Establishment and validation of a critical care echocardiography-based predictive model for sepsis-induced cardiomyopathy: A prospective cohort study
Authors' conclusion
The nomogram, incorporating GLS, E, and TAPSE, emerged as a reliable non-invasive tool for assessing the risk of SIC.
Study context
- Population
- Septic patients undergoing critical care echocardiography
- Participants
- 181 patients
- Exposure
- Training dataset vs Validation dataset
- Training datasetn = 126
- Validation datasetn = 55
- Outcome
- Echocardiographic diagnosis of sepsis-induced cardiomyopathy (SIC)
Methodological detailspredictive model development and validation, nomogram, random split training/validation
- Predictive model development and validation
- Nomogram
- Random split training/validation
Main finding
As reported in the abstract
Discrimination of SIC model (GLS, E, TAPSE)
| Group | AUC |
|---|---|
| Training dataset | 0.879 |
| Validation dataset | 0.888 |
AUC
Training dataset0.879
Validation dataset0.888
- Three key predictors (global longitudinal strain (GLS), E, TAPSE) selected from 14 variables
- 3
Observational data: associations, not effects.
Further findings
| Outcome | Training dataset | Validation dataset |
|---|---|---|
| Clinical utility (decision curve analysis) | ||
| Risk threshold range with net clinical benefit | 5-100 % | 21-100 % |
Calibration (Hosmer-Lemeshow test)
- Consistency between predicted probabilities and actual outcomes in both datasets
Original publication
Establishment and validation of a critical care echocardiography-based predictive model for sepsis-induced cardiomyopathy: A prospective cohort study. J Crit Care. Published 24 March 2025. doi:10.
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