Local recurrence and survival after treatment of oral squamous cell carcinoma of the maxilla: A systematic review and meta-analysis

OBJECTIVE: Oral squamous cell carcinoma involving the maxilla (MSCC) is a rare malignancy. The aim was to perform a systematic review and meta-analysis of available literature on local recurrence (LR), overall survival (OS), and associated risk factors of MSCC. STUDY DESIGN: The Cochrane, PubMed, and EMBASE databases were searched with related keywords and synonyms. The pooled proportions of both LR and OS were subsequently calculated with 95% confidence intervals. RESULTS: In total, 2638 articles were screened on title and abstract, 131 articles were screened on full text, and 20 were included. The pooled 5-year LR rate was 19.3%, and the 5-year OS rate was 53.7%. The subgroup analysis between surgery only and surgery with (neo)adjuvant treatment resulted in an odds ratio (OR) of .76 (95% confidence interval [CI]; .41-1.40). CONCLUSIONS: Postoperative (chemo)radiotherapy or preoperative intra-arterial chemoradiotherapy improves survival when adverse tumor characteristics are present. Posterior tumor extension into the soft palate, pterygoid muscle, pterygoid process, and infratemporal fossa was significantly associated with decreased OS in multiple studies. More research into the risk-reduction of local recurrence is warranted

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al. Methods: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. Results: Subgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators. Graphical Abstract

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

Long-Term Follow-Up of Pericardium for the Ventricular Component in Atrioventricular Septal Defect Repair

Background: Despite the improved outcome in complete atrioventricular septal defect (AVSD) repair, reoperations for left atrioventricular valve (LAVV) dysfunction are common. The aim of this study was to evaluate the effect of fresh untreated autologous pericardium for ventricular septal defect (VSD) closure on atrioventricular valve function and compare the results with the use of treated bovine pericardial patch material. Methods: Clinical and echocardiographic data were collected of patients with complete AVSD with their VSD closed with either untreated autologous pericardial or treated bovine pericardial patch material between January 1, 1996, and December 31, 2003. Evaluation closed in September 2019. Results: A total of 77 patients were analyzed (untreated autologous pericardial VSD patch: 59 [77%], treated bovine pericardial VSD patch: 18 [23%]). Median age at surgery was 3.6 (interquartile range [IQR]: 2.7-4.5) months, and median weight was 4.5 (IQR: 3.9-5.1) kg. Trisomy 21 was present in 70 (91%) patients. Median follow-up time was 17.5 (IQR: 12.6-19.8) years. Death <30 days occurred in two (3%) patients. Reinterventions occurred in eight patients (early [within 30 days] in two, early and late in one, and late in five), all in the autologous pericardium group. Log-rank tests showed no significant difference in mortality (P =.892), LAVV reinterventions (P =.228), or LAVV regurgitation (P =.770). Conclusions: In AVSD, the VSD can safely be closed with either untreated autologous pericardium or xeno-pericardium. We found no difference in LAVV regurgitation or the need for reoperation between the two patches

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

Branched-chain amino acid levels are inversely associated with incident and prevalent chronic kidney disease in people with type 2 diabetes

AimTo investigate the association of plasma metabolites with incident and prevalent chronic kidney disease (CKD) in people with type 2 diabetes and establish whether this association is causal.Materials and MethodsThe Hoorn Diabetes Care System cohort is a large prospective cohort consisting of individuals with type 2 diabetes from the northwest part of the Netherlands. In this cohort we assessed the association of baseline plasma levels of 172 metabolites with incident (Ntotal = 462/Ncase = 81) and prevalent (Ntotal = 1247/Ncase = 120) CKD using logistic regression. Additionally, replication in the UK Biobank, body mass index (BMI) mediation and causality of the association with Mendelian randomization was performed.ResultsElevated levels of total and individual branched-chain amino acids (BCAAs)-valine, leucine and isoleucine-were associated with an increased risk of incident CKD, but with reduced odds of prevalent CKD, where BMI was identified as an effect modifier. The observed inverse effects were replicated in the UK Biobank. Mendelian randomization analysis did not provide evidence for a causal relationship between BCAAs and prevalent CKD.ConclusionsOur study shows the intricate relationship between plasma BCAA levels and CKD in individuals with type 2 diabetes. While an association exists, its manifestation varies based on disease status and BMI, with no definitive evidence supporting a causal link between BCAAs and prevalent CKD.Nephrolog