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> Chemicals & Drugs > Amino Acid > Procalcitonin

Procalcitonin

Procalcitonin is a peptide precursor of the hormone calcitonin, which is primarily produced by the C cells of the thyroid gland.
It has been identified as a valuable biomarker for the diagnosis and monitoring of bacterial infections, sepsis, and other inflammatory conditions.
Procalcitonin levels rise rapidly in response to bacterial stimuli, making it a useful tool for differentiating bacterial from viral infections.
Accurate and reproducible methods for procalcitonin measurement are crucial for effective clinical decision-making and patient management.
PubCompare.ai, an AI-driven platform, can help researchers optimize their procalcitonin studies by identifying the best protocols from literature, preprints, and patents, enhancing the quality and reliability of their research.

Most cited protocols related to «Procalcitonin»

1
Epidemiological Analysis of Early COVID-19 Cases
Cited 213 times
We obtained the medical records and compiled data for hospitalized patients and outpatients with laboratory-confirmed Covid-19, as reported to the National Health Commission between December 11, 2019, and January 29, 2020; the data cutoff for the study was January 31, 2020. Covid-19 was diagnosed on the basis of the WHO interim guidance.14 A confirmed case of Covid-19 was defined as a positive result on high-throughput sequencing or real-time reverse-transcriptase–polymerase-chain-reaction (RT-PCR) assay of nasal and pharyngeal swab specimens.1 (link) Only laboratory-confirmed cases were included in the analysis.
We obtained data regarding cases outside Hubei province from the National Health Commission. Because of the high workload of clinicians, three outside experts from Guangzhou performed raw data extraction at Wuhan Jinyintan Hospital, where many of the patients with Covid-19 in Wuhan were being treated.
We extracted the recent exposure history, clinical symptoms or signs, and laboratory findings on admission from electronic medical records. Radiologic assessments included chest radiography or computed tomography (CT), and all laboratory testing was performed according to the clinical care needs of the patient. We determined the presence of a radiologic abnormality on the basis of the documentation or description in medical charts; if imaging scans were available, they were reviewed by attending physicians in respiratory medicine who extracted the data. Major disagreement between two reviewers was resolved by consultation with a third reviewer. Laboratory assessments consisted of a complete blood count, blood chemical analysis, coagulation testing, assessment of liver and renal function, and measures of electrolytes, C-reactive protein, procalcitonin, lactate dehydrogenase, and creatine kinase. We defined the degree of severity of Covid-19 (severe vs. nonsevere) at the time of admission using the American Thoracic Society guidelines for community-acquired pneumonia.15 (link)All medical records were copied and sent to the data-processing center in Guangzhou, under the coordination of the National Health Commission. A team of experienced respiratory clinicians reviewed and abstracted the data. Data were entered into a computerized database and cross-checked. If the core data were missing, requests for clarification were sent to the coordinators, who subsequently contacted the attending clinicians.
Guan W.J., Ni Z.Y., Hu Y., Liang W.H., Ou C.Q., He J.X., Liu L., Shan H., Lei C.L., Hui D.S., Du B., Li L.J., Zeng G., Yuen K.Y., Chen R.C., Tang C.L., Wang T., Chen P.Y., Xiang J., Li S.Y., Wang J.L., Liang Z.J., Peng Y.X., Wei L., Liu Y., Hu Y.H., Peng P., Wang J.M., Liu J.Y., Chen Z., Li G., Zheng Z.J., Qiu S.Q., Luo J., Ye C.J., Zhu S.Y, & Zhong N.S. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. The New England Journal of Medicine.
Publication 2020
Biological Assay Blood Chemical Analysis Complete Blood Count COVID 19 C Reactive Protein Creatine Kinase Electrolytes Kidney Lactate Dehydrogenase Liver Nose Outpatients Patients Pharynx Physicians Pneumonia Procalcitonin Radiography, Thoracic Radionuclide Imaging Real-Time Polymerase Chain Reaction Respiratory Rate Reverse Transcriptase Polymerase Chain Reaction RNA-Directed DNA Polymerase X-Ray Computed Tomography
2
Laboratory Confirmation of SARS-CoV-2 Infection
Cited 27 times

