Risk Of Mortality In COVID-19 Patients Research Articles

NT-proBNP ratio is a potential predictor for COVID-19 outcomes in adult Chinese patients: a retrospective study

Despite the progressive decline in the virulence of the novel coronavirus, there has been no corresponding reduction in its associated hospital mortality. Our aim was to redefine an accurate predictor of mortality risk in COVID-19 patients, enabling effective management and resource allocation. We conducted a retrospective analysis of 2917 adult Chinese patients diagnosed with COVID-19 who were admitted to our hospital during two waves of epidemics, involving the Beta and Omicron variants. Upon admission, NT-proBNP levels were measured, and we collected demographic, clinical, and laboratory data. We introduced a new concept called the NT-proBNP ratio, which measures the NT-proBNP level relative to age-specific maximum normal values. The primary outcome was all-cause in-hospital mortality. Our analysis revealed a higher in-hospital mortality rate in 2022, as shown by the Kaplan–Meier Survival Curve. To assess the predictive value of the NT-proBNP ratio, we employed the time-dependent receiver operating characteristic (ROC) curve. Notably, the NT-proBNP ratio emerged as the strongest predictor of mortality in adult Chinese hospitalized COVID-19 patients (area under the curve, AUC = 0.826; adjusted hazard ratio [HR], 3.959; 95% confidence interval [CI] 3.001–5.221; P < 0.001). This finding consistently held true for both the 2020 and 2022 subgroups. The NT-proBNP ratio demonstrates potential predictive capability compared to several established risk factors, including NT-proBNP, hsCRP, and neutrophil-to-lymphocyte ratio, when it comes to forecasting in-hospital mortality among adult Chinese patients with COVID-19.Trial registration Clinical Trial Registration: www.clinicaltrials.gov NCT05615792.

Molecular Determination of Tumor Necrosis Factor-alpha, Interleukin-8, Interleukin-10, and C-X-C Chemokine Receptor-2 Genetic Variations and their Association with Disease Susceptibility and Mortality in COVID-19 Patients.

Altered cytokine levels have been associated with poor outcomes among COVID-19 patients. TNF-α, IL-8 and IL-10 are key cytokines in COVID-19 pathogenesis, and CXCR-2 is a major chemokine receptor involved in inflammatory response. Polymorphisms in the genes of these proteins are proposed to influence disease outcomes. In this study, we aimed to find out the association of genetic polymorphisms in TNF-α, IL-8, IL-10 and CXCR-2 genes with susceptibility to and mortality of COVID-19. The present case-control study was conducted on 230 subjects, among whom 115 were clinically diagnosed and RT-PCR-confirmed COVID-19 patients and 115 healthy control subjects. The polymorphisms in TNFα -308 G>A (rs1800629), IL-8 -251T>A (rs4073), CXCR2 +785 C>T (rs2230054) genes were detected by ARMS -PCR assay whereas for IL-10 (-1082 G>A), rs1800896 G>A allele-specific PCR assay was used and their association with COVID-19 susceptibility and mortality was estimated by multivariate analysis. The results were analyzed for risk of infection and mortality through different inheritance models. Frequencies of TNF-α rs1800629 GA, AA, IL-8 rs4073 TA, AA, IL-10 (-1082 G>A), rs1800896 GA and GG, and CXCR2 rs2230054 CT genotypes were significantly higher in COVID-19 patients compared to the control group (p < 0.05). Furthermore, COVID-19 patients had a higher frequency of the polymorphic A allele of TNF-α, the A allele of IL-8, the G allele of IL-10, and the T allele of CXCR2. The risk of susceptibility to COVID-19 was significantly associated with TNF-α rs1800629 GA, GA+AA genotypes and the A allele, IL-8 rs4073 TA, AA genotypes and A allele, IL-10 rs1800872 GA and CC genotypes and C allele, and CXCR2 rs2230054 CT and CT+CC genotypes. TNF-α-GA and AA genotypes and A allele, IL-8 TA and AA genotypes and A allele and CXCR-2 CC and CT genotypes have significant associations with mortality risk in COVID-19 patients, while GA and GG genotypes of the IL-10 are shown to confer significant protection against mortality from COVID-19. The findings of this study provide important insights into the COVID-19 disease and susceptibility risk. The polymorphisms in TNFα -308 G>A (rs1800629), IL-8 -251T>A (rs4073), IL-10 (-1082 G>A), rs1800896 and CXCR2 +785 C>T (rs2230054) are associated with the risk of susceptibility to COVID-19 and with mortality in COVID-19 patients. Further studies with larger sample sizes are necessary to confirm our findings.

Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets.

Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.

Correlation between Chest Computed Tomography Score and Laboratory Biomarkers in the Risk Stratification of COVID-19 Patients Admitted to the Emergency Department.

It has been reported that mid-regional proadrenomedullin (MR-proADM) could be considered a useful tool to stratify the mortality risk in COVID-19 patients upon admission to the emergency department (ED). During the COVID-19 outbreak, computed tomography (CT) scans were widely used for their excellent sensitivity in diagnosing pneumonia associated with SARS-CoV-2 infection. However, the possible role of CT score in the risk stratification of COVID-19 patients upon admission to the ED is still unclear. The main objective of this study was to assess if the association of the CT findings alone or together with MR-proADM results could ameliorate the prediction of in-hospital mortality of COVID-19 patients at the triage. Moreover, the hypothesis that CT score and MR-proADM levels together could play a key role in predicting the correct clinical setting for these patients was also evaluated. Epidemiological, demographic, clinical, laboratory, and outcome data were assessed and analyzed from 265 consecutive patients admitted to the triage of the ED with a SARS-CoV-2 infection. The accuracy results by AUROC analysis and statistical analysis demonstrated that CT score is particularly effective, when utilized together with the MR-proADM level, in the risk stratification of COVID-19 patients admitted to the ED, thus helping the decision-making process of emergency physicians and optimizing the hospital resources.

Normal high-sensitivity cardiac troponin for ruling-out inpatient mortality in acute COVID-19.

Assessment of inpatient mortality risk in COVID-19 patients is important for guiding clinical decision-making. High sensitivity cardiac troponin T (hs-cTnT) is a biomarker of cardiac injury associated with a worse prognosis in COVID-19. We explored how hs-cTnT could potentially be used in clinical practice for ruling in and ruling out mortality in COVID-19. We tested the diagnostic value of hs-cTnT in laboratory-confirmed COVID-19 patients (≥18 years old) admitted to the Royal Berkshire Hospital (UK) between 1st March and 10th May 2020. A normal hs-cTnT was defined as a value within the 99th percentile of healthy individuals (≤14 ng/L), and an elevated hs-cTnT was defined as >14 ng/L. Adverse clinical outcome was defined as inpatient mortality related to COVID-19. A total of 191 COVID-19 patients (62% male; age 66±16 years) had hs-cTnT measured on admission. Of these patients, 124 (65%) had elevated hs-cTnT and 67 (35%) had normal hs-cTnT. On a group level, patients with elevated hs-cTnT had worse inpatient survival (p = 0.0014; Kaplan-Meier analysis) and higher risk of inpatient mortality (HR 5.84 [95% CI 1.29-26.4]; p = 0.02; Cox multivariate regression) compared to patients with normal hs-cTnT. On a per-patient level, a normal hs-cTnT had a negative predictive value of 94% (95% CI: 85-98%) for ruling out mortality, whilst an elevated hs-cTnT had a low positive predictive value of 38% (95% CI: 39-47%) for ruling in mortality. In this study cohort of COVID-19 patients, the potential clinical utility of hs-cTnT appears to rest in ruling out inpatient mortality. This finding, if prospectively validated in a larger study, may allow hs-cTnT to become an important biomarker to facilitate admission-avoidance and early safe discharge.

