Metodo LAMP como alternativa diagnostica para la deteccion del virus SARS-CoV-2

Summary Introduction: The SARS-CoV-2 coronavirus, that causes the COVID-19 disease, has become a global public health problem that requires the implementation of rapid and sensitive diagnostic tests. Aim(s): To evaluate and compare the sensitivity of LAMP assay to a standard method and use RT-LAMP for the diagnosis of SARS-CoV-2 in clinical samples from Colombian patients. Method(s): A descriptive and cross-sectional study was conducted. A total of 25 nasopharyngeal swab samples including negative and positive samples for SARS-CoV-2 were analyzed, through the RT-LAMP method compared to the RT-qPCR assay. Result(s): LAMP method detected ~18 copies of the N gene, in 30 min, evidenced a detection limit similar to the standard method, in a shorter time and a concordance in RT-LAMP of 100% with the results. Conclusion(s): RT-LAMP is a sensitive, specific, and rapid method that can be used as a diagnostic aid of COVID-19 disease.Copyright © 2021. All Rights Reserved.

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Advantages of Total Bilirubin for Predicting Malignant Obstructive Jaundice, A Combination of the Pandemic Era and Limited Resources Settings

BACKGROUND: Hepatopancreatobiliary (HPB) cancer incidence and mortality are increasing worldwide. An initial diagnostic predictor is needed for recommending further diagnostic modalities, referral, and curative or palliative decisions. There were no studies conducted in area with limited accessibility setting of the COVID-19 pandemic, coupled with limited human resources and facilities. AIM: We aimed to investigate the advantages of total bilirubin for predicting malignant obstructive jaundice, a combination of the pandemic era and limited resources settings. METHOD(S): Data from all cholestasis jaundice patients at M. Djamil Hospital in Pandemic COVID-19 period from July 2020 to May 2022 were retrospectively collected. The data included demographics, bilirubin fraction results, and final diagnosis. Bivariate analysis for obtain demographic risk factor, and Receiver Operating Characteristics (ROC) analysis for getting bilirubin value. RESULT(S): Of a total 132 patients included, 35.6% were malignant obstructive jaundice, and Pancreatic adeno ca was the most malignant etiology (34.4%). Bivariate analysis showed a significant correlation between age and malignant etiology (p = 0,024). Direct and total Bilirubin reach the same level of Area Under Curve (AUC). Total bilirubin at the cutoff point level of 10.7 mg/dl had the most optimal results on all elements of ROC output, AUC 0.88, sensitivity 76.6%, specificity 90.1%, +LR 8.14, and-LR 0.26. CONCLUSION(S): The bilirubin fraction is a good initial indicator for differentiating benign and malignant etiology (AUC 0.8-0.9) in pandemic era and resource-limited areas to improve diagnostic effectiveness and reduce referral duration.Copyright © 2023 Avit Suchitra, M. Iqbal Rivai, Juni Mitra, Irwan Abdul Rachman, Rini Suswita, Rizqy Tansa.

Single-step multiplex reverse transcription-polymerase chain reaction for the detection and differentiation of QX-like infectious bronchitis virus from the Thai variant and vaccine strains H120, Ma5, and 4/91

