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Development and external validation of an interpretable machine learning model for predicting prolonged postoperative ICU length of stay in coronary artery bypass grafting patients using MIMIC-IV 3.1 and eICU-CRD 2.0

Crossref DOI link: https://doi.org/10.1186/s12911-026-03659-y

Published Online: 2026-06-27

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Liu, Dayan

Lu, Pengyu https://orcid.org/0009-0005-8930-0791
Meng, Yulan

Liu, Xianglong

Huang, Wei
License Information

Text and Data Mining valid from 2026-06-27

Accepted Manuscript valid from 2026-06-27
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Article History

Received: 2 February 2026

Accepted: 22 June 2026

First Online: 27 June 2026

Declarations

:

: This study was conducted using de-identified data from publicly available databases (MIMIC-IV 3.1 and eICU-CRD 2.0). All data were de-identified in accordance with the Health Insurance Portability and Accountability Act (HIPAA) standards and institutional privacy protocols. This retrospective analysis of de-identified data was exempt from institutional review board approval. The study was conducted in accordance with the Declaration of Helsinki and all applicable ethical guidelines for research with secondary data sources.

: Not applicable. This study used only de-identified data from public databases; no individual-level identifying information is presented in the manuscript.

: The authors declare no competing interests.

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