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Publication 2020
Blood Chest Chinese Complete Blood Count Cough COVID 19 Creatine Kinase Dyspnea Electrolytes Enzymes Ferritin Fever Inpatient Interleukin-6 Kidney Lactate Dehydrogenase Liver Lung Myocardium Patient Discharge Pharynx Physical Examination Physicians Procalcitonin Radiography, Thoracic Real-Time Polymerase Chain Reaction Respiratory Rate SARS-CoV-2 Serum Signs and Symptoms, Respiratory Tests, Blood Coagulation X-Ray Computed Tomography
3
Factors Associated with COVID-19 Hospitalization and Severity
Cited 11 times
We used descriptive statistics to characterize each cohort of patients: those not admitted to hospital, all those admitted, those admitted without critical illness, and those with critical illness (care in ICU, mechanical ventilation, discharge to hospice, or death). We then fitted multivariable logistic regression models with admission and with critical illness as the outcomes to identify factors associated with those outcomes. In analyses using hospital admission as the outcome, we included only patient characteristics and comorbidities, since 77% of the patients who were not admitted were evaluated in ambulatory testing centers and did not have vitals or laboratory studies collected. For the critical illness analyses, we included the above predictors and for one of the models added temperature and oxygen saturation on presentation, as well as the first result of C reactive protein, D-dimer, ferritin, procalcitonin, and troponin when obtained. We included all selected predictors based on a priori clinical significance after testing for collinearity using the variance inflation factor and ensuring none had a variance inflation factor greater than 2.18 (link) We also tested for overall multicollinearity among all variables simultaneously using the determinant of correlation matrix implemented in R’s mctest library and found no significant results.19
For the admission model, we included all patients testing positive (excluding the 287 patients with no data besides age and sex). We constructed two models for association with critical illness. First, we constructed a model restricted to patients admitted to hospital, including all personal and comorbidity predictors, and a random effect for hospital to account for clustering by facility. Second, we added to that model vital signs and the first set of laboratory results, to assess clinical associations with critical illness among patients admitted to hospital. We excluded from the second model four patients who died in the emergency department before vital signs or laboratory results could be collected. We obtained odds ratios from the models and profiled confidence intervals for the odds ratios using the approach of Venables and Ripley,20 (link) since assuming normality of the maximum likelihood estimate to estimate Wald-type confidence intervals can lead to biased estimates.21 (link) We also calculated average marginal effects for each predictor by using the margins library in R, which uses a discrete first difference in predicted outcomes to obtain the average marginal effect.
Finally, we fitted a competing risk model for the mortality or hospice outcome with time from first positive test result as the start point, including only patients admitted to hospital.22 (link) We considered discharge from hospital to be a competing risk, since mortality data are limited after that point unless the patient is readmitted to our system (in which case the newest hospital admission would be included). Patients still in hospital as of 5 May 2020 were counted as censored. The model was fitted with the R library cmprisk,23 (link) and the proportionality assumption was checked with the goffte library.24 We fitted two competing risk models, one adjusting for personal characteristics and comorbidities, and one adding admission vitals and laboratory studies.
All statistical analyses were conducted with R, version 3.6.3. All analyses used two sided statistical tests and we considered a P value less than 0.05 to be statistically significant without adjustment for multiple testing.
Petrilli C.M., Jones S.A., Yang J., Rajagopalan H., O’Donnell L., Chernyak Y., Tobin K.A., Cerfolio R.J., Francois F, & Horwitz L.I. (2020). Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. The BMJ, 369, m1966.
Publication 2020
cDNA Library C Reactive Protein Critical Illness Ferritin fibrin fragment D Hospice Care Inpatient Mechanical Ventilation Oxygen Saturation Patient Discharge Patients Procalcitonin Signs, Vital Troponin
4
Validation of SARS-CoV-2 Antibody Multiplex Assay
Cited 10 times