Background and Aim: QX-like infectious bronchitis virus (IBV) is a highly infectious avian coronavirus that causes respiratory and kidney disease. It is linked to increased mortality and loss of performance in infected chickens worldwide, including Thailand. Thus, a simple and rapid diagnostic method for the diagnosis of QX-like IBV is needed. This study aimed to develop a single-step multiplex reverse transcription-polymerase chain reaction (mRT-PCR) assay to detect and differentiate QX-like IBV from Thai IBV and vaccine strains used in the poultry industry (H120, Ma5, and 4/91). Materials and Methods: Primer sets specific for QX-like and Thai IBV were designed to target the S1 gene. The specificity of the technique was verified using nine isolates of QX-like IBV, four isolates of Thai IBV, and other avian viral respiratory pathogens. The detection limit was evaluated using a serial ten-fold dilution of QX-like and Thai IBV. Results: The results showed that single-step mRT-PCR could detect QX-like IBV and differentiate it from Thai IBV and the vaccine strains H120, Ma5, and 4/91. The limit of detection of the developed assay was 102.2 embryo infectious dose (EID)50/mL for QX-like IBV and 101.8 EID50/mL for Thai IBV. Interestingly, the developed assay could identify mixed infection by both IBVs in a single sample. Conclusion: The single-step mRT-PCR assay developed in this study can potentially discriminate QX-like IBV from Thai IBV and the vaccine strains H120, Ma5, and 4/91 in a single reaction. It is also suitable for use in all laboratories with access to conventional PCR equipment. [ FROM AUTHOR] Copyright of Veterinary World is the property of Veterinary World and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Procalcitonin as a Prognostic Marker of Complicated Appendicitis

Introduction: Acute appendicitis is the most common cause of acute abdominal pain as well as one of the most frequently performed procedures in general surgery. Different prognostic laboratory markers have been studied to identify patients with complicated appendicitis and it is unknown whether the level of procalcitonin in adults could be used as a predictive marker. From a cut-off point, Does procalcitonin have predictive value for complicated appendicitis? Methods: Prospective, observational study. Patients from the Civil Hospital of Guadalajara with a diagnosis of Appendicitis, presurgical laboratory studies and Procalcitonin, and undergo appendectomy in this institution. A calculated sample was obtained based on the surgeries performed annually. Result(s): 80 appendicectomies were performed in the 12-month period (2021;COVID pandemic) obtaining: 37 patients with uncomplicated appendicitis (Phase I and II) 43 patients with complicated appendicitis (Phase III and IV) The procalcitonin levels of both groups were analyzed to demonstrate differences between them, Mann-Whitney U test gives us as a result a p value <0.05. For the cut-off point at the most suitable procalcitonin level for this sample we decided to use the Yauden index method in the analysis of the ROC curve: it is observed that the cut-off point with a sensitivity of 72.1% and a specificity of 81.1% for the sample is 0.305. Conclusion(s): Procalcitonin has been shown to be a useful marker for discriminating the severity of appendicitis and that the best cutoff point for this sample is 0.3 ng/dl.

Direct bilirubin and the neutrophil-to-monocyte ratio timely predict intensive care unit admission in patients with severe acute respiratory syndrome coronavirus-2 infection (COVID-19)

Objective: Intensive care units (ICUs) collapsed under the global wave of coronavirus disease 2019 (COVID-19). Thus, we designed a clinical decision-making model that can help predict at hospital admission what patients with COVID-19 are at higher risk of requiring critical care. Method(s): This was a cross-sectional study in 119 patients that met hospitalization criteria for COVID-19 including less than 30 breaths per minute, peripheral oxygen saturation < 93%, and/or >= 50% lung involvement on imaging. Depending on the need for critical care, patients were retrospectively assigned to ICU and non-ICU groups. Demographic, clinical, and laboratory parameters were collected at admission and analyzed by classification and regression tree (CRT). Result(s): Forty-five patients were admitted to ICU and 80% of them were men older than 57.13 +/- 12.80 years on average. The leading comorbidity in ICU patients was hypertension. The CRT revealed that direct bilirubin (DB) > 0.315 mg/dl together with the neutrophil-to-monocyte ratio (NMR) > 15.90 predicted up to correctly in 92% of the patients the requirement of intensive care management, with sensitivity of 93.2%. Preexisting comorbidities did not influence on the tree growing. Conclusion(s): At hospital admission, DB and NMR can help identify nine in 10 patients with COVID-19 at higher risk of ICU admission.Copyright © 2022 Sociedad Medica del Hospital General de Mexico.

