Association of procalcitonin levels with the progression and prognosis of hospitalized patients with COVID-19

Potential impacts of SARS-CoV-2 on parathyroid: current advances and trends

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection affects several important organs including endocrine glands. Experimental studies demonstrated that the virus exploits the ACE2, a transmembrane glycoprotein on the cell surface as a receptor for cellular entry. This entry process is exclusively facilitated by other intracellular protein molecules such as TMPRSS2, furin, NRP1, and NRP2. Recent findings documented the involvement of the SARS-CoV-2 in inducing various parathyroid disorders including hypoparathyroidism and hypocalcemia, which received significant attention. This review extensively describes rapidly evolving knowledge on the potential part of SARS-CoV-2 in emerging various parathyroid disorders due to SARS-CoV-2 infection particularly parathyroid malfunction in COVID-19 cases, and post-COVID-19 conditions. Further, it presents the expression level of various molecules such as ACE2, TMPRSS2, furin, NRP1, and NRP2 in the parathyroid cells that facilitate the SARS-CoV-2 entry into the cell, and discusses the possible mechanism of parathyroid gland infection. Besides, it explores parathyroid malfunction in COVID-19 vaccine-administered cases. It also explains the possible long-COVID-19 effect on parathyroid and post-COVID-19 management of parathyroid. A complete understanding of the mechanisms of SARS-CoV-2-triggered pathogenesis in parathyroid dysfunctions may curtail treatment options and aid in the management of SARS-CoV-2-infected cases.

Procalcitonin as a biomarker to guide treatments for patients with lower respiratory tract infections

INTRODUCTION

Lower respiratory tract infections are amongst the main causes for hospital/intensive care unit admissions and antimicrobial prescriptions. In order to reduce antimicrobial pressure, antibiotic administration could be optimized through procalcitonin-based algorithms.

AREAS COVERED

In this review, we discuss the performances of procalcitonin for the diagnosis and the management of community-acquired and ventilator-associated pneumonia. We provide up-to-date evidence and deliver clear messages regarding the purpose of procalcitonin to reduce unnecessary antimicrobial exposure.

EXPERT OPINION

Antimicrobial pressure and resulting antimicrobial resistances are a major public health issue as well as a daily struggle in the management of patients with severe infectious diseases, especially in intensive care units where antibiotic exposure is high. Procalcitonin-guided antibiotic administration has proven its efficacy in reducing unnecessary antibiotic use in lower respiratory tract infections without excess in mortality, hospital length of stay or disease relapse. Procalcitonin-guided algorithms should be implemented in wards taking care of patients with severe infections. However, procalcitonin performances are different regarding the setting of the infection (community versus hospital-acquired infections) the antibiotic management (start or termination of antibiotic) as well as patient's condition (immunosuppressed or in shock) and we encourage the physicians to be aware of these limitations.

Association between inflammatory cytokines/chemokines, clinical laboratory parameters, disease severity and in-hospital mortality in critical and mild COVID-19 patients without comorbidities or immune-mediated diseases

There are limited data on inflammatory cytokines and chemokines; the humoral immune response; and main clinical laboratory parameters as indicators for disease severity and mortality in patients with critical and mild COVID-19 without comorbidities or immune-mediated diseases in Saudi Arabia. We determined the expression levels of major proinflammatory cytokines and chemokines; C-reactive protein (CRP); procalcitonin; SARS-CoV-2 IgM antibody and twenty-two clinical laboratory parameters and assessed their usefulness as indicators of disease severity and in-hospital death. Our results showed a significant increase in the expression levels of SARS-CoV-2 IgM antibody; IL1-β; IL-6; IL-8; TNF-α and CRP in critical COVID-19 patients; neutrophil count; urea; creatinine and troponin were also increased. The elevation of these biomarkers was significantly associated and positively correlated with in-hospital death in critical COVID-19 patients. Our results suggest that the levels of IL1-β; IL-6; IL-8; TNF-α; and CRP; neutrophil count; urea; creatinine; and troponin could be used to predict disease severity in COVID-19 patients without comorbidities or immune-mediated diseases. These inflammatory mediators could be used as predictive early biomarkers of COVID-19 disease deterioration; shock and death among COVID-19 patients without comorbidities or immune-mediated diseases.

