A possible new approach in the prediction of late gestational hypertension: The role of the fetal aortic intima-media thickness

The aim was to determine the predictive role of combined screening for late-onset gestational hypertension by fetal ultrasound measurements, third trimester uterine arteries (UtAs) Doppler imaging, and maternal history. This prospective study on singleton pregnancies was conducted at the tertiary center of Maternal and Fetal Medicine of the University of Padua during the period between January 2012 and December 2014. Ultrasound examination (fetal biometry, fetal wellbeing, maternal Doppler study, fetal abdominal aorta intima-media thickness [aIMT], and fetal kidney volumes), clinical data (mother age, prepregnancy body mass index [BMI], and parity), and pregnancy outcomes were collected. The P value <0.05 was defined significant considering a 2-sided alternative hypothesis. The distribution normality of variables were assessed using Kolmogorov–Smirnoff test. Data were presented by mean (±standard deviation), median and interquartile range, or percentage and absolute values. We considered data from 1381 ultrasound examinations at 29 to 32 weeks’ gestation, and in 73 cases late gestational hypertension developed after 34 weeks’ gestation. The final multivariate model found that fetal aIMT as well as fetal umbilical artery pulsatility index (PI), maternal age, maternal prepregnacy BMI, parity, and mean PI of maternal UtAs, assessed at ultrasound examination of 29 to 32 weeks’ gestation, were significant and independent predictors for the development of gestational hypertension after 34 weeks’ gestation. The area under the curve of the model was 81.07% (95% confidence interval, 75.83%–86.32%). A nomogram was developed starting from multivariate logistic regression coefficients. Late-gestational hypertension could be independently predicted by fetal aIMT assessment at 29 to 32 weeks’ gestation, ultrasound Doppler waveforms, and maternal clinical parameters

Evaluation of Glycated Albumin (GA) and GA/Hba1c Ratio for Diagnosis of Diabetes and Glycemic Control: A Comprehensive Review

Diabetes Mellitus (DM) is a group of metabolic diseases characterized by chronic high blood glucose concentrations (hyperglycemia). When it is left untreated or improperly managed, it can lead to acute complications including diabetic ketoacidosis and non-ketotic hyperosmolar coma. In addition, possible long-term complications include impotence, nerve damage, stroke, chronic kidney failure, cardiovascular disease, foot ulcers, and retinopathy. Historically, universal methods to measure glycemic control for the diagnosis of diabetes included fasting plasma glucose level (FPG), 2-h plasma glucose (2HP), and random plasma glucose. However, these measurements did not provide information about glycemic control over a long period of time. To address this problem, there has been a switch in the past decade to diagnosing diabetes and its severity through measurement of blood glycated proteins such as Hemoglobin A1c (HbA1c) and glycated albumin (GA). Diagnosis and evaluation of diabetes using glycated proteins has many advantages including high accuracy of glycemic control over a period of time. Currently, common laboratory methods used to measure glycated proteins are high-performance liquid chromatography (HPLC), immunoassay, and electrophoresis. HbA1c is one of the most important diagnostic factors for diabetes. However, some reports indicate that HbA1c is not a suitable marker to determine glycemic control in all diabetic patients. GA, which is not influenced by changes in the lifespan of erythrocytes, is thought to be a good alternative indicator of glycemic control in diabetic patients. Here, we review the literature that has investigated the suitability of HbA1c, GA and GA:HbA1c as indicators of long-term glycemic control and demonstrate the importance of selecting the appropriate glycated protein based on the patient’s health status in order to provide useful and modern point-of-care monitoring and treatment

How to assess and manage hypertension during and after pregnancy.

