Assessing common classification methods for the identification of abnormal repolarization using indicators of T-wave morphology and QT interval

Effect of hydroxychloroquine on the cardiac ventricular repolarization: A randomized clinical trial

Aims

Hydroxychloroquine has been suggested as possible treatment for severe acute respiratory syndrome‐coronavirus‐2. Studies reported an increased risk of QTcF‐prolongation after treatment with hydroxychloroquine. The aim of this study was to analyse the concentration‐dependent effects of hydroxychloroquine on the ventricular repolarization, including QTcF‐duration and T‐wave morphology.

Methods

Twenty young (≤30 y) and 20 elderly (65–75 y) healthy male subjects were included. Subjects were randomized to receive either a total dose of 2400 mg hydroxychloroquine over 5 days, or placebo (ratio 1:1). Follow‐up duration was 28 days. Electrocardiograms (ECGs) were recorded as triplicate at baseline and 4 postdose single recordings, followed by hydroxychloroquine concentration measurements. ECG intervals (RR, QRS, PR, QTcF, J‐Tpc, Tp‐Te) and T‐wave morphology, measured with the morphology combination score, were analysed with a prespecified linear mixed effects concentration–effect model.

Results

There were no significant associations between hydroxychloroquine concentrations and ECG characteristics, including RR‐, QRS‐ and QTcF‐interval (P = .09, .34, .25). Mean ΔΔQTcF‐interval prolongation did not exceed 5 ms and the upper limit of the 90% confidence interval did not exceed 10 ms at the highest measured concentrations (200 ng/mL). There were no associations between hydroxychloroquine concentration and the T‐wave morphology (P = .34 for morphology combination score). There was no significant effect of age group on ECG characteristics.

Conclusion

In this study, hydroxychloroquine did not affect ventricular repolarization, including the QTcF‐interval and T‐wave morphology, at plasma concentrations up to 200 ng/mL. Based on this analysis, hydroxychloroquine does not appear to increase the risk of QTcF‐induced arrhythmias.

Mortality characteristics in Sudan in national psychiatric hospitals: 5-year review of hospital mortality

Background

Re-auditing mortality of inpatient psychiatric service for a span of time extending to 14 years is scarce in low-resource countries. We are reporting a 5-year mortality study to complete an audit cycle with a previous 9-year mortality research published a few years ago. It serves to improve the standard of care in low-resource settings.

Results

Out of a total of 7340 (4890 men and 2450 women) admissions to all national psychiatric hospitals in Khartoum over a 5-year period, 36 (23 men and 13 women) patients died while receiving inpatient psychiatric treatment. The mortality rate in this sample was 4.9 (4.7 for men and 5.3 for women). Among all deaths, 30.6% were diagnosed with schizophrenia, while mania constituted 25%, major depression 15% and organic psychosis 11.3%, and 11.1% were considered to be due to alcohol- and/or drug-related disorders. There was no single case of unnatural death in this sample.

Twenty patients (55.6%) died following circulatory failure (sudden death), four (11.1%) as a result of an infection, three (8.3%) from renal failure, two (5.6%) secondarily to NMS, two (5.6%) from diabetic complications and four patients as a result of ECT, GIT bleeding, ischaemic heart disease and alcohol complications.

Conclusion

Circulatory failure and infection are the main causes of death in psychiatric hospitals in Sudan, calling for an intense investigation to resolve these preventable problems.

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features.