Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-2000
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-20T10:42:12.744983Z
status: published
lens: g_space
sector: health
headline: Development and Prospective Validation of an Interpretable Machine Learning Model Integrating Cardiac MRI Extracellular Volume for 1-Year MACE Prediction Post-STEMI
dek: Rationale and objectives Extracellular volume (ECV) quantified by cardiac magnetic resonance (CMR) is a key indicator of myocardial fibrosis and strongly linked to outcomes after ST-segment elevation myocardial infarction (STEMI). This research focused on the development and validation of an interpretable machine learning (ML) model that incorporates ECV to predict 1-year major adverse cardiac events (MACE) in patients with STEMI following percutaneous coronary intervention. Materials and methods MACE prediction…
gain_title: A random forest model combining clinical variables with cardiac MRI extracellular volume predicted 1-year major adverse cardiac events in STEMI patients, with explanations that clinicians can inspect.
problem_title: (none)
trace_subject: (none)
gain_reading: A random forest model combining clinical variables with cardiac MRI extracellular volume predicted 1-year major adverse cardiac events in STEMI patients, with explanations that clinicians can inspect.
gain_evidence: enables accurate 1-year MACE risk prediction following STEMI
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built machine learning models to forecast major adverse cardiac events within a year for patients treated with percutaneous coronary intervention after ST-segment elevation myocardial infarction. The models used clinical variables together with extracellular volume measured by cardiac magnetic resonance, a marker of myocardial fibrosis.

The work matters because it pairs predictive accuracy with SHAP-based explanations and an online nomogram aimed at personalized perioperative risk management. The authors describe it only as a proof-of-concept tool, so its use in routine care remains unestablished.
limitation: The authors describe the model only as a proof-of-concept tool, so it is not shown to be ready for routine clinical use.
tag: Evidence-backed gain
key_points: The cohort had 673 STEMI patients, with 398 in derivation and 275 in prospective validation. | Five key predictors were identified: NT-proBNP, LVEF, MI-ECV, age, and creatine kinase-MB. | The random forest model had an AUC of 0.825 with a 95% confidence interval of 0.763-0.876. | SHAP analysis ranked NT-proBNP as the most impactful feature, followed by LVEF class and MI-ECV.
rundown: Feature selection used the Boruta and LASSO algorithms, and eight machine learning algorithms were trained. Performance was assessed by discrimination and calibration.

The best-performing model was interpreted with Shapley Additive exPlanations, which showed how each predictor contributed to risk estimates. NT-proBNP had the largest influence.

An interactive online nomogram was deployed to support personalized perioperative risk management. The article describes the overall approach as a proof-of-concept tool for precision cardiovascular care.
sources:
- peer_reviewed | Academic Radiology | https://doi.org/10.1016/j.acra.2026.09.054 | 2026-10-14
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

c86cb351c24cfccbe4478edb726f6d48ff9fda361bc2225119c12b21299678e3

previous

0000000000000000000000000000000000000000000000000000000000000000