Liver biomarkers for prognosis in COVID-19

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Mortality attributable to COVID-19 remains considerably high, with case fatality rates as high as 8-11%. Early medical intervention in patients who are seriously and critically ill with COVID-19 reduces fatal outcomes. Thus, there is an urgent need to identify biomarkers that could help clinicians determine which patients with SARS-CoV-2 infection are at a higher risk of developing the most adverse outcomes, which include intensive care unit (ICU) admission, invasive ventilation, and death. In COVID-19 patients experiencing the most severe form of the disease, tests of liver function are frequently abnormal and liver enzymes are found to be elevated. For this reason, we examine the most promising liver biomarkers for COVID-19 prognosis in an effort to help clinicians predict the risk of ARDS, ICU admission, and death at hospital admission. In patients meeting hospitalization criteria for COVID-19, serum albumin < 36 g/L is an independent risk factor for ICU admission, with an AUC of 0.989, whereas lactate dehydrogenase (LDH) values > 365 U/L accurately predict death with an AUC of 0.943.The clinical scores COVID-GRAM and SOFA that include measures of liver function such as albumin, LDH, and total bilirubin are also good predictors of pneumonia development, ICU admission, and death, with AUC values ranging from 0.88 to 0.978.Thus, serum albumin and LDH, together with clinical risk scores such as COVID-GRAM and SOFA, are the most accurate biomarkers in the prognosis of COVID-19.Copyright © 2021 Sociedad Medica del Hospital General de Mexico. Published by Permanyer.

An audit of paroxysmal nocturnal haemoglobinuria (PNH) requesting at University Hospitals of North Midlands

Early identification of PNH, a rare life-threatening disease is essential to ensure appropriate management via the UK PNH service. Since testing for PNH is expensive (75.97 per test), we set out to assess the suitability of PNH requests against guidelines with the aim to feedback to colleagues and reduce unnecessary testing. To determine whether PNH requests at UHNM were in line with criteria in British Society for Haematology (BSH) guidelines. All patients over 18 years of age who had PNH testing for the first time and those who had repeat testing for monitoring between 01/04/2019 and 31/03/2020 were included. Patients were selected from electronic records of PNH sample receipt to laboratories. Hospital records were reviewed for clinical details and investigation Results. 82 requests including 79 individual patients were audited. 57% were male and 43% were female. Median age was 56 years. 97.6% of PNH tests were requested by a haematologist whilst only 2.5% requests were done by non-haematology clinicians. 52.4% requests were in keeping with BSH recommendations, whilst 47.6% tests did not meet criteria for testing. All patients tested outside of guideline recommendations were negative. Of the reasonable requests, only 23.3% (10) were positive. Of the PNH positive patients, 8 patients were known to have a PNH clone with aplastic anaemia;one patient had a hypoplastic bone marrow and a known PNH clone whilst only one patient with cytopenia had a new positivity for PNH. The frequency monitoring for aplastic anaemia and a PNH clone was 100% concordant with BSH recommendations. With appropriate testing, only one new patient was identified. Our audit has limitations. We have not been able to assess whether any patients outside of those monitored for PNH in aplastic anaemia have been overlooked for testing. Also, the time period includes the COVID-19 pandemic so our findings may not reflect usual practice. New BSH guidelines for thrombophilia testing were published in 2022 and recommend testing for PNH in patients with thrombosis at unusual sites and abnormal haematological parameters and for patients with arterial thrombosis and abnormal blood parameters. This will likely limit excess tests although the sensitivity and specificity of such an approach has not been formally evaluated. In a finite health system, it is our responsibility to rationalise investigations. The cost of a year's testing was 6229.54 and that of inappropriate testing was 2962.83. As a department, we could, therefore, save 2962.83.

