Artificial Intelligence for early detection of circulatory shock in ICU patients

Abstract

Circulatory shock is one of the leading causes of mortality in intensive care units (ICUs), and its early detection is critical to enable timely treatment and improve clinical outcomes. This study aimed to develop and evaluate a two-stage cascade machine learning framework for the early detection and etiological classification of circulatory shock in critically ill patients. Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, four patient groups were defined: septic shock, cardiogenic shock, hypovolemic shock, and a non?shock control group, comprising a total of 32,907 patients. Vital signs and laboratory data were collected during the first six hours after ICU admission. After data cleaning and missing-value imputation, the mean value of each variable was used for model development. Several machine learning algorithms were compared, including logistic regression, Random Forest, XGBoost, and multilayer perceptron (MLP) networks. Random Forest and XGBoost achieved the highest overall performance, with an AUROC of approximately 0.82-0.83 for shock detection, and a macro-averaged sensitivity of approximately 0.61 and precision of approximately 0.58 across all four classes. Classification performance was highest for the non?shock group, followed by septic and cardiogenic shock, while hypovolemic shock showed the lowest performance. These results indicate that machine learning models can identify early signs of hemodynamic deterioration associated with circulatory shock and may support clinical decision-making in the ICU. However, further improvements are needed for the classification of specific shock subtypes, particularly hypovolemic and cardiogenic shock, as well as for real-time clinical implementation.

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BibTeX RIS

BibTeX

@misc{pina2026artificial,
  title = {{Artificial Intelligence for early detection of circulatory shock in ICU patients}},
  author = {Jaume Aguiló Piña and Laia Subirats and Aina Frau-Pascual and Alba Gorriz and Rudys Magrans Nicieza and Jordi Morillas Perez},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.34991},
  eprint = {2609.34991},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.34991},
}

RIS

TY  - UNPB
TI  - Artificial Intelligence for early detection of circulatory shock in ICU patients
AU  - Jaume Aguiló Piña
AU  - Laia Subirats
AU  - Aina Frau-Pascual
AU  - Alba Gorriz
AU  - Rudys Magrans Nicieza
AU  - Jordi Morillas Perez
PY  - 2026
DA  - 2026/09/28
PB  - arXiv
DO  - 10.48550/arxiv.2609.34991
UR  - https://arxiv.org/abs/2609.34991
AB  - Circulatory shock is one of the leading causes of mortality in intensive care units (ICUs), and its early detection is critical to enable timely treatment and improve clinical outcomes. This study aimed to develop and evaluate a two-stage cascade machine learning framework for the early detection and etiological classification of circulatory shock in critically ill patients. Using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, four patient groups were defined: septic shock, cardiogenic shock, hypovolemic shock, and a non?shock control group, comprising a total of 32,907 patients. Vital signs and laboratory data were collected during the first six hours after ICU admission. After data cleaning and missing-value imputation, the mean value of each variable was used for model development. Several machine learning algorithms were compared, including logistic regression, Random Forest, XGBoost, and multilayer perceptron (MLP) networks. Random Forest and XGBoost achieved the highest overall performance, with an AUROC of approximately 0.82-0.83 for shock detection, and a macro-averaged sensitivity of approximately 0.61 and precision of approximately 0.58 across all four classes. Classification performance was highest for the non?shock group, followed by septic and cardiogenic shock, while hypovolemic shock showed the lowest performance. These results indicate that machine learning models can identify early signs of hemodynamic deterioration associated with circulatory shock and may support clinical decision-making in the ICU. However, further improvements are needed for the classification of specific shock subtypes, particularly hypovolemic and cardiogenic shock, as well as for real-time clinical implementation.
ER  -

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