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Updated: Oct 20, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Comparison of risk prediction models in infarct-related cardiogenic shock
Anne Freund1,2,3, Janine Pöss1, Suzanne de Waha-Thiele4
1Department of Internal Medicine/Cardiology, Heart Center Leipzig at the University of Leipzig, Strümpellstr. 39, D-04289 Leipzig, Germany.
Insights
The IABP-SHOCK II score best predicts short-term mortality in infarct-related cardiogenic shock (CS). This study validated several models, finding the IABP-SHOCK II score most suitable for immediate risk assessment in CS patients.
Area of Science:
- Cardiology
- Critical Care Medicine
- Health Outcomes Research
Background:
- Accurate risk stratification is crucial for managing infarct-related cardiogenic shock (CS).
- Several prediction models exist, but comparative external validation data are limited.
- This study addresses the need for comparative analysis of these models in a real-world setting.
Purpose of the Study:
- To externally validate and compare the predictive performance of different risk prediction models for infarct-related CS.
- To assess the models' ability to predict 30-day all-cause mortality in the early clinical course.
- To identify the most suitable model for immediate risk assessment in patients with infarct-related CS.
Main Methods:
- External validation of the Simplified Acute Physiology Score (SAPS) II, CardShock score, IABP-SHOCK II score, and SCAI classification.
- Utilized data from 1055 patients with infarct-related CS from the CULPRIT-SHOCK trial and registry.
- Assessed discriminative power using Area Under the Curve (AUC) and calibration via the Hosmer-Lemeshow test.
Main Results:
- The IABP-SHOCK II score demonstrated the best discrimination (AUC=0.74), followed by CardShock (AUC=0.69) and SAPS II (AUC=0.63).
- All continuous scores showed acceptable calibration.
- The SCAI classification showed good prognostic assessment for the highest risk group but poor discrimination between intermediate risk stages.
Conclusions:
- The IABP-SHOCK II score is the most suitable model for immediate risk prediction in infarct-related CS.
- Further prospective evaluations and potential development of new scores are warranted to improve discrimination.
- These findings aid in refining risk assessment and treatment guidance for CS patients.
Aims:
Several prediction models have been developed to allow accurate risk assessment and provide better treatment guidance in patients with infarct-related cardiogenic shock (CS). However, comparative data between these models are still scarce. The objective of the study is to externally validate different risk prediction models in infarct-related CS and compare their predictive value in the early clinical course.
Methods And Results:
The Simplified Acute Physiology Score (SAPS) II Score, the CardShock score, the IABP-SHOCK II score, and the Society for Cardiovascular Angiography and Intervention (SCAI) classification were each externally validated in a total of 1055 patients with infarct-related CS enrolled into the randomized CULPRIT-SHOCK trial or the corresponding registry. The primary outcome was 30-day all-cause mortality. Discriminative power was assessed by comparing the area under the curves (AUC) in case of continuous scores. In direct comparison of the continuous scores in a total of 161 patients, the IABP-SHOCK II score revealed best discrimination [area under the curve (AUC = 0.74)], followed by the CardShock score (AUC = 0.69) and the SAPS II score, giving only moderate discrimination (AUC = 0.63). All of the three scores revealed acceptable calibration by Hosmer-Lemeshow test. The SCAI classification as a categorical predictive model displayed good prognostic assessment for the highest risk group (Stage E) but showed poor discrimination between Stages C and D with respect to short-term-mortality.
Conclusion:
Based on the present findings, the IABP-SHOCK II score appears to be the most suitable of the examined models for immediate risk prediction in infarct-related CS. Prospective evaluation of the models, further modification, or even development of new scores might be necessary to reach higher levels of discrimination.