Detection and Classification of Pneumonia Using Deep Learning by the Dense Net-121 Model

A chronic respiratory disease known as pneumonia can be devastating if it is not identified and treated in a timely manner. For successful treatment and better patient outcomes, pneumonia must be identified early and properly classified. Deep learning has recently demonstrated considerable promise in the area of medical imaging and has successfully applied for a few image-based diagnosis tasks, including the identification and classification of pneumonia. Pneumonia is a respiratory illness that produces pleural effusion (a condition in which fluids flood the lungs). COVID-19 is becoming the major cause of the global rise in pneumonia cases. Early detection of this disease provides curative therapy and increases the likelihood of survival. CXR (Chest X-ray) imaging is a common method of detecting and diagnosing pneumonia. Examining chest X-rays is a difficult undertaking that often results in variances and inaccuracies. In this study, we created an automatic pneumonia diagnosis method, also known as a CAD (Computer-Aided Diagnosis), which may significantly reduce the time and cost of collecting CXR imaging data. This paper uses deep learning which has the potential to revolutionize in the area of medical imaging and has shown promising results in the detection and classification of pneumonia. Further research and development in this area is needed to improve the accuracy and reliability of these models and make them more accessible to healthcare providers. These models can provide fast and accurate results, with high sensitivity and specificity in identifying pneumonia in chest X-rays. © 2023 IEEE.

Silicon photonic chip-based biosensor for COVID-19 and flu detection with high sensitivity and specificity

Since the end of 2021, Omicron, the new variant of SARS-CoV-2, has continued to spread as the predominant strain of COVID-19. Compared to previous variants, Omicron causes milder symptoms, which are similar to symptoms of other common respiratory infections, such as flu. In this work, we develop a silicon photonic chip-based biosensor for COVID-19 and flu detection using subwavelength grating micro-ring resonator. The biosensor realizes the detection of two pathogens with high sensitivity (1.31 fg/mL) and specificity. Besides, the microfluidic channel offers a promising solution for point-of-care detection. © 2023 SPIE.

Immunotherapy: RAPID ACCESS TO VIRUS-SPECIFIC T CELLS FOR ADOPTIVE IMMUNOTHERAPY FOR COMPASSIONATE USE IN AUSTRALIA

Background & Aim: Adoptive T cell immunotherapy holds great promise for the treatment of viral complications. Our group has been developing and trialling virus-specific T cell therapies for more than 20 years. Recently, we have generated a repository of multi-virus-specific T cells for our clinical trials. Unfortunately, for many patients with viral complications, there is no suitable trial through which to access these therapies. In Australia, the Therapeutic Goods Administration has a Special Access Scheme (SAS) to enable provision of unapproved therapies for compassionate use. Our research group is now a leading Australian provider of "off-the-shelf" and custom-grown allogeneic virus-specific T cells to hospitals for patients with no other treatment options. Methods, Results & Conclusion(s): We have generated a repository of multi-virus-specific T cells from 20 healthy donors, with up to 150 doses of T cells per donor generated from a single blood sample. Each product batch is thoroughly characterised in terms of viral antigen specificity, HLA restriction and alloreactivity. These T cells target a combination of Epstein-Barr virus, cytomegalovirus, BK polyomavirus, John Cunningham virus and adenovirus epitopes. We have also generated a repository of SARS-CoV-2-specific T cells and occasionally grow custom patient-specific batches of T cells from nominated donors, on request. Since 2008, we have provided virus-specific T cells to 15 hospitals across Australia, and the volume of supply requests has significantly increased in recent years, as clinicians have gained interest in adoptive immunotherapy. In 2022, we provided T cells for 26 patients via the SAS. The majority were experiencing post-transplant complications, including cytomegalovirus disease, BK virus-associated haemorrhagic cystitis and post-transplant lymphoproliferative disorder. Through our clinical trials, we have developed rigorous processes for T cell therapy manufacture and characterisation, in addition to a computer-based selection algorithm, which we apply to SAS cases. As these cases are not part of a clinical trial, concomitant therapy varies, and monitoring is not uniform. However, we have received reports of clinical benefit from adoptive T cell therapy. These include cases of reduction in viral load, improvement in symptoms, and complete resolution of infection. We believe that these promising T cell therapies should be available to hospitals through a nationally funded centre for cellular therapies for critically ill patients.Copyright © 2023 International Society for Cell & Gene Therapy

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.

