A novel haemocytometric COVID-19 prognostic score developed and validated in an observational multicentre European hospital-based study

Association between objectively measured protein intake and muscle status, health-related quality of life, and mortality in hemodialysis patients

BACKGROUND

Protein intake is known to be associated with muscle mass, health-related quality of life (HRQoL), and mortality in patients with stage 5 chronic kidney disease undergoing dialysis. However, most studies evaluated protein intake based on 24 h dietary recall or food frequency questionnaire, and these methods are prone to bias. Therefore, this study aimed to evaluate the association of objectively measured protein intake with muscle mass and strength, HRQoL, and mortality.

METHODS

Dietary protein intake was calculated based on the combined (urinary and dialysate) urea excretion rate according to the Maroni formula and indexed to body weight. Muscle mass was calculated based on the combined dialysate and urinary creatinine excretion rate, and muscle strength was assessed by handgrip strength. HRQoL was based on the Short Form 36. Linear and Cox regression were used for the analyses.

RESULTS

We included 59 hemodialysis patients (mean age 65 ± 15 years, 37% female, median hemodialysis vintage 15 [6-39] months). Mean protein intake was 0.82 ± 0.23 g/kg/day, and 76% had a low protein intake (<1.0 g/kg/day). Higher protein intake was independently associated with higher muscle mass (Standardized beta (St. β) [95% confidence interval (95%CI) = 0.56 [0.34 to 0.78]) and higher scores on the physical functioning domain of HRQoL (St. β [95%CI] = 0.49 [0.25 to 0.73]), but not with muscle strength (St. β [95%CI] = 0.17 [-0.10 to 0.43]). During a median follow-up of 21.6 [8.6-36.6] months, 16 (27.1%) patients died. Higher protein intake was associated with lower mortality risk (hazard ratio [95%CI] = 0.34 [0.16-0.73]). This association remained significant after adjustment for potential confounders.

CONCLUSIONS

Protein intake is independently associated with muscle mass, physical functioning domain of HRQOL, and mortality. Clinicians and dietitians should closely monitor the protein intake of hemodialysis patients.

White blood cells scattergram as a valuable tool for COVID-19 screening: A multicentric study

INTRODUCTION

New tools have been developed to distinguish the COVID-19 diagnosis from other viral infections presenting similar symptomatology and mitigate the lack of sensitivity of molecular testing. We previously identified a specific "sandglass" aspect on the white blood cells (WBC) scattergram of COVID-19 patients, as a highly reliable COVID-19 screening test (sensitivity: 85.9%, specificity: 83.5% and positive predictive value: 94.3%). We then decided to validate our previous data in a multicentric study.

METHODS

This retrospective study involved 817 patients with flu-like illness, among 20 centers, using the same CBC instrument (XN analyzer, SYSMEX, Japan). After training, one specialist per center independently evaluated, under the same conditions, the presence of the "sandglass" aspect of the WDF scattergram, likely representing plasmacytoid lymphocytes.

RESULTS

Overall, this approach showed sensitivity: 59.0%, specificity: 72.9% and positive predictive value: 77.7%. Sensitivity improved with subgroup analysis, including in patients with lymphopenia (65.2%), patients presenting symptoms for more than 5 days (72.3%) and in patients with ARDS (70.1%). COVID-19 patients with larger plasmacytoid lymphocyte cluster (>15 cells) more often have severe outcomes (70% vs. 15% in the control group).

CONCLUSION

Our findings confirm that the WBC scattergram analysis could be added to a diagnostic algorithm for screening and quickly categorizing symptomatic patients as either COVID-19 probable or improbable, especially during COVID-19 resurgence and overlapping with future influenza epidemics. The observed large size of the plasmacytoid lymphocytes cluster appears to be a hallmark of COVID-19 patients and was indicative of a severe outcome. Furthers studies are ongoing to evaluate the value of the new hematological parameters in combination with WDF analysis.

Sysmex-derived COVID-19 prognostic score as an early prognostic marker for severity of the COVID-19 disease

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a life-threatening disease with a heterogeneous course. Even some young patients are at increased risk of severe course or death, as they can face severe complications. It would be very useful to have a cheap and easily available marker to predict COVID-19 course in the early stages of the disease. The COVID-19 prognostic score could be a very useful clinical indicator available at the time of primary contact with the patient.