In order to test the repeatability of the MultiCoV-Ab three quality control samples (QCs) were processed in duplicate on each test plate (n = 17) during the sample screening and inter-assay variance was assessed for each antigen in the multiplex. For intra-assay variance, 24 replicates for each of the three QC samples were analyzed on one plate. Results from this are presented in Supplementary Table 1 and Supplementary Fig. 2. A limit of detection (LOD) for each antigen was determined by processing a blank in 24 replicates and the LOD was set as mean MFI + 3 standard deviations. Sample parallelism and comparability of paired serum and plasma samples were assessed over eight dilution steps ranging from 1:100 to 1:12,800 (Supplementary Fig. 2). A set of samples derived from 205 SARS-CoV-2-infected and 72 uninfected individuals was tested repeatedly with two different kit batches. The samples classification in both runs matched 100%. Furthermore, as part of our negative sample panel, we have analyzed samples with potentially interfering characteristics (i.e., samples from patients with PCR-confirmed hCoV infection, presences of HAMA (human anti-mouse antibodies) and rheumatoid factor (RF), with high procalcitonin values (> 3 ng/mL), as well as from pregnant women and patients with neuroinflammatory diseases) (Supplementary Table 2).
Becker M., Strengert M., Junker D., Kaiser P.D., Kerrinnes T., Traenkle B., Dinter H., Häring J., Ghozzi S., Zeck A., Weise F., Peter A., Hörber S., Fink S., Ruoff F., Dulovic A., Bakchoul T., Baillot A., Lohse S., Cornberg M., Illig T., Gottlieb J., Smola S., Karch A., Berger K., Rammensee H.G., Schenke-Layland K., Nelde A., Märklin M., Heitmann J.S., Walz J.S., Templin M., Joos T.O., Rothbauer U., Krause G, & Schneiderhan-Marra N. (2021). Exploring beyond clinical routine SARS-CoV-2 serology using MultiCoV-Ab to evaluate endemic coronavirus cross-reactivity. Nature Communications, 12, 1152.
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Publication 2021
Anti-Antibodies Antigens Biological Assay Homo sapiens Infection Mus Neuroinflammatory Diseases Patients Plasma Pregnant Women Procalcitonin Rheumatoid Factor SARS-CoV-2 Serum Technique, Dilution
5
Liver Fibrosis Biomarker Evaluation
Cited 8 times
The study protocol was approved by the local ethics committee (ethics committee of University Hospital Aachen, RWTH Aachen), and written informed consent was obtained from each patient. The study was conducted according to the principles expressed in the Declaration of Helsinki. Inclusion criteria were either any CLD with a predisposition to liver fibrosis or an already established liver fibrosis/cirrhosis of any origin. Established cirrhosis (in contrast to non-cirrhotic CLD) was defined, if imaging (ultrasound, CT or MRI scan), biopsy or laparoscopy indicated liver cirrhosis or if cirrhosis-related complications were present. Patients with established liver cirrhosis were staged according to Child-Pugh's criteria [32] (link). Patients with acute liver failure or acute hepatitis B or C were not included. Exclusion criteria were conditions known to directly affect monocyte subset distributions in humans, specifically ongoing bacterial infections (procalcitonin concentration above normal value [<0.5 µg/L]), HIV-infection, systemic steroid medication (prednisolone >7.5 mg/d or equivalent doses) and malignant tumor(s) except hepatocellular or cholangiocellular carcinoma. Furthermore, patients were excluded in case of systemic inflammatory response syndrome (SIRS) or sepsis criteria [33] (link). The etiologies of liver diseases comprised viral hepatitis (n = 89, 39.4%; HBV n = 38, HCV n = 51), biliary or autoimmune disease (n = 27, 11.9%; autoimmune hepatitis n = 10, primary biliary cirrhosis n = 8, primary sclerosing cholangitis n = 9), alcoholic liver disease (n = 65, 28.7%) and other liver diseases (n = 45, 20%, e.g. non-alcoholic steatohepatitis n = 7, hemochromatosis n = 4, cryptogenic n = 23). Grading and staging of liver samples (biopsies and explants) were performed according to Desmet-Scheuer score by one experienced pathologist, who was fully blinded to any experimental data [34] (link).
As a control group, 181 healthy volunteers were recruited from the local blood transfusion institute that had normal aminotransferase activities, no history of liver disease or alcohol abuse and tested negative for HBV, HCV and HIV infections.
Zimmermann H.W., Seidler S., Nattermann J., Gassler N., Hellerbrand C., Zernecke A., Tischendorf J.J., Luedde T., Weiskirchen R., Trautwein C, & Tacke F. (2010). Functional Contribution of Elevated Circulating and Hepatic Non-Classical CD14+CD16+ Monocytes to Inflammation and Human Liver Fibrosis. PLoS ONE, 5(6), e11049.
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Publication 2010
Abuse, Alcohol Alcoholic Liver Diseases Autoimmune Chronic Hepatitis Autoimmune Diseases Bacterial Infections Bile Biopsy Blood Transfusion Child Cholangiocarcinoma Ethics Committees, Clinical Fibrosis Fibrosis, Liver Healthy Volunteers Hemochromatosis Hepatitis B Hepatitis Viruses HIV Infections Homo sapiens Laparoscopy Liver Liver Cirrhosis Liver Diseases Liver Failure, Acute Malignant Neoplasms Monocytes MRI Scans Nonalcoholic Steatohepatitis Pathologists Patients Pharmaceutical Preparations Prednisolone Primary Biliary Cholangitis Primary Sclerosing Cholangitis Procalcitonin Regional Ethics Committees Septicemia Steroids Susceptibility, Disease Systemic Inflammatory Response Syndrome Transaminases Ultrasonics

