Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning.

BACKGROUND AND PURPOSE: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. METHODS: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. RESULTS: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65-0.79), 0.76 (95% CI 0.68-0.82) and 0.77 (95% CI 0.70-0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. CONCLUSION: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features.

[Decompression illness: minor symptoms, major consequences]. / Decompressieziekte: geringe klachten, ernstige gevolgen.

Nowadays, diving is being performed ever more frequently; it is thus important to take diving injuries into consideration in patients presenting with even minor complaints after diving. Every dive is risky and could result in decompression illness, barotrauma and/or death. We report on two cases of decompression illness: a 30-year old man, an occupational diver, and a 46-year old man, an experienced diver, who were both clinically suspected of having decompression illness and were treated with hyperbaric oxygen in a recompression chamber. Both were eventually symptom-free after several treatments. Decompression illness is caused by a reduction in ambient pressure, which results in intra- or extravascular bubbles. Symptoms vary and are dependent on the site affected: from minor pain to neurological symptoms and death. If patients are suspected of having diving injuries, we recommend contacting a centre specialised in diving and hyperbaric medicine. Recompression in a hyperbaric chamber is the definitive treatment for decompression illness and should be performed as soon as possible.