Mortality In Coronavirus Disease-2019 Cases Research Articles

Identified Factors in COVID-19 Patients in Predicting Mortality.

The coronavirus disease 2019 (COVID-19) pandemic has led to a significant increase in global mortality rates. Numerous studies have been conducted to identify the factors associated with mortality in COVID-19 cases. In these studies, overall mortality was evaluated in patients, and no distinction was made as ward or intensive care mortality. This study aims to determine mortality-related factors in patients who died while in the ward. This could enable us to review the indications for intensive care hospitalization in possible pandemics. This retrospective study was conducted on a cohort of 237 patients who applied to our institution between January 2020 and December 2021 with the diagnosis of COVID-19. Demographic characteristics, length of stay, type of admission (emergency ward or outpatient clinic), presence of comorbidities, thoracic computerized tomography (CT) findings, and laboratory findings were extracted from the hospital database. The demographic and laboratory results of both deceased and recovered patients were compared. While many demographic and laboratory findings were statistically significant in the initial analysis, multiple logistic regression analysis showed that decreased albumin levels (adjusted OR = 0.23, 95% CI = 0.09 - 0.57), increased troponin (adjusted OR = 1.03, 95% CI = 1.02 - 1.05), and procalcitonin (adjusted OR = 3.46, 95% CI = 1.04 - 11.47) levels and higher partial thromboplastin time (PTT) (adjusted OR = 1.18, 95% CI = 1.09 - 1.28) values, presence of diabetes mellitus (DM) in patients (adjusted OR = 2.18, 95% CI = 1.01 - 4.69, P = 0.047), and admission to hospital from the emergency department (adjusted OR = 5.15, 95% CI = 1.45 - 18.27, P = 0.011) were significantly associated with mortality when adjusted for age. When a predictive model is constructed with these variables, this model predicted mortality statistically significant (AUC = 0.904, 95% CI = 0.856 - 0.938, P < 0.001), with a sensitivity of 77.2% (95% CI, 67.8 - 85), a specificity of 91.2% (95% CI, 85.1 - 95.4), a positive predictive value (PPV) of 86.7% (95% CI, 72 - 85.3), and an negative predictive value (NPV) of 84.4% (95% CI, 79.4 - 89.6). In this study, we may predict mortality among COVID-19-diagnosed patients admitted to the ward via this model which has the potential to provide guidance for reconsidering the indications for intensive care unit (ICU) admission.

A Retrospective Analysis of the Importance of Biochemical and Hematological Parameters for Mortality Prediction in COVID-19 Cases.

Introduction The recent coronavirus disease 2019 (COVID-19) pandemic has devastated the world's health and economy and has devastatingly affected social and emotional spheres. Although it was the older population who faced the worst, a good number of the younger population also lost their lives. It was very important to predict who will progress to the worst clinical outcome. Consequently, quick and accurate ways of forecasting mortality in COVID-19 cases are essential to save lives. In this study, 11 predictive parameters, namely, D-dimer, creatinine, C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), systemic inflammatory index (SII), platelet count, absolute neutrophil count (ANC), and absolute lymphocyte count (ALC), were studied for determining their significance as predictive parameters of mortality in COVID-19-affected patients. Methods We conducted a retrospective study of essential parameters, namely, D-dimer, creatinine, CRP, LDH, ferritin, NLR, PLR, SII, platelet count, ANC, and ALC, in confirmed COVID-19 cases for one year between 2020 and 2021. The medical information was obtained from the digital storage sources of the hospital. All cases were segregated into surviving and non-surviving cases. All parameters were collected, results were tabulated, and each individual parameter wasthen analyzed to see if it showed any significantdeviations in non-surviving cases and could help predict mortalities. Statistical analysis was conducted using the latest version of the Statistical Package for the Social Sciences(SPSS) software (IBM SPSS Statistics, Armonk, NY, USA). Results Each of the parameters was individuallystudied. D-dimer, creatinine, LDH, ferritin, CRP, NLR, PLR, SII, and ANC showed a statistically significant increase in non-surviving cases. Compared to surviving cases, ALC and platelets showed a statistically significant decrease in non-surviving instances. Conclusion All the studied parameters showed significant deviations in non-surviving cases and could help predict mortalities. This study also stresses the utility of readily available hematological ratios such as NLR and SII for prognosis in COVID-19 subjects.