METHODS

The COVID-19 prognostic score and the clinical condition together with selected laboratory parameters were evaluated in patients with respiratory tract infection and a positive PCR test for the SARS-CoV-2 during the first contact with the patient. Prognostic significance was evaluated using receiver operating characteristic curves (ROC) and area under the curve (AUC). Selected parameters of the blood count and hemostasis, as well as selected biochemical indicators, were examined too.

RESULTS

Thirty-seven of 164 patients developed serious symptoms. The COVID-19 score had one of the highest AUC values (0.855) of all markers. The highest combination of sensitivity (91.9%) and specificity (71.7%) for identifying patients with a subsequent moderate and severe course of the disease was achieved at the threshold 1.5. The predictive value of a negative test is beneficial too (0.968).

CONCLUSIONS

The COVID-19 prognostic score is a promising indicator stratifying patients with COVID-19 into prognostic groups at the time of the first contact, thus allowing the timely provision of increased care in patients at high risk of severe development.

Performance evaluation of the new Sysmex XR-Series haematology analyser

Background

The new XR-Series haematology analyser from Sysmex provides increased throughput and automation, along with a new reagent in WDF channel for optimised WBC differential.

Methods

An analytical performance study for the XR analyser was conducted to evaluate the WDF channel parameters in comparison to the instrument specifications. Additionally, 7460 samples were measured on XR and XN analysers to compare selected parameters and flags, and 930 randomly selected samples were further evaluated with microscopy.

Results

All investigated aspects of the analytical performance study for the XR fell within the manufacturer specifications. The correlation coefficients between the two systems for the parameters tested were greater than 0.983 for the main CBC and DIFF parameters, greater than 0.909 for the Extended Inflammation Parameters, and greater than 0.932 for the parameters used in the workflow rulesets of the Extended IPU. Similarly high sensitivities for the detection of abnormal cells were observed for the 'Blasts/Abn Lympho?' flag (XN: 100%, XR: 99.0%) and WPC abnormal flags ('Blasts?' or 'Abn Lympho?') (XN: 97.0%, XR: 96.0%). XN with WPC channel had a 26% reduction of false positive smears compared to XR with 22% reduction, a statistically non-significant difference.

Conclusion

The XR analyser had very good analytical performance, and highly comparable results to the predecessor XN analyser in all investigated parameters, flags and workflow aspects.

It's in the blood: a review of the hematological system in SARS-CoV-2-associated COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global healthcare crisis. While SARS-CoV-2-associated COVID-19 affects primarily the respiratory system, patients with COVID-19 frequently develop extrapulmonary manifestations. Notably, changes in the hematological system, including lymphocytopenia, neutrophilia and significant abnormalities of hemostatic markers, were observed early in the pandemic. Hematological manifestations have since been recognized as important parameters in the pathophysiology of SARS-CoV-2 and in the management of patients with COVID-19. In this narrative review, we summarize the state-of-the-art regarding the hematological and hemostatic abnormalities observed in patients with SARS-CoV-2-associated COVID-19, as well as the current understanding of the hematological system in the pathophysiology of acute and chronic SARS-CoV-2-associated COVID-19.

Vascular risk factors for COVID-19 ARDS: endothelium, contact-kinin system

SARS-CoV-2 binds to ACE2 receptors, expressed within the lungs. Risk factors for hospitalization include hypertension, diabetes, ischaemic heart disease and obesity-conditions linked by the presence of endothelial pathology. Viral infection in this setting causes increased conversion of circulating Factor XII to its active form (FXIIa). This is the first step in the contact-kinin pathway, leading to synchronous activation of the intrinsic coagulation cascade and the plasma Kallikrein-Kinin system, resulting in clotting and inflammatory lung disease. Temporal trends are evident from blood results of hospitalized patients. In the first week of symptoms the activated partial thromboplastin time (APTT) is prolonged. This can occur when clotting factors are consumed as part of the contact (intrinsic) pathway. Platelet counts initially fall, reflecting their consumption in coagulation. Lymphopenia occurs after approximately 1 week, reflecting the emergence of a lymphocytic pneumonitis [COVID-19 acute respiratory distress syndrome (ARDS)]. Intrinsic coagulation also induces the contact-kinin pathway of inflammation. A major product of this pathway, bradykinin causes oedema with ground glass opacities (GGO) on imaging in early COVID-19. Bradykinin also causes release of the pleiotrophic cytokine IL-6, which causes lymphocyte recruitment. Thromobosis and lymphocytic pneumonitis are hallmark features of COVID-19 ARDS. In this review we examine the literature with particular reference to the contact-kinin pathway. Measurements of platelets, lymphocytes and APTT should be undertaken in severe infections to stratify for risk of developing ARDS.

