Hypervitaminosis A is prevalent in children with CKD and contributes to hypercalcemia.

Vitamin A accumulates in renal failure, but the prevalence of hypervitaminosis A in children with predialysis chronic kidney disease (CKD) is not known. Hypervitaminosis A has been associated with hypercalcemia. In this study we compared dietary vitamin A intake with serum retinoid levels and their associations with hypercalcemia

An Analysis of Sentence-Like Utterances of IELTS Mock Speaking Test Scripts

The present study examines non-Malaysian candidates’ performance in an IELTS mock speaking test with regards to their use of different types of sentence structure utterances. Audio recorded data was obtained from eight students of three different levels of proficiency, namely; foundation, intermediate and advanced, in which thereafter an analysis was carried out using Radford (1990; 1997) sentence types. A semi-structured interview was also employed to gauge the candidates’ opinions on answering the test questions as well the interlocutor’s views on the candidates’ performance. The test was conducted by an IELTS trained interlocutor. It was found that most candidates were able to understand the questions, their responses were mainly simple sentence utterances indicated by many disjointed and choppy ideas. The semi-structured interview answers show that most candidates’ high level of nervousness and anxiety caused them not to be able to speak fluently, and as a result, their ideas were expressed in simple sentence structures that lacked logical coordination. It is hoped that the findings of the present study would help the current IETLS course module developers to integrate lessons on the different types of sentence structures in training test candidates to express complete and complex structured responses

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

Abstract Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.Abstract Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

BACKGROUND AND AIMS: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. METHODS: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. RESULTS: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. CONCLUSIONS: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors

An Analysis of Sentence-Like Utterances of IELTS Mock Speaking Test Scripts

The present study examines non-Malaysian candidates’ performance in an IELTS mock speaking test with regards to their use of different types of sentence structure utterances. Audio recorded data was obtained from eight students of three different levels of proficiency, namely; foundation, intermediate and advanced, in which thereafter an analysis was carried out using Radford (1990; 1997) sentence types. A semi-structured interview was also employed to gauge the candidates’ opinions on answering the test questions as well the interlocutor’s views on the candidates’ performance. The test was conducted by an IELTS trained interlocutor. It was found that most candidates were able to understand the questions, their responses were mainly simple sentence utterances indicated by many disjointed and choppy ideas. The semi-structured interview answers show that most candidates’ high level of nervousness and anxiety caused them not to be able to speak fluently, and as a result, their ideas were expressed in simple sentence structures that lacked logical coordination. It is hoped that the findings of the present study would help the current IETLS course module developers to integrate lessons on the different types of sentence structures in training test candidates to express complete and complex structured responses.</jats:p

Development and Validation of a Multivariable Risk Prediction Model for Sudden Cardiac Death after Myocardial Infarction (PROFID Risk Model): Study Rationale, Design and Protocol

AbstractIntroductionSudden cardiac death (SCD) is the leading cause of death in patients with myocardial infarction (MI) and can be prevented by the implantable cardioverter defibrillator (ICD). Currently, risk stratification for SCD and decision on ICD implantation are based solely on impaired left ventricular ejection fraction (LVEF). However, this strategy leads to over- and under-treatment of patients because LVEF alone is insufficient for accurate assessment of prognosis. Thus, there is a need for better risk stratification. This is the study protocol for developing and validating a prediction model for risk of SCD in patients with prior MI.Methods and AnalysisThe EU funded PROFID project will analyse 23 datasets from Europe, Israel and the US (∼225,000 observations). The datasets include patients with prior MI or ischemic cardiomyopathy with reduced LVEF&lt;50%, with and without a primary prevention ICD. Our primary outcome is SCD in patients without an ICD, or appropriate ICD therapy in patients carrying an ICD as a SCD surrogate. For analysis, we will stack 18 of the datasets into a single database (datastack), with the remaining analysed remotely for data governance reasons (remote data). We will apply 5 analytical approaches to develop the risk prediction model in the datastack and the remote datasets, all under a competing risk framework: 1) Weibull model, 2) flexible parametric survival model, 3) random forest, 4) likelihood boosting machine, and 5) neural network. These dataset-specific models will be combined into a single model (one per analysis method) using model aggregation methods, which will be externally validated using systematic leave-one-dataset-out cross-validation. Predictive performance will be pooled using random effects meta-analysis to select the model with best performance.Ethics and disseminationLocal ethical approval was obtained. The final model will be disseminated through scientific publications and a web-calculator. Statistical code will be published through open-source repositories.</jats:sec