The Predictive Value of Cystatin C for AKI in Patients with COVID-19.

Objective. To evaluate a potential of cystatin C blood concentration to predict acute kidney injury (AKI) in patients with severe and extremely severe pneumonia associated with a COVID-19. Materials and methods. An observational prospective study of 117 patients with severe and extremely severe pneumonia associated with a COVID-19 in an ICU setting was conducted in 2020-2022 (site: multifunctional Medical Center, 1586 Military Clinical Hospital of the Ministry of Defense of Russia, Moscow Region, Russia). Routine laboratory tests and instrumental examinations were performed according to generally accepted protocols. Cystatin C concentrations in blood (s-CysC) and urine (u-CysC) were measured by immunoturbidimetric method. Results. AKI was diagnosed in 21 (17.9%) patients, kidney dysfunction without AKI was found in 22 (18.8%) patients with severe and extremely severe pneumonia associated with COVID-19. s-CysC and u-CysC levels in the group of patients with AKI were statistically significantly higher compared to the levels in the group of patients without AKI. The levels of s-CysC obtained within Day 1 - T (-1), and Day 2 - T (-2) prior to AKI onset turned out to be the independent factors for AKI development in patients with severe and extremely severe pneumonia associated with COVID-19: OR 5.37, Wald chi-square 5.534 (CI: 1.324;21.788);P=0.019 and OR 3.225, Wald chi-square 4.121 (CI: 1.041;9.989);P=0.042, respectively. s-CysC T (-2) value is informative, and s- CysC T (-1) is a highly informative predictor of AKI development in severe and extremely severe pneumonia associated with COVID-19: ROC AUC 0.853 (95% CI, 0.74-0.966), P_0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.67 mg/L, and ROC AUC 0.905 (95% CI, 0.837-0.973), P_0.001) with 90% sensitivity and 73% specificity at a cut-off of 1.69 mg/l, respectively. Serum CysC levels started increasing 3 days prior to AKI onset, outpacing the increase of SCr levels. The u-CysC levels were not predictive of AKI development. Impaired renal function probability was increasing with patients' age (P_0.0001). Conclusions. Serum CysC seems to be a statistically significant predictor of AKI. s-CysC levels started increasing 3 days prior to AKI onset, surpassing the increase of SCr levels in patients with severe and extremely severe pneumonia associated with COVID-19. Urine CysC did not achieve statistical significance as a predictor for AKI, although u-CysC concentrations were significantly higher on days 3, 2, 1 prior to AKI onset and on the day of AKI onset in the group of patients with AKI.Copyright © 2023, V.A. Negovsky Research Institute of General Reanimatology. All rights reserved.

Digital Contact Tracing for COVID-19: Overview of Existing Systems and Framework for Future Innovation

Background: The COVID-19 pandemic catalyzed innovation in infection control measures, including widespread deployment of digital contact tracing systems. However, these technologies were not well understood by the general public and were complex for the public health community to implement, hampering adoption. Objective(s): To provide an overview of existing digital contact tracing systems, creating a framework for understanding design elements that impact their effectiveness as public health tools and offering a rubric for decision-makers to evaluate different systems for selection and implementation. Method(s): Scientific literature and publicly available information from relevant health authorities and other stakeholders was reviewed. Information was synthesized to develop a conceptual framework explaining how key design elements impact effectiveness of digital contact tracing systems and highlighting opportunities for future improvement. Result(s): A range of digital contact tracing interventions were deployed by governments worldwide and several professional sports leagues. Key design elements of the systems include: (1) data architecture (i.e., centralized versus decentralized systems, impacting privacy guarantees and data availability);(2) proximity detection technology (e.g., type of device signaling);(3) alert logic and timing (e.g., time- and distance-based criteria affecting sensitivity and specificity of alerts;real-time proximity alerts and/or bidirectional contact tracing, determining scope of infection prevention);(4) population (eligibility and availability);and (5) the structural and public health context of intervention (e.g., availability and timeliness of testing). Several systems demonstrated effectiveness in preventing transmission during COVID-19, though numerous limitations have also been documented in the literature. Conclusion(s): Digital contact tracing systems have the potential to mitigate the economic and public health impact of future infectious disease outbreaks, reducing community transmission and detecting potential cases earlier in the disease course. Lessons learned from solutions deployed during the COVID-19 pandemic provide an opportunity to improve multiple aspects of these systems, enhancing preparedness for future outbreaks.Copyright © 2023

