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  • Author: Ron Kusters x
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Abstract

Objectives

The novel coronavirus disease 19 (COVID-19), caused by SARS-CoV-2, spreads rapidly across the world. The exponential increase in the number of cases has resulted in overcrowding of emergency departments (ED). Detection of SARS-CoV-2 is based on an RT-PCR of nasopharyngeal swab material. However, RT-PCR testing is time-consuming and many hospitals deal with a shortage of testing materials. Therefore, we aimed to develop an algorithm to rapidly evaluate an individual’s risk of SARS-CoV-2 infection at the ED.

Methods

In this multicenter retrospective study, routine laboratory parameters (C-reactive protein, lactate dehydrogenase, ferritin, absolute neutrophil and lymphocyte counts), demographic data and the chest X-ray/CT result from 967 patients entering the ED with respiratory symptoms were collected. Using these parameters, an easy-to-use point-based algorithm, called the corona-score, was developed to discriminate between patients that tested positive for SARS-CoV-2 by RT-PCR and those testing negative. Computational sampling was used to optimize the corona-score. Validation of the model was performed using data from 592 patients.

Results

The corona-score model yielded an area under the receiver operating characteristic curve of 0.91 in the validation population. Patients testing negative for SARS-CoV-2 showed a median corona-score of 3 vs. 11 (scale 0–14) in patients testing positive for SARS-CoV-2 (p<0.001). Using cut-off values of 4 and 11 the model has a sensitivity and specificity of 96 and 95%, respectively.

Conclusions

The corona-score effectively predicts SARS-CoV-2 RT-PCR outcome based on routine parameters. This algorithm provides the means for medical professionals to rapidly evaluate SARS-CoV-2 infection status of patients presenting at the ED with respiratory symptoms.

Abstract

Background: Query fever (Q fever) is a zoonotic infection, caused by the intracellular Gram-negative coccobacillus Coxiella burnetii. From 2007 until 2010, a large Q fever outbreak has occurred in the Netherlands. We studied traditional and less common inflammation markers in seronegative and seropositive patients with acute Q fever pneumonia to identify markers that distinguish different disease stages and predict disease severity.

Methods: A total of 443 adult patients presenting at the Emergency Department with community-acquired pneumonia were included in a prospective etiologic study. Patients with acute Q fever pneumonia were identified by PCR and/or serology. Patient characteristics, clinical symptoms, pneumonia severity and inflammation markers were assessed upon presentation. Duration of symptoms, prior therapy and length of hospital stay were retrieved from the hospital information system.

Results: In all, 40 patients with acute Q fever pneumonia were identified. Of these, 29 were seronegative and 11 seropositive at presentation. C-reactive protein (CRP) was the only inflammation marker increased in all seronegative and seropositive patients but no significant difference was observed between groups. In seronegative patients, hypophosphatemia was more common (p=0.01), and length of hospital stay was longer (p=0.02). However, there was no significant difference in pneumonia severity index. Furthermore, phosphate levels were inversely correlated with body temperature (p=0.003).

Conclusions: In acute Q fever pneumonia, CRP is the only traditional inflammation marker adequately reflecting disease activity. Patients with seronegative acute