Validating a clinical laboratory parameter-based deisolation algorithm for patients with COVID-19 in the intensive care unit using viability PCR: the CoLaIC multicentre cohort study protocol

INTRODUCTION

To investigate whether biochemical and haematological changes due to the patient's host response (CoLab algorithm) in combination with a SARS-CoV-2 viability PCR (v-PCR) can be used to determine when a patient with COVID-19 is no longer infectious.We hypothesise that the CoLab algorithm in combination with v-PCR can be used to determine whether or not a patient with COVID-19 is infectious to facilitate the safe release of patients with COVID-19 from isolation.

METHODS AND ANALYSIS

This study consists of three parts using three different cohorts of patients. All three cohorts contain clinical, vital and laboratory parameters, as well as logistic data related to isolated patients with COVID-19, with a focus on intensive care unit (ICU) stay. The first cohort will be used to develop an algorithm for the course of the biochemical and haematological changes of the host response of the COVID-19 patient. Simultaneously, a second prospective cohort will be used to investigate the algorithm derived in the first cohort, with daily measured laboratory parameters, next to conventional SARS-CoV-2 reverse transcriptase PCRs, as well as v-PCR, to confirm the presence of intact SARS-CoV-2 particles in the patient. Finally, a third multicentre cohort, consisting of retrospectively collected data from patients with COVID-19 admitted to the ICU, will be used to validate the algorithm.

ETHICS AND DISSEMINATION

This study was approved by the Medical Ethics Committee from Maastricht University Medical Centre+ (cohort I: 2020-1565/300523) and Zuyderland MC (cohorts II and III: METCZ20200057). All patients will be required to provide informed consent. Results from this study will be disseminated via peer-reviewed journals and congress/consortium presentations.

T-cell cellular stress and reticulocyte signatures, but not loss of naïve T lymphocytes, characterize severe COVID-19 in older adults

In children and younger adults up to 39 years of age, SARS-CoV-2 usually elicits mild symptoms that resemble the common cold. Disease severity increases with age starting at 30 and reaches astounding mortality rates that are ~330 fold higher in persons above 85 years of age compared to those 18-39 years old. To understand age-specific immune pathobiology of COVID-19, we have analyzed soluble mediators, cellular phenotypes, and transcriptome from over 80 COVID-19 patients of varying ages and disease severity, carefully controlling for age as a variable. We found that reticulocyte numbers and peripheral blood transcriptional signatures robustly correlated with disease severity. By contrast, decreased numbers and proportion of naïve T-cells, reported previously as a COVID-19 severity risk factor, were found to be general features of aging and not of COVID-19 severity, as they readily occurred in older participants experiencing only mild or no disease at all. Single-cell transcriptional signatures across age and severity groups showed that severe but not moderate/mild COVID-19 causes cell stress response in different T-cell populations, and some of that stress was unique to old severe participants, suggesting that in severe disease of older adults, these defenders of the organism may be disabled from performing immune protection. These findings shed new light on interactions between age and disease severity in COVID-19.

Early biochemical analysis of COVID-19 patients helps severity prediction

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

The 2000HIV study: Design, multi-omics methods and participant characteristics

Background

Even during long-term combination antiretroviral therapy (cART), people living with HIV (PLHIV) have a dysregulated immune system, characterized by persistent immune activation, accelerated immune ageing and increased risk of non-AIDS comorbidities. A multi-omics approach is applied to a large cohort of PLHIV to understand pathways underlying these dysregulations in order to identify new biomarkers and novel genetically validated therapeutic drugs targets.

Methods

The 2000HIV study is a prospective longitudinal cohort study of PLHIV on cART. In addition, untreated HIV spontaneous controllers were recruited. In-depth multi-omics characterization will be performed, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and metagenomics, functional immunological assays and extensive immunophenotyping. Furthermore, the latent viral reservoir will be assessed through cell associated HIV-1 RNA and DNA, and full-length individual proviral sequencing on a subset. Clinical measurements include an ECG, carotid intima-media thickness and plaque measurement, hepatic steatosis and fibrosis measurement as well as psychological symptoms and recreational drug questionnaires. Additionally, considering the developing pandemic, COVID-19 history and vaccination was recorded. Participants return for a two-year follow-up visit. The 2000HIV study consists of a discovery and validation cohort collected at separate sites to immediately validate any finding in an independent cohort.

