Electrocardiographic (ECG) changes have been investigated in the condition of coronavirus disease (COVID-19) indicating that COVID-19 infection exacerbates arrhythmias and triggers conduction abnormalities. However, the specific type of ECG abnormalities in COVID-19 and their impact on mortality fail to have been fully elucidated. The present retrospective, tertiary care hospital-based cross-sectional study was conducted by reviewing the medical records of all patients diagnosed with COVID-19 infection who were admitted to Booali Sina Hospital in Qazvin, Iran from March to July 2020. Demographic information, length of hospital stay, treatment outcome, and electrocardiographic information (heart rate, QTc interval, arrhythmias, and blocks) were extracted from the medical records of the patients. Finally, a total of 231 patients were enrolled in the study. Atrial fibrillation was a common arrhythmia, and the left anterior fascicular block was a common cardiac conduction defect other than sinus arrhythmia. The deceased patients were significantly older than the recovered ones (71 ± 14 vs. 57 ± 16 years, p < 0.001). Longer hospital stay (p = 0.036), non-sinus rhythm (p < 0.001), bundle and node blocks (p = 0.002), ST-T waves changes (p = 0.003), and Tachycardia (p = 0.024) were significantly prevalent in the deceased group. In baseline ECGs, no significant difference was observed in terms of the absolute size of QT; however, a prolonged QTc in the deceased was about twice of the recovered patients (using Bazett, Sagie, and Fridericia’s formula). Serial ECGs are recommended to be taken from all hospitalized patients with COVID-19 due to increased in-hospital mortality in patients with prolonged QTc interval, non-sinus rhythms, ST-T changes, tachycardia, and bundle, and node blocks.

Humanity today is suffering from one of the most dangerous pandemics in history, the Coronavirus Disease of 2019 (COVID-19). Although today there is immense advancement in the medical field with the latest technology, the COVID-19 pandemic has affected us severely. The virus is spreading rapidly, resulting in an escalation in the number of patients admitted. We propose a contextual patient classification system for better analysis of the data from the discharge summary available from the research hospital. The classification was done using the Knuth–Morris–Pratt algorithm. We have also analyzed the data of COVID-19 and non-COVID-19 patients. During the analysis, studies on the medicines, medical services and tests, pulse count, body temperature, and the overall effect of age and gender was done. The death versus survival ratio for the COVID-19 positive patients has also been studied. The classification accuracy of the contextual patient classification system achieved was 97.4%. The combination of data analysis and contextual patient classification will be helpful to all the sectors to be better prepared for any future waves of the COVID-19 pandemic.