Vorhofflimmern bei nichtkardialen Infektionen und Sepsis

Rath, Benjamin; Niehues, Philipp; Leitz, Patrick; Eckardt, Lars

doi:10.1007/s00399-019-0633-z

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…Some diseases or conditions have been shown to trigger paroxysmal atrial fibrillation. Infectious diseases are the leading causes (11) . It has been shown in some case reports that patients with COVID-19 infection have atrial and ventricular tachyarrhythmias (12,13) .…”

Section: Effects Of Covid-19 Infection On P-wave Dispersion P-wave Pe...mentioning

confidence: 99%

Effects of COVID-19 Infection on P-Wave Dispersion, P-Wave Peak Time and Atrial Conduction Times

Boyraz¹,

İbişoğlu²

2021

ejcm

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Objectives:The relationship between atrial conduction times and paroxysmal atrial fibrillation has been demonstrated in some studies. There have been case reports showing that coronavirus disease-2019 (COVID-19) infection caused arrhythmic cases including atrial fibrillation. We investigate the effect of different clinical presentations of COVID-19 infection on these parameters in this study. Materials and Methods:We divided the patients who were infected by COVID-19 into three groups according to computerized tomography and real-time polymerase chain reaction test results. The longest P-wave duration, shortest P-wave durations, P-wave dispersion and P-wave peak duration were calculated in the surface electrocardiography of these patients.Results: Patients with both real-time polymerase chain reaction test positive and pneumonia had the highest P-wave maximum duration (113.08±9.671 ms vs 102.44±7.412 ms. and 99.18±9.292 ms; p=0.000) and the highest P-wave dispersion (53.34±7.705 ms vs 40.58±4.813 ms. and 35.42±4.116 ms; p=0.000) and the longest P-wave peak time (56.79±7.767 ms vs 51.92 ms ±6.443 ms and 50.55±11.63 ms; p=0.008). P-wave dispersion was found longer in patients with real-time polymerase chain reaction test only compared to patients with only pneumonia (40.58±4.813 ms. vs 35.42±4.116 ms; p=0.000). Conclusion:Patients with COVID-19 pneumonia with real-time polymerase chain reaction test positivity have longest P-wave dispersion, P-wave maximum duration and P-wave peak duration. It seems that they have a risk of paroxysmal atrial fibrillation. That's why, that group may benefit most from strict electrocardiography follow-up.

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Section: Effects Of Covid-19 Infection On P-wave Dispersion P-wave Pe...mentioning

confidence: 99%

Effects of COVID-19 Infection on P-Wave Dispersion, P-Wave Peak Time and Atrial Conduction Times

Boyraz¹,

İbişoğlu²

2021

ejcm

View full text Add to dashboard Cite

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“…Atrial fibrillation (AF), which accounts for approximately 44% of arrhythmia in critically ill patients, is the most common type (7). Atrial fibrillation is also an indicator of poor prognosis in septic patients (8,9). Studies have indicated that patients with AF during sepsis are at greater risk of heart failure than patients without AF (10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

Development of a Machine Learning Model for Predicting 28-Day Mortality of Septic Patients With Atrial Fibrillation

Wang

Zhang

Chao

et al. 2023

Shock

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Introduction: Septic patients with atrial fibrillation (AF) are common in the intensive care unit accompanied by high mortality. The early prediction of prognosis of these patients is critical for clinical intervention. This study aimed to develop a model by using machine learning (ML) algorithms to predict the risk of 28-day mortality in septic patients with AF. Methods: In this retrospective cohort study, we extracted septic patients with AF from the Medical Information Mart for Intensive Care III (MIMIC-III) and IV database. Afterward, only MIMIC-IV cohort was randomly divided into training or internal validation set. External validation set was mainly extracted from MIMIC-III database. Propensity score matching was used to reduce the imbalance between the external validation and internal validation data sets. The predictive factors for 28-day mortality were determined by using multivariate logistic regression. Then, we constructed models by using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve, sensitivity, specificity, recall, and accuracy. Results: A total of 5,317 septic patients with AF were enrolled, with 3,845 in the training set, 960 in the internal testing set, and 512 in the external testing set, respectively. Then, we established four prediction models by using ML algorithms. AdaBoost showed moderate performance and had a higher accuracy than the other three models. Compared with other severity scores, the AdaBoost obtained more net benefit. Conclusion: We established the first ML model for predicting the 28-day mortality of septic patients with AF. Compared with conventional scoring systems, the AdaBoost model performed moderately. The model established will have the potential to improve the level of clinical practice.

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