Immunotherapy: OFF-THE-SHELF PARTIAL HLA MATCHING SARS-COV-2 ANTIGEN SPECIFIC T CELL THERAPY FOR SEVERE COVID-19 PATIENTS

Background & Aim: Immunological characteristics of COVID-19 show pathological hyperinflammation associated with lymphopenia and dysfunctional T cell responses. These features provide a rationale for restoring functional T cell immunity in COVID-19 patients by adoptive transfer of SARS-CoV-2 specific T cells. Methods, Results & Conclusion(s): To generate SARS-CoV-2 specific T cells, we isolated peripheral blood mononuclear cells from 7 COVID-19 recovered and 13 unexposed donors. Consequently, we stimulated cells with SARS-CoV-2 peptide mixtures covering spike, membrane and nucleocapsid proteins. Then, we culture expanded cells with IL-2 for 21 days. We assessed immunophenotypes, cytokine profiles, antigen specificity of the final cell products. Our results show that SARSCoV- 2 specific T cells could be expanded in both COVID-19 recovered and unexposed groups. Immunophenotypes were similar in both groups showing CD4+ T cell dominance, but CD8+ and CD3+CD56+ T cells were also present. Antigen specificity was determined by ELISPOT, intracellular cytokine assay, and cytotoxicity assays. One out of 14 individuals who were previously unexposed to SARS-CoV-2 failed to show antigen specificity. Moreover, ex-vivo expanded SARS-CoV-2 specific T cells mainly consisted of central and effector memory subsets with reduced alloreactivity against HLA-unmatched cells suggesting the possibility for the development of third-party partial HLA-matching products. In conclusion, our findings show that SARSCoV- 2 specific T cell can be readily expanded from both COVID-19 and unexposed individuals and can therefore be manufactured as a biopharmaceutical product to treat severe COVID-19 patients.Copyright © 2023 International Society for Cell & Gene Therapy

Comparison of Diagnostic Accuracy of CRP Versus Ferritin in COVID Patients as a Marker of Severity of Disease Taking who Severity Classification as Gold Standard

Objective: To determine the diagnostic accuracy of elevated C reactive protein (CRP) and ferritin in predicting severe Covid-19 infection using the World Health Organization's (WHO) Covid-19 severity classification as gold standard. Study Design: Descriptive study. Place and Duration of Study: This study was conducted at the Pak Emirates Military Hospital, Rawalpindi, from January 1st 2021 till April 30th 2021. Ethical review committee's (ERC) approval was taken and good clinical practice guidelines were followed. Material(s) and Method(s): Baseline blood samples were sent to the hospital laboratory for the measurement of C reactive protein and ferritin levels. PCR was taken as gold standard for the diagnosis of Corona virus disease. Patients were classified into severe and non-severe categories using WHO classification of severity. Sensitivity, specificity, diagnostic accuracy, negative predictive value and positive predictive value were calculated for elevated CRP and ferritin. Result(s): There were 65 (57.5%) patients who had severe Covid-19 disease and 48 (42.5%) patients who had non-severe Covid-19 disease. Among the patients with severe Covid-19, 57 (87.7%) had elevated CRP levels, and 50 (76.9%) patients had elevated ferritin levels. Testing ferritin levels, against the severity of Covid-19 patients, there was a sensitivity of 76.9%, specificity of 79.2%, positive predictive value (PPV) of 83.3%, negative predictive value (NPV) of 71.7% and diagnostic accuracy of 77.8%. Testing CRP levels, there was a sensitivity of 87.7%, specificity of 85.4%, PPV of 89.1%, NPV of 83.6% and diagnostic accuracy of 86.7%. Conclusion(s): The results from our study show that CRP has a slightly improved diagnostic accuracy as compared to ferritin. However, both these markers have value in the prediction of severity of Covid-19 infection.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Predictors of poor outcomes in patients with pandemic viral infections (influenza AH1N1pdm09, COVID-19) on a ventilator.

