Sepsis-induced cardiac dysfunction (SIMD) is a well-known phenomenon yet its diagnosis remains elusive with no accepted definition, or defining pathophysiological mechanism associated with this disease. Systolic dysfunction occurs in 20-70% of patients, and may be severe, yet does not appear to have any prognostic value for mortality. Diastolic function has also been variably described and seems to be related to short-term mortality. However, the contribution of left ventricular systolic and diastolic dysfunction to mortality in sepsis are still far from clear, with uncertain contribution from previous cardiovascular disease, vasopressor and inotropic drugs and mechanical ventilation. Another poorly investigated area is right ventricular dysfunction. Cor pulmonale occurs in up to 25% of patients with septic shock, and is invariably related to pulmonary haemodynamics and mechanical ventilation, yet very little is known about how this affects prognosis. Finally, although the outcome of disease is a function of multiple parameters, septic cardiomyopathy is most frequently characterized based on individual echocardiographic parameters, without considering their interactions or placing them in the context of biomarkers and clinically available haemodynamic data. Available relevant studies are often monocentric, and many fail to consider the various confounders that influence the clinical outcome in sepsis. Therefore, the diagnostic and prognostic value of combinations of clinical, biochemical and haemodynamic variables remains to be established. Accordingly, the purpose of this study is to identify biomarkers and echocardiographic and haemodynamic signatures characteristic of specific outcomes in SIMD to support the diagnosis and prognosis in SIMD. Specific aims are: 1. To determine the association between left ventricular systolic and diastolic dysfunction, and adverse outcome in SIMD; 2. To determine the association between right ventricular systolic and diastolic dysfunction, and adverse outcome in SIMD; 3. To determine the association between novel biomarkers and adverse outcome in SIMD; 4. To determine the combined value of biomarker, echocardiographic, and haemodynamic variables for predicting adverse outcomes in SIMD; 5. To explore if there are different phenotypes of SIMD using unsupervised machine learning algorithms, and whether they are associated with adverse outcomes. 50 patients will be enrolled in a feasibility study to evaluate the logistical setup for acute echocardiography and biobanking facilities. A further 300 patients will be enrolled with inclusion from peripheral centers once feasibility is confirmed.