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Background: Currently, there is lack of evidence regarding the long-term follow-up of coronavirus disease 2019 (COVID-19) patients. The aim of this study is to present a 6-month follow-up of COVID-19 patients who were discharged from hospital after their recovery. Methods: This retrospective cohort study was performed to assess the six-month follow-up of COVID-19 patients who were discharged from the hospital between February 18 and July 20, 2020. The primary outcome was 6-month all-cause mortality. Results: Data related to 614 patients were included to this study. Of these 614 patients, 48 patients died (7.8%). The cause of death in 26 patients (54.2%) was the relapse of COVID-19. Also, 44.2% of deaths happened in the first week after discharge and 74.4% in the first month. Risk factors of all-cause mortality included increase in age (odds ratio [OR]=1.09;P<0.001), increase in neutrophil percentage (OR=1.05;P=0.009) and increase in heart rate (OR=1.06;P=0.002) on the first admission. However, the risk of all-cause death was lower in patients who had higher levels of hematocrit (OR=0.93;P=0.021), oxygen saturation (OR=0.90;P=0.001) and mean arterial pressure (OR=0.93;P=0.001). In addition, increase in age (OR=1.11;P<0.001) was an independent risk factor for COVID-19-related death, while higher levels of lymphocyte percentage (OR=0.96;P=0.048), mean arterial pressure (OR=0.93;P=0.006) and arterial oxygen saturation (OR=0.91;P=0.009) were protective factors against COVID-19-related deaths during the 6-month period after discharge. Conclusion: Death is relatively common in COVID-19 patients after their discharge from hospital. In light of our findings, we suggest that elderly patients who experience a decrease in their mean arterial pressure, oxygen saturation and lymphocyte count during their hospitalization, should be discharged cautiously. In addition, we recommend that one-month follow-up of discharged patients should be take place, and urgent return to hospital should be advised when the first signs of COVID-19 relapse are observed.
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Introduction: The available literature regarding the rate of readmission of COVID-19 patients after discharge is rather scarce. Thus, the aim in the current study was to evaluate the readmission rate of COVID-19 patients and the components affecting it, including clinical symptoms and relevant laboratory findings. Methods: In this retrospective cohort study, COVID-19 patients who were discharged from Imam Hossein hospital, Tehran, Iran, were followed for six months. Data regarding their readmission status were collected through phone calls with COVID-19 patients or their relatives, as well as hospital registry systems. Eventually, the relationship between demographic and clinical characteristics and readmission rate was assessed. Results: 614 patients were entered to the present study (mean age 58.7±27.2 years; 51.5% male). 53 patients were readmitted (8.6%), of which 47 patients (7.6%) had a readmission during the first 30 days after discharge. The reasons for readmission were relapse of COVID-19 symptoms and its pulmonary complications in 40 patients (6.5%), COVID-19 related cardiovascular complications in eight patients (1.3%), and non-COVID-19 related causes in five patients (0.8%). Older age (OR=1.04; 95% CI: 1.01, 1.06; p=0.002) and increased mean arterial pressure during the first admission (OR=1.04; 95% CI: 1.01, 1.08; p=0.022) were found to be independent prognostic factors for the readmission of COVID-19 patients. Conclusion: Readmission is relatively frequent in COVID-19 patients. Lack of adequate hospital space may be the reason behind the early discharge of COVID-19 patients. Hence, to reduce readmission rate, extra care should be directed towards the discharge of older or hypertensive patients.
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BACKGROUND: The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. METHODS: This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. RESULTS: The COVID-19 patients' median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25-9.91), males (OR = 1.51, 95% CI: 1.007-2.29), ICU need (OR = 16.32, 95% CI 10.13-26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26-2.83), lymphocytes <17% (OR = 2.33, 95%CI: 1.54-3.50), RBC ≤4, 10 6/L (OR = 2.10, 95% CI: 1.35-3.26), LDH >700 U/L (OR = 1.68, 95%CI: 1.13-2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17-2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. CONCLUSION: This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease .
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INTRODUCTION: Adults with underlying medical disorders are at increased risk for severe illness from the virus that causes COVID-19. This study aimed to compare the effect of underlying diseases on the mortality of male and female patients as a primary objective. We also evaluated the effect of drugs previously used by COVID-19 patients on their outcome. METHODS: This retrospective cohort study was carried out on confirmed cases of COVID-19 who were admitted to a teaching hospital in Tehran, Iran. Data was gathered from patients' files. Log binomial model was used for investigating the association of underlying diseases and in-hospital mortality of these patients. RESULTS: A total of 991 patients (mean age 61.62±17.02; 54.9% male) were recruited. Hypertension (41.1%), diabetes mellitus (30.6%), and coronary artery disease (19.6%) were the most common underlying diseases. The multivariable model showed that hypertension (RR = 1.62; 95% CI: 1.22-2.14, p = 0.001) in male patients over 55 years old and coronary artery disease (RR = 2.40; 95% CI: 1.24-4.46, p = 0.009) in female patients under 65 years old were risk factors of mortality. In females over 65 years old, the history of taking Angiotensin Converting Enzyme inhibitors (ACEi) and Angiotensin Receptor Blockers (ARB) (RR = 0.272; 95% CI: 0.17-0.41, p = 0.001) was a significant protective factor for death. CONCLUSIONS: COVID-19 patients with a history of cardiovascular diseases such as hypertension and coronary artery disease, especially those in specific age and sex groups, are high-risk patients for in-hospital mortality. Additionally, a previous history of taking ACEi and ARB medications in females over 65 tears old was a protective factor against in-hospital mortality of COVID-19 patients.
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<h4>Background</h4> The aim of this study is to develop and validate a scoring system as a tool for predicting the in-hospital mortality in COVID-19 patients in early stage of disease. <h4>Methods</h4> This retrospective cohort study, conducted on 893 COVID-19 patients in Tehran from February 18 to July 20, 2020. Potential factors were chosen via stepwise selection and multivariable logistic regression model. Cross-validation method was employed to assess the predictive performance of the model as well as the scoring system such as discrimination, calibration, and validity indices. <h4>Results</h4> The COVID-19 patients’ median age was 63 yrs (54.98% male) and 233 (26.09%) patients expired during the study. The scoring system was developed based on 8 selected variables: age ≥55 yrs (OR = 5.67, 95% CI: 3.25–9.91), males (OR = 1.51, 95% CI: 1.007–2.29), ICU need (OR = 16.32, 95% CI 10.13–26.28), pulse rate >90 (OR = 1.89, 95% CI: 1.26–2.83), lymphocytes <17% (OR = 2.33, 95%CI: 1.54–3.50), RBC ≤4, 10 6/L (OR = 2.10, 95% CI: 1.35–3.26), LDH >700 U/L (OR = 1.68, 95%CI: 1.13–2.51) and troponin I level >0.03 ng/mL (OR = 1.75, 95%CI: 1.17–2.62). The AUC and the accuracy of scoring system after cross-validation were 79.4% and 79.89%, respectively. <h4>Conclusion</h4> This study showed that developed scoring system has a good performance and can use to help physicians for identifying high-risk patients in early stage of disease.
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BACKGROUND: Although current evidence points to the possible prognostic value of electrocardiographic (ECG) findings for in-hospital mortality of COVID-19 patients, most of these studies have been performed on a small sample size. In this study, our aim was to investigate the ECG changes as prognostic indicators of in-hospital mortality. METHODS: In a retrospective cohort study, the findings of the first and the second ECGs of COVID-19 patients were extracted and changes in the ECGs were examined. Any abnormal finding in the second ECG that wasn't present in the initial ECG at the time of admission was defined as an ECG change. ECGs were interpreted by a cardiologist and the prognostic value of abnormal ECG findings for in-hospital mortality of COVID-19 patients was evaluated using multivariate analysis and the report of the relative risk (RR). RESULTS: Data of the ECGs recorded at the time of admission were extracted from the files of 893 patients; likewise, the second ECGs could be extracted from the records of 328 patients who had an initial ECG. The presence of sinus tachycardia (RR = 2.342; p <0.001), supraventricular arrhythmia (RR = 1.688; p = 0.001), ventricular arrhythmia (RR = 1.854; p = 0.011), interventricular conduction delays (RR = 1.608; p = 0.009), and abnormal R wave progression (RR = 1.766; p = 0.001) at the time of admission were independent prognostic factors for in-hospital mortality. In the second ECG, sinus tachycardia (RR = 2.222; p <0.001), supraventricular arrhythmia (RR = 1.632; p <0.001), abnormal R wave progression (RR = 2.151; p = 0.009), and abnormal T wave (RR = 1.590; p = 0.001) were also independent prognostic factors of in-hospital mortality. Moreover, by comparing the first and the second ECGs, it was found that the incidence of supraventricular arrhythmia (RR = 1.973; p = 0.005) and ST segment elevation/depression (RR = 2.296; p <0.001) during hospitalization (ECG novel changes) are two independent prognostic factors of in-hospital mortality in COVID-19 patients. CONCLUSION: Due to the fact that using electrocardiographic data is easy and accessible and it is easy to continuously monitor patients with this tool, ECGs can be useful in identifying high-risk COVID-19 patients for mortality.