Key result
An AI-based CABG risk prediction model overestimated overall in-hospital mortality (predicted 2.67% vs actual 1.05%) and specifically overestimated mortality in intermediate-risk patients.
Why the study?
The objective was to predict the risk of mortality in patients undergoing CABG using a risk prediction model based on artificial intelligence and big data technologies.
Does an AI and big data-based risk prediction model accurately predict in-hospital mortality in patients undergoing CABG?
Observational (n=2,364)
No
Does an AI and big data-based risk prediction model accurately predict in-hospital mortality in patients undergoing CABG?
An AI-based risk prediction model for CABG patients overestimated in-hospital mortality, particularly in intermediate-risk patients, highlighting the need for further refinement before clinical application.
Answers are grounded in this paper and cite their sources.
AI-CABG mortality model showed moderate discrimination but overestimated risk; leaves open whether it improves stratification or outcomes over existing scores.
The objective of this work was to predict the risk of mortality rate in patients with coronary artery bypass grafting (CABG) based on the risk prediction model of CABG using artificial intelligence (AI) and big data technologies. The clinical data of 2,364 patients undergoing CABG in our hospital from January 2019 to August 2021 were collected in this work. Based on AI and big data technology, business requirement analysis, system requirement analysis, complication prediction module, big data mining technology, and model building are carried out, respectively; the successful CABG risk prediction system includes case feature analysis service, risk warning service, and case retrieval service. The commonly used precision, recall, and F1-score were adopted to evaluate the quality of the gradient-boosted tree (GBT) model. The analysis proved that the GBT model was the best in terms of precision, F1-score, and area under the receiver operating characteristic curve (ROC). According to the CABG risk prediction model, 1,382 patients had a score of <0, 463 patients had a score of 0 ≤ score ≤ 2, 252 patients had a score of 2 < score ≤ 5, and 267 patients had a score of >5, which were stratified into four groups: A, B, C, and D. The actual number of in-hospital deaths was 25, and the in-hospital mortality rate was 1.05%. The mortality rate predicted by the CABG risk prediction model was 2.67 ± 1.82% (95% confidential interval (CI) (2.87-2.98)), which was higher than the actual value. The CABG risk prediction model showed the credible results only in group B with AUC = 0.763 > 0.7. In group B, 3 patients actually died, the actual mortality rate was 0.33%, and the predicted mortality rate was 0.96 ± 0.78 (95% CI (0.82-0.87)), which overestimated the mortality rate of patients in group B. It successfully constructed a CABG risk prediction model based on the AI and big data technologies, which would overestimate the mortality of patients with intermediate risk, and it is suitable for different types of heart diseases through continuous research and development and innovation, and provides clinical guidance value.
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Tang et al. (2023) conducted an observational in Coronary artery bypass grafting (CABG) (n=2,364). AI and big data-based CABG risk prediction model vs. Actual clinical outcomes was evaluated on In-hospital mortality rate (95% CI 2.87-2.98). An AI-based CABG risk prediction model overestimated overall in-hospital mortality (predicted 2.67% vs actual 1.05%) and specifically overestimated mortality in intermediate-risk patients.
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