Glycemic Control in Critically Ill COVID-19 Patients: Systematic Review and Meta-Analysis.

Background: Given the mortality risk in COVID-19 patients, it is necessary to estimate the impact of glycemic control on mortality rates among inpatients by designing and implementing evidence-based blood glucose (BG) control methods. There is evidence to suggest that COVID-19 patients with hyperglycemia are at risk of mortality, and glycemic control may improve outcomes. However, the optimal target range of blood glucose levels in critically ill COVID-19 patients remains unclear, and further research is needed to establish the most effective glycemic control strategies in this population. Methods: The investigation was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Data sources were drawn from Google Scholar, ResearchGate, PubMed (MEDLINE), Cochrane Library, and Embase databases. Randomized controlled trials, non-randomized controlled trials, retrospective cohort studies, and observational studies with comparison groups specific to tight glycemic control in COVID-19 patients with and without diabetes. Results: Eleven observational studies (26,953 patients hospitalized for COVID-19) were included. The incidence of death was significantly higher among COVID-19 patients diagnosed with diabetes than those without diabetes (OR = 2.70 [2.11, 3.45] at a 95% confidence interval). Incidences of death (OR of 3.76 (3.00, 4.72) at a 95% confidence interval) and complications (OR of 0.88 [0.76, 1.02] at a 95% confidence interval) were also significantly higher for COVID-19 patients with poor glycemic control. Conclusion: These findings suggest that poor glycemic control in critically ill patients leads to an increased mortality rate, infection rate, mechanical ventilation, and prolonged hospitalization.

Evaluation of Inflammation-Based Prognostic Risk Scores in Predicting in-Hospital Mortality Risk in COVID-19 Patients: A Cross-Sectional Retrospective Study.

Systemic inflammatory parameters are predictors of poor prognosis in COVID-19 patients. This study evaluated whether the prognostic nutritional index, which was also related to nutrition risk and other inflammation-based prognostic scores, was predictive of in-hospital mortality in COVID-19 patients. This was a retrospective cross-sectional single-center study. Based on the exclusion criteria, 151 patients over 18 years old diagnosed with COVID-19 and hospitalized in the intensive care unit between March 2020 and December 2020 were eligible for this study. Multivariable logistic regression analysis was performed to evaluate the predictive value of the Glasgow Prognostic Score (GPS), Prognostic Index (PI), Prognostic Nutritional Index (PNI), and Systemic Inflammatory Index (SII). In the univariate analyses, age, diabetes mellitus (DM), chronic kidney disease, acute kidney injury, hypothyroidism, hospitalization stay, lactate dehydrogenase (LDH), aspartate aminotransferase (AST), D-dimer, ferritin, C-reactive protein (CRP), albumin, hemoglobin level, platelet count, urea, creatinine level, PNI, GPS were significantly associated with mortality. However, in the multivariable logistic regression analysis of the inflammation-based prognostic scores, only PNI was statistically significant in predicting in-hospital mortality (OR=0.83; [95% CI=0.71-0.97]; p =0.019). PNI is a more useful and powerful tool among these inflammation-based prognostic risk scores in predicting in-hospital mortality in COVID-19 patients.

Liver Damage Associated with SARS-CoV-2 Infection—Myth or Reality?