Serum procalcitonin level is independently associated with mechanical ventilation and case-fatality in hospitalized COVID-19-positive US veterans-A potential marker for disease severity

The Coronavirus-19 disease (COVID-19) has claimed over 6.8 million lives since first being reported in late 2019. The virus that causes COVID-19 disease is highly contagious and spreads rapidly. To date, there are no approved prognostic tools that could predict why some patients develop severe or fatal disease outcomes. Early COVID-19 studies found an association between procalcitonin (PCT) and hospitalization or duration of mechanical ventilation and death but were limited by the cohort sizes. Therefore, this study was designed to confirm the associations of PCT with COVID-19 disease severity outcomes in a large cohort. For this retrospective data analysis study, 27,154 COVID-19-positive US veterans with post-infection PCT laboratory test data and their disease severity outcomes were accessed using the VA electronic healthcare data. Cox regression models were used to test the association between serum PCT levels and disease outcomes while controlling for demographics and relevant confounding variables. The models demonstrated increasing disease severity (ventilation and death) with increasing PCT levels. For PCT serum levels above 0.20 ng/ml, the unadjusted risk increased nearly 2.3-fold for mechanical ventilation (hazard ratio, HR, 2.26, 95%CI: 2.11-2.42) and in-hospital death (HR, 2.28, 95%CI: 2.16-2.41). Even when adjusted for demographics, diabetes, pneumonia, antibiotic use, white blood cell count, and serum C-reactive protein levels, the risks remained relatively high for mechanical ventilation (HR, 1.80, 95%CI: 1.67-1.94) and death (HR, 1.76, 95%CI: 1.66-1.87). These data suggest that higher PCT levels have independent associations with ventilation and in-hospital death in veterans with COVID-19 disease, validating previous findings. The data suggested that serum PCT level may be a promising prognostic tool for COVID-19 severity assessment and should be further evaluated in a prospective clinical trial.

The Association between Admission Procalcitonin Level and The Severity of COVID-19 Pneumonia: A Retrospective Cohort Study

Background and Objectives: An elevated procalcitonin level has classically been linked to bacterial infections. Data on the association between elevated procalcitonin and the outcome of coronavirus disease 2019 (COVID-19) are conflicting. Some linked it to associated bacterial co-infections, while others correlated the elevation with disease severity without coexisting bacterial infections. We aimed to investigate the association between high procalcitonin and the severity of COVID-19. Materials and Methods: Hospitalized patients with confirmed COVID-19 pneumonia were divided into two groups: the normal-procalcitonin group and the high-procalcitonin group (>0.05 ng/mL). Patients with concomitant bacterial infections on admission were excluded. The primary outcomes were the need for intensive care unit (ICU) admission, progression to invasive mechanical ventilation (IMV), and in-hospital 28-day mortality. Results: We included 260 patients in the normal procalcitonin group and 397 patients in the high procalcitonin group. The mean age was 55 years and 49% were females. A higher number of patients in the elevated procalcitonin group required ICU admission (32.7% vs. 16.2%, p < 0.001) and IMV (27.2% vs. 13.5%, p < 0.001). In-hospital mortality was significantly higher in the elevated procalcitonin group (18.9% vs. 8.5%, p < 0.001). After adjusting for other covariates, procalcitonin > 0.05 ng/mL was an independent predictor of progression to IMV (OR, 1.71; 95% CI, 1.08−2.71; p = 0.022), ICU admission (OR, 1.73; 95% CI, 1.13−2.66; p = 0.011), and in-hospital mortality (OR, 1.99; 95% CI, 1.14−3.47; p = 0.015). An elevated procalcitonin level was the strongest predictor of in-hospital mortality. Conclusions: Measurement of procalcitonin can have a prognostic role among COVID-19 patients. The admission procalcitonin level can identify patients at risk of ICU admission, progression to IMV, and in-hospital mortality.

Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study

Background

COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality.

Methods

The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method.

Results

While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy.

Conclusion

Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.

