001051639 001__ 1051639
001051639 005__ 20260120203622.0
001051639 0247_ $$2doi$$a https://doi.org/10.1101/2025.09.18.25336054
001051639 0247_ $$2doi$$ahttps://doi.org/10.1101/2025.09.18.25336054
001051639 0247_ $$2doi$$a10.1101/2025.09.18.25336054
001051639 0247_ $$2datacite_doi$$a10.34734/FZJ-2026-00553
001051639 037__ $$aFZJ-2026-00553
001051639 041__ $$aEnglish
001051639 1001_ $$0P:(DE-Juel1)194707$$aNieto, Nicolas$$b0$$eCorresponding author
001051639 245__ $$aMachine learning models for early prognosis prediction in cardiogenic shock
001051639 260__ $$c2025
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001051639 520__ $$aCardiogenic shock (CS) is a severe and frequent complication of acute myocardial infarction (AMI), necessitating rapid and accurate prognosis as-sessment to guide treatment and intensive care unit (ICU) resource allocation. We developed two machine learning models to predict 30-day outcomes following CS in AMI: an Admission model (using only data available at admission, like demography, comorbidities) and a Full model (incorporating additional laboratory values obtained within 24 hours). The models were trained on the CULPRIT-SHOCK dataset and externally validated using the eICU database. The Admission model achieved an out-of-sample AUC of 0.71 (95% CI: 0.6–0.83) in the development cohort and 0.68 in the validation cohort, while the Full model attained significantly higher performance, with AUCs of 0.80 (95% CI: 0.69–0.9) and 0.78, respectively. The Full model’s superior performance underscores the prognostic value of early laboratory trends, suggesting that dynamic data integration improves risk stratification. Both models outperformed existing risk scores across multiple metrics, provided well-calibrated probabilistic predictions, and demonstrated robustness to missing data. Additionally, they offered patient-level explainability, enhancing clinical interpretability. While promising, the models’ generalizability may be influenced by differences between the CULPRIT-SHOCK and eICU cohorts (e.g., demographics, CS severity thresholds); further validation in larger, prospective cohorts is warranted.
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001051639 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b1
001051639 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b2
001051639 7001_ $$0P:(DE-HGF)0$$aPoess, Janine$$b3
001051639 7001_ $$0P:(DE-HGF)0$$aDesch, Steffen$$b4
001051639 7001_ $$0P:(DE-HGF)0$$aFeistritzer, Hans-Josef$$b5
001051639 7001_ $$0P:(DE-HGF)0$$aLichtenberg, Artur$$b6
001051639 7001_ $$0P:(DE-HGF)0$$aMasyuk, Maryna$$b7
001051639 7001_ $$0P:(DE-HGF)0$$aKelm, Malte$$b8
001051639 7001_ $$0P:(DE-HGF)0$$aThiele, Holger$$b9
001051639 7001_ $$0P:(DE-HGF)0$$aJung, Christian$$b10
001051639 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh$$b11$$eLast author
001051639 773__ $$a10.1101/2025.09.18.25336054$$tMedRxiv$$y2025
001051639 8564_ $$uhttps://juser.fz-juelich.de/record/1051639/files/Machine%20learning%20models%20for%20early%20prognosis%20prediction%20in%20cardiogenic%20shock%20-%20Nieto%202025.pdf$$yOpenAccess
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