Early changes in laboratory parameters are predictors of mortality and ICU admission in patients with COVID-19: a systematic review and meta-analysis

The prognostic value of absolute lymphocyte count and neutrophil-to-lymphocyte ratio for patients with metastatic breast cancer: a systematic review and meta-analysis

Background

Neutrophil-to-lymphocyte ratio (NLR) is considered a potential prognostic marker in early breast cancer. However, the prognosis of absolute lymphocyte count (ALC) and NLR in metastatic breast cancer (MBC) has been reported in a few studies, and conclusions are still conflicting. This present manuscript aims to provide further solid evidence regarding the prognostic values of ALC and NLR in MBC patients.

Method

Eligible studies that reported the associations between ALC or NLR and MBC were included by searching relative electronic databases. Overall survival (OS) and progression-free survival (PFS) were used as outcome measures. The hazard ratio (HR) values and 95% confidence interval (CI) of the outcome measures were collected as effect sizes, and further analysis and discussion were conducted according to the pooled HR, subgroup analysis, publication bias, and interstudy heterogeneity.

Results

Twenty-nine studies comprising 3,973 patients with MBC were included. According to our findings, lower ALC was significantly associated with poorer prognosis of OS (HR = 0.57, 95% CI 0.48 to 0.68) and PFS (HR = 0.68, 95% CI 0.58 to 0.79), and greater NLR was associated with poorer OS (HR = 1.50, 95% CI 1.35 to 1.67) and PFS (HR = 1.82, 95% CI 1.42 to 2.35). Furthermore, the prognostic values of ALC and NLR in MBC were also observed in the subgroup analyses regarding cutoff values and ethnicities.

Conclusion

Low ALC and elevated NLR were observed to be significantly associated with adverse OS and PFS in MBC, indicating that ALC and NLR may act as potential prognostic biomarkers of MBC patients. Meanwhile, our results will also provide some novel evidence and research clues for the selection and development of clinical treatment strategies for MBC patients.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42021224114.

Establishment of sex-specific predictive models for critical illness in Chinese people with the Omicron variant

Introduction

The Omicron variant has rapidly spread throughout the world compared to the Delta variant and poses a great threat to global healthcare systems due to its immune evasion and rapid spread. Sex has been identified as a factor significantly associated with COVID-19 mortality, but it remains unclear which clinical indicators could be identified as risk factors in each sex group and which sex-specific risk factors might shape the worse clinical outcome, especially for Omicrons. This study aimed to confirm the relationship between sex and the progression of the Omicron variant and to explore its sex-biased risk factors.

Methods

We conducted a retrospective study including 1,132 hospitalized patients with the COVID-19 Omicron variant from 5 December 2022 to 25 January 2023 at Shanghai General Hospital, and the medical history data and clinical index data of the inpatients for possible sex differences were compared and analyzed. Then, a sex-specific Lasso regression was performed to select the variables significantly associated with critical illness, including intensive care unit admission, invasive mechanical ventilation, or death. A logistic regression was used to construct a sex-specific predictive model distinctively for the critical illness outcome using selected covariates.

Results

Among the collected 115 clinical indicators, up to 72 showed significant sex differences, including the difference in merit and the proportion of people with abnormalities. More importantly, males had greater critical illness (28.4% vs. 19.9%) and a significantly higher intensive care unit occupancy (20.96% vs. 14.49%) and mortality (13.2% vs. 4.9%), and males over 80 showed worse outcomes than females. Predictive models (AUC: 0.861 for males and 0.898 for females) showed 12 risk factors for males and 10 for females. Through a comprehensive sex-stratified analysis of a large cohort of hospitalized Omicron-infected patients, we identified the specific risk factors for critical illness by developing prediction models.

Discussion

Sex disparities and the identified risk factors should be considered, especially in the personalized prevention and treatment of the COVID-19 Omicron variant.

Baseline and early changes in laboratory parameters predict disease severity and fatal outcomes in COVID-19 patients

Introduction

Coronavirus disease 2019 (COVID-19) has become the worst catastrophe of the twenty-first century and has led to the death of more than 6.9 million individuals across the globe. Despite the growing knowledge of the clinicopathological features of COVID-19, the correlation between baseline and early changes in the laboratory parameters and the clinical outcomes of patients is not entirely understood.

Methods

Here, we conducted a time series cross-sectional study aimed at assessing different measured parameters and socio-demographic factors that are associated with disease severity and the outcome of the disease in 268 PCR-confirmed COVID-19 Patients.

