On-admission and dynamic trend of laboratory profiles as prognostic biomarkers in COVID-19 inpatients.

This large-scale study aimed to investigate the trend of laboratory tests of patients with COVID-19. Hospitalized confirmed and probable COVID-19 patients in three general hospitals were examined from March 20, 2020, to June 18, 2021. The confirmed and probable COVID-19 patients with known outcomes and valid laboratory results were included. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to select admittance prognostic features. Parallel Pairwise Comparison of mortality versus survival was used to examine the trend of markers. In the final cohort, 11,944 patients were enrolled, with an in-hospital mortality rate of 21.8%, mean age of 59.4 ± 18.0, and a male-to-female ratio of 1.3. Abnormal admittance level of white blood cells, neutrophils, lymphocytes, mean cellular volume, urea, creatinine, bilirubin, creatine kinase-myoglobin binding, lactate dehydrogenase (LDH), Troponin, c-reactive protein (CRP), potassium, and creatinine phosphokinase reduced the survival of COVID-19 inpatients. Moreover, the trend analysis showed lymphocytes, platelet, urea, CRP, alanine transaminase (ALT), and LDH have a dissimilar trend in non-survivors compared to survived patients. This study proposed a novel approach to find serial laboratory markers. Serial examination of platelet count, creatinine, CRP, LDH, and ALT can guide healthcare professionals in finding patients at risk of deterioration.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Effect of Electrolyte Imbalance on Mortality and Late Acute Kidney Injury in Hospitalized COVID-19 Patients.

INTRODUCTION: As a multisystem illness, Coronavirus disease 2019 (COVID-19) can damage different organs. This study investigated the effect of electrolyte imbalance (EI), with or without concomitant renal dysfunction, on the prognosis of COVID-19 in hospitalized patients. METHODS: We evaluated 499 hospitalized patients with confirmed COVID-19, without a history of chronic kidney disease. The patients' demographic data, laboratory values, and outcomes were retrospectively collected from the hospital information system. Serumelectrolytes including sodium, potassium, magnesium, calcium, and phosphorus abnormalities were analyzed on admission and during the hospitalization period. The outcomes of this study were the occurrence of acute kidney injury (AKI) after the first week of hospitalization and in-hospital mortality rate. Multivariate analyses were carried out to obtain the independent risk of each EI on mortality, by adjusting for age, gender, and AKI occurrence. RESULTS: Among the 499 COVID-19 patients (60.9% male), AKI occurred in 168 (33.7%) and mortality in 92 (18.4%) cases. Hypocalcemia (38%) and hyponatremia (22.6%) were the most prevalent EIs, and all EIs were more common in the AKI group than in the non-AKI group. Hyponatremia (Adjusted Odds ratio [AOR] = 2.34, 95% CI: 1.30 to 4.18), hypernatremia (AOR = 8.52, 95% CI: 1.95 to 37.32), and hyperkalemia (AOR = 4.63, 95% CI: 1.65 to 13) on admission were associated with poor prognosis. Moreover, hyponatremia (AOR = 3.02, 95% CI: 1.28 to 7.15) and hyperphosphatemia (AOR = 5.12, 95% CI: 1.24 to 21.09) on admission were associated with late AKI occurrence. CONCLUSION: This study highlights the role of hyponatremia, hypernatremia, hyperkalemia, and hyperphosphatemia in poor prognosis of COVID-19. According to the independent effect of EI on late AKI and mortality, we recommend physicians to raise awareness to closely monitor and correct EI during hospitalization.  DOI: 10.52547/ijkd.6904.

Epidemiology of COVID-19 in Tehran, Iran: A Cohort Study of Clinical Profile, Risk Factors, and Outcomes.