Results

Overall, 1895 PLHIV from four sites were included for analysis, 1559 in the discovery and 336 in the validation cohort. The study population was representative of a Western European HIV population, including 288 (15.2%) cis-women, 463 (24.4%) non-whites, and 1360 (71.8%) MSM (Men who have Sex with Men). Extreme phenotypes included 114 spontaneous controllers, 81 rapid progressors and 162 immunological non-responders. According to the Framingham score 321 (16.9%) had a cardiovascular risk of >20% in the next 10 years. COVID-19 infection was documented in 234 (12.3%) participants and 474 (25.0%) individuals had received a COVID-19 vaccine.

Conclusion

The 2000HIV study established a cohort of 1895 PLHIV that employs multi-omics to discover new biological pathways and biomarkers to unravel non-AIDS comorbidities, extreme phenotypes and the latent viral reservoir that impact the health of PLHIV. The ultimate goal is to contribute to a more personalized approach to the best standard of care and a potential cure for PLHIV.

Use of an algorithm based on routine blood laboratory tests to exclude COVID-19 in a screening-setting of healthcare workers

Background

COVID-19 is an ongoing pandemic leading to exhaustion of the hospital care system. Our health care system has to deal with a high level of sick leave of health care workers (HCWs) with COVID-19 related complaints, in whom an infection with SARS-CoV-2 has to be ruled out before they can return back to work. The aim of the present study is to investigate if the recently described CoLab-algorithm can be used to exclude COVID-19 in a screening setting of HCWs.

Methods

In the period from January 2021 till March 2021, HCWs with COVID-19-related complaints were prospectively collected and included in this study. Next to the routinely performed SARS-CoV-2 RT-PCR, using a set of naso- and oropharyngeal swab samples, two blood tubes (one EDTA- and one heparin-tube) were drawn for analysing the 10 laboratory parameters required for running the CoLab-algorithm.

Results

In total, 726 HCWs with a complete CoLab-laboratory panel were included in this study. In this group, 684 HCWs were tested SARS-CoV-2 RT-PCR negative and 42 cases RT-PCR positive. ROC curve analysis showed an area under the curve (AUC) of 0.853 (95% CI: 0.801–0.904). At a safe cut-off value for excluding COVID-19 of -6.525, the sensitivity was 100% with a specificity of 34% (95% CI: 21 to 49%). No SARS-CoV-2 RT-PCR cases were missed with this cut-off and COVID-19 could be safely ruled out in more than one third of HCWs.

Conclusion

The CoLab-score is an easy and reliable algorithm that can be used for screening HCWs with COVID-19 related complaints. A major advantage of this approach is that the results of the score are available within 1 hour after collecting the samples. This results in a faster return to labour process of a large part of the COVID-19 negative HCWs (34%), next to a reduction in RT-PCR tests (reagents and labour costs) that can be saved.

Reference intervals for Sysmex XN hematological parameters as assessed in the Dutch Lifelines cohort

OBJECTIVES

Our aim was to derive reference intervals for all Sysmex XN hematology analyzer parameters. The rationale behind the study was the lack of reference intervals for the XN analyzer cell population data (CPD) and functional parameters.

METHODS

Fresh fasting blood samples from 18,484 participants in the Dutch Lifelines study were analyzed using two automated XN analyzers. Structured health questionnaire data were used to select a subgroup of 15,803 apparently healthy individuals for inclusion in the reference population. The Latent Abnormal Values Exclusion (LAVE) approach was used to reduce the influence of latent diseases in the reference population on the resulting reference intervals. We applied analysis of variance to judge the need for partitioning of the reference intervals by sex or age.

RESULTS

We report reference intervals for 105 XN analyzer hematological parameters with and without applying LAVE. Sex-related partitioning was required for red blood cells, (RBC, RBC-O), hemoglobin (HGB, HGB-O), hematocrit (HCT), mean corpuscular hemoglobin concentration (MCHC), reticulocyte production index (RPI), and side scattered light intensity of the red blood cell population in the RET channel (RBC-Z). Partitioning for age was not warranted. Body mass index (BMI) and smoking had moderate influence on a minority of the parameters.