Considering the commonality of the pathogenetic links of the critical forms of COVID-19 and influenza AH1N1pdm09 (cytokine over-release syndrome), the question arises: will the predictors of an unfavorable outcome in patients on mechanical ventilation and, accordingly, the universal tactics of respiratory support in these diseases be identical? Objective. In a comparative aspect, to characterize patients with influenza AH1N1pdm09 and COVID-19 who were on mechanical ventilation, to identify additional clinical and laboratory risk factors for death, to determine the degree of influence of respiratory support (RP) tactics on an unfavorable outcome in the studied category of patients. Patients and methods. Patients treated on the basis of resuscitation and intensive care departments of the State Budgetary Healthcare Institution "SKIB" in Krasnodar and the State Budgetary Healthcare Institution "IB No 2" in Sochi were studied: group 1 - 31 people with influenza AH1N1pdm09 (21 people died - subgroup 1A;10 people survived - subgroup 1B) and group 2 - 50 people with COVID-19 (29 patients died - subgroup 2A;21 people survived - subgroup 2B). All patients developed hypoxemic ARF. All patients received step-by-step tactics of respiratory support, starting with oxygen therapy and ending with the use of "traditional" mechanical ventilation. Continuous variables were compared in subgroups of deceased and surviving patients for both nosologies at the stages: hospital admission;registration of hypoxemia and the use of various methods of respiratory therapy;development of multiple organ dysfunctions. With regard to the criteria for which a statistically significant difference was found (p < 0.05), we calculated a simple correlation, the relative risk of an event (RR [CI 25-75%]), the cut-off point, which corresponded to the best combination of sensitivity and specificity. Results. Risk factors for death of patients with influenza AH1N1pdm09 on mechanical ventilation: admission to the hospital later than the 8th day of illness;the fact of transfer from another hospital;leukocytosis >=10.0 x 109/l, granulocytosis >=5.5 x 109/l and LDH level >=700.0 U/l at admission;transfer of patients to mechanical ventilation on the 9th day of illness and later;SOFA score >=8;the need for pressor amines and replacement of kidney function. Predictors of poor outcome in ventilated COVID-19 patients: platelet count <=210 x 109/L on admission;the duration of oxygen therapy for more than 4.5 days;the use of HPNO and NIV as the 2nd step of RP for more than 2 days;transfer of patients to mechanical ventilation on the 14th day of illness and later;oxygenation index <=80;the need for pressors;SOFA score >=8. Conclusion. When comparing the identified predictors of death for patients with influenza and COVID-19 who needed mechanical ventilation, there are both some commonality and differences due to the peculiarities of the course of the disease. A step-by-step approach to the application of respiratory support methods is effective both in the case of patients with influenza AH1N1pdm09 and patients with COVID-19, provided that the respiratory support method used is consistent with the current state of the patient and his respiratory system, timely identification of markers of ineffectiveness of the respiratory support stage being carried out and determining the optimal moment escalation of respiratory therapy.Copyright © 2022, Dynasty Publishing House. All rights reserved.