Background: The outbreak of coronavirus disease 2019 (COVID-19) dates back to December 2019 in China. Iran has been among the most prone countries to the virus. The aim of this study was to report demographics, clinical data, and their association with death and CFR. Methods: This observational cohort study was performed from 20th March 2020 to 18th March 2021 in three tertiary educational hospitals in Tehran, Iran. All patients were admitted based on the WHO, CDC, and Iran's National Guidelines. Their information was recorded in their medical files. Multivariable analysis was performed to assess demographics, clinical profile, outcomes of disease, and finding the predictors of death due to COVID-19. Results: Of all 5318 participants, the median age was 60.0 years, and 57.2% of patients were male. The most significant comorbidities were hypertension and diabetes mellitus. Cough, dyspnea, and fever were the most dominant symptoms. Results showed that ICU admission, elderly age, decreased consciousness, low BMI, HTN, IHD, CVA, dialysis, intubation, Alzheimer disease, blood injection, injection of platelets or FFP, and high number of comorbidities were associated with a higher risk of death related to COVID-19. The trend of CFR was increasing (WPC: 1.86) during weeks 25 to 51. Conclusions: Accurate detection of predictors of poor outcomes helps healthcare providers in stratifying patients, based on their risk factors and healthcare requirements to improve their survival chance.

Medical treatment for paradoxical and saddle pulmonary embolism in a young man with patent foramen ovale and coronavirus disease 2019.

Here, we reported a 32-year-old male presenting to the emergency department with respiratory symptoms and coronavirus disease 2019 (COVID-19) diagnosis. Multiple thrombi were detected in his heart and inferior vena cava, probably due to former deep-vein thrombosis. The presence of patent foramen ovale and high pressure of the right heart caused the clots to enter the heart's left side. He received fibrinolytics, and his condition improved with no need for surgery. Hence, patients with impending paradoxical embolism may take advantage of medical treatment, such as fibrinolytics. Moreover, COVID-19 appears to be associated with a strong thrombotic tendency, and anticoagulants might be helpful.

Correlation between Chest Computed Tomography Scan Findings and Mortality of COVID-19 Cases; a Cross sectional Study.

INTRODUCTION: Predicting the outcomes of COVID-19 cases using different clinical, laboratory, and imaging parameters is one of the most interesting fields of research in this regard. This study aimed to evaluate the correlation between chest computed tomography (CT) scan findings and outcomes of COVID-19 cases. METHODS: This cross sectional study was carried out on confirmed COVID-19 cases with clinical manifestations and chest CT scan findings based on Iran's National Guidelines for defining COVID-19. Baseline and chest CT scan characteristics of patients were investigated and their correlation with mortality was analyzed and reported using SPSS 21.0. RESULTS: 380 patients with the mean age of 53.62 ± 16.66 years were evaluated (66.1% male). The most frequent chest CT scan abnormalities were in peripheral (86.6%) and peribronchovascular interstitium (34.6%), with ground glass pattern (54.1%), and round (53.6%) or linear (46.7%) shape. There was a significant correlation between shape of abnormalities (p = 0.003), CT scan Severity Score (CTSS) (p <0.0001), and pulmonary artery CT diameter (p = 0. 01) with mortality. The mean CTSS of non-survived cases was significantly higher (13.68 ± 4.59 versus 8.72 ± 4.42; <0.0001). The area under the receiver operating characteristic (ROC) curve of CTSS in predicting the patients' mortality was 0.800 (95% CI: 0.716-0.884). The best cut off point of chest CTSS in this regard was 12 with 75.82% (95% CI: 56.07%-88.98%) sensitivity and 75.78% (95% CI: 70.88%-80.10%) specificity. The mean main pulmonary artery diameter in patients with CTSS ≥ 12 was higher than cases with CTSS < 12 (27.89 ± 3.73 vs 26.24 ± 3.14 mm; p < 0.0001). CONCLUSION: Based on the results of the present study it seems that there is a significant correlation between chest CT scan characteristics and mortality of COVID-19 cases. Patients with lower CTSS, lower pulmonary artery CT diameter, and round shape opacity had lower mortality.