Most recents protocols related to «Procalcitonin»

1
Appendicitis-MISC Score for Diagnosis
Both groups were evaluated using NSS parameters [10 (link)]. These parameters were;

sex

type (continuous or intermittent), duration and migration of abdominal pain

anorexia, bilious vomiting, pyrexia (body temperature ≥ 38.0 °C [11 (link)])

presence of localized right lower quadrant abdominal tenderness, guarding, gurgling, a positive heel drop test, and rebound tenderness in physical examination

leukocytosis (> 10.600/mm3), neutrophilia (> 75%), elevated C-reactive protein (CRP) levels (> 5 mg/L) in blood examination

scoliosis on the right side, localized air-fluid level, gas deposition in the right lower quadrant on standing abdominal radiography

appendix diameter (> 7 mm), presence of a thickened wall, and surrounding loculated fluid collection on US

Consistent with the previous study, an NSS score ≥ 12 was accepted as the cutoff level for the diagnosis of AA [10 (link)].
Both groups were compared by new parameters thought to be MISC-specific (fatigue (feeling extra tired [12 ]), headache, maximum body temperature, and total fever [11 (link)] days in the history, serum lymphocyte and platelet counts, serum procalcitonin (PRC), alanine transferase (ALT), CRP, and D-dimer value). Statistically significant parameters were included in the scoring. A scoring system named the Appendicitis–MISC Score (AMS) was created using eight new parameters including the NSS score.
Toker Kurtmen B., Ekemen Keles Y., Tekindal M.A., Koyluoglu G, & Yilmaz Ciftdogan D. (2023). Differentiating abdominal pain due to COVID-19 associated multisystem inflammatory syndrome from children with acute appendicitis: a score system. Pediatric Surgery International, 39(1), 151.
Publication 2023
Abdomen Alanine Appendicitis BLOOD Body Temperature C Reactive Protein Diagnosis Fever fibrin fragment D Headache Heel Lymphocyte Neutrophil Physical Examination Platelet Counts, Blood Procalcitonin Serum Transferase
2
Diagnostic Workflow for Appendicitis and MIS-C
For discrete and continuous variables, descriptive statistics (mean, standard deviation, median, minimum, and maximum values) were calculated. In addition, the homogeneity of variances, which is one of the prerequisites of parametric tests, was checked using Levene’s test. The assumption of normality was tested using the Shapiro–Wilk test. To compare the differences between the two groups, an independent sample t-test was used when the parametric test prerequisites were fulfilled, and the Mann–Whitney U test was used when such prerequisites were not fulfilled. The chi-squared test was used to determine the relationships between the two discrete variables. When the expected sources were less than 20%, values were determined using the Monte Carlo simulation method to include these sources in the analysis. Age was determined as covariates (to be excluded), and the groups were compared using covariance analysis.
The cutoff points for the parameters were evaluated using receiver operating characteristic (ROC) curve analysis. Area under curve (AUC) of the ROC curve was calculated. We identified the value for each that maximized the Youden index (J), a summary statistic based on receiver operating characteristic curves that equally weights sensitivity and specificity (sensitivity + specificity − 1). The scores were estimated by constructing a multivariable logistic regression model considering the following covariates: NSS score, age, absence of fatigue, CRP, d-dimer, procalcitonin values, lymphocyte, and platelet counts. Due to the study design, we expected potential imbalances between groups. Propensity score matching was used to reduce potential selection bias between appendicitis group and MIS-C group. Data were evaluated using SPPS, version 25 (IBM Statistics, New York, USA). Statistical significance was set at p < 0.05 and p < 0.01.
Toker Kurtmen B., Ekemen Keles Y., Tekindal M.A., Koyluoglu G, & Yilmaz Ciftdogan D. (2023). Differentiating abdominal pain due to COVID-19 associated multisystem inflammatory syndrome from children with acute appendicitis: a score system. Pediatric Surgery International, 39(1), 151.
Publication 2023
Appendicitis Fatigue fibrin fragment D Lymphocyte Platelet Counts, Blood Procalcitonin stable plasma protein solution
3
High-Flow Nasal Oxygen Therapy for COVID-19-Induced Acute Respiratory Failure
Cited 1 time
This retrospective and cross-sectional study was conducted in Trakya University Hospital Respiratory Intensive Care Units which was approved by the Trakya University Clinical Research Ethics Committee (TÜTF-BAEK 2021/275) and the Turkish Ministry of Health (2021-06-07T10_06_44). Patients diagnosed with ARF due to lung involvement of laboratory-confirmed (RT-PCR) COVID-19 and managed with HFNC at ICU admission were included in the study between April 2020 and January 2022.