CONCLUSIONS

We provide reference intervals for all Sysmex XN analyzer routine, CPD and functional parameters, using a direct approach in a large cohort in the Netherlands.

Intensive Care Infection Score (ICIS) is elevated in patients with moderate and severe COVID-19 in the early stages of disease

Background

Coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is still a very dangerous and life-threatening disease with an extremely heterogeneous course. Older patients and those with comorbidities are at increased risk of death from the disease but young patients can develop potentially lethal complications too. For those reasons, numerous recent studies focus on the analysis of markers associated with early assessment of COVID-19 prognosis. Previous publications provided evidence for the Intensive Care Infection Score (ICIS) as an easy to use tool to assess the risk for bacterial infection in ICU patients based on a combination of haematologic parameters. This study evaluated the performance of ICIS as a prognostic marker of stages of disease in COVID-19 patients.

Methods

A total of 205 COVID-19 patients admitted to the University Hospital Hradec Kralove, Czech Republic, with symptoms of respiratory tract infection and a positive RT-PCR test for SARS-CoV-2 virus were enrolled in this study. Forty-nine patients developed mild COVID-19 symptoms (no oxygen therapy needed), 156 patients developed moderate or severe symptoms (supplemental oxygen therapy or death).

Results

ICIS predicted the mild or moderate/severe course with the highest AUC (0.773). The cut-off value (ICIS = 3.5) was selected as the value with the highest Youden index (0.423). The cut-off value could predict a mild or moderate/severe course of the disease with the highest specificity (77.6%) and positive predictive value (90.2%) of all markers used in this study. Sensitivity was 64.7%.

Conclusion

ICIS is a reliable, cheap, fast and simply interpretable score for the early identification of moderate/severe course of COVID-19 in an early stage of the disease. ICIS> 3 predicts a severe course of the disease with high specificity and positive predictive value.

A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients

In this article, we discuss the development of prognostic machine learning (ML) models for COVID-19 progression, by focusing on the task of predicting ICU admission within (any of) the next 5 days. On the basis of 6,625 complete blood count (CBC) tests from 1,004 patients, of which 18% were admitted to intensive care unit (ICU), we created four ML models, by adopting a robust development procedure which was designed to minimize risks of bias and over-fitting, according to reference guidelines. The best model, a support vector machine, had an AUC of .85, a Brier score of .14, and a standardized net benefit of .69: these scores indicate that the model performed well over a variety of prediction criteria. We also conducted an interpretability study to back up our findings, showing that the data on which the developed model is based is consistent with the current medical literature. This also demonstrates that CBC data and ML methods can be used to predict COVID-19 patients' ICU admission at a relatively low cost: in particular, since CBC data can be quickly obtained by means of routine blood exams, our models could be used in resource-constrained settings and provide health practitioners with rapid and reliable indications.

Lymphopenia and Early Variation of Lymphocytes to Predict In-Hospital Mortality and Severity in ED Patients with SARS-CoV-2 Infection

(1) Introduction: Multiple studies have demonstrated that lymphocyte count monitoring is a valuable prognostic tool for clinicians during inflammation. The aim of our study was to determine the prognostic value of delta lymphocyte H24 from admission from the emergency department for mortality and severity of SARS-CoV-2 infection. (2) Methods: We have made a retrospective and multicentric study in six major hospitals of northeastern France. The patients were hospitalized and had a confirmed diagnosis of SARS-CoV-2 infection. (3): Results: A total of 1035 patients were included in this study. Factors associated with infection severity were CRP > 100 mg/L (OR: 2.51, CI 95%: (1.40−3.71), p < 0.001) and lymphopenia < 800/mm3 (OR: 2.15, CI 95%: (1.42−3.27), p < 0.001). In multivariate analysis, delta lymphocytes H24 (i.e., the difference between lymphocytes values at H24 and upon admission to the ED) < 135 was one of the most significant biochemical factors associated with mortality (OR: 2.23, CI 95%: (1.23−4.05), p = 0.009). The most accurate threshold for delta lymphocytes H24 was 75 to predict severity and 135 for mortality. (4) Conclusion: Delta lymphocytes H24 could be a helpful early screening prognostic biomarker to predict severity and mortality associated with COVID-19.