Procalcitonin as a Predictive Factor for the Clinical Outcome of Patients with Coronavirus Disease 2019

Purpose: The coronavirus disease 2019 (COVID-19) pandemic continues. It has been reported that patients with bacterial coinfection have a higher mortality rate than patients without coinfection. However, there are no clear standard guidelines for the use of antibacterial drugs. Therefore, the purpose of this study was to determine the usefulness of procalcitonin, a specific indicator of bacterial infection, as a biomarker for predicting death in COVID-19 patients.Methods: This was a retrospective study of confirmed COVID-19 patients (<i>N</i> = 283) between December 2020 and February 2021 who survived or died. Logistic regression analysis was performed to determine whether there was an association between the level of procalcitonin and death. In addition, receiver operating characteristic curve analysis was performed to determine the usefulness of procalcitonin as a predictor of death.Results: In the non-survivor group, age, the number of patients transferred from a health care center, segment neutrophil ratio, C reactive protein, ferritin, and procalcitonin were significantly higher in the survivor group. In multivariate analysis, procalcitonin was identified as an independent factor associated with death (hazard ratio 6.162, confidential interval 2.285-26.322, <i>p</i> = 0.014). In addition, the predictive power of procalcitonin level and mortality was statistically significant using receiver operating characteristic curve analysis which gave an area under the curve value of 0.823, a cut-off value of 0.05, a sensitivity of 72.2%, a specificity of 87.5% (<i>p</i> < 0.001).Conclusion: Measurement of procalcitonin and other biomarkers may be useful to determine whether to use or discontinue use of antibacterial drugs in patients with COVID-19.

Evaluation of Procalcitonin's Utility to Predict Concomitant Bacterial Pneumonia in Critically Ill COVID-19 Patients

Background: Historically, procalcitonin(PCT) has been used as a predictor of bacterial infection and to guide antibiotic therapy in hospitalized patients. The purpose of this study was to determine PCT's diagnostic utility in predicting secondary bacterial pneumonia in critically ill patients with severe COVID-19 pneumonia. Methods: A retrospective cohort study was conducted in COVID-19 adults admitted to the ICU between March 2020, and March 2021. All included patients had a PCT level within 72 h of presentation and serum creatinine of <1.5mg/dL. A PCT threshold of 0.5ng/mL was used to compare patients with high( ≥ 0.5ng/mL) versus low(< 0.5ng/mL) PCT. Bacterial pneumonia was defined by positive respiratory culture. A receiver operating characteristics (ROC) curve was utilized to evaluate PCT as a diagnostic test for bacterial pneumonia, with an area under the curve(AUC) threshold of 0.7 to signify an accurate diagnostic test. A multivariable model was constructed to identify variables associated with in-hospital mortality. Results: There were 165 patients included: 127 low PCT versus 38 high PCT. There was no significant difference in baseline characteristics, vital signs, severity of disease, or outcomes among low versus high PCT groups (all p > 0.05). While there was no difference in bacterial pneumonia in low versus high groups (34(26.8%) versus 12(31.6%), p = 0.562), more patients in the high PCT group had bacteremia (19(15%) versus 11(28.9%), p = 0.050). Sensitivity was 26.1% and specificity was 78.2% for PCT to predict bacterial pneumonia coinfection in ICU patients with COVID-19 pneumonia. ROC yielded an AUC 0.54 (p = 0.415). After adjusting for LDH>350U/L and creatinine in multivariable regression, PCT did not enhance performance of the regression model. Conclusions: PCT offers little to no predictive utility in diagnosing concomitant bacterial pneumonia in critically ill patients with COVID-19 nor in predicting increased severity of disease or worse outcomes including mortality.

Routine laboratory parameters, including complete blood count, predict COVID-19 in-hospital mortality in geriatric patients

To reduce the mortality of COVID-19 older patients, clear criteria to predict in-hospital mortality are urgently needed. Here, we aimed to evaluate the performance of selected routine laboratory biomarkers in improving the prediction of in-hospital mortality in 641 consecutive COVID-19 geriatric patients (mean age 86.6 ± 6.8) who were hospitalized at the INRCA hospital (Ancona, Italy). Thirty-four percent of the enrolled patients were deceased during the in-hospital stay. The percentage of severely frail patients, assessed with the Clinical Frailty Scale, was significantly increased in deceased patients compared to the survived ones. The age-adjusted Charlson comorbidity index (CCI) score was not significantly associated with an increased risk of death. Among the routine parameters, neutrophilia, eosinopenia, lymphopenia, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein, procalcitonin, IL-6, and NT-proBNP showed the highest predictive values. The fully adjusted Cox regressions models confirmed that high neutrophil %, NLR, derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), and low lymphocyte count, eosinophil %, and lymphocyte-to-monocyte ratio (LMR) were the best predictors of in-hospital mortality, independently from age, gender, and other potential confounders. Overall, our results strongly support the use of routine parameters, including complete blood count, in geriatric patients to predict COVID-19 in-hospital mortality, independent from baseline comorbidities and frailty.