Results

We found COVID-19 patients who died had a median age of 61 years (IQR, 50 y - 70 y), which is significantly higher (p < 0.05) compared to those who survived and had a median age of 54 years (IQR, 42y - 65y). The median RBC count of COVID-19 survivors was 4.9 × 106/μL (IQR 4.3 × 106/μL - 5.2 × 106/μL) which is higher (p < 0.05) compared to those who died 4.4 × 106/μL (3.82 × 106/μL - 5.02 × 106/μL). Similarly, COVID-19 survivors had significantly (p < 0.05) higher lymphocyte and monocyte percentages compared to those who died. One important result we found was that COVID-19 patients who presented with severe/critical cases at the time of first admission but managed to survive had a lower percentage of neutrophil, neutrophil to lymphocyte ratio, higher lymphocyte and monocyte percentages, and RBC count compared to those who died.

Conclusion

To conclude here, we showed that simple laboratory parameters can be used to predict severity and outcome in COVID-19 patients. As these parameters are simple, inexpensive, and radially available in most resource-limited countries, they can be extrapolated to future viral epidemics or pandemics to allocate resources to particular patients.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BACKGROUND

With the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain.

OBJECTIVE

In this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making.

METHODS

Specifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk.

RESULT

The proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes.

CONCLUSION

The experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

Associated Biochemical and Hematological Markers in COVID-19 Severity Prediction

Background

The global threat of COVID-19 has created the need for researchers to investigate the disease's progression, especially through the use of biomarkers to inform interventions. This study aims to assess the correlations of laboratory parameters to determine the severity of COVID-19 infection.

Methods

This study was conducted among 191 COVID-19 patients in Sumeru Hospital, Lalitpur, Nepal. According to their clinical outcomes, these patients were divided into severe and nonsevere groups. Inflammatory markers such as LDH, D-dimer, CRP, ferritin, complete blood cell count, liver function tests, and renal function tests were performed. Binary logistic regression analysis determined relative risk factors associated with severe COVID-19. The area under the curve (AUC) was calculated with ROC curves to assess the potential predictive value of risk factors.

Results

Out of 191 patients, 38 (19.8%) subjects died due to COVID-19 complications, while 156 (81.7%) survived and were discharged from hospital. The COVID-19 severity was found in patients with older age and comorbidities such as CKD, HTN, DM, COPD, and pneumonia. Parameters such as d-dimer, CRP, LDH, SGPT, neutrophil, lymphocyte count, and LMR were significant independent risk factors for the severity of the disease. The AUC was highest for d-dimer (AUC = 0.874) with a sensitivity of 82.2% and specificity of 81.2%. Similarly, the cut-off values for other factors were age >54.5 years, D-dimer >0.91 ng/ml, CRP >82.4 mg/dl, neutrophil >78.5%, LDH >600 U/L, and SGPT >35.5 U/L, respectively.

Conclusion

Endorsement of biochemical and hematological parameters with their cut-off values also aids in predicting COVID-19 severity. The biomarkers such as D-dimer, CRP levels, LDH, ALT, and neutrophil count could be used to predict disease severity. So, timely analysis of these markers might allow early prediction of disease progression.

C-reactive protein and clinical outcome in COVID-19 patients: the importance of harmonized measurements

C-reactive protein (CRP) is a cytokine-mediated acute phase reactant with a recognized role in inflammatory conditions and infectious disease. In coronavirus disease 2019 (COVID-19), elevated CRP concentrations in serum were frequently detected and significantly associated with poor outcome in terms of disease severity, need for intensive care, and in-hospital death. For these reasons, the marker was proposed as a powerful test for prognostic classification of COVID-19 patients. In most of available publications, there was however confounding information about how interpretative criteria for CRP in COVID-19 should be derived, including quality of employed assays and optimal cut-off definition. Assuring result harmonization and controlling measurement uncertainty in terms of performance specifications are fundamental to allow worldwide application of clinical information according to specific CRP thresholds and to avoid risk of patient misclassification.

Shock index in the emergency department as a predictor for mortality in COVID-19 patients: A systematic review and meta-analysis

Background

The shock index (SI) ratio serves as a straightforward predictor to identify patients who are either at risk of or experiencing shock. COVID-19 patients with shock face increased mortality risk and reduced chances of recovery. This review aims to determine the role of SI in the emergency department (ED) to predict COVID-19 patient outcomes.

Methods

The systematic search was conducted in PubMed, ProQuest, Scopus, and ScienceDirect on June 16, 2023. We included observational studies evaluating SI in ED and COVID-19 patient outcomes. Random-effect meta-analysis was done to generate odds ratios of SI as the predictor of intensive care unit (ICU) admission and mortality. The sensitivity and specificity of SI in predicting these outcomes were also pooled, and a summary receiver operating characteristics (sROC) curve was generated.

