Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical but frequently overlooked step to establish the reliability of predicted risk scores to translate them into clinical practice. We systematically reviewed how regularly external validation of ML-based risk scores is performed and how their performance changed in external data. Methods: We searched MEDLINE, Web of Science, and arXiv for studies using ML to predict deterioration of ICU patients from routine data. We included primary research published in English before April 2022. We summarised how many studies were externally validated, assessing differences over time, by outcome, and by data source. For validated studies, we evaluated the change in area under the receiver operating characteristic (AUROC) attributable to external validation using linear mixed-effects models. Results: We included 355 studies, of which 39 (11.0%) were externally validated, increasing to 17.9% by 2022. Validated studies made disproportionate use of open-source data, with two well-known US datasets (MIMIC and eICU) accounting for 79.5% of studies. On average, AUROC was reduced by -0.037 (95% CI -0.064 to -0.017) in external data, with >0.05 reduction in 38.6% of studies. Discussion: External validation, although increasing, remains uncommon. Performance was generally lower in external data, questioning the reliability of some recently proposed ML-based scores. Interpretation of the results was challenged by an overreliance on the same few datasets, implicit differences in case mix, and exclusive use of AUROC.

The authors have declared no competing interest.

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=311514

This work was supported through a postdoc grant awarded to PR by the Alexander von Humboldt Foundation (Grant Nr. 1221006). This work also received funding from the European Commission via the Horizon 2020 program for PRECISE4Q (No. 777107, lead: DF).

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