(1) Introduction: While the primary impairment in COVID-19 is pulmonary, the ubiquitous distribution of angiotensin-converting enzyme 2 (ACE2) demonstrates the possible presence of systemic disease with involvement of the heart, kidneys, liver and other organs. (2) Methods: We retrospectively studied the observation sheets of patients diagnosed with SARS-CoV-2 infection hospitalized in the "Sf. Parascheva" Clinical Hospital of Infectious Diseases from Iasi for a period of 3 months. The aim of the study was to identify the frequency of liver injury due to SARS-CoV-2 infection among patients and its impact on the course of the disease. (3) Results: Out of the total number of hospitalized cases (1552), 207 (13.34%) were the subjects of our analysis. The severe form of SARS-CoV-2 infection predominated (108 cases; 52.17%) and in terms of liver damage, in all cases increased transaminase levels predominated and were determined to be secondary to the viral infection. We divided the lot into two groups, A (23 cases; 23.19%) and B (159 cases; 76.81%), depending on the time of onset of liver dysfunction, either at the time of hospitalization or during hospitalization. The evolution of liver dysfunction was predominant in most cases, with an average time of onset at 12.4 days of hospitalization. Death occurred in 50 cases. (4) Conclusions: This study revealed that high AST and ALT at hospital admission was associated with a high mortality risk in COVID-19 patients. Therefore, abnormal liver test results can be a significant prognostic indicator of outcomes in COVID-19 patients.

ACUTE KIDNEY INJURY IN COVID-19 PATIENTS

Acute kidney injury (AKI) often complicates the progression of COVID-19 and increases in-hospital mortality.&#x0D; The aim of the study is to analyze AKI frequency, the time of its development and the possibility of using the ratio blood urea nitrogen/blood creatinine (BUN/Cr) as a biomarker for AKI progression in COVID-19 patients.&#x0D; Materials and methods. The authors examined 329 patients hospitalized with COVID-19 (157 women (47.7 %) and 172 men (52.3 %), mean age 58.0±14.3 years). The follow-up period was 12 months. COVID-19 was confirmed by a PCR test. AKI frequency, severity and time of development were studied in all patients. Moreover, the authors calculated the ratio blood urea nitrogen/blood creatinine (BUN/Cr, mg/dl:mg/dl).&#x0D; Results. AKI was diagnosed in 70 patients (21.3 %), including 12 patients (17.1 %) with an increase in creatinine level after hospitalization (in-hospital AKI) and 58 patients (82.9 %) with a high creatinine level (pre-hospital AKI). AKI stage 1 was observed in 55 patients (78.6 %), stage 2 – in 11 patients (15.7 %), stage 3 – in 4 patients (5.7 %). In-hospital mortality in COVID-19 patients with AKI was 10 %, the relative mortality risk in COVID-19 patients with AKI was 5.3 (95 %, CI 1.7–16.1; p=0.01). In patients hospitalized with AKI, AUB/Cr&gt;20 was observed on hospitalization in 16 patients (27.6 %). In patients with in-hospital AKI, AUB/Cr&gt;20 was detected only in 1 person (8 %).&#x0D; Conclusion. One in four patients hospitalized with COVID-19 develop AKI, predominantly stage 1. AKI increases in-hospital mortality. In most patients, AKI develops before hospitalization. In 27.6 % of patients with pre-hospital AKI, AUB/Cr&gt;20 on hospitalization, which indicates the prerenal nature of AKI and the importance of dehydration (hypovolemia) as a risk factor for AKI progression in COVID-19 patients.

Effect of Antidiabetic Therapy on Clinical Outcomes of COVID-19 Patients With Type 2 Diabetes: A Systematic Review and Meta-Analysis.

No study has yet systematically evaluated the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with type 2 diabetes (T2D). We aimed to evaluate the effect of different antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. We comprehensively retrieved the published research which examined the effect of antidiabetic therapy on clinical outcomes of COVID-19 patients with T2D. The odds ratio (OR) and its 95% confidence interval (95% CI) for clinical outcomes were calculated using the random-effects model, and meta-regression was adopted to evaluate the potential sources of heterogeneity between studies. A total of 54 studies were included in this study. We found that the use of metformin (OR = 0.66, 95% CI: 0.58-0.75), SGLT-2i (OR = 0.80, 95% CI: 0.73-0.88), and GLP-1ra (OR = 0.83, 95% CI: 0.70-0.98) were significantly associated with lower mortality risk in COVID-19 patients with T2D, while insulin use might unexpectedly increase the ICU admission rate (OR = 2.32, 95% CI: 1.34-4.01) and risk of death (OR = 1.52, 95% CI: 1.32-1.75). No statistically significant associations were identified for DPP-4i, SUs, AGIs, and TZDs. We demonstrated that the usage of metformin, SGLT-2i, and GLP-1ra could significantly decrease mortality in COVID-19 patients with T2D. The heterogeneity across the studies, baseline characteristics of the included patients, shortage of dosage and the duration of antidiabetic drugs and autonomy of drug selection might limit the objectivity and accuracy of results. Further adequately powered and high-quality randomized controlled trials are warranted for conclusive findings.