As per the Turkish Ministry of Health COVID-19 management guideline,21 HFNC is indicated for patients with persistent hypoxemia or respiratory distress symptoms under low flow oxygen therapy systems. HFNC was administered in the ICU with HI-Flow StarTM (Dragerwerk AG & Co., Germany), which is set to deliver a flow rate up to 50 l/min with FiO2 to keep the patient’s SpO2 above 90%.
If deterioration in the patient’s level of consciousness, worsening dyspnea, malign arrhythmia, or hemodynamic instability were detected or more than 60% FiO2 under 50 l/min flow rate was required to keep the patient’s PaO2/FiO2 over 150 mmHg, it was considered a treatment failure. Non-invasive ventilation (NIV) or IMV was initiated as rescue therapy.
Data were abstracted from the hospital records and nurse charts. Patients’ demographics, body mass indices, comorbidities, Charlson Comorbidity Indices,22 (link) disease severity scores [Acute Physiology and Chronic Health Assessment (APACHE),23 (link) Sequential Organ Failure Assessment (SOFA)24 (link)] and laboratory findings (hemogram, d-dimer, ferritin, C-reactive protein, procalcitonin, arterial blood gas parameters within 2 hours thereafter HFNC initiation) at ICU admission; ROX indices at initiation, 2nd, 8th, 12th, 24th and 48th hours of HFNC; and out-comes (ICU and hospital length of stay, in 28-day mortality) were recorded (Figure 2). ROX index was calculated using the formula (SpO2/FiO2)/respiratory rate.18 (link) Patients were excluded who were younger than 18 years old and HFNC failed within 2 hours of the therapy.
Hancı P., Uysal A., Yüksel B, & İnal V. (2023). Rox Index Dynamics According to High Flow Nasal Cannula Success in Intensive Care Unit Patients with COVID-19-Related Acute Respiratory Failure. Balkan Medical Journal, 40(2), 111-116.
Publication 2023
Arteries Blood Cardiac Arrhythmia Consciousness COVID 19 C Reactive Protein Dyspnea Ethics Committees, Research Ferritin fibrin fragment D Hemodynamics Index, Body Mass Lung Noninvasive Ventilation Nurses Patients physiology Procalcitonin Respiratory Rate Respiratory System Reverse Transcriptase Polymerase Chain Reaction Saturation of Peripheral Oxygen Therapeutics Therapies, Oxygen Inhalation Youth
4
Acute Osteomyelitis and Septic Arthritis in Children
All consecutive patients aged 3 months to 18 years old with a diagnosis of acute OM and/or SA according to the International Classification of Diseases, 9th Revision, Clinical Modification code were evaluated for inclusion. A case was defined by diagnosis of OM or SA on imaging, preferably magnetic resonance imaging (MRI, gold standard) for OM, or in alternative computed tomography (CT scan), Tc99 bone scintiscan, PET-TC scan, or ultrasound (US)/MRI for SA. Long bones were considered the typical site of infection for OM. The hips were considered a high-risk site for both OM and SA. Exclusion criteria were diagnosis of immunodeficiency or hemoglobinopathy or chronic granulomatous disease, immunosuppressive therapy, concomitant systemic bacterial infection, and ongoing antibiotic treatment on admission. Patients with complicated infections, not fully vaccinated, and/or with incomplete follow-up were excluded, as well as those with chronic osteomyelitis and Brodie's abscess.
The population was divided into two main groups, OM and SA. Each group was further divided into three groups: pre-intervention, post-intervention not following the guidelines (no GL), and post-intervention group with adherence to the guidelines (GL).
The following variables, selected a priori, were evaluated: age, sex, weight, fever, vaccination status, white blood cells, and neutrophil count, CRP, erythrocyte sedimentation rate (ESR), and procalcitonin (PCT) at onset, IV and oral antibiotic treatment with duration, diagnosis and imaging type, typical vs. atypical site, results of blood, pus, synovial fluid cultures, MRSA colonization status, Quantiferon results, PVL test positivity, treatment failure (defined as treatment escalation to broad spectrum antibiotics and/or need for surgery) and relapse at six months of follow-up. PCR tests for identification of K. kingae or other pathogens in case of culture-negative infections were not performed, as not included as standard of care at our facility.