Bacterial and Fungal Coinfection in Critically Ill COVID-19 Cases and Predictive Role of Procalcitonin During the First Wave at an Academic Health Center

BACKGROUND

Coinfection at various sites can complicate the clinical course of coronavirus disease of 2019 (COVID-19) patients leading to worse prognosis and increased mortality. We aimed to investigate the occurrence of coinfection in critically ill COVID-19 cases, and the predictive role of routinely tested biomarkers on admission for mortality.

METHODS

This is a retrospective study of all SARS-CoV-2-infected cases, who were admitted to King Fahad Hospital of the University between March 2020 and December 2020. We reviewed the data in the electronic charts in the healthcare information management system including initial presentation, clinical course, radiological and laboratory findings and reported all significant microbiological cultures that indicated antimicrobial therapy. The mortality data were reviewed for severely ill patients who were admitted to critical care units.

RESULTS

Of 1091 admitted patients, there were 70 fatalities (6.4%). 182 COVID-19 persons were admitted to the critical care service, of whom 114 patients (62.6%) survived. The in-hospital mortality was 13.4%. Coinfection was noted in 67/68 non-survivors, and Gram-negative pathogens (Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumanni) represented more than 50% of the etiological agents. We noted that the serum procalcitonin on admission was higher for non-survivors (Median = 1.6 ng/mL ± 4.7) than in survivors (Median = 0.2 ng/mL ± 4.2) (p ≤ 0.05).

CONCLUSION

Coinfection is a serious complication for COVID-19 especially in the presence of co-morbidities. High levels of procalcitonin on admission may predict non-survival in critically ill cases in whom bacterial or fungal co-infection is likely.

Epidemiological, Radiographical, and Laboratorial Characteristics of Chinese Asymptomatic Cases With COVID-19: A Systematic Review and Meta-Analysis

The comprehensive understanding of the characteristics of asymptomatic cases are helpful for the identification and management of patients with asymptomatic COVID-19 infection. Four electronic databases were searched from December 1, 2019 to February 8, 2022 for relevant articles. Data synthesis, subgroup analysis, and sensitivity analysis were performed on the included studies. I² and Q tests were applied to evaluate heterogeneity across studies. The risk of publication bias was assessed and visualized using a funnel plot. A total of 45 studies consisting of 2,655 patients with no symptoms at the screening point were included. Pooled results showed that in China, 65% of initial no-symptoms COVID-19 patients did not present any COVID-19-related symptom during follow-up or by end of disease course (asymptomatic infections). High proportions of initial no-symptoms COVID-19 patients (76%) and patients with asymptomatic infection (55%) had abnormal CT features at the screening point. High proportion of patients with asymptomatic infection had been detected Ig G⁺ (72%) and/or Ig M⁺ (57%) at the screening point. The chest CT scan and SARS-CoV-2-specific antibody testing could serve as effective supplementary methods to identify asymptomatic cases in the early stage of SARS-CoV-2 infection. However, the chest CT scan and the SARS-CoV-2-specific IgM and IgG testing should not replace reverse transcription–polymerase chain reaction (RT-PCR) for screening in asymptomatic patients. The combination of repeated RT-PCR, chest CT scans, and the SARS-CoV-2-specific IgM and IgG testing should be performed for those highly suspected SARS-CoV-2 infections.

Systematic Review of Cardiovascular Manifestations in COVID-19 and Management Consideration