Definition of the Immune Parameters Related to COVID-19 Severity

A relevant portion of patients with disease caused by the severe acute respiratory syndrome coronavirus 2 (COVID-19) experience negative outcome, and several laboratory tests have been proposed to predict disease severity. Among others, dramatic changes in peripheral blood cells have been described. We developed and validated a laboratory score solely based on blood cell parameters to predict survival in hospitalized COVID-19 patients. We retrospectively analyzed 1,619 blood cell count from 226 consecutively hospitalized COVID-19 patients to select parameters for inclusion in a laboratory score predicting severity of disease and survival. The score was derived from lymphocyte- and granulocyte-associated parameters and validated on a separate cohort of 140 consecutive COVID-19 patients. Using ROC curve analysis, a best cutoff for score of 30.6 was derived, which was associated to an overall 82.0% sensitivity (95% CI: 78–84) and 82.5% specificity (95% CI: 80–84) for detecting outcome. The scoring trend effectively separated survivor and non-survivor groups, starting 2 weeks before the end of the hospitalization period. Patients’ score time points were also classified into mild, moderate, severe, and critical according to the symptomatic oxygen therapy administered. Fluctuations of the score should be recorded to highlight a favorable or unfortunate trend of the disease. The predictive score was found to reflect and anticipate the disease gravity, defined by the type of the oxygen support used, giving a proof of its clinical relevance. It offers a fast and reliable tool for supporting clinical decisions and, most important, triage in terms of not only prioritization but also allocation of limited medical resources, especially in the period when therapies are still symptomatic and many are under development. In fact, a prolonged and progressive increase of the score can suggest impaired chances of survival and/or an urgent need for intensive care unit admission.

COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients: Harnessing clinical data for public health benefits

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.

Prognostic factors associated with COVID-19 related severity in sickle cell disease

BACKGROUND

Equipoise exists regarding sickle cell disease (SCD) as a risk factor for COVID-19 disease severity and variables that increase risk of COVID-19 severity in SCD. Given our health system's large SCD patient catchment, we analyzed our own experience in this regard.

STUDY METHODS

Retrospective analysis of the clinical course and factors associated with need for hospitalization and ICU admission of SCD patients diagnosed with COVID-19 through the Northwell Health system from March 1 to Dec 31, 2020.

RESULTS

Of 1098 patients with SCD, 3.3% were diagnosed with COVID-19. Overall rates of hospitalization, ICU admission, cohort mortality, and in-hospital mortality were 80%, 19%, 2.5%,and 3.1%, respectively. By multivariable analysis, hospitalization risk was decreased by 60% for every 1 g/dL increase in admission Hb. ICU admission risk was increased by 84% as a health care worker; increased by 45% for every 1000/uL increase in admission immature granulocyte count; and decreased by 17% with hydroxyurea use.

DISCUSSION

High hospitalization rates are compatible with worsened severity upon COVID-19 infection in SCD compared to the general population. Patients should be placed on hydroxyurea to increase their Hb and perhaps lower their neutrophil counts. Health care workers with SCD may warrant special safety precautions.

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Platelets in the perspective of COVID-19; pathophysiology of thrombocytopenia and its implication as prognostic and therapeutic opportunity

Despite endorsed and exponential research to improve diagnostic and therapeutic strategies, efforts have not yet converted into a better prospect for patients infected with the novel coronavirus (2019nCoV), and still, the name of SARS-CoV-2 is coupled with numerous unanswered questions. One of these questions is concerning how this respiratory virus reduces the number of platelets (PLTs)? The results of laboratory examinations showed that about a quarter of COVID-19 cases experience thrombocytopenia, and more remarkably, about half of these patients succumb to the infection due to coagulopathy. These findings have positioned PLTs as a pillar in the management as well as stratifying COVID-19 patients; however, not all the physicians came into a consensus about the prognostic value of these cells. The current review aims to unravel the contributory role of PLTs s in COVID-19; and alsoto summarize the original data obtained from international research laboratories on the association between COVID-19 and PLT production, activation, and clearance. In addition, we provide a special focus on the prognostic value of PLTs and their related parameters in COVID-19. Questions on how SARS-CoV-2 induces thrombocytopenia are also responded to. The last section provides a general overview of the most recent PLT- or thrombocytopenia-related therapeutic approaches. In conclusion, since SARS-CoV-2 reduces the number of PLTs by eliciting different mechanisms, treatment of thrombocytopenia in COVID-19 patients is not as simple as it appears and serious cautions should be considered to deal with the problem through scrutiny awareness of the causal mechanisms.