Minotti C., Tirelli F., Guariento C., Sturniolo G., Giaquinto C., Da Dalt L., Zulian F., Meneghel A., Martini G, & Donà D. (2023). Impact of guidelines implementation on empiric antibiotic treatment for pediatric uncomplicated osteomyelitis and septic arthritis over a ten-year period: Results of the ELECTRIC study (ostEomyeLitis and sEptiC arThritis tReatment in children). Frontiers in Pediatrics, 11, 1135319.
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Publication 2023
Abscess Administration, Oral Antibiotics Bacterial Infections Blood Bones Coxa Diagnosis Fever Gold Granulomatous Disease, Chronic Hemoglobinopathies Immunologic Deficiency Syndromes Immunosuppression Infection Leukocytes Methicillin-Resistant Staphylococcus aureus Neutrophil Operative Surgical Procedures Osteomyelitis pathogenesis Patients Positron-Emission Tomography Procalcitonin Relapse Sedimentation Rates, Erythrocyte Synovial Fluid Ultrasonics Vaccination X-Ray Computed Tomography
5
Factors Influencing COVID-19 Admission Patterns
Participant characteristics were summarized descriptively. Comparisons between patients discharged home, admitted to the medical ward, or admitted directly to the ICU were made with Wilcoxson rank sum and Pearson chi-square tests for continuous and categorical variables, respectively. Impact of timing during the pandemic was assessed as days since data collection started (March 8, 2020).
All tests were 2-sided and a P value < .05 was considered statistically significant. All variables were initially assessed for significance using univariable analysis comparing: Patients discharged home versus admitted to the medical ward and; Patients admitted to the medical ward versus ICU (see Tables S1 and S2, Supplemental Digital Content, http://links.lww.com/MD/I601, which shows the results of univariable analysis). A Multivariable logistic regression was fitted separately comparing: Patients discharged home versus admitted to the medical ward and; Patients admitted to the medical ward versus ICU. We opted for 2 logistic regression models to reflect the distinct clinical decision making processes in the ED (i.e., “discharge home” vs “admit to medical ward,” and “admit to medical ward” vs “admit to ICU”).”
Our key associations of interest were race, ethnicity, ADI, English as a primary language, homelessness, and illicit substance use (opiates, cocaine, methamphetamine); variables also included age, gender, and clinical comorbidities, including body mass index (mg/kg2) and clinical severity. We evaluated disease severity using clinical severity scores (sequential organ failure assessment, Charlson comorbidity index) and laboratory markers found in other risk severity scores,[27 (link),28 ] specifically, C-reactive protein (mg/L), ferritin (ug/L), D-dimer (ng/mL), creatine kinase (U/L), troponin (ng/L), procalcitonin (ng/mL), absolute lymphocyte count (K/mL), and blood urea nitrogen (mg/dL). Timing of admission was calculated as days after the first date of data collection (March 8, 2020). In our regression, we controlled for timing of admission and included the square of timing of admission to evaluate how the effect changed over time. To build our regression models, we first included a priori variables based on clinical understanding (i.e., age, sex, sequential organ failure assessment, C-reactive protein, ferritin, and troponin), and then added variables that were significant on univariable analysis.” Variables were excluded if they showed significant co-linearity (variance inflation factors over 10). We used stepwise, backward selection for our logistic regression model, using a P value of over 0.2 as a cutoff to remove variables. Potential interaction between significant variables was explored.
Additionally, we divided differences in number of admissions in 3 groups to visually evaluate changes in admission over time. Groups were created as general phases of the surge in SARS-CoV-2 admissions in our hospital, representing changes in comfort with diagnosis and clinical management of COVID-19. Changes in admission patterns over time were assessed using the Jonckheere–Terpstra test for trend. All data were analyzed using Stata Statistical Software (Release 16. College Station, TX: StataCorp LLC).
Olds P.K., Musinguzi N., Geisler B.P, & Haberer J.E. (2023). Evaluating disparities by social determinants in hospital admission decisions for patients with COVID-19 quaternary hospital early in the pandemic. Medicine, 102(10), e33178.
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Publication 2023
Cocaine COVID 19 C Reactive Protein Creatine Kinase Diagnosis Ethnicity Factor X Ferritin fibrin fragment D Index, Body Mass Lymphocyte Count Methamphetamine Opiate Alkaloids Pandemics Patients Procalcitonin SARS-CoV-2 Substance Use Troponin Urea Nitrogen, Blood

