Rukiye Derin Atabey

Bio: Rukiye Derin Atabey is an academic researcher from University of Health Sciences Antigua. The author has contributed to research in topics: Mortality rate & Intensive care unit. The author has an hindex of 1, co-authored 1 publications receiving 13 citations.

The role of concomitant cardiovascular diseases and cardiac biomarkers for predicting mortality in critical COVID-19 patients.

Abstract: to identify the potential cardiovascular risk factors associated with mortality in hospitalised COVID-19 patients. All consecutive patients admitted to intensive care unit (ICU) of our institute fo...

Comparison of some parameters in estimating in-hospital mortality risk in patients undergoing open-heart surgery.

Abstract: OBJECTIVE Despite accumulating evidence showing the importance of various scoring systems in predicting preoperative mortality rates among patients who are undergoing open cardiac surgery, the prediction of in-hospital mortality is still limited. This study aimed to investigate the contributing factors associated with in-hospital mortality in patients who undergo cardiac surgery. PATIENTS AND METHODS All patients, aged 19 to 80 years, who underwent cardiac surgery between February 2019 and November 2020 at our tertiary healthcare institute were analyzed retrospectively. Demographic details, transthoracic echocardiography data, operation-related details, cardiopulmonary bypass time and laboratory measurement results were retrieved from the institutional digital database. RESULTS Data was available for a total of 311 subjects whose median age was 59 (52-67) years, and 65% of which were male. Among these 311 subjects, 296 (95%) were successfully discharged; however, in-hospital mortality was observed in 15 (5%) patients. Multiple logistic regression analysis revealed that low ejection fraction (p=0.049 and p=0.018), emergency surgery (p=0.022), low postoperative platelet (p=0.002) and high postoperative creatinine (p=0.007) were the most significant risk factors of mortality. CONCLUSIONS In conclusion, in a population of subjects undergoing cardiac and thoracic surgery, the in-hospital mortality rate was 4.8%. Left ventricular ejection fraction (LVEF) <40%, emergency surgery, postoperative platelet count and postoperative creatinine were significant risk factors for mortality.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Non-respiratory presentations of COVID-19, a clinical review.

Abstract: Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) is a highly infectious viral syndrome currently threatening millions of people worldwide. It is widely recognized as a disease of the pulmonary system, presenting with fever, cough, and shortness of breath. However, a number of extrapulmonary manifestations have been described in the literature. Objective In this review, we seek to provide a comprehensive summary of the hematologic, gastroenterological, renal, dermatologic, neurologic, and psychiatric manifestations of COVID-19. Discussion Hematological presentations of COVID-19 include laboratory abnormalities such as decreased total lymphocyte count, prolonged prothrombin time (PT), elevated d-dimer, and increased lactate dehydrogenase (LDH). Several of these findings are associated with increased mortality among infected patients. The most common gastrointestinal symptoms include nausea, vomiting, diarrhea, and abdominal pain. Furthermore, presence of viral RNA in patient stool suggests the possibility of additional testing modalities for COVID-19. Nephrological findings such as proteinuria, hematuria, and elevated BUN and creatinine levels have been observed. Additionally, several studies demonstrated that patients with COVID-19 who developed acute kidney injury (AKI) had a greater risk of mortality. The virus can also present with cutaneous symptoms such as erythematous rashes, urticaria, and chicken pox-like lesions. Neuropsychiatric symptoms have been described in the literature, and patients can exhibit findings consistent with viral encephalitis, cerebral vascular disease, peripheral nerve disorders, and psychosis. Conclusion Although COVID-19 does usually present primarily with respiratory symptoms, the extra-pulmonary manifestations of the virus are unpredictable and varied. Better understanding and awareness of these symptoms can lead to more efficient diagnosis, rapid treatment, isolation, and decreased spread of the disease.

Factors associated with mortality in patients with COVID-19 admitted to intensive care: a systematic review and meta-analysis.

Abstract: Identification of high-risk patients admitted to intensive care with COVID-19 may inform management strategies. The objective of this meta-analysis was to determine factors associated with mortality among adults with COVID-19 admitted to intensive care by searching databases for studies published between 1 January 2020 and 6 December 2020. Observational studies of COVID-19 adults admitted to critical care were included. Studies of mixed cohorts and intensive care cohorts restricted to a specific patient sub-group were excluded. Dichotomous variables were reported with pooled OR and 95%CI, and continuous variables with pooled standardised mean difference (SMD) and 95%CI. Fifty-eight studies (44,305 patients) were included in the review. Increasing age (SMD 0.65, 95%CI 0.53-0.77); smoking (OR 1.40, 95%CI 1.03-1.90); hypertension (OR 1.54, 95%CI 1.29-1.85); diabetes (OR 1.41, 95%CI 1.22-1.63); cardiovascular disease (OR 1.91, 95%CI 1.52-2.38); respiratory disease (OR 1.75, 95%CI 1.33-2.31); renal disease (OR 2.39, 95%CI 1.68-3.40); and malignancy (OR 1.81, 95%CI 1.30-2.52) were associated with mortality. A higher sequential organ failure assessment score (SMD 0.86, 95%CI 0.63-1.10) and acute physiology and chronic health evaluation-2 score (SMD 0.89, 95%CI 0.65-1.13); a lower PaO2 :FI O2 (SMD -0.44, 95%CI -0.62 to -0.26) and the need for mechanical ventilation at admission (OR 2.53, 95%CI 1.90-3.37) were associated with mortality. Higher white cell counts (SMD 0.37, 95%CI 0.22-0.51); neutrophils (SMD 0.42, 95%CI 0.19-0.64); D-dimers (SMD 0.56, 95%CI 0.43-0.69); ferritin (SMD 0.32, 95%CI 0.19-0.45); lower platelet (SMD -0.22, 95%CI -0.35 to -0.10); and lymphocyte counts (SMD -0.37, 95%CI -0.54 to -0.19) were all associated with mortality. In conclusion, increasing age, pre-existing comorbidities, severity of illness based on validated scoring systems, and the host response to the disease were associated with mortality; while male sex and increasing BMI were not. These factors have prognostic relevance for patients admitted to intensive care with COVID-19.

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique.

Abstract: Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.