Reticulocyte and Erythrocyte Hemoglobin Parameters for Iron Deficiency and Anemia Diagnostics in Patient Blood Management. A Narrative Review

Anemia, iron deficiency and other hematinic deficiencies are a major cause of perioperative transfusion needs and are associated with increased morbidity and mortality. Anemia can be caused either by decreased production of hemoglobin or red blood cells or by increased consumption and blood loss. Decreased production can involve anything from erythropoietin or vitamin B12 insufficiency to absolute or functional lack of iron. Thus, to achieve the goal of patient blood management, anemia must be addressed by addressing its causes. The traditional parameters to diagnose anemia, despite offering elaborate options, are not ideally suited to giving a simple overview of the causes of anemia, e.g., iron status for erythropoiesis, especially during the acute phase of inflammation, acute blood loss or iron deficiency. Reticulocyte hemoglobin can thus help to uncover the cause of the anemia and to identify the main factors inhibiting erythropoiesis. Regardless of the cause of anemia, reticulocyte hemoglobin can also quickly track the success of therapy and, together with the regular full blood count it is measured alongside, help in clearing the patient for surgery.

A new haematocytometric index: Predicting severity and mortality risk value in COVID-19 patients

INTRODUCTION

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 virus, is a major public health concern spanning from healthy carriers to patients with life-threatening conditions. Although most of COVID-19 patients have mild-to-moderate clinical symptoms, some patients have severe pneumonia leading to death. Therefore, the early prediction of disease prognosis and severity is crucial in COVID-19 patients. The main objective of this study is to evaluate the haemocytometric parameters and identify severity score associated with SARS-CoV-2 infection.

METHODS

Clinical and laboratory records were retrospectively reviewed from 97 cases of COVID-19 admitted to hospitals in Istanbul, Turkey. The patient groups were subdivided into three major groups: Group 1 (Non-critical): 59 patients, Group 2 (Critical-Survivors): 23 patients and Group 3 (Critical-Non-survivors):15 patients. These data was tested for correlation, including with derived haemocytometric parameters. The blood analyses were performed the Sysmex XN-series automated hematology analyser using standard laboratory protocols. All statistical testing was undertaken using Analyse-it software.

RESULTS

97 patients with COVID-19 disease and 935 sequential complete blood count (CBC-Diff) measurements (days 0-30) were included in the final analyses. Multivariate analysis demonstrated that red cell distribution width (RDW) (>13.7), neutrophil to lymphocyte ratio (NLR) (4.4), Hemoglobin (Hgb) (<11.4 gr/dL) and monocyte to neutrophil ratio (MNR) (0.084) had the highest area under curve (AUC) values, respectively in discrimination critical patients than non-critical patients. In determining Group 3, MNR (<0.095), NLR (>5.2), Plateletcount (PLT) (>142 x103/L) and RDW (>14) were important haemocytometric parameters, and the mortality risk value created by their combination had the highest AUC value (AUC = 0.911, 95% CI, 0886-0.931). Trend analysis of CBC-Diff parameters over 30 days of hospitalization, NLR on day 2, MNR on day 4, RDW on day 6 and PLT on day 7 of admission were found to be the best time related parameters in discrimination non-critical (mild-moderate) patient group from critical (severe and non-survivor) patient group.

CONCLUSION

NLR is a strong predictor for the prognosis for severe COVID-19 patients when the cut-off chosen was 4.4, the combined mortality risk factor COVID-19 disease generated from RDW-CV, NLR, MNR and PLT is best as a mortality haematocytometric index.

