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. 2021 Oct 22;1(2):110-116.
doi: 10.1016/j.jointm.2021.09.002. eCollection 2021 Oct.

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Jonathan Montomoli  1 , Luca Romeo  2 , Sara Moccia  2   3 , Michele Bernardini  2 , Lucia Migliorelli  2 , Daniele Berardini  2 , Abele Donati  4   5 , Andrea Carsetti  4   5 , Maria Grazia Bocci  6 , Pedro David Wendel Garcia  7 , Thierry Fumeaux  8 , Philippe Guerci  9 , Reto Andreas Schüpbach  7 , Can Ince  10 , Emanuele Frontoni  2 , Matthias Peter Hilty  7 ; RISC-19-ICU Investigators
Collaborators, Affiliations

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Jonathan Montomoli et al. J Intensive Med. .

Abstract

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care.

Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort.

Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]).

Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.

Keywords: COVID-19; Clinical decision support system (CDSS); Extreme gradient boosting (XGBoost); Machine learning; Multiple organ failure; Organ dysfunction score.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1:
Fig. 1
The timeline used to collect features to include in the model and the predicted outcomes. ICU: Intensive care unit; SOFA: Sequential organ failure assessment.
Fig 2:
Fig. 2
Description of the steps applied to build and test the prediction model. ICU: Intensive care unit; ML: Machine learning; RISC-19-ICU: RIsk Stratification in COVID-19 patients in the Intensive Care Unit; XGBoost: Extreme gradient boosting.
Fig 3:
Fig. 3
The description of the model performance (A) and the most important features identified by the model (B). APACHE: Acute Physiology and Chronic Health disease Classification System; COVID-19: Coronavirus disease 2019; ICU: Intensive Care Unit; ML: Machine Learning; RISC-19-ICU: RIsk Stratification in COVID-19 patients in the Intensive Care Unit; ROC: Receiving Operating Characteristics; SAPS: Simplified Acute Physiologic Score; SOFA: Sequential Organ Failure Assessment.
See this image and copyright information in PMC

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