
ARTICLE HIGHLIGHTS
Research background

The monitoring of clinical and laboratory parameters of patients in the intensive care unit (ICU) is an extremely important part of the routine of intensive care staff. Additionally, several scores already utilize these parameters to guide the assistance of these patients. In the meantime, the advance of technological resources, such as the machine learning approach, allows the development of predictive models capable of being applied to medical practice.

Research motivation

Mortality in the ICU is something that worries and drives the search for alternatives that can help the team in directing treatment to avoid this negative outcome. Therefore, a predictive model that uses the patient’s parameters can precisely influence this treatment guidance, improving the cost-effectiveness quickly and safely.

Research objectives

The objective of our study is the development of a binary classifier predictive model between the outcomes of death and non-death in ICU patients. This paper demonstrates the potency of emerging technological realities within the medical field and how it is possible to harness them to improve healthcare practices.

Research methods

Initially, we obtained a set of 1087 instances and 50 variables related to patients admitted to an ICU by using a public database. We calculated frequency and risk rate for categorical variables and means, standard deviations, and the Mann-Whitney U test for numerical variables. Afterwards, we divided the data for the application in training of the predictive model based on the Random Forest algorithm and then to test the effectiveness of the model.

Research results

Among the 50 variables associated with death outcome, the maximum and minimum lactate values were the most important predictors (15.54%) followed by temperature (6.47%), and motor Glasgow coma scale punctuation (5.25%). The Random Forest binary classifier predictive model (death and no death) showed accuracy of 80.28%, sensitivity of 81.82%, specificity of 79.43%, positive predictive value of 73.26%, negative predictive value of 84.85%, F1 score of 0.74, and area under the curve score of 0.85.

Research conclusions

This study demonstrated the development of a predictive model with high accuracy, sensitivity, and specificity for ICU patients by applying a machine learning approach, the Random Forest algorithm, to clinical and laboratory data.

Research perspectives

The proper registration of patient parameters, as well as the availability of more and larger databases and even further development of digital tools, can enhance machine learning approaches, enabling the refinement of predictive models and patient care.