Top products related to «Procalcitonin»

1
Cobas 6000 by Roche
Sourced in Switzerland, Germany, United States, Japan, Denmark, Italy, France, United Kingdom, Belgium, Norway, China, Israel, Poland, Netherlands, Sweden
The Cobas 6000 is an automated clinical chemistry and immunoassay analyzer system designed for high-volume laboratory testing. It combines the features of two separate instruments, the Cobas c 501 module for clinical chemistry and the Cobas e 601 module for immunoassays, into a single integrated platform. The Cobas 6000 system is capable of performing a wide range of diagnostic tests, including biochemical, immunological, and specialty assays.
2
Cobas e411 by Roche
Sourced in Germany, Switzerland, United States, Japan, United Kingdom, Denmark, France, Poland
The Cobas e411 is a compact and fully automated immunoassay analyzer designed for clinical laboratory settings. It is capable of performing a wide range of immunoassay tests, including those for hormones, therapeutic drug monitoring, and infectious disease markers. The Cobas e411 is known for its reliability, ease of use, and efficient throughput.
3
Cobas e601 by Roche
Sourced in Germany, Switzerland, United States, China, Sweden, United Kingdom, Japan, Canada
The Cobas e601 is an automated immunochemistry analyzer used for in vitro diagnostic testing. It is designed to perform a wide range of immunoassay tests, including those for hormones, tumor markers, and infectious diseases. The Cobas e601 utilizes electrochemiluminescence technology to provide accurate and reliable results.
4
Cobas 8000 by Roche
Sourced in Switzerland, Germany, United States, Japan, France, Italy, China, United Kingdom, Sweden
The Cobas 8000 is a modular, automated in-vitro diagnostic system designed for high-throughput clinical chemistry and immunochemistry testing. It is used to perform a wide range of laboratory tests, including those for chemistry, immunoassay, and electrolyte analysis. The Cobas 8000 is capable of processing a large volume of samples efficiently and accurately.
5
Vidas brahms pct assay by bioMérieux
Sourced in France, United States
The VIDAS BRAHMS PCT assay is a quantitative test that measures the procalcitonin (PCT) level in human serum or plasma samples. PCT is a biomarker that can be used to aid in the diagnosis of sepsis and to monitor the progression of bacterial infections.
6
Minividas immunoassay by bioMérieux
The MiniVIDAS is a compact immunoassay analyzer designed for the detection and quantification of a variety of analytes. It utilizes a fluorescent detection method to provide automated, high-quality results. The MiniVIDAS is a self-contained system that performs all necessary steps, from sample preparation to data analysis, in a streamlined process.
7
Kryptor by Thermo Fisher Scientific
Sourced in Germany
The KRYPTOR is a compact, automated immunoassay analyzer designed for the quantitative determination of various analytes in biological samples. It utilizes fluorescence detection technology to perform high-throughput immunoassays. The KRYPTOR is capable of running a wide range of assays, including those for therapeutic drug monitoring, infectious diseases, and endocrinology.
8
Lh750 by Beckman Coulter
Sourced in United States
The LH750 is a hematology analyzer designed for clinical laboratory use. It provides automated analysis of complete blood count (CBC) parameters, including red blood cells, white blood cells, and platelets. The core function of the LH750 is to perform rapid and accurate blood cell counting and differentiation for diagnostic purposes.
9
Au5800 by Beckman Coulter
Sourced in United States, Japan, Germany, United Kingdom, China, Italy, Canada
The AU5800 is a chemistry analyzer designed for high-throughput clinical laboratory testing. It features advanced optics and automation to provide reliable and efficient sample processing. The core function of the AU5800 is to perform a variety of clinical chemistry tests, including immunoassays, on patient samples.
10
Brahms pct sensitive kryptor by Thermo Fisher Scientific
Sourced in Germany
The BRAHMS PCT-sensitive Kryptor is an automated immunoassay analyzer designed for the quantitative determination of procalcitonin (PCT) in human serum and plasma samples. It utilizes a luminescence detection method to measure PCT levels.