Results

A total of eight studies involving 4557 participants were included in the pooled analysis. High SI was found to be associated with an increased risk of ICU admission (OR 5.81 [95%CI: 1.18-28.58], p = 0.03). Regarding mortality, high SI was linked to higher rates of in-hospital (OR 7.45 [95%CI: 2.44-22.74], p = 0.0004), within 30-day (OR 7.34 [95%CI: 5.27-10.21], p < 0.00001), and overall (OR 7.52 [95%CI: 3.72-15.19], p < 0.00001) mortality. The sensitivity and specificity of SI for predicting ICU admission were 76.2% [95%CI: 54.6%-89.5%] and 64.3% [95%CI: 19.6%-93.0%], respectively. In terms of overall mortality, the sensitivity and specificity were 54.0% (95%CI: 34.3%-72.6%) and 85.9% (95%CI: 75.8%-92.3%), respectively, with only subtle changes for in-hospital and within 30-day mortality. Adjustment of SI cut-off to >0.7 yielded improved sensitivity (95%CI: 78.0% [59.7%-89.4%]) and specificity (95%CI: 76.8% [41.7%-93.9%]) in predicting overall mortality.

Conclusion

SI in emergency room may be a simple and useful triage instrument for predicting ICU admission and mortality in COVID-19 patients. Future well-conducted studies are still needed to corroborate the findings of this study.

Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study

INTRODUCTION

In the current COVID-19 pandemic, clinicians require a manageable set of decisive parameters that can be used to (i) rapidly identify SARS-CoV-2 positive patients, (ii) identify patients with a high risk of a fatal outcome on hospital admission, and (iii) recognize longitudinal warning signs of a possible fatal outcome.

METHODS

This comparative study was performed in 515 patients in the Maria Skłodowska-Curie Specialty Voivodeship Hospital in Zgierz, Poland. The study groups comprised 314 patients with COVID-like symptoms who tested negative and 201 patients who tested positive for SARS-CoV-2 infection; of the latter, 72 patients with COVID-19 died and 129 were released from hospital. Data on which we trained several machine learning (ML) models included clinical findings on admission and during hospitalization, symptoms, epidemiological risk, and reported comorbidities and medications.

RESULTS

We identified a set of eight on-admission parameters: white blood cells, antibody-synthesizing lymphocytes, ratios of basophils/lymphocytes, platelets/neutrophils, and monocytes/lymphocytes, procalcitonin, creatinine, and C-reactive protein. The medical decision tree built using these parameters differentiated between SARS-CoV-2 positive and negative patients with up to 90-100% accuracy. Patients with COVID-19 who on hospital admission were older, had higher procalcitonin, C-reactive protein, and troponin I levels together with lower hemoglobin and platelets/neutrophils ratio were found to be at highest risk of death from COVID-19. Furthermore, we identified longitudinal patterns in C-reactive protein, white blood cells, and D dimer that predicted the disease outcome.

CONCLUSIONS

Our study provides sets of easily obtainable parameters that allow one to assess the status of a patient with SARS-CoV-2 infection, and the risk of a fatal disease outcome on hospital admission and during the course of the disease.

Biomarkers Predicting Poor Prognosis in Covid-19 Patients: A Survival Analysis

Introduction With the spread of the Covid-19 pandemic and its overwhelming impact on health systems in several countries, the importance of identifying predictors of severity is of paramount importance. The objective of this study is to determine the relationship between death and the biological parameters of patients with Covid-19. Materials and methods This is an analytical retrospective cohort study conducted on 326 patients admitted to the Mohammed VI University Hospital in Oujda, Morocco. The statistical analysis concerned the biological parameters carried out on the admission of the patients, in addition to age and sex. The comparison between the two surviving and non-surviving groups was made by a simple analysis than a multivariate analysis by logistic regression. Next, a survival analysis was performed by the Kaplan-Meier method and then by Cox regression. Results A total of 326 patients were included in the study, including 108 fatal cases. The mean age was 64.66 ± 15.51 and the sex ratio was 1.08:1 (M:F). Age, procalcitonin, liver enzymes, and coagulation factors were significantly higher in patients who died of Covid-19 and are therefore considered to be the main prognostic factors identified in this study. Conclusion Knowledge and monitoring of predictive biomarkers of poor prognosis in patients with Covid-19 could be of great help in the identification of patients at risk and in the implementation of an effective diagnostic and therapeutic strategy to predict severe disease forms.