The oxygen dissociation curve of blood in COVID-19

COVID-19 hinders oxygen transport to the consuming tissues by at least two mechanisms: In the injured lung, saturation of hemoglobin is compromised, and in the tissues, an associated anemia reduces the volume of delivered oxygen. For the first problem, increased hemoglobin oxygen affinity [left shift of the oxygen dissociation curve (ODC)] is of advantage, for the second, however, the contrary is the case. Indeed a right shift of the ODC has been found in former studies for anemia caused by reduced cell production or hemolysis. This resulted from increased 2,3-bisphosphoglycerate (2,3-BPG) concentration. In three investigations in COVID-19, however, no change of hemoglobin affinity was detected in spite of probably high [2,3-BPG]. The most plausible cause for this finding is formation of methemoglobin (MetHb), which increases the oxygen affinity and thus apparently compensates for the 2,3-BPG effect. However, this "useful effect" is cancelled by the concomitant reduction of functional hemoglobin. In the largest study on COVID-19, even a clear left shift of the ODC was detected when calculated from measurements in fresh blood rather than after equilibration with gases outside the body. This additional "in vivo" left shift possibly results from various factors, e.g., concentration changes of Cl-, 2,3-BPG, ATP, lactate, nitrocompounds, glutathione, glutamate, because of time delay between blood sampling and end of equilibration, or enlarged distribution space including interstitial fluid and is useful for O2 uptake in the lungs. Under discussion for therapy are the affinity-increasing 5-hydroxymethyl-2-furfural (5-HMF), erythropoiesis-stimulating substances like erythropoietin, and methylene blue against MetHb formation.

SARS-CoV-2 Antigenemia as a Confounding Factor in Immunodiagnostic Assays: A Case Study

Humoral immunity has emerged as a vital immune component against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nevertheless, a subset of recovered Coronavirus Disease-2019 (COVID-19) paucisymptomatic/asymptomatic individuals do not generate an antibody response, constituting a paradox. We assumed that immunodiagnostic assays may operate under a competitive format within the context of antigenemia, potentially explaining this phenomenon. We present a case where persistent antigenemia/viremia was documented for at least 73 days post-symptom onset using ‘in-house’ methodology, and as it progressively declined, seroconversion took place late, around day 55, supporting our hypothesis. Thus, prolonged SARS-CoV-2 antigenemia/viremia could mask humoral responses, rendering, in certain cases, the phenomenon of ‘non-responders’ a misnomer.

Extended Inflammation Parameters (EIP) as Markers of Immune System Cell Activation in Psoriasis

Psoriasis is an inflammatory, autoimmune disease that affects approximately 2% of the population. The inflammation in psoriasis can be systemic, so despite a predominantly cutaneous manifestation, it also affects the internal organs. The diagnosis and monitoring of the disease are based on the clinical picture. To assess the disorders of other organs, additional tests need to be performed. Recently, the examination of blood morphology has been enriched with modern haematological parameters, i.e., Extended Inflammation Parameters (EIP), which include RE-LYMPH (activated lymphocytes), AS-LYMPH (antibody-producing B lymphocytes), and NEUT-RI and NEUT-GI (activated neutrophils). In the study, higher values of new haematological parameters were observed in individuals with psoriasis than in healthy controls. A higher EIP value was noted in the group of individuals with plaque psoriasis than in the group of individuals with psoriatic arthritis. Implementation of these parameters into routine laboratory analysis will likely make it possible to estimate the severity of the inflammation and improve its assessment.

Leukocyte differential and reactive lymphocyte counts from Sysmex XN analyzer in the evaluation of SARS-CoV-2 infection

BACKGROUND

Leukocyte differential present certain features in COVID 19 patients. RE-LYMP (reactive lymphocytes) is an extended inflammation parameter (EIP) reported by XN analyzer (Sysmex Corporation, Kobe, Japan) reflect the activation of lymphocytes triggered by infections. We aimed to assess the clinical utility of these parameters as biomarkers for the rapid detection of COVID 19.

METHODS

The study group included 200 COVID 19 and 167 patients with other infections at admission. Differences of leukocyte differential, neutrophil/lymphocyte ratio (NLR) and EIP among groups were assessed with the Kruskal-Wallis test; parameters statiscally different in the groups were tested with Receiver operating characteristic (ROC) curve analysis to assess their diagnostic performance in distinguishing SARS-CoV-2 infections. The reliability of the selected parameters was evaluated in a validation group of 347 patients (160 COVID 19 and 187 other infections).

RESULTS

NLR performed well to discard viral infections, area under curve (AUC) 0.988 (95%CI 0.973 - 0.991) and RE-LYMP was useful to distinguish COVID 19 and bacterial infections AUC 0.920 (95%CI 0.884 - 0.948); the two conditions NLR> 3.3 RE-LYMP> 0.6% was applied to the validation group and 153 out of 160 COVID 19 patients were correctly distinguished (95.6%).

CONCLUSIONS

Early diagnosis of SARS-CoV-2 infection is critical for better caring of patients and to reduce the threat of nosocomial infection for professionals. Leukocyte differential and RE-LYMPH could assist in a preliminary differential diagnosis of the disease.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.