Using ISARIC 4C mortality score to predict dynamic changes in mortality risk in COVID-19 patients during hospital admission.

As SARS-CoV-2 infections continue to cause hospital admissions around the world, there is a continued need to accurately assess those at highest risk of death to guide resource use and clinical management. The ISARIC 4C mortality score provides mortality risk prediction at admission to hospital based on demographic and physiological parameters. Here we evaluate dynamic use of the 4C score at different points following admission. Score components were extracted for 6,373 patients admitted to Barts Health NHS Trust hospitals between 1st August 2020 and 19th July 2021 and total score calculated every 48 hours for 28 days. Area under the receiver operating characteristic (AUC) statistics were used to evaluate discrimination of the score at admission and subsequent inpatient days. Patients who were still in hospital at day 6 were more likely to die if they had a higher score at day 6 than others also still in hospital who had the same score at admission. Discrimination of dynamic scoring in those still in hospital was superior with the area under the curve 0.71 (95% CI 0.69-0.74) at admission and 0.82 (0.80-0.85) by day 8. Clinically useful changes in the dynamic parts of the score are unlikely to be associated with subject-level measurements. Dynamic use of the ISARIC 4C score is likely to provide accurate and timely information on mortality risk during a patient's hospital admission.

Role of biological markers and CT severity score in predicting mortality in patients with COVID‑19: An observational retrospective study.

COVID-19 pandemic is a continuing ongoing emergency of public concern. Early identification of markers associated with disease severity and mortality can lead to a prompter therapeutic approach. The present study conducted a multivariate analysis of different markers associated with mortality in order to establish their predictive role. Confirmed cases of 697 patients were examined. Demographic data, clinical symptoms and comorbidities were evaluated. Laboratory and imaging severity scores were reviewed. A total of 133 (19.1%) out of 697 patients succumbed during hospitalization. Obesity was the most common comorbidity, followed by hypertension, diabetes, coronary heart disease and chronic kidney disease. Compared with the survivor patients, non-survivors had a higher prevalence of diabetes, chronic kidney disease and coronary heart disease, as well as higher values of laboratory markers such as neutrophil-lymphocyte ratio (NLR), D-dimer, procalcitonin, IL-6 and C Reactive protein (CRP) and respectively high values of imaging severity scores. Multivariate regression analysis showed that high values of the proposed markers and chest computerized tomography (CT) severity imaging score were predictive for in hospital death: NLR [hazard ratio (HR): 3.127 confidence interval (CI) 95: 2.137-4.576]; D-dimer [HR: 6.223 (CI 95:3.809-10.167)]; procalcitonin [HR: 4.414 (CI 95:2.804-6.948)]; IL-6 [HR: 3.344 (CI 95:1.423-7.855)]; CRP [HR:2.997 (CI 95:1.940-4.630)]; and CT severity score [HR: 3.068 (CI 95:1.777-5.299)]. Laboratory markers and imaging severity scores could be used to stratify mortality risk in COVID-19 patients.

Low HDL Cholesterol Efflux Capacity Indicates a Fatal Course of COVID-19.