Clinical progress in MSC-based therapies for the management of severe COVID-19

Considering the high impact that severe Coronavirus disease 2019 (COVID-19) cases still pose on public health and their complex pharmacological management, the search for new therapeutic alternatives is essential. Mesenchymal stromal cells (MSCs) could be promising candidates as they present important immunomodulatory and anti-inflammatory properties that can combat the acute severe respiratory distress syndrome (ARDS) and the cytokine storm occurring in COVID-19, two processes that are mainly driven by an immunological misbalance. In this review, we provide a comprehensive overview of the intricate inflammatory process derived from the immune dysregulation that occurs in COVID-19, discussing the potential that the cytokines and growth factors that constitute the MSC-derived secretome present to treat the disease. Moreover, we revise the latest clinical progress made in the field, discussing the most important findings of the clinical trials conducted to date, which follow 2 different approaches: MSC-based cell therapy or the administration of the secretome by itself, as a cell-free therapy.

Assessment and Monitoring of Coagulation in Patients with COVID-19: A Review of Current Literature

Coagulation abnormalities are common in patients with COVID-19 and associated with high morbidity and mortality. It became a daily challenge to navigate through these abnormal laboratory findings and deliver the best possible treatment to the patients. The unique character of COVID-19-induced coagulopathy necessitates not only a dynamic follow-up of the patients in terms of hemostatic findings but also the introduction of new diagnostic methods to determine the overall function of the coagulation system in real time. After the recognition of the high risk of thromboembolism in COVID-19, several professional societies published their recommendations regarding anticoagulation in patients with COVID-19. This review summarizes common hemostatic findings in COVID-19 patients and presents the societal recommendations regarding the use of coagulation laboratory findings in clinical decision-making. Although several studies have investigated coagulation parameters in patients with COVID-19, the methodological shortcomings of published studies as well as the differences in employed anticoagulation regimens that have changed over time, depending on national and international guidelines, limit the applicability of these findings in other clinical settings. Accordingly, evidence-based recommendations for diagnostics during acute COVID-19 infection are still lacking. Future studies should verify the role of coagulation parameters as well as viscoelastic methods in the management of patients with COVID-19.

Analysis of Mortality in Unvaccinated Patients with COVID-19 and Cardiovascular Risk

COVID-19 is a contagious disease that has spread globally, killing millions of people around the world. In order to reduce the likelihood of in-hospital death due to COVID-19, it is reasonable to select a group of patients with a low probability of survival and to implement measures in advance to minimize the risk of death. One way to do this is to establish cut-off values for the most commonly performed blood laboratory tests, above or below which the likelihood of death increases significantly. The aim of the study was to determine the basic laboratory parameters among unvaccinated patients hospitalized for COVID-19 with concomitant cardiovascular disease, which are the predictors of in-hospital death. Out of 1234 patients, 446 people who met the specific inclusion criteria were enrolled in the study. The multivariate regression analysis has shown that the independent predictors of death are: troponin levels of at least 0.033 μg/L (OR = 2.04 [1.10; 3.79]), creatinine of at least 1.88 mg/dL (OR = 2.88 [1.57; 5.30]), D-dimers of at least 0.97 g/L (OR = 2.04 [1.02; 4.07]), and C-reactive protein minimum of 0.89 mg/L (OR = 2.28 [1.24; 4.18]).

Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

Background

In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.

Objective

The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.

Methods

We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.

Results

The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.

Conclusions

In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.

Comprehensive Cytokine Profiling of Patients with COVID-19 Receiving Tocilizumab Therapy

Coronavirus disease 2019 (COVID-19) is characterized by immune activation in response to viral spread, in severe cases leading to the development of cytokine storm syndrome (CSS) and increased mortality. Despite its importance in prognosis, the pathophysiological mechanisms of CSS in COVID-19 remain to be defined. Towards this goal, we analyzed cytokine profiles and their interrelation in regard to anti-cytokine treatment with tocilizumab in 98 hospitalized patients with COVID-19. We performed a multiplex measurement of 41 circulating cytokines in the plasma of patients on admission and 3-5 days after, during the follow-up. Then we analyzed the patient groups separated in two ways: according to the clusterization of their blood cytokines and based on the administration of tocilizumab therapy. Patients with and without CSS formed distinct clusters according to their cytokine concentration changes. However, the tocilizumab therapy, administered based on the standard clinical and laboratory criteria, did not fully correspond to those clusters of CSS. Furthermore, among all cytokines, IL-6, IL-1RA, IL-10, and G-CSF demonstrated the most prominent differences between patients with and without clinical endpoints, while only IL-1RA was prognostically significant in both groups of patients with and without tocilizumab therapy, decreasing in the former and increasing in the latter during the follow-up period. Thus, CSS in COVID-19, characterized by a correlated release of multiple cytokines, does not fully correspond to the standard parameters of disease severity. Analysis of the cytokine signature, including the IL-1RA level in addition to standard clinical and laboratory parameters may be useful to define the onset of a cytokine storm in COVID-19 as well as the indications for anti-cytokine therapy.

