RESUMEN
Atrial fibrillation (AF), a prevalent cardiac arrhythmia, is associated with increased morbidity and mortality worldwide. Stroke, the leading cause of serious disability in the United States, is among the important complications of this arrhythmia. Recent studies have demonstrated that certain clinical variables can be useful in the prediction of AF development in the future. The electrocardiogram (ECG) is a simple and cost-effective technology that is widely available in various healthcare settings. An emerging body of evidence has suggested that ECG tracings preceding the development of AF can be useful in predicting this arrhythmia in the future. Various variables on ECG especially different P wave parameters have been investigated in the prediction of new-onset AF and found to be useful. Several risk models were also introduced using these variables along with the patient's clinical data. However, current guidelines do not provide a clear consensus regarding implementing these prediction models in clinical practice for identifying patients at risk of AF. Also, the role of intensive screening via ECG or implantable devices based on this scoring system is unclear. The purpose of this review is to summarize AF and various related terminologies and explain the pathophysiology and electrocardiographic features of this tachyarrhythmia. We also discuss the predictive electrocardiographic features of AF, review some of the existing risk models and scoring system, and shed light on the role of monitoring device for screening purposes.
RESUMEN
Background Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology is recommended only for seroprevalence. We think it could be useful in differentiating coronavirus disease 2019 (COVID-19) stages, which could in terms of helping improve our therapeutic interventions. Methods The medical records of adult patients admitted to the hospital with probable COVID-19 were extracted and analyzed. We excluded patients with no serology and no clear outcome at the end of data collection. Patient demographics, medical history, and biochemical and clinical data were retrieved. Results A total of 202 patients were included; 57% were males, the majority were Hispanic (45%), followed by African Americans (22%). Hypertension is the most common comorbidity, followed by diabetes mellitus and chronic kidney disease. We classified them into three groups based on their serology: subacute stage (47 patients) with both immunoglobulin M (IgM) and IgG negative; acute stage (116 patients) with IgM positive and late-stage (39 patients) with IgM negative and IgG positive. We found that elevated lactate dehydrogenase (LDH) and ferritin were present in the IgM+ and IgM-/IgG+ subgroups (p-value of 0.0061 and p-value 0.0013, respectively) while C-reactive protein (CRP) and D-dimer were more elevated in the IgM-/IgG- and IgM+ subgroups (P <0.0001 and p-value of 0.0452, respectively). The IgM+ group had the worst prognosis, with high mortality despite receiving remdesivir and dexamethasone. Conclusion Our findings suggest that the use of serology in patients hospitalized with COVID-19 could predict prognosis; this will need to be validated in a larger prospective study.
