Six-Month Follow-up of COVID-19 Patients: Mortality and Related Factors

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.

Comparing the Characteristics of Mucormycosis Between Cases with and without COVID-19; a Cross-sectional Study.

Introduction: Mucormycosis as a rare but life-threatening disease with 46-96% mortality, which challenged the healthcare system during the COVID-19 pandemic. This study aimed to compare the characteristics of mucormycosis between cases with and without COVID-19. Methods: This cross-sectional study was done in two referral hospitals, Imam Hossein and Labbafinezhad Hospitals, Tehran, Iran, between 21 March to 21 December 2021. Data related to all hospitalized adults subject with the diagnosis of mucormycosis during the study period was collected from patients' profiles and they were divided into two groups of with and without COVID-19 based on the results of real time PCR. Then demographic, clinical, and laboratory findings as well as outcomes were compared between the two groups. Results: 64 patients with the mean age of 53.40±10.32 (range: 33-74) years were studied (53.1% male). Forty-three (67.2%) out of the 64 subjects had a positive COVID-19 PCR test. The two groups had significant differences regarding some symptoms (cough (p < 0.001), shortness of breath (p = 0.006)), acute presentation (p = 0.027), using immunosuppressive (p = 0.013), using corticosteroid (p < 0.001), and outcomes (mortality (p = 0.018), need for intubation (p < 0.001)). 22 (34.3%) patients expired during hospital admission. Univariate analysis showed the association of in-hospital mortality with need for ventilation (p < 0.001), sinus involvement (p = 0.040), recent use of dexamethasone (p = 0.011), confirmed COVID-19 disease (p = 0.025), mean body mass index (BMI) (p =0.035), hemoglobin A1c (HbA1c) (p = 0.022), and median of blood urea nitrogen (BUN) (p =0.034). Based on the multivariate model, confirmed COVID-19 disease (OR = 5.01; 95% CI: 1.14-22.00; p = 0.033) and recent use of dexamethasone (OR= 4.08, 95% CI: 1.05-15.84, p = 0.042) were independent predictors of mortality in this series. Conclusion: The mucormycosis cases with concomitant COVID-19 disease had higher frequency of cough and shortness of breath, higher frequency of acute presentation, higher need for immunosuppressive, corticosteroid, and ventilator support, and higher mortality rate. The two groups were the same regarding age, gender, BMI, risk factors, underlying diseases, symptoms, and sites of involvement.

Risk Factors of Readmission in COVID-19 Patients; a Retrospective 6-Month Cohort Study.

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.

Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients.

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 .

Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients

<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.

Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

Electrocardiographic Findings and In-Hospital Mortality of COVID-19 Patients; a Retrospective Cohort Study.

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.

Global Incidence and Mortality Rate of COVID-19; Special Focus on Iran, Italy and China.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a new coronavirus, was diagnosed in China in December 2019. Around the globe, a total of 71429 were infected up to February 17, 2020, with 98.9% of cases in China. On March 11, 2020, the World Health Organization (WHO) characterized the COVID-19 as 'pandemic'. Rapid positive worldwide incidence was the motivation behind this study to investigate the incidence and mortality globally. METHODS: We used the data published by the WHO until March 9, 2020. Non-parametric tests and change point analysis were used for inferences. RESULTS: Change point analysis for Iran and China and the world excluding China for the first 20 days revealed around 78, 195 and 2 further new cases per day, respectively. Italy had a big jump in incidence on the 36th day. Similarly, a sharp rise of positive cases was reported for the world on the 35th day. China successfully controlled the ascending reports of incidence on the 23rd day. Mortality in China and the world were almost similar for the first 20 days. There was an ascending incidence trend with two change points in Italy (30th and 36th days) and one change point in Iran on the 17th day. Mortality in the world jumped remarkably after day 42 with an estimation of almost more than 25 deaths per day. CONCLUSION: The incidence of COVID-19 varied by regions; however, after March 11, it became 'pandemic'. It was observed that after about 6 days with an emergence of sharp increase in incidences, there would be a mutation in mortality rate. On the other hand, the importance of 'on-time' quarantine programs in controlling this virus was confirmed.