Introduction: Coronavirus disease 2019 (COVID-19) is a health problem that is still engulfing the world that contributes to the high mortality rate globally. Death arises from the severity of the disease due to complications in important organs such as the heart. The purpose of this study was to systematically review the manifestations of cardiovascular disease in COVID-19 patients and their management in terms of published articles. Method: This research is a systematic review research. The research was conducted using the PRISMA method. Article searches are carried out by online publications throughPubMed, Science Direct and Google Scholarthat meet the inclusion and exclusion criteria. The population is articles about the manifestations of cardiovascular disease in COVID-19 patients and their management between 2011 and 2021. Inclusion criteria are studies that examine the manifestations of cardiovascular disease in COVID-19 patients and their management using primary data in the form of cohort research designs, in English and full text available. The exclusion criteria were a case study, review study and used secondary data. The data were analyzed by univariate analysis by calculating the frequency and percentage. Result: The results show that several manifestations of cardiovascular disease in COVID-19 patients include cardiac injury, heart failure, myocardial infarction, myocarditis, cardiomegaly and others.Complications of these diseases occur with or without comorbidities and the risk increases with comorbid cardiovascular disease. The management of COVID-19 patients is basically done with antiviral agents, reducing symptoms and protecting important organs such as the heart. Conclusion: In the treatment of COVID-19 patients with cardiovascular complications, the use of antiviral agents such as lopinavir or ritonavir should be used with caution because: may interact with cardiovascular drugs. Mechanical circulation support is suggested and the use of extracorporeal membrane oxygenation (ECMO) can also be performed totreat cardiovascular complications in COVID-19 patients. Keywords: COVID-19, cardiovascular, manifestation, management, systematic review.  

Non-invasive Vagus Nerve Stimulation for COVID-19: Results From a Randomized Controlled Trial (SAVIOR I)

Background

Severe coronavirus disease 2019 (COVID-19) is characterized, in part, by an excessive inflammatory response. Evidence from animal and human studies suggests that vagus nerve stimulation can lead to reduced levels of various biomarkers of inflammation. We conducted a prospective randomized controlled study (SAVIOR-I) to assess the feasibility, efficacy, and safety of non-invasive vagus nerve stimulation (nVNS) for the treatment of respiratory symptoms and inflammatory markers among patients who were hospitalized for COVID-19 (ClinicalTrials.gov identifier: NCT04368156).

Methods

Participants were randomly assigned in a 1:1 allocation to receive either the standard of care (SoC) alone or nVNS therapy plus the SoC. The nVNS group received 2 consecutive 2-min doses of nVNS 3 times daily as prophylaxis. Efficacy and safety were evaluated via the incidence of specific clinical events, inflammatory biomarker levels, and the occurrence of adverse events.

Results

Of the 110 participants who were enrolled and randomly assigned, 97 (nVNS, n = 47; SoC, n = 50) had sufficient available data and comprised the evaluable population. C-reactive protein (CRP) levels decreased from baseline to a significantly greater degree in the nVNS group than in the SoC group at day 5 and overall (i.e., all postbaseline data points collected through day 5, combined). Procalcitonin level also showed significantly greater decreases from baseline to day 5 in the nVNS group than in the SoC group. D-dimer levels were decreased from baseline for the nVNS group and increased from baseline for the SoC group at day 5 and overall, although the difference between the treatment groups did not reach statistical significance. No significant treatment differences were seen for clinical respiratory outcomes or any of the other biochemical markers evaluated. No serious nVNS-related adverse events occurred during the study.

Conclusions

nVNS therapy led to significant reductions in levels of inflammatory markers, specifically CRP and procalcitonin. Because nVNS has multiple mechanisms of action that may be relevant to COVID-19, additional research into its potential use earlier in the course of COVID-19 and its potential to mitigate some of the symptoms associated with post-acute sequelae of COVID-19 is warranted.

The Association between Nutritional Status and In-Hospital Mortality of COVID-19 in Critically-Ill Patients in the ICU

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become one of the leading causes of death worldwide. The impact of poor nutritional status on increased mortality and prolonged ICU (intensive care unit) stay in critically ill patients is well-documented. This study aims to assess how nutritional status and BMI (body mass index) affected in-hospital mortality in critically ill COVID-19 patients Methods: We conducted a retrospective study and analysed medical records of 286 COVID-19 patients admitted to the intensive care unit of the University Clinical Hospital in Wroclaw (Poland).

RESULTS

A total of 286 patients were analysed. In the sample group, 8% of patients who died had a BMI within the normal range, 46% were overweight, and 46% were obese. There was a statistically significantly higher death rate in men (73%) and those with BMIs between 25.0-29.9 (p = 0.011). Nonsurvivors had a statistically significantly higher HF (Heart Failure) rate (p = 0.037) and HT (hypertension) rate (p < 0.001). Furthermore, nonsurvivors were statistically significantly older (p < 0.001). The risk of death was higher in overweight patients (HR = 2.13; p = 0.038). Mortality was influenced by higher scores in parameters such as age (HR = 1.03; p = 0.001), NRS2002 (nutritional risk score, HR = 1.18; p = 0.019), PCT (procalcitonin, HR = 1.10; p < 0.001) and potassium level (HR = 1.40; p = 0.023).