Significance of hemogram-derived ratios for predicting in-hospital mortality in COVID-19: A multicenter study

Background

To address the problem of resource limitation, biomarkers having a potential for mortality prediction are urgently required. This study was designed to evaluate whether hemogram-derived ratios could predict in-hospital deaths in COVID-19 patients.

Materials and Methods

This multicenter retrospective study included hospitalized COVID-19 patients from four COVID-19 dedicated hospitals in Sylhet, Bangladesh. Data on clinical characteristics, laboratory parameters, and survival outcomes were analyzed. Logistic regression models were fitted to identify the predictors of in-hospital death.

Results

Out of 442 patients, 55 (12.44%) suffered in-hospital death. The proportion of male was higher in nonsurvivor group (61.8%). The mean age was higher in nonsurvivors (69 ± 13 vs. 59 ± 14 years, p < 0.001). Compared to survivors, nonsurvivors exhibited higher frequency of comorbidities, such as chronic kidney disease (34.5% vs. 15.2%, p ≤ 0.001), chronic obstructive pulmonary disease (23.6% vs. 10.6%, p = 0.011), ischemic heart disease (41.8% vs. 19.4%, p < 0.001), and diabetes mellitus (76.4% vs. 61.8%, p = 0.05). Leukocytosis and lymphocytopenia were more prevalent in nonsurvivors (p < 0.05). Neutrophil-to-lymphocyte ratio (NLR), derived NLR (d-NLR), and neutrophil-to-platelet ratio (NPR) were significantly higher in nonsurvivors (p < 0.05). After adjusting for potential covariates, NLR (odds ratio [OR] 1.05; 95% confidence interval [CI] 1.009-1.08), d-NLR (OR 1.08; 95% CI 1.006-1.14), and NPR (OR 1.20; 95% CI 1.09-1.32) have been found to be significant predictors of mortality in hospitalized COVID-19 patients. The optimal cut-off points for NLR, d-NLR, and NPR for prediction of in-hospital mortality for COVID-19 patients were 7.57, 5.52 and 3.87, respectively.

Conclusion

Initial assessment of NLR, d-NLR, and NPR values at hospital admission is of good prognostic value for predicting mortality of patients with COVID-19.

Clinical Characteristics and Predictors of Mortality in Elderly Patients Hospitalized with COVID-19 in Bangladesh: A Multicenter, Retrospective Study

Purpose

Elderly patients are at high risk of fatality from COVID-19. The present work aims to describe the clinical characteristics of elderly inpatients with COVID-19 and identify the predictors of in-hospital mortality at admission.

Materials and Methods

In this retrospective, multicenter cohort study, we included elderly COVID-19 inpatients (n = 245) from four hospitals in Sylhet, Bangladesh, who had been discharged between October 2020 and February 2021. Demographic, clinical, and laboratory data were extracted from hospital records and compared between survivors and nonsurvivors. We used univariable and multivariable logistic regression analysis to explore the risk factors associated with in-hospital death. Principal Results. Of the included patients, 202 (82.44%) were discharged and 43 (17.55%) died in hospital. Except hypertension, other comorbidities like diabetes, chronic kidney disease, ischemic heart disease, and chronic obstructive pulmonary disease were more prevalent in nonsurvivors. Nonsurvivors had a higher prevalence of leukocytosis (51.2 versus 30.7; p=0.01), lymphopenia (72.1 versus 55; p=0.05), and thrombocytopenia (20.9 versus 9.9; p=0.07). Multivariable regression analysis showed an increasing odds ratio of in-hospital death associated with older age (odds ratio 1.05, 95% CI 1.01–1.10, per year increase; p=0.009), thrombocytopenia (OR = 3.56; 95% CI 1.22–10.33, p=0.019), and admission SpO₂ (OR 0.91, 95% CI 0.88–0.95; p=0.001).

Conclusions

Higher age, thrombocytopenia, and lower initial level of SpO₂ at admission are predictors of in-hospital mortality in elderly patients with COVID-19.