More about "Procalcitonin"

Procalcitonin (PCT) is a valuable biomarker for the diagnosis and monitoring of bacterial infections, sepsis, and other inflammatory conditions.
It is a peptide precursor of the hormone calcitonin, primarily produced by the C cells of the thyroid gland.
Procalcitonin levels rise rapidly in response to bacterial stimuli, making it a useful tool for differentiating bacterial from viral infections.
Accurate and reproducible methods for procalcitonin measurement are crucial for effective clinical decision-making and patient management.
Several diagnostic platforms and assays are available for procalcitonin testing, including the Cobas 6000, Cobas e411, Cobas e601, Cobas 8000, VIDAS BRAHMS PCT assay, MiniVIDAS immunoassay, KRYPTOR, LH750, and AU5800 systems.
These platforms provide automated, sensitive, and precise measurements of procalcitonin levels, allowing healthcare professionals to make informed decisions about patient care.
The BRAHMS PCT-sensitive Kryptor is a particularly sensitive and specific assay for procalcitonin measurement, with the ability to detect even low levels of the biomarker.
This can be especially helpful in early stages of infection or in patients with milder inflammatory conditions.
Optimizing your procalcitonin research can be facilitated by PubCompare.ai, an AI-driven platform that helps researchers identify the best protocols from literature, preprints, and patents.
By leveraging AI-driven comparisons, you can enhance the quality and reliability of your procalcitonin studies, taking your research to the next level.