Do Certain Biomarkers Predict Adverse Outcomes in Coronavirus Disease 2019?

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Given the rapid spread of the disease, the World Health Organization (WHO) declared the 2019 - 2020 coronavirus outbreak a Public Health Emergency of International Concern (PHEIC) on January 30, 2020, and a pandemic on March 11, 2020. There have been several reports of the limited resources including the lack of intensive care unit (ICU) beds and mechanical ventilators. Thus, biomarkers that predict ICU stay and mortality will be an important tool to appropriately allocate the limited resources. The aim of this review was to identify laboratory markers that can effectively predict the risk of severe infection and increased mortality in COVID-19 cases. We conducted a systematic review of existing literature in six databases to evaluate the predictive value of various biomarkers. We used the keywords “COVID-19”, “SARS-CoV-2”, “Novel corona virus pneumonia”, “Biomarkers”, “Adverse outcomes”, “Mortality”, etc. among many others to refine our search. Several biomarkers were identified to be associated with adverse outcomes in the above studies. These biomarkers can be used as a tool to identify patients at increased risk for adverse outcomes so that the need for aggressive critical care in such patients is met.

Evaluation of serum ferritin for prediction of severity and mortality in COVID-19- A cross sectional study

BackgroundFerritin even though widely recognized as a representative of total body iron stores, its prognostic utility is linked with COVID-19. This study was aimed at evaluation of the association of ferritin with severity in Coronavirus disease 2019 (COVID-19), hospitalized patients and to test the hypothesis that it is an independent predictor of mortality. Material and methodsThis study was conducted at Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University (AKU), Karachi. Medical records of all in-patients including both genders, and all age groups with documented COVID-19 from 1st March to 10th August 2020 were reviewed. The subjects were divided into two categories severe and non-severe COVID-19; and survivors and non-survivors. The details were recorded on a pre-structured performa. Between-group differences were tested using the Mann–Whitney's U-test. The receiver operating characteristic curve was plotted for ferritin with severity and mortality. A binary logistic regression was used to identify variables independently associated with mortality. The data was analyzed using Statistical Package for the Social Sciences (SPSS). ResultsA total of 336 in patients were reviewed as declared COVID-19 positive during the study duration, and 157 were included in the final analysis including 108 males and 49 females. Statistically significant difference in ferritin was found in the two categories based on severity and mortality. Binary logistic regression showed ferritin to be an independent predictor of all-cause mortality supplemented with an AUC of 0.69 on ROC analysis. ConclusionsSerum ferritin concentration is a promising predictor of mortality in COVID-19 cases.

Association of different biomarkers and CT value of RT-PCR with mortality in COVID-19 cases attended at tertiary care centre in India

Background: Severe Acute Respiratory Syndrome- Corona Virus-2 (SARS-CoV-2) infection becomes the pandemic of coronavirus disease 2019 (COVID-19) shows drastic changes in health care system. Aims and objectives: (1) To assess different biomarkers in COVID-19 cases, (2) To correlate biomarkers with cycle threshold (CT) value of Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and it's role for prognosis of severe cases. Material and Methods: Consecutive patients with positive for SARS-CoV-2 by RT-PCR with CT value less than 36 were included. A CT values were found for envelope (E-gene) as a screening gene and RNA dependent RNA polymerase (RdRp) as a confirmatory gene were amplified during RT-PCR. The severity of illness was assessed using different biochemical parameters for C-reactive protein (CRP), serum ferritin, D-dimer, lactate dehydrogenase (LDH), serum urea, and creatinine.

