Identifying relevant studies for systematic reviews

Assessment of Cardiac Arrhythmic Risk in Children With Covid-19 Infection

Coronavirus disease of 2019 (COVID-19) is a cause of significant morbidity and mortality worldwide. Although COVID-19 clinical manifestations are mainly respiratory, major cardiac complications are being reported. The mechanism of cardiac injury and arrhythmias is unclear. Also, drugs currently used to treat the COVID-19 may prolong the QT interval and may have a proarrhythmic propensity. The study aims to investigate the effects of COVID-19 infection with asymptomatic and mild symptoms on trans-myocardial repolarization parameters in children without treatment. A total of 105 COVID-19 patients were compared with 40 healthy children. The patient and control group data were compared by calculating the QT interval, corrected QT (QTc), QT dispersion (QTd), QTc dispersion (QTcd), Tp-e, Tp-e dispersion, Tp-e/QT ratio, and Tp-e/QTc ratio on the 12-lead surface electrocardiogram. The mean age was determined as 11.2 ± 0.3 years in the patient group, and 10.8 ± 2.1 years in the control group. In the COVID-19 group, QTd, QTcd, Tp-e, Tp-e dispersion, Tp-e/QT ratio and Tp-e/QTc ratio were statistically higher than the control group. The ventricular repolarization was impaired even in asymptomatic children with COVID-19 infection. These results suggest the need to further assess the long terms risks of prolonged QT dispersion in the setting of COVID-19 infection.

Systematic reviews of randomized controlled trials: the need for complete data

If the relative effectiveness of different treatments that might be used in clinical practice is to be evaluated reliably, it is very important that the evaluation is carried out in an appropriate manner. This is especially true where the differences between treatments are expected to be moderate, and so easily obscured by the play of chance or systematic bias. Although such differences are often of considerable clinical importance, they can be difficult to assess and require a large amount of randomized evidence. This evidence can be obtained through prospective randomized controlled trials, meta-analysis of results from past randomized trials, or ideally a combination of the two, with prospective trials contributing to future meta-analyses. Whichever technique is adopted, all possible biases must be minimized through the collection of as much randomized evidence as possible. In meta-analyses, this is best achieved by ensuring that all relevant trials, and all randomized participants in these trials, are included in the analysis. The gold standard for this might be a meta-analysis of individual patient data, in which details for each participant in every trial are collected and analysed centrally. This approach requires considerable time and effort. However, it will add to the analyses that can be performed and will remove many of the problems associated with a reliance on published data alone and some of the problems that can arise from the use of aggregate data. This paper sets out some of the reasons for this and some of the techniques used for individual patient data-based meta-analysis.