Plasma membrane cholesterol is required for proper trafficking and localization of receptors that facilitate severe acute respiratory syndrome coronavirus 2 infection. High-density lipoproteins (HDL) mobilize plasma membrane cholesterol, and HDL-cholesterol levels are associated with the severity of COVID-19 disease and mortality. However, HDL-cholesterol levels poorly reflect the function of this complex family of particles, and a detailed assessment of COVID-19-associated changes in HDL functionality and its prognostic value is lacking. In the present study, we assessed HDL cholesterol efflux capacity, HDL anti-inflammatory and antioxidant properties, and changes in HDL composition and metabolism in COVID-19 (n = 48) and non-COVID pneumonia patients (n = 32). COVID-19 infection markedly reduced the activity of lecithin-cholesteryl-acyltransferase and functional parameters of HDL, such as the cholesterol efflux capacity, arylesterase activity of paraoxonase 1, and anti-oxidative capacity of apoB-depleted serum when compared to non-COVID pneumonia at baseline, paralleled by markedly reduced levels of HDL-cholesterol. Of particular interest, low HDL cholesterol efflux capacity was associated with increased mortality risk in COVID-19 patients, independent of HDL-C levels. Our results highlight profound effects of COVID-19 infection on HDL function, metabolism, and composition. Low HDL cholesterol efflux capacity indicates a fatal course of COVID-19, independent of HDL-cholesterol levels.

Preexisting coronary artery disease among coronavirus disease 2019 patients: a systematic review and meta-analysis.

The prevalence and prognostic implications of coronary artery disease (CAD) in patients infected by the novel coronavirus 2019 (COVID-19) disease remain unclear. We conducted a systematic review and meta-analysis to investigate the prevalence and mortality risk in COVID-19 patients with preexisting CAD. We searched Medline and Scopus to locate all articles published up to December 8, 2021, reporting data of COVID-19 survivors and nonsurvivors with preexisting CAD. Data were pooled using the Mantel-Haenszel random effects models with odds ratio (OR) as the effect measure with the related 95% confidence interval (CI). Thirty-eight studies including 27 435 patients (mean age 61.5 and 70.9 years) were analysed. The pooled prevalence of preexisting CAD was 12.6% (95% CI: 11.2-16.5%, I2 : 95.6%), and resulted as higher in intensive care unit patients (17.5%, 95% CI: 11.9-25.1, I2 : 88.4%) and in European cohorts (13.1%, 95% CI: 7.8-21.6%, P < 0.001, I2 : 98.4%). COVID-19 patients with preexisting CAD had a two-fold risk of short-term mortality (OR 2.61, 95% CI 2.10-3.24, P < 0.001, I2 = 73.6%); this risk was higher among Asian cohorts (OR: 2.66, 95% CI: 1.79-3.90, P < 0.001, I2 : 77.3%) compared with European (OR: 2.44, 95% CI: 1.90-3.14, P < 0.001, I2 : 56.9%) and American (OR: 1.86, 95% CI: 1.41-2.44, P < 0.001, I2 : 0%) populations. The association between CAD and poor short-term prognosis was influenced by age, prevalence of hypertension (HT), DM and CKD. Preexisting CAD is present in approximately 1 in 10 patients hospitalized for COVID-19 and significantly associated with an increased risk of short-term mortality, which is influenced by age, HT, DM and CKD.

External Validation of the Modified 4C Deterioration Model and 4C Mortality Score for COVID-19 Patients in a Swiss Tertiary Hospital.

Prognostic models to predict the deterioration and mortality risk in COVID-19 patients are utterly needed to assist in informed decision making. Most of these models, however, are at high risk of bias, model overfitting, and unclear reporting. Here, we aimed to externally validate the modified (urea was omitted) 4C Deterioration Model and 4C Mortality Score in a cohort of Swiss COVID-19 patients and, second, to evaluate whether the inclusion of the neutrophil-to-lymphocyte ratio (NLR) improves the predictive performance of the models. We conducted a retrospective single-centre study with adult patients hospitalized with COVID-19. Both prediction models were updated by including the NLR. Model performance was assessed via the models’ discriminatory performance (area under the curve, AUC), calibration (intercept and slope), and their performance overall (Brier score). For the validation of the 4C Deterioration Model and Mortality Score, 546 and 527 patients were included, respectively. In total, 133 (24.4%) patients met the definition of in-hospital deterioration. Discrimination of the 4C Deterioration Model was AUC = 0.78 (95% CI 0.73–0.82). A total of 55 (10.44%) patients died in hospital. Discrimination of the 4C Mortality Score was AUC = 0.85 (95% CI 0.79–0.89). There was no evidence for an incremental value of the NLR. Our data confirm the role of the modified 4C Deterioration Model and Mortality Score as reliable prediction tools for the risk of deterioration and mortality. There was no evidence that the inclusion of NLR improved model performance.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Abstract Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power. Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death. Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients. Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