Association between renin-angiotensin-aldosterone system inhibitor use and COVID-19 hospitalization and death: a 1.4 million patient nationwide registry analysis.

AimsRenin–angiotensin–aldosterone system inhibitors (RAASi) improve outcomes in cardiorenal disease but concerns have been raised over increased risk of incident hospitalization and death from coronavirus disease 2019 (COVID‐19). We investigated the association between use of angiotensin‐converting enzyme inhibitors (ACEi), angiotensin receptor blockers (ARBs) or mineralocorticoid receptor antagonists (MRAs) and COVID‐19 hospitalization/death in a large nationwide population.Methods and resultsPatients with hypertension, heart failure, diabetes, kidney disease, or ischaemic heart disease registered in the Swedish National Patient Registry until 1 February 2020 were included and followed until 31 May 2020. COVID‐19 cases were defined based on hospitalization/death for COVID‐19. Multivariable logistic and Cox regressions were fitted to investigate the association between ACEi/ARB and MRA and risk of hospitalization/death for COVID‐19 in the overall population, and of all‐cause mortality in COVID‐19 cases. We performed consistency analysis to quantify the impact of potential unmeasured confounding. Of 1 387 746 patients (60% receiving ACEi/ARB and 5.8% MRA), 7146 (0.51%) had incident hospitalization/death from COVID‐19. After adjustment for 45 variables, ACEi/ARB use was associated with a reduced risk of hospitalization/death for COVID‐19 (odds ratio 0.86, 95% confidence interval 0.81–0.91) in the overall population, and with reduced mortality in COVID‐19 cases (hazard ratio 0.89, 95% confidence interval 0.82–0.96). MRA use was not associated with risk of any outcome. Consistency analysis showed that unmeasured confounding would need to be large for there to be harmful signals associated with RAASi use.ConclusionsIn a 1.4 million nationwide cohort, use of RAASi was not associated with increased risk of hospitalization for or death from COVID‐19.

COVID-19 and chronic renal disease: clinical characteristics and prognosis.

BackgroundPatients on dialysis were susceptible to COVID-19 and were prone to severe clinical characteristics after infection; acute kidney injury was related to mortality in COVID-19 cases. Limited is known about the characteristics of COVID-19 patients with end-stage renal disease not requiring renal replacement therapy (RRT).AimEvaluate clinical characteristics, course and outcomes of COVID-19 patients with chronic kidney disease (CKD) who did not require RRT and those on dialysis.DesignA two-center retrospective study.Methods836 adult patients with COVID-19 (24 CKD not on dialysis; 15 dialysis-dependent CKD) were included. The study includes no patients with renal transplantation. Risk factors were explored.ResultsCKD not requiring RRT is an independent risk factor for in-hospital death [adjusted OR (aOR) 7.35 (95%CI 2.41-22.44)] and poor prognosis [aOR 3.01 (95%CI 1.23-7.33)]. Compared to COVID-19 cases without CKD, those with CKD not requiring RRT showed similar percentage of initial moderate cases (75.00% vs. 73.65%) but higher incidence of in-hospital neutrophilia (50.00% vs. 27.30%) or death (50.00% vs. 9.03%). The odds ratio of dialysis associated to mortality in CKD patients was 2.00 (95%CI 0.52-7.63), suggesting COVID-19 patients with dialysis-dependent CKD were at greater risk of in-hospital death. For COVID-19 patients with CKD not requiring RRT, statins reduced the risk of neutrophilia [OR 0.10 (95%CI 0.01-0.69)] while diuretics increased the risk of neutrophilia [OR 15.4 (95%CI 1.47-160.97)], although both showed no association to mortality.ConclusionCOVID-19 patients with CKD presented high incidence of neutrophilia, poor prognosis and in-hospital death, with dialysis patients being more vulnerable.