NDLI: Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction

Content Provider	Springer Nature : BioMed Central
Author	Bakkes, Tom H.G.F. Mestrom, Eveline H.J. Ourahou, Nassim Kaymak, Uzay de Andrade Serra, Paulo J. Mischi, Massimo Bouwman, Arthur R. Turco, Simona
Abstract	Objective This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes. Materials and Methods With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost. Results During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction. Discussion The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies. Conclusions This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.
Related Links	https://perioperativemedicinejournal.biomedcentral.com/counter/pdf/10.1186/s13741-024-00420-9.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	20470525
DOI	10.1186/s13741-024-00420-9
Journal	Perioperative Medicine
Issue Number	1
Volume Number	13
Language	English
Publisher	BioMed Central
Publisher Date	2024-07-03
Access Restriction	Open
Subject Keyword	Intensive Critical Care Medicine Emergency Medicine Surgery Human Physiology Perioperative patient deterioration Unanticipated ICU admission In-hospital mortality Predictive modeling Univariate analysis Multivariate analysis
Content Type	Text
Resource Type	Article
Subject	Surgery
Journal Impact Factor	2/2023
5-Year Journal Impact Factor	2.6/2023

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