Utilizing FEM-Software to quantify pre- and post-interventional cardiac reconstruction data based on modelling data sets from surgical ventricular repair therapy (SVRT) and cardiac resynchronisation therapy (CRT)

BACKGROUND: Left ventricle (LV) 3D structural data can be easily obtained using standard transesophageal echocardiography (TEE) devices but quantitative pre- and intraoperative volumetry and geometry analysis of the LV is presently not feasible in the cardiac operation room (OR). Finite element method (FEM) modelling is necessary to carry out precise and individual volume analysis and in the future will form the basis for simulation of cardiac interventions. METHOD: A Philips/HP Sonos 5500 ultrasound device stores volume data as time-resolved 4D volume data sets. In this prospective study TomTec LV Analysis TEE(© )Software was used for semi-automatic endocardial border detection, reconstruction, and volume-rendering of the clinical 3D echocardiographic data. With the software FemCoGen(© )a quantification of partial volumes and surface directions of the LV was carried out for two patients data sets. One patient underwent surgical ventricular repair therapy (SVR) and the other a cardiac resynchronisation therapy (CRT). RESULTS: For both patients a detailed volume and surface direction analysis is provided. Partial volumes as well as normal directions to the LV surface are pre- and post-interventionally compared. CONCLUSION: The operation results for both patients are quantified. The quantification shows treatment details for both interventions (e.g. the elimination of the discontinuities for CRT intervention and the segments treated for SVR intervention). The LV quantification is feasible in the cardiac OR and it gives a detailed and immediate quantitative feedback of the quality of the intervention to the medical

Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone

Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning:a retrospective observational study

BACKGROUND: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction.METHODS: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction.FINDINGS: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36).INTERPRETATION: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results.FUNDING: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].</p

Determinants of breastfeeding initiation within the first hour of life in a Brazilian population: cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Breastfeeding within the first hour of life is a potential mechanism for health promotion. The purpose of this study was to evaluate the prevalence of breastfeeding initiation within the first hour of life in Feira de Santana, Bahia, Brazil, between 2004 and 2005, and investigate the influence of maternal, child and prenatal factors on this practice.</p> <p>Methods</p> <p>This is a cross-sectional study extracted from the results of a contemporary cohort conducted in 10 maternity hospitals in the city of Feira de Santana, Bahia, Brazil. A group of 1,309 mother-child pairs was included in the study. Information about mother's and baby's characteristics, pregnancy, birth, and time of breastfeeding initiation was collected in the first 72 hours after delivery, through interview with mothers and hospital records. The data gathered were stored and analyzed using the SPSS 16.0 and R 8.0. The chi-square test and binary logistic regression analysis were used to examine the relationship between breastfeeding within the first hour and different variables.</p> <p>Results</p> <p>47.1% of the mothers initiated breastfeeding within the first hour after birth. Early initiation of breastfeeding was associated with birth at full term pregnancy (adjusted Prevalence Ratio 1.43; 95% confidence interval 1.10 to 2.00), mothers who received prenatal guidance regarding the advantages of breastfeeding (aPR1.23; 95% CI 1.11 to 1.41) and vaginal delivery (aPR 2.78; 95% CI 2.38 to 3.23).</p> <p>Conclusions</p> <p>In order to improve the rates of breastfeeding within the first hour of life, health care professionals must promote the factors favoring this practice such as prenatal guidance regarding the advantages of breastfeeding, vaginal delivery and full term birth, and stimulate this practice in vulnerable situations such as mothers with cesarean section and preterm birth.</p

Activated Platelets in Carotid Artery Thrombosis in Mice Can Be Selectively Targeted with a Radiolabeled Single-Chain Antibody

BACKGROUND: Activated platelets can be found on the surface of inflamed, rupture-prone and ruptured plaques as well as in intravascular thrombosis. They are key players in thrombosis and atherosclerosis. In this study we describe the construction of a radiolabeled single-chain antibody targeting the LIBS-epitope of activated platelets to selectively depict platelet activation and wall-adherent non-occlusive thrombosis in a mouse model with nuclear imaging using in vitro and ex vivo autoradiography as well as small animal SPECT-CT for in vivo analysis. METHODOLOGY/PRINCIPAL FINDINGS: LIBS as well as an unspecific control single-chain antibody were labeled with (111)Indium ((111)In) via bifunctional DTPA ( = (111)In-LIBS/(111)In-control). Autoradiography after incubation with (111)In-LIBS on activated platelets in vitro (mean 3866 ± 28 DLU/mm(2), 4010 ± 630 DLU/mm(2) and 4520 ± 293 DLU/mm(2)) produced a significantly higher ligand uptake compared to (111)In-control (2101 ± 76 DLU/mm(2), 1181 ± 96 DLU/mm(2) and 1866 ± 246 DLU/mm(2)) indicating a specific binding to activated platelets; P<0.05. Applying these findings to an ex vivo mouse model of carotid artery thrombosis revealed a significant increase in ligand uptake after injection of (111)In-LIBS in the presence of small thrombi compared to the non-injured side, as confirmed by histology (49630 ± 10650 DLU/mm(2) vs. 17390 ± 7470 DLU/mm(2); P<0.05). These findings could also be reproduced in vivo. SPECT-CT analysis of the injured carotid artery with (111)In-LIBS resulted in a significant increase of the target-to-background ratio compared to (111)In-control (1.99 ± 0.36 vs. 1.1 ± 0.24; P < 0.01). CONCLUSIONS/SIGNIFICANCE: Nuclear imaging with (111)In-LIBS allows the detection of platelet activation in vitro and ex vivo with high sensitivity. Using SPECT-CT, wall-adherent activated platelets in carotid arteries could be depicted in vivo. These results encourage further studies elucidating the role of activated platelets in plaque pathology and atherosclerosis and might be of interest for further developments towards clinical application