|D055370||Lung Injury NIH||0.23|
|D011024||Pneumonia, Viral NIH||0.14|
|D055371||Acute Lung Injury NIH||0.12|
|D012127||Respiratory Distress Syndrome, Newborn NIH||0.12|
|D012128||Respiratory Distress Syndrome, Adult NIH||0.10|
There is one clinical trial.
In December 2019, a new viral disease called COVID-19 emerged. It is caused by the new corona virus SARS-CoV-2. It was initially described in the chinese city of Wuhan. In the following months, the disease developed into a pandemic, which is currently an immense international challenge. So far, there is little scientific evidence on risk stratification, especially on the prognostic value of biomarkers (laboratory-chemical, clinical and digital) with regard to clinical deterioration of patients with COVID-19. Further scientific studies are needed to establish optimal risk stratification and early detection of clinical deterioration. In this study, the investigators aim to observe patients with COVID-19 via SmartWatches on top of their clinical routine. The investigators aim to determine, whether the addition of SmartWatches enhances risk stratification, early detection of complications and prognostics in patients with COVID-19, who have cardiovascular diseases or receive medication with arrhythmogenic risk.
Description: Identification of biomarkers (laboratory-chemical, clinical, digital) for risk stratification, early detection of complications and prognosisMeasure: Biomarker Time: 3 months
Description: Identification of laboratory-chemical, clinical or digitally measured protective factors, that indicate good prognosisMeasure: Protective factors Time: 3 months
Description: The amount of time is compared between participants regarding the wearing of a SmartWatch as a monitoring toolMeasure: SmartWatch compliance Time: 3 months
Description: Detection of an irregular heartbeat (PPG) as a sign of atrial or ventricular arrhythmias and correlation to intermittently recorded ECGs by SmartWatchMeasure: Arrhythmias Time: 3 months
Description: Detection of QT time changes (prolongation) in intermittently recorded ECGs by SmartWatch and correlation with clinical variables (change of medication, fever, etc.)Measure: QT time changes Time: 3 months
Description: Application of artificial intelligence and machine learning techniques for longitudinal risk models by using collected data (e.g. metabolomics)Measure: Longitudinal risk models Time: 3 months