Mechanical Mitral Valve Replacement:A Multicenter Study of Outcomes With Use of 15-to 17-mm Prostheses

Background. The aim of this study was to evaluate early and mid-term outcomes (mortality and prosthetic valve reintervention) after mitral valve replacement with 15- to 17-mm mechanical prostheses. Methods. A multicenter, retrospective cohort study was performed among patients who underwent mitral valve replacement with a 15- to 17-mm mechanical prosthesis at 6 congenital cardiac centers: 5 in The Netherlands and 1 in the United States. Baseline, operative, and follow-up data were evaluated. Results. Mitral valve replacement was performed in 61 infants (15 mm, n = 17 [28%]; 16 mm, n = 18 [29%]; 17 mm, n = 26 [43%]), of whom 27 (47%) were admitted to the intensive care unit before surgery and 22 (39%) required ventilator support. Median age at surgery was 5.9 months (interquartile range [IQR] 3.2-17.4), and median weight was 5.7 kg (IQR, 4.5-8.8). There were 13 in-hospital deaths (21%) and 8 late deaths (17%, among 48 hospital survivors). Major adverse events occurred in 34 (56%). Median follow-up was 4.0 years (IQR, 0.4-12.5) First prosthetic valve replacement (n = 27 [44%]) occurred at a median of 3.7 years (IQR, 1.9-6.8). Prosthetic valve endocarditis was not reported, and there was no mortality related to prosthesis replacement. Other reinterventions included permanent pacemaker implantation (n = 9 [15%]), subaortic stenosis resection (n = 4 [7%]), aortic valve repair (n = 3 [5%], and aortic valve replacement (n = 6 [10%]). Conclusions. Mitral valve replacement with 15- to 17-mm mechanical prostheses is an important alternative to save critically ill neonates and infants in whom the mitral valve cannot be repaired. Prosthesis replacement for outgrowth can be carried out with low risk. (C) 2020 by The Society of Thoracic Surgeons. Published by Elsevier Inc

Mechanical Mitral Valve Replacement: A Multicenter Study of Outcomes with Use of 15- to 17-mm Prostheses

Background: The aim of this study was to evaluate early and mid-term outcomes (mortality and prosthetic valve reintervention) after mitral valve replacement with 15- to 17-mm mechanical pr

The tissue-specific aspect of genome-wide DNA methylation in newborn and placental tissues: Implications for epigenetic epidemiologic studies

Epigenetic programming is essential for lineage differentiation, embryogenesis and placentation in early pregnancy. In epigenetic association studies, DNA methylation is often examined in DNA derived from white blood cells, although its validity to other tissues of interest remains questionable. Therefore, we investigated the tissue specificity of epigenome-wide DNA methylation in newborn and placental tissues. Umbilical cord white blood cells (UC-WBC, n = 25), umbilical cord blood mononuclear cells (UC-MNC, n = 10), human umbilical vein endothelial cells (HUVEC, n = 25) and placental tissue (n = 25) were obtained from 36 uncomplicated pregnancies. Genome-wide DNA methylation was measured by the Illumina HumanMethylation450K BeadChip. Using UC-WBC as a reference tissue, we identified 3595 HUVEC tissue-specific differentially methylated regions (tDMRs) and 11,938 placental tDMRs. Functional enrichment analysis showed that HUVEC and placental tDMRs were involved in embryogenesis, vascular development and regulation of gene expression. No tDMRs were identified in UC-MNC. In conclusion, the extensive amount of genome-wide HUVEC and placental tDMRs underlines the relevance of tissue-specific approaches in future epigenetic association studies, or the use of validated representative tissues for a certain disease of interest, if available. To this purpose, we herewith provide a relevant dataset of paired, tissue-specific, genome-wide methylation measurements in newborn tissues

An omics-based machine learning approach to predict diabetes progression:a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.).</p

Prognostic models for predicting the risk of foot ulcer or amputation in people with type 2 diabetes: a systematic review and external validation study

Aims/hypothesis Approximately 25% of people with type 2 diabetes experience a foot ulcer and their risk of amputation is 10-20 times higher than that of people without type 2 diabetes. Prognostic models can aid in targeted monitoring but an overview of their performance is lacking. This study aimed to systematically review prognostic models for the risk of foot ulcer or amputation and quantify their predictive performance in an independent cohort.Methods A systematic review identified studies developing prognostic models for foot ulcer or amputation over minimal 1 year follow-up applicable to people with type 2 diabetes. After data extraction and risk of bias assessment (both in duplicate), selected models were externally validated in a prospective cohort with a 5 year follow-up in terms of discrimination (C statistics) and calibration (calibration plots).Results We identified 21 studies with 34 models predicting polyneuropathy, foot ulcer or amputation. Eleven models were validated in 7624 participants, of whom 485 developed an ulcer and 70 underwent amputation. The models for foot ulcer showed C statistics (95% CI) ranging from 0.54 (0.54, 0.54) to 0.81 (0.75, 0.86) and models for amputation showed C statistics (95% CI) ranging from 0.63 (0.55, 0.71) to 0.86 (0.78, 0.94). Most models underestimated the ulcer or amputation risk in the highest risk quintiles. Three models performed well to predict a combined endpoint of amputation and foot ulcer (C statistics >0.75).Conclusions/interpretation Thirty-four prognostic models for the risk of foot ulcer or amputation were identified. Although the performance of the models varied considerably, three models performed well to predict foot ulcer or amputation and may be applicable to clinical practice.Therapeutic cell differentiatio

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p

Patient information portal for congenital aortic and pulmonary valve disease:A stepped-wedge cluster randomised trial

Background In response to an increased need for patient information in congenital heart disease, we previously developed an online, evidence-based information portal for patients with congenital aortic and pulmonary valve disease. To assess its effectiveness, a stepped-wedge cluster randomised trial was conducted. Methods Adult patients and caregivers of paediatric patients with congenital aortic and/or pulmonary valve disease and/or tetralogy of Fallot who visited the outpatient clinic at any of the four participating centres in the Netherlands between 1 March 2016-1 July 2017 were prospectively included. The intervention (information portal) was introduced in the outpatient clinic according to a stepped-wedge randomised design. One month after outpatient clinic visit, each participant completed a questionnaire on disease-specific knowledge, anxiety, depression, mental quality of life, involvement and opinion/attitude concerning patient information and involvement. Results 343 participants were included (221 control, 122 intervention). Cardiac diagnosis (p=0.873), educational level (p=0.153) and sex (p=0.603) were comparable between the two groups. All outcomes were comparable between groups in the intention-to-treat analyses. However, only 51.6% of subjects in the intervention group (n=63) reported actually visiting the portal. Among these subjects (as-treated), disease-specific knowledge (p=0.041) and mental health (p=0.039) were significantly better than in control subjects, while other baseline and outcome variables were comparable. Conclusion Even after being invited by their cardiologists, only half of the participants actually visited the information portal. Only in those participants that actually visited the portal, knowledge of disease and mental health were significantly better. This underlines the importance of effective implementation of online evidence-based patient information portals in clinical practice.Cardiolog