SARS-CoV-2 Spike Protein Binding of Glycated Serum Albumin-Its Potential Role in the Pathogenesis of the COVID-19 Clinical Syndromes and Bias towards Individuals with Pre-Diabetes/Type 2 Diabetes and Metabolic Diseases

The immune response to SARS-CoV-2 infection requires antibody recognition of the spike protein. In a study designed to examine the molecular features of anti-spike and anti-nucleocapsid antibodies, patient plasma proteins binding to pre-fusion stabilised complete spike and nucleocapsid proteins were isolated and analysed by matrix-assisted laser desorption ionisation–time of flight (MALDI-ToF) mass spectrometry. Amongst the immunoglobulins, a high affinity for human serum albumin was evident in the anti-spike preparations. Careful mass comparison revealed the preferential capture of advanced glycation end product (AGE) forms of glycated human serum albumin by the pre-fusion spike protein. The ability of bacteria and viruses to surround themselves with serum proteins is a recognised immune evasion and pathogenic process. The preference of SARS-CoV-2 for AGE forms of glycated serum albumin may in part explain the severity and pathology of acute respiratory distress and the bias towards the elderly and those with (pre)diabetic and atherosclerotic/metabolic disease.

D-dimer, CRP, PCT, and IL-6 Levels at Admission to ICU Can Predict In-Hospital Mortality in Patients with COVID-19 Pneumonia

INTRODUCTION

Health care workers have had a challenging task since the COVID-19 outbreak. Prompt and effective predictors of clinical outcomes are crucial to recognize potentially critically ill patients and improve the management of COVID-19 patients. The aim of this study was to identify potential predictors of clinical outcomes in critically ill COVID-19 patients.

METHODS

The study was designed as a retrospective cohort study, which included 318 patients treated from June 2020 to January 2021 in the Intensive Care Unit (ICU) of the Clinical Hospital Center "Bezanijska Kosa" in Belgrade, Serbia. The verified diagnosis of COVID-19 disease, patients over 18 years of age, and the hospitalization in ICU were the criteria for inclusion in the study. The optimal cutoff value of D-dimer, CRP, IL-6, and PCT for predicting hospital mortality was determined using the ROC curve, while the Kaplan-Meier method and log-rank test were used to assess survival.

RESULTS

The study included 318 patients: 219 (68.9%) were male and 99 (31.1%) female. The median age of patients was 69 (60-77) years. During the treatment, 195 (61.3%) patients died, thereof 130 male (66.7%) and 65 female (33.3%). 123 (38.7%) patients were discharged from hospital treatment. The cutoff value of IL-6 for in-hospital death prediction was 74.98 pg/mL (Sn 69.7%, Sp 62.7%); cutoff value of CRP was 81 mg/L (Sn 60.7%, Sp 60%); cutoff value of procalcitonin was 0.56 ng/mL (Sn 81.1%, Sp 76%); and cutoff value of D-dimer was 760 ng/mL FEU (Sn 63.4%, Sp 57.1%). IL-6 ≥ 74.98 pg/mL, CRP ≥ 81 mg/L, PCT ≥ 0.56 ng/mL, and D-dimer ≥ 760 ng/mL were statistically significant predictors of in-hospital mortality.

CONCLUSION

IL-6 ≥ 74.98 pg/mL, CRP values ≥ 81 mg/L, procalcitonin ≥ 0.56 ng/mL, and D-dimer ≥ 760 ng/mL could effectively predict in-hospital mortality in COVID-19 patients.

Hematological changes associated with COVID-19 infection

BACKGROUND

The unresolved COVID-19 pandemic considerably impacts the health services in Iraq and worldwide. Consecutive waves of mutated virus increased virus spread and further constrained health systems. Although molecular identification of the virus by polymerase chain reaction is the only recommended method in diagnosing COVID-19 infection, radiological, biochemical, and hematological studies are substantially important in risk stratification, patient follow-up, and outcome prediction.

AIM

This narrative review summarized the hematological changes including the blood indices, coagulative indicators, and other associated biochemical laboratory markers in different stages of COVID-19 infection, highlighting the diagnostic and prognostic significance.

METHODS

Literature search was conducted for multiple combinations of different hematological tests and manifestations with novel COVID-19 using the following key words: "hematological," "complete blood count," "lymphopenia," "blood indices," "markers" "platelet" OR "thrombocytopenia" AND "COVID-19," "coronavirus2019," "2019-nCoV," OR "SARS-CoV-2." Articles written in the English language and conducted on human samples between December 2019 and January 2021 were included.