Circulating miR-320b and miR-483-5p levels are associated with COVID-19 in-hospital mortality

The stratification of mortality risk in COVID-19 patients remains extremely challenging for physicians, especially in older patients. Innovative minimally invasive molecular biomarkers are needed to improve the prediction of mortality risk and better customize patient management. In this study, aimed at identifying circulating miRNAs associated with the risk of COVID-19 in-hospital mortality, we analyzed serum samples of 12 COVID-19 patients by small RNA-seq and validated the findings in an independent cohort of 116 COVID-19 patients by qRT-PCR. Thirty-four significantly deregulated miRNAs, 25 downregulated and 9 upregulated in deceased COVID-19 patients compared to survivors, were identified in the discovery cohort. Based on the highest fold-changes and on the highest expression levels, 5 of these 34 miRNAs were selected for the analysis in the validation cohort. MiR-320b and miR-483-5p were confirmed to be significantly hyper-expressed in deceased patients compared to survived ones. Kaplan-Meier and Cox regression models, adjusted for relevant confounders, confirmed that patients with the 20% highest miR-320b and miR-483-5p serum levels had three-fold increased risk to die during in-hospital stay for COVID-19. In conclusion, high levels of circulating miR-320b and miR-483-5p can be useful as minimally invasive biomarkers to stratify older COVID-19 patients with an increased risk of in-hospital mortality.

Mortality Risk in COVID-19 Patients with HIV-Comorbidity: Meta-Analysis

Background: Coronavirus disease 2019 or known as COVID-19 is a disease caused by severe acute respiratory coronavirus 2 (SARS-CoV-2). Comorbidities that are risk factors for COVID-19 death include hypertension, diabetes, heart disease, COPD, HIV (Human Immunodeficiency Virus), kidney failure, and cancer. This study aims to estimate the magnitude of the risk of death in COVID-19 patients with comorbid HIV, with a meta-analysis of the primary studies conducted by the previous authors. Subjects and Method : This study is a systematic review and meta-analysis with the following PICO, population: COVID-19 patients. Intervention: HIV comorbidity. Comparison: without co-morbid HIV. Outcome: Mortality. The articles used in this study were obtained from four data­bases, namely PubMed, Google Scholar, Springerlink, and Science direct, using the search keys "HIV/AIDS" AND "Mortality" OR "death" AND "COVID-19 OR SARS-CoV-2. The included article is a full-text English language with a cohort study design from 2020 to 2021 and reports the adjusted Odds Ratio (aOR) in a multivariate analysis. Article selection is done by using PRISMA flow diagram. Articles were analyzed using the Review Manager 5.3 application. Results: A total of 9 cohort studies involving 3,397,186 COVID-19 patients from America, Europe, and Africa were selected for a systematic review and meta-analysis. The data collected showed that COVID-19 patients with HIV comorbidities had a mortality risk of 3.30 times compared to COVID-19 patients without HIV comorbidities (aOR = 3.30; 95% CI = 2.87 to 3.81; p&lt;0.001). Conclusion: HIV increases mortality risk in COVID-19 patients. Keywords: HIV, COVID-19, SARS-CoV-2, mortality Correspondence: Nindita Arum Veibiani. Masters Program in Public Health, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: nindita.veibiani@gmail.com. Mobile: 085800­216969. Journal of Epidemiology and Public Health (2022), 07(04): 431-440 https://doi.org/10.26911/jepublichealth.2022.07.04.02 .

