Aortoiliac occlusive disease is a subset of peripheral arterial disease involving an atheromatous occlusion of the infrarenal aorta, common iliac arteries, or both. The disease, as it is known today, was described by the French surgeon René Leriche as a thrombotic occlusion of the end of the aorta. Leriche successfully linked the anatomic location of the occlusion with a unique triad of symptoms, including claudication, impotence, and decreased peripheral pulses. The anatomical location of the atheromatous lesions also has a direct influence on classification of the disease, as well as choice of treatment modality. Considering its impact on diagnosis and treatment, we aimed to provide a detailed understanding of the anatomical structures involved in aortoiliac occlusive disease. Familiarity with these structures will aid the physician in interpretation of radiologic images and surgical planning.

Key Clinical MessageThe uses of amniocentesis are numerous, including determination of chromosomal abnormalities, lung maturity, and infections. A common complication of amniocentesis is loss of the pregnancy, but rare complications should be considered. The role of patient history and clinical observation of uncommon presentations are critical in the management of the patient.

Journal of Cardiovascular Magnetic Resonance 2009, 11(Suppl 1):O1Introduction: Prophylactic implantation of a cardioverter/ defibrillator (ICD) has been shown to reduce mortality in patients with chronic myocardial infarction (CMI) and an increased risk for life threatening ventricular arrhythmia (VA). The use of ICDs in this large patient population is still limited by high costs and possible adverse events including inappropriate discharges and progression of heart failure. VA is related to infarct size and seems to be related to infarct morphology. Contrast enhanced cardiovascular magnetic resonance imaging (ceCMR) can detect and quantify myocardial fibrosis in the setting of CMI and might therefore be a valuable tool for a more accurate risk stratification in this setting. Hypothesis: ceCMR can identify the subgroup developing VA in patients with prophylactic ICD implantation following MADIT criteria. Methods: We prospectively enrolled 52 patients (49 males, age 69 ± 10 years) with CMI and clinical indication for ICD therapy following MADIT criteria. Prior to implantation (36 ± 78 days) patients were investigated on a 1.5 T clinical scanner (Siemens Avanto © , Germany) to assess left ventricular function (LVEF), LV end-diastolic volume (LVEDV) and LV mass (sequence parameters: GRE SSFP, matrix 256 × 192, short axis stack; full LV coverage, no gap; slice thickness 6 mm). For quantitative assessment of infarct morphology late gadolinium enhancement (LGE) was performed including measurement of total and relative infarct mass (related to LV mass) and the degree of transmurality (DT) as defined by the percentage of transmurality in each scar. (sequence parameters: inversion recovery gradient echo; matrix 256 × 148, imaging 10 min after 0.2 μg/kg gadolinium DTPA; slice orientation equal to SSFP). MRI images were analysed using dedicated software (MASS © , Medis,

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): HEART PRESERVED onbehalf HEART PRESERVED Background Heart failure with preserved ejection fraction (HFpEF) is now recognized as a major and growing public health problem worldwide. Clinical trials investigating different treatment strategies had disappointing results. Several biomarkers of inflammation, endothelial dysfunction, and myocardial fibrosis appeared to be promising in understanding HFpEF pathophysiology. Methods. We enrolled prospectively 94 patients with HFpEF in sinus rhythm  (according to 2019 scoring system) (67 ± 9 yrs, 33 men). We evaluated them by 2D and speckle tracking echocardiography (STE). 80 patients had also a cardiac magnetic resonance (CMR) 1.5T evaluation. We measured LV ejection fraction (LVEF), mean E’ (E’m), E/E’ ratio, sPAP, left atrial volume indexed (LAVi), and global longitudinal stain by STE (GLS).  By CMR we evaluated LVEFcmr, LV mass, T1 mapping with mean extracellular volume (ECVm), and pre-gadolinium mean times quantification (preGDT1m) as markers of myocardial fibrosis. All patients had NTproBNP and biomarkers for systemic inflammation (IL6, cystatin C, pentraxin-3, GDF15), endothelial dysfunction: soluble E -selectin, VCAM, von Willebrand factor (vWf), and myocardial fibrosis: Galectin-3. Results. LVEF was 60.5 ± 6 % and LVEFcmr 61 ± 6.6%. All parameters from the scoring system were as we expected: E’m = 7.6 ± 1.8 cm/s, E/E’ ratio = 11 ± 3.4, sPAP = 34 ± 8 mmHg, LAVi = 47 ± 11 ml/m2, GLS=-18.3 ± 2.9, and NTproBNP of 282 ± 294 pg/ml. NTproBNP significantly correlated with sPAP, LAVi, preGDT1m, ECVm, galectin-3, GDF15, and pentraxin-3 (all r > 0.4, p < 0.05). The best predictor for NTproBNP level was GDF15 (r = 0.4, r2 = 0.25, p = 0.001). LAVi significantly corelated with E/E’ ratio, sPAP, NTproBNP, galectin-3 (r > 0.4, p < 0.05). GLS correlated with LVEFcmr, LV mass, ECVm, preGDT1m, LAVi, E/E’ ratio, NTproBNP, GDF15, vWf, Eselectin, VCAM (all r = 0.4, p < 0.05). The best predictor model for GLS was LV mass, NTproBNP, E-selectine, and vWf (r = 0.67, r2 = 0.45, P < 0.001). sPAP was best predicted by a model composed by IL6, VCAM, LAVi (r = 0.5, r2 = 0.25, p < 0.001). E’m significantly correlated with vWf, GHD15, VCAM, LV mass, and preGDT1 (all r > 0.4, p < 0.05), but the best predictor model included only LV mass and GDF15 (r = 0.57, r2 = 0.32, P < 0.001). Galectin-3 significantly correlated with LAVi, preGDT1m, and NTproBNP, but the only predictor for galectin-3 level was preGDT1 (r = 0.4, r2 = 0.2, p = 0.007). Conclusions In HFpEF NTproBNP is significantly correlated with markers of inflammatory status, endothelial dysfunction, and fibrosis, but the level is mainly determined by inflammation (GDF15). Diastolic dysfunction parameters are mainly correlated with inflammatory and endothelial dysfunction biomarkers . Only LAVi was correlated with myocardial fibrosis. Sub-clinical systolic dysfunction is mainly determined by proinflamatory status and endothelial dysfunction, but not by fibrosis.

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