CONCLUSIONS

Being overweight in critically ill COVID-19 patients requiring invasive mechanical ventilation increases their risk of death significantly. Additional factors indicating a higher risk of death include the patient's age, high PCT, potassium levels, and NRS ≥ 3 measured at the time of admission to the ICU.

Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19

Background

The COVID-19 pandemic has necessitated efficient and accurate triaging of patients for more effective allocation of resources and treatment.

Objectives

The objectives are to investigate parameters and risk stratification tools that can be applied to predict mortality within 90 days of hospital admission in patients with COVID-19.

Methods

A literature search of original studies assessing systems and parameters predicting mortality of patients with COVID-19 was conducted using MEDLINE and EMBASE.

Results

589 titles were screened, and 76 studies were found investigating the prognostic ability of 16 existing scoring systems (area under the receiving operator curve (AUROC) range: 0.550–0.966), 38 newly developed COVID-19-specific prognostic systems (AUROC range: 0.6400–0.9940), 15 artificial intelligence (AI) models (AUROC range: 0.840–0.955) and 16 studies on novel blood parameters and imaging.

Discussion

Current scoring systems generally underestimate mortality, with the highest AUROC values found for APACHE II and the lowest for SMART-COP. Systems featuring heavier weighting on respiratory parameters were more predictive than those assessing other systems. Cardiac biomarkers and CT chest scans were the most commonly studied novel parameters and were independently associated with mortality, suggesting potential for implementation into model development. All types of AI modelling systems showed high abilities to predict mortality, although none had notably higher AUROC values than COVID-19-specific prediction models. All models were found to have bias, including lack of prospective studies, small sample sizes, single-centre data collection and lack of external validation.

Conclusion

The single parameters established within this review would be useful to look at in future prognostic models in terms of the predictive capacity their combined effect may harness.

Metabolomics in the Diagnosis and Prognosis of COVID-19

Coronavirus disease 2019 (COVID-19) pandemic triggered an unprecedented global effort in developing rapid and inexpensive diagnostic and prognostic tools. Since the genome of SARS-CoV-2 was uncovered, detection of viral RNA by RT-qPCR has played the most significant role in preventing the spread of the virus through early detection and tracing of suspected COVID-19 cases and through screening of at-risk population. However, a large number of alternative test methods based on SARS-CoV-2 RNA or proteins or host factors associated with SARS-CoV-2 infection have been developed and evaluated. The application of metabolomics in infectious disease diagnostics is an evolving area of science that was boosted by the urgency of COVID-19 pandemic. Metabolomics approaches that rely on the analysis of volatile organic compounds exhaled by COVID-19 patients hold promise for applications in a large-scale screening of population in point-of-care (POC) setting. On the other hand, successful application of mass-spectrometry to detect specific spectral signatures associated with COVID-19 in nasopharyngeal swab specimens may significantly save the cost and turnaround time of COVID-19 testing in the diagnostic microbiology and virology laboratories. Active research is also ongoing on the discovery of potential metabolomics-based prognostic markers for the disease that can be applied to serum or plasma specimens. Several metabolic pathways related to amino acid, lipid and energy metabolism were found to be affected by severe disease with COVID-19. In particular, tryptophan metabolism via the kynurenine pathway were persistently dysregulated in several independent studies, suggesting the roles of several metabolites of this pathway such as tryptophan, kynurenine and 3-hydroxykynurenine as potential prognostic markers of the disease. However, standardization of the test methods and large-scale clinical validation are necessary before these tests can be applied in a clinical setting. With rapidly expanding data on the metabolic profiles of COVID-19 patients with varying degrees of severity, it is likely that metabolomics will play an important role in near future in predicting the outcome of the disease with a greater degree of certainty.