Prediction of Respiratory Failure and Mortality in COVID-19 Patients Using Long Pentraxin PTX3

The course of COVID-19 is unpredictable, ranging from asymptomatic to respiratory failure and death. Prognostic biomarkers are urgently needed. We hypothesized that long pentraxin PTX3 could be a valuable plasma biomarker due to its essential role in inflammatory processes. In a prospective hospitalized COVID-19 derivation cohort (n = 126) during the spring of 2020, we measured PTX3 within 4 days of admission. The predictive value of mechanical ventilation (MV) and 30-day mortality compared with clinical parameters and other markers of inflammation were assessed by logistic regression analysis and expressed as odds ratio (OR) with 95% confidence interval (CI). Analyses were repeated in a prospective validation cohort (n = 112) of hospitalized patients with COVID-19 treated with remdesivir and dexamethasone. Thirty-day mortality in the derivation cohort was 26.2%. In patients who died, the median PTX3 concentration upon admission was 19.5 ng/mL (IQR: 12.5–33.3) versus 6.6 ng/mL (IQR 2.9–12.3) (p < 0.0001) for survivors. After adjustment for covariates, the odds of 30-day mortality increased two-fold for each doubling of PTX3 (OR 2.03 [95% CI: 1.23–3.34], p = 0.006), which was also observed in the validation cohort (OR 1.70 [95% CI: 1.09–2.67], p = 0.02). Similarly, PTX3 levels were associated with MV. After adjustment for covariates, OR of MV was 2.34 (95% CI: 1.33–4.12, p = 0.003) in the derivation cohort and 1.64 (95% CI: 1.03–2.62, p = 0.04) in the validation cohort. PTX3 appears to be a useful clinical biomarker to predict 30-day respiratory failure and mortality risk in COVID-19 patients treated with and without remdesivir and dexamethasone.

Meta-Analysis: The Effect of D-Dimer on Mortality in Patients with COVID-19

Background: The severity of COVID-19 can be detected by examining elevated levels of CRP, ferritin, IL-6 and LDH. Elevated D-dimer levels are often found in patients with severe COVID-19 which can predict the occurrence of ARDS (Acute Respiratory Distress Syndrome) and patient mortality. The purpose of this study was to determine how much influence D-dimer levels had on mortality of COVID-19 patients. Subjects and Method: This study used a systematic review and meta-analysis with following PICO, population: patients with a clinical diagnosis of COVID-19. Intervention: high D-dimer level. Comparison: low D-dimer level. Outcome: mortality. The data used were obtained from scientific research articles from Pubmed, Google Scholar, Science Direct, and Proquest electronic databases with a cohort design in 2020 until 2022 and report on aOR in multivariate analysis. The keywords used in the search for scientific articles were “D-dimer level” OR “D-dimer” AND “COVID-19” AND “mortality”. The selection of articles is done by using PRISMA flow diagram. Data were analyzed using Review Manager software version 5.4.1. Results: A total of 12 studies with a cohort design that met the criteria from Asia, America, and Europe were selected for a systematic review and meta-analysis. Based on the meta-analysis, it was found that increased levels of D-dimer (&gt;0.5 g/mL) increased mortality in COVID-19 patients by 6.40 times compared to low levels of D-dimer (≤0.5 g/mL) and was statistically significant (aOR = 6.40; 95% CI= 4.51 to 9.08; p&lt;0.001). Conclusion: Elevated levels of D-dimer may increase mortality risk in COVID-19 patients. Keywords: d-dimer level, d-dimer, covid-19, mortality. Correspondence: Yusuf Ryadi. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Jawa Tengah. Email: yusuf.ryadi@gmail.com. Mobile: 085715605268 Journal of Epidemiology and Public Health (2022), 07(04): 552-561 https://doi.org/10.26911/jepublichealth.2022.07.04.11.