Precision medicine models for sepsis-induced myocardial dysfunction

Myocardial dysfunction is one of the most severe manifestations of critically ill patients with sepsis, affecting 20-70% of this population.

The absence of consensus criteria for the diagnosis of sepsis-induced myocardial dysfunction (SIMD), coupled with no defining pathophysiological process hampers diagnosis and prognosis of SIMD.

Current prognostic indicators, including echocardiographic parameters and biomarkers, exhibit significant variability in their predictive accuracy, often failing to account for the complex interplay of chronic comorbidities and therapeutic interventions such as mechanical ventilation, vasopressors/inotropes, and fluid therapy.We hypothesize that a comprehensive integration of echocardiographic, biomarker, haemodynamic, and clinical data holds the key to accurate prognostication in SIMD.

To elucidate this, our proposed clinical study aims to uncover nuanced prognostic patterns, leveraging a multidimensional approach using traditional statistical inference and machine learning approaches.

Concurrently, findings from the clinical study will be mechanistically explored in animal models to better understand underlying pathophysiological mechanisms, offering invaluable insights into potential therapeutic avenues.

Furthermore, we hope to establish a foundation for the development of consensus guidelines for the identification and management of SIMD, ultimately facilitating more precise and effective patient care strategies.