RESULTS

Hematological changes are not reported in asymptomatic or presymptomatic COVID-19 patients. In nonsevere cases, hematological changes are subtle, included mainly lymphocytopenia (80.4%). In severe, critically ill patients and those with cytokine storm, neutrophilia, lymphocytopenia, elevated D-dimer, prolonged PT, and reduced fibrinogen are predictors of disease progression and adverse outcome.

CONCLUSION

Monitoring hematological changes in patients with COVID-19 can predict patients needing additional care and stratify the risk for severe course of the disease. More studies are required in Iraq to reflect the hematological changes in COVID-19 as compared to global data.

Synergistic Effect of Static Compliance and D-dimers to Predict Outcome of Patients with COVID-19-ARDS: A Prospective Multicenter Study

The synergic combination of D-dimer (as proxy of thrombotic/vascular injury) and static compliance (as proxy of parenchymal injury) in predicting mortality in COVID-19-ARDS has not been systematically evaluated. The objective is to determine whether the combination of elevated D-dimer and low static compliance can predict mortality in patients with COVID-19-ARDS. A “training sample” (March–June 2020) and a “testing sample” (September 2020–January 2021) of adult patients invasively ventilated for COVID-19-ARDS were collected in nine hospitals. D-dimer and compliance in the first 24 h were recorded. Study outcome was all-cause mortality at 28-days. Cut-offs for D-dimer and compliance were identified by receiver operating characteristic curve analysis. Mutually exclusive groups were selected using classification tree analysis with chi-square automatic interaction detection. Time to death in the resulting groups was estimated with Cox regression adjusted for SOFA, sex, age, PaO₂/FiO₂ ratio, and sample (training/testing). “Training” and “testing” samples amounted to 347 and 296 patients, respectively. Three groups were identified: D-dimer ≤ 1880 ng/mL (LD); D-dimer > 1880 ng/mL and compliance > 41 mL/cmH₂O (LD-HC); D-dimer > 1880 ng/mL and compliance ≤ 41 mL/cmH₂O (HD-LC). 28-days mortality progressively increased in the three groups (from 24% to 35% and 57% (training) and from 27% to 39% and 60% (testing), respectively; p < 0.01). Adjusted mortality was significantly higher in HD-LC group compared with LD (HR = 0.479, p < 0.001) and HD-HC (HR = 0.542, p < 0.01); no difference was found between LD and HD-HC. In conclusion, combination of high D-dimer and low static compliance identifies a clinical phenotype with high mortality in COVID-19-ARDS.

The incremental value of computed tomography of COVID-19 pneumonia in predicting ICU admission

Triage is crucial for patient’s management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient’s admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73–0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56–0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp. The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.

An update review of globally reported SARS-CoV-2 vaccines in preclinical and clinical stages

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the rapidly spreading pandemic COVID-19 in the world. As an effective therapeutic strategy is not introduced yet and the rapid genetic variations in the virus, there is an emerging necessity to design, evaluate and apply effective new vaccines. An acceptable vaccine must elicit both humoral and cellular immune responses, must have the least side effects and the storage and transport systems should be available and affordable for all countries. These vaccines can be classified into different types: inactivated vaccines, live-attenuated virus vaccines, subunit vaccines, virus-like particles (VLPs), nucleic acid-based vaccines (DNA and RNA) and recombinant vector-based vaccines (replicating and non-replicating viral vector). According to the latest update of the WHO report on April 2nd, 2021, at least 85 vaccine candidates were being studied in clinical trial phases and 184 candidate vaccines were being evaluated in pre-clinical stages. In addition, studies have shown that other vaccines, including the Bacillus Calmette-Guérin (BCG) vaccine and the Plant-derived vaccine, may play a role in controlling pandemic COVID-19. Herein, we reviewed the different types of COVID-19 candidate vaccines that are currently being evaluated in preclinical and clinical trial phases along with advantages, disadvantages or adverse reactions, if any.

Clinical Course and Outcome of ESRD Patients on Maintenance Hemodialysis Infected with COVID-19: A Single-Center Study

Background

In an ESRD subset of patients, COVID-19 infection is associated with increased disease burden and higher mortality rates.

Methods

We conducted a retrospective single-center cohort study in which 43 ESRD patients had a diagnosis of COVID-19. Association of risk factors with mortality was assessed by chi-square test and logistic regression analysis. Data were collected on a structured performa which included variables like age, gender, comorbid conditions, drug history, clinical presentation, hemodynamic status and laboratory parameters. Outcome variables were recovery and death. All patients received standard treatment for COVID-19 according to hospital protocols, along with hemodialysis and continuous renal replacement therapy (CRRT) when needed.

