Human in silico clinical trials in post myocardial infarction: mechanistic investigations into phenotypic electromechanical variability and response to treatment
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AI plain-English summaryDoctors cannot reliably predict which heart attack survivors will suffer a fatal arrhythmia or develop heart failure, because the standard risk marker—ejection fraction—misses many high-risk patients and wrongly flags others. This project builds thousands of personalised computer models of damaged human hearts, simulating both electrical and mechanical behaviour from the level of individual ion channels up to the whole organ. By running virtual clinical trials on these models, the researcher aims to identify the specific electromechanical mechanisms that determine why two patients with similar damage can have completely different outcomes. If successful, the work could transform how clinicians stratify risk after a heart attack, replacing a single crude measurement with a detailed, patient-specific profile that predicts both arrhythmic danger and mechanical decline. This would allow doctors to target expensive treatments—such as implantable defibrillators or drugs that modify heart contraction—only to those who will actually benefit, while sparing low-risk patients from unnecessary procedures. The approach also offers a platform for testing new pharmacological interventions in silico before moving to human trials, potentially accelerating drug development for post-infarction care.
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