Electrochemical Immunosensors for Quantification of Procalcitonin: Progress and Prospects

Human procalcitonin (PCT) is a peptide precursor of the calcium-regulating hormone calcitonin. Traditionally, PCT has been used as a biomarker for severe bacterial infections and sepsis. It has also been recently identified as a potential marker for COVID-19. Normally, serum PCT is intracellularly cleaved to calcitonin, which lowers the levels of PCT (<0.01 ng/mL). In severe infectious diseases and sepsis, serum PCT levels increase above 100 ng/mL in response to pro-inflammatory stimulation. Development of sensors for specific quantification of PCT has resulted in considerable improvement in the sensitivity, linear range and rapid response. Among the various sensing strategies, electrochemical platforms have been extensively investigated owing to their cost-effectiveness, ease of fabrication and portability. Sandwich-type electrochemical immunoassays based on the specific antigen–antibody interactions with an electrochemical transducer and use of nanointerfaces has augmented the electrochemical response of the sensors towards PCT. Identification of a superior combination of electrode material and nanointerface, and translation of the sensing platform into flexible and disposable substrates are under active investigation towards development of a point-of-care device for PCT detection. This review provides an overview of the existing detection strategies and limitations of PCT electrochemical immunosensors, and the emerging directions to address these lacunae.

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model

BACKGROUND

COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients.

MATERIALS AND METHODS

We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot.

RESULTS

242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82).

CONCLUSIONS

We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation

Background

The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality.

Objective

The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression.

Methods

The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU).

Results

The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO₂ (peripheral oxygen saturation) and being aged 75 years and over. Performance of this model to predict the deceased outcome extended 5 days prior, with AUC=0.81, specificity=70%, and sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU-admitted outcomes, respectively. Although respiration rate and SpO₂ levels offered the highest feature importance, other canonical markers, including diabetic history, age, and temperature, offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen and lactate dehydrogenase (LDH). Features that were predictive of morbidity included LDH, calcium, glucose, and C-reactive protein.

Conclusions

Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.

Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications

Clinical activity of 3740 de-identified COVID-19 positive patients treated at NYU Langone Health (NYULH) were collected between January and August 2020. XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 and age 75+. Performance of this model to predict the deceased outcome extended 5 days prior with AUC=0.81, specificity=70%, sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU admitted, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers including diabetic history, age and temperature offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen (BUN) and lactate dehydrogenase (LDH). Features predictive of morbidity included LDH, calcium, glucose, and C-reactive protein (CRP). Together this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.

Is procalcitonin a part of human immunological response to SARS-CoV-2 infection or "just" a marker of bacterial coinfection?

Elevated PCT level in COVID-19 was associated with higher risk of severe disease and higher risk of overall mortality. An increased PCT level of PCT in COVID-19 patients especially in severe cases would be assumed as bacterial coinfection. Could PCT level increase in SARS-CoV-2 infection without bacterial coinfection? Several SARS-CoV-2 proteins activate STAT3-dependent transcriptional pathways particularly in monocytes, that could lead to increased PCT production. STAT3α isoform could cause increased ACE2 expression, resulting more SARS-CoV-2 infected cells and further production of PCT.

Myoglobin and C-reactive protein are efficient and reliable early predictors of COVID-19 associated mortality

Since the emergence of SARS-CoV-2, numerous studies have been attempting to determine biomarkers, which could rapidly and efficiently predict COVID-19 severity, however there is lack of consensus on a specific one. This retrospective cohort study is a comprehensive analysis of the initial symptoms, comorbidities and laboratory evaluation of patients, diagnosed with COVID-19 in Huoshenshan Hospital, Wuhan, from 4th February to 12th March, 2020. Based on the data collected from 63 severely ill patients from the onset of symptoms till the full recovery or demise, we found not only age (average 70) but also blood indicators as significant risk factors associated with multiple organ failure. The blood indices of all patients showed hepatic, renal, cardiac and hematopoietic dysfunction with imbalanced coagulatory biomarkers. We noticed that the levels of LDH (85%, P < .001), HBDH (76%, P < .001) and CRP (65%, P < .001) were significantly elevated in deceased patients, indicating hepatic impairment. Similarly, increased CK (15%, P = .002), Cre (37%, P = 0.102) and CysC (74%, P = 0.384) indicated renal damage. Cardiac injury was obvious from the significantly elevated level of Myoglobin (52%, P < .01), Troponin-I (65%, P = 0.273) and BNP (50%, P = .787). SARS-CoV-2 disturbs the hemolymphatic system as WBC# (73%, P = .002) and NEUT# (78%, P < .001) were significantly elevated in deceased patients. Likewise, the level of D-dimer (80%, P < .171), PT (87%, P = .031) and TT (57%, P = .053) was elevated, indicating coagulatory imbalances. We identified myoglobin and CRP as specific risk factors related to mortality and highly correlated to organ failure in COVID-19 disease.