Impact of ABO Blood Group on Thromboembolic and Bleeding Complications in Patients with Left Ventricular Assist Devices.

BACKGROUND: The ABO blood group system is linked to hemostasis via its relationship with von Willebrand factor (VWF) and factor VIII (FVIII). In the current study, we investigated the association of the ABO system with clinical outcomes as well as VWF and platelet function in patients with left ventricular assist devices (LVADs). METHODS: Bleeding and thromboembolic complications were assessed in 111 patients during 1 year after LVAD implantation. In 67 LVAD patients, VWF antigen, VWF activity, VWF ristocetin cofactor, VWF collagen-binding, and FVIII activity were assessed. Platelet surface P-selectin and activated glycoprotein IIb/IIIa were determined by flow cytometry, and soluble P-selectin was measured with an enzyme-linked immunoassay. Platelet aggregation was assessed by light transmission and impedance aggregometry. RESULTS: Thirty-six patients (32.4%) experienced a bleeding and 22 patients (19.8%) a thromboembolic event. In univariate analyses, patients with blood group O had numerically more bleeding complications and less thromboembolic events as compared to patients with blood group non-O (both p ≥ 0.05). After multivariable adjustment, blood group O was significantly associated with a higher risk of bleeding (hazard ratio 2.42 [95% confidence interval 1.03-5.70], p = 0.044) but not linked to thromboembolic complications. CONCLUSION: Patients with blood group O had significantly lower levels of VWF and FVIII (all p < 0.05), whereas P-selectin expression in response to thrombin-receptor activating peptide and soluble P-selectin were higher as compared to patients with blood group non-O (both p < 0.05). LVAD patients with blood group O are at an increased bleeding risk, potentially due to lower VWF and FVIII levels.

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy.

Growth Differentiation Factor-15 Correlates Inversely with Protease-Activated Receptor-1-Mediated Platelet Reactivity in Patients with Left Ventricular Assist Devices.

Growth differentiation factor (GDF)-15 inhibits platelet activation, prevents thrombus formation, and has been linked to bleeding events. This was a prospective study including 51 left-ventricular assist device (LVAD) patients on aspirin and phenprocoumon. Platelet surface expression of activated glycoprotein (GP) IIb/IIIa was assessed by flow cytometry, and platelet aggregation was measured by multiple electrode aggregometry (MEA) in response to arachidonic acid (AA), adenosine diphosphate (ADP), and thrombin receptor-activating peptide (TRAP), a protease-activated-receptor-1 (PAR-1) agonist. GDF-15 was determined with a commercially-available assay. There was a trend towards an inverse correlation of GDF-15 with activated GPIIb/IIIa in response to TRAP (r = -0.275, p = 0.0532) but not in response to AA and ADP. Moreover, GDF-15 correlated with MEA TRAP (r = -0.326, p = 0.0194), whereas it did not correlate with MEA ADP and MEA AA. In a second step, GDF-15 levels in the fourth quartile were defined as high GDF-15. Patients with high GDF-15 showed significantly lower TRAP-inducible platelet aggregation by MEA compared to patients in the first quartile (63 AU vs. 113 AU, p = 0.0065). In conclusion, in LVAD patients receiving state-of-the-art antithrombotic therapy, GDF-15 correlates inversely with residual platelet reactivity via PAR-1.

Platelet activation and aggregation in different centrifugal-flow left ventricular assist devices.

Left-ventricular assist devices (LVADs) improve outcomes in end-stage heart failure patients. Two centrifugal-flow LVAD systems are currently approved, HeartMate 3 (HM3) and Medtronic/Heartware HVAD (HVAD). Clinical findings suggest differences in thrombogenicity between both systems. We compared markers of platelet activation and aggregation between HM3 and HVAD. We prospectively included 59 LVAD patients (40 HM3, 19 HVAD). Platelet P-selectin expression, activated glycoprotein (GP) IIb/IIIa and monocyte-platelet aggregates (MPA) were assessed by flow-cytometry. Platelet aggregation was measured by light-transmission aggregometry (LTA) and multiple-electrode aggregometry (MEA). Von-Willebrand factor (VWF) antigen (VWF:Ag), VWF activity (VWF:Ac), and VWF multimer pattern analysis were determined. Soluble P-selectin (sP-selectin) was measured with an enzyme-linked immunoassay. P-selectin, GPIIb/IIIa and MPA levels in vivo and in response to arachidonic acid, adenosine diphosphate, and thrombin receptor activating peptide were similar between HM3 and HVAD (all p > .05). Likewise, agonist-inducible platelet aggregation by LTA and MEA did not differ between HM3 and HVAD (all p > .05). VWF:Ag levels and FVIII:C were similar between both systems (both p > .05), but patients with HVAD had significantly lower VWF:Ac (p = .011) and reduced large VWF multimers (p = .013). Finally, sP-selectin levels were similar in patients with HVAD and HM3 (p = .845). In conclusion, on-treatment platelet activation and aggregation are similar in HM3 and HVAD patients. Potential clinical implications of observed differences in VWF profiles between both LVAD systems need to be addressed in future clinical trials.

Retinal and Corneal Neurodegeneration and Their Association with Systemic Signs of Peripheral Neuropathy in Type 2 Diabetes.

PURPOSE: To determine the extent of retinal and corneal neurodegeneration and investigate the association with intraepidermal neuronal loss and diabetic peripheral neuropathy (DPN) in type 2 diabetes. DESIGN: Prospective, cross-sectional study. METHODS: Single-center study of 94 patients with type 2 diabetes patients (157 eyes), divided into groups: the groups without diabetic retinopathy (DR) (n = 68); the nonproliferative DR (NPDR) group (n = 48); and the proliferative DR (PDR) group (n = 41). Patients were imaged with optical coherence tomography and confocal microscopy for macular and peripapillary neuroretinal layer thicknesses and corneal nerve length/density, respectively. Distal leg skin punch biopsies and 2 neurological scores were used to depict intraepidermal nerve fiber density (IENFD) and clinical DPN. RESULTS: Among neuroretinal layers, solely the peripapillary retinal nerve fiber layer was decreased in PDR (96 µm; 95% confidence interval [CI], 92-100 µm) versus no DR (103 µm; 95% CI, 100-106 µm) eyes and only after exclusion of outliers (P = .01). Corneal nerve fiber length and density were statistically significantly reduced in the NPDR group (23.0 mm/mm2; 95% CI, 20.0-26.00 mm/mm2 and 14.3 mm; 95% CI, 14.5-16.63 mm, respectively) and the PDR group (18.6 mm/mm2; 95% CI, 14.9-22.30 mm/mm2 and 11.7 mm; 95% CI, 10.2-13-3 mm, respectively) versus the no DR group (25.5 mm/mm2; 95% CI, 23.3-27.70 mm/mm2 and 15.6 mm; 95% CI, 14.5-16.6 mm, respectively), and in the PDR versus the NPDR group. IENFD was statistically significantly reduced in the NPDR (2.0/mm; 95% CI, 1.4-2.7/mm) and PDR stage (1.4/mm; 95% CI, 0.9-2.1/mm) versus in eyes without DR (3.6/mm; 95% CI, 2.9-4.6/mm). A low correlation between intraepidermal and corneal fiber loss was found with both neurological scores (P < .05). CONCLUSIONS: Retinal neurodegenerative changes may develop independently of the microvascular alterations defining DR. Corneal and intraepidermal neuronal loss is more pronounced in advanced stages of DR, indicating a positive severity correlation between DR and DPN.