Results

Those most affected were found to be male, 25 (58.1%), while the number of females affected was 18 (41.9%). The most frequent comorbid condition was hypertension (HTN), seen in 35 (81.4%) patients; however, thromboembolic complications were very few in these patients. The mortality rate in our study was 25.6%, and the population most susceptible to poor outcomes in the ESRD subgroup was elderly people (45.5%), while younger patients recovered the most from COVID-19 (53.1%). Hypoalbuminemia, leukocytosis, lymphopenia and raised LDH were also found to be associated with death in ESRD patients suffering from COVID-19 (81.8, 72.7, 100 and 100%, respectively). In multivariate logistic regression analysis, we found that the odds ratio of dying from COVID-19 was 19.5 times higher in patients aged >65 years as compared to patients aged 18–50 years (p=0.039). Similarly, patients with a high TLC were 24.1 times more likely to die than patients with a normal TLC (p=0.008).

Conclusion

In our center, the mortality rate of ESRD patients affected with COVID-19 disease was 25.6%, and older age, leukocytosis, lymphopenia, hypoalbuminemia and high LDH were significantly associated with mortality.

Effect of all-in-one nursing model on ICU ventilator-associated pneumonia

OBJECTIVE

To study the effect of an all-in-one nursing model on ICU ventilator-associated pneumonia (VAP).

METHODS

A total of 100 ICU patients needing ventilator assistance who were admitted to our hospital from March 2018 to December 2019 were equally randomized into two groups by a lottery system, with 50 cases in each group. Patients in the control group received routine nursing, and patients in the experimental group received all-in-one nursing. The number of ICU VAP patients, time transferring from ICU to an ordinary ward, hospital stay, mechanical ventilation time, nursing efficiency, and the changes of blood pressure, heart rate and oxygen saturation during nursing was compared between the two groups.

RESULTS

Regarding the number of cases of VAP, the length of stay in the ICU, and the length of hospital stay, and the mechanical ventilation time, the experimental group was markedly shorter than that of the control group (P<0.05). With respect to the effective rates of nursing care, the experimental group (96%) was better than the control group (80%) (P<0.05). When considering the changes of hemodynamic indexes during the nursing process, the two groups exhibited no marked difference (P>0.05). After intervention, the control group was inferior in terms of the oxygen partial pressure and carbon dioxide partial pressure compared to the experimental group (P<0.05).

CONCLUSION

All-in-one nursing can reduce the incidence of VAP in ICU patients, significantly shorten the length of ICU stay, hospital stay and mechanical ventilation time, thus improving overall nursing efficiency.

Predictors of Mortality in Critically Ill COVID-19 Patients Demanding High Oxygen Flow: A Thin Line between Inflammation, Cytokine Storm, and Coagulopathy

Introduction

Mortality among critically ill COVID-19 patients remains relatively high despite different potential therapeutic modalities being introduced recently. The treatment of critically ill patients is a challenging task, without identified credible predictors of mortality.

Methods

We performed an analysis of 160 consecutive patients with confirmed COVID-19 infection admitted to the Respiratory Intensive Care Unit between June 23, 2020, and October 2, 2020, in University Hospital Center Bezanijska kosa, Belgrade, Serbia. Patients on invasive, noninvasive ventilation and high flow oxygen therapy with moderate to severe ARDS, according to the Berlin definition of ARDS, were selected for the study. Demographic data, past medical history, laboratory values, and CT severity score were analyzed to identify predictors of mortality. Univariate and multivariate logistic regression models were used to assess potential predictors of mortality in critically ill COVID-19 patients.

Results

The mean patient age was 65.6 years (range, 29–92 years), predominantly men, 68.8%. 107 (66.9%) patients were on invasive mechanical ventilation, 31 (19.3%) on noninvasive, and 22 (13.8%) on high flow oxygen therapy machine. The median total number of ICU days was 10 (25^th to 75^th percentile: 6–18), while the median total number of hospital stay was 18 (25^th to 75^th percentile: 12–28). The mortality rate was 60% (96/160). Univariate logistic regression analysis confirmed the significance of age, CRP, and lymphocytes at admission to hospital, serum albumin, D-dimer, and IL-6 at admission to ICU, and CT score. Serum albumin, D-dimer, and IL-6 at admission to ICU were independently associated with mortality in the final multivariate analysis.

Conclusion

In the present study of 160 consecutive critically ill COVID-19 patients with moderate to severe ARDS, IL-6, serum albumin, and D-dimer at admission to ICU, accompanied by chest CT severity score, were marked as independent predictors of mortality.