Hypertensive disorders of pregnancy are increasingly important complications of which clinicians should have an up-to-date knowledge to facilitate prompt recognition, diagnosis and management. These disorders affect a growing number of pregnancies worldwide, with incidence rates likely to increase in the future commensurate with increasing maternal age and maternal comorbidities independent of age, with consequent effects on maternal and fetal/neonatal morbidity and mortality rates. This article mainly focuses on management within the UK of these disorders, examining their current working definitions, detection methods and recent developments in screening tool development. The current NICE-recommended strategies for treating these disorders and minimizing their occurrence in pregnancy are also explored. In addition, the association between adverse pregnancy outcome and increased risk of future maternal and offspring cardiovascular disease is described, with comments on future strategies to help minimize these potential risks

Jefferson Digital Commons quarterly report: January-March 2020

This quarterly report includes: New Look for the Jefferson Digital Commons Articles COVID-19 Working Papers Educational Materials From the Archives Grand Rounds and Lectures JeffMD Scholarly Inquiry Abstracts Journals and Newsletters Master of Public Health Capstones Oral Histories Posters and Conference Presentations What People are Saying About the Jefferson the Digital Common

Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Systematic review and network meta-analysis with individual participant data on cord management at preterm birth (iCOMP): study protocol

Introduction Timing of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups. Previous and current trials compare various policies, including time-based or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enable exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons. Objectives (1) To evaluate the effectiveness of cord management strategies for preterm infants on neonatal mortality and morbidity overall and for different participant characteristics using IPD meta-analysis. (2) To evaluate and rank the effect of different cord management strategies for preterm births on mortality and other key outcomes using NMA. Methods and analysis Systematic searches of Medline, Embase, clinical trial registries, and other sources for all ongoing and completed randomised controlled trials comparing cord management strategies at preterm birth (before 37 weeks’ gestation) have been completed up to 13 February 2019, but will be updated regularly to include additional trials. IPD will be sought for all trials; aggregate summary data will be included where IPD are unavailable. First, deferred clamping and cord milking will be compared with immediate clamping in pairwise IPD meta-analyses. The primary outcome will be death prior to hospital discharge. Effect differences will be explored for prespecified participant subgroups. Second, all identified cord management strategies will be compared and ranked in an IPD NMA for the primary outcome and the key secondary outcomes. Treatment effect differences by participant characteristics will be identified. Inconsistency and heterogeneity will be explored. Ethics and dissemination Ethics approval for this project has been granted by the University of Sydney Human Research Ethics Committee (2018/886). Results will be relevant to clinicians, guideline developers and policy-makers, and will be disseminated via publications, presentations and media releases

Gestational diabetes mellitus- right person, right treatment, right time?

Background: Personalised treatment that is uniquely tailored to an individual’s phenotype has become a key goal of clinical and pharmaceutical development across many, particularly chronic, diseases. For type 2 diabetes, the importance of the underlying clinical heterogeneity of the condition is emphasised and a range of treatments are now available, with personalised approaches being developed. While a close connection between risk factors for type 2 diabetes and gestational diabetes has long been acknowledged, stratification of screening, treatment and obstetric intervention remains in its infancy. Conclusions: Although there have been major advances in our understanding of glucose tolerance in pregnancy and of the benefits of treatment of gestational diabetes, we argue that far more vigorous approaches are needed to enable development of companion diagnostics, and to ensure the efficacious and safe use of novel therapeutic agents and strategies to improve outcomes in this common condition

First Evaluation of an Index of Low Vagally-Mediated Heart Rate Variability as a Marker of Health Risks in Human Adults: Proof of Concept.

Multiple studies have demonstrated low vagally-mediated heart rate variability (HRV) being associated with a range of risk factors for heart disease and stroke, including inflammation, hyperglycemia, hyperlipidemia, and hypertension. Yet, no cut point exists that indicates elevated risk. In the present study we sought to identify a cut point-value for HRV that is associated with elevated risk across a range of known risk factors. METHODS:A total of 9550 working adults from 19 study sites took part in a health assessment that included measures of inflammation, hyperglycemia, hyperlipidemia, and hypertension and vagally-mediated HRV (Root mean square of successive differences between normal heartbeats (RMSSD)). Multiple age and sex adjusted logistic regressions were calculated per risk factor (normal versus clinical range), with RMSSD being entered in binary at different cut points ranging from 15-39 msec with a 2 msec increment. RESULTS:For daytime RMSSD, values below 25 ± 4 indicated elevated risk (odds ratios (OR) 1.5-3.5 across risk factors). For nighttime RMSSD, values below 29 ± 4 indicated elevated risk (OR 1.2-2.0). CONCLUSION:These results provide the first evidence that a single value of RMSSD may be associated with elevated risk across a range of established cardiovascular risk factors and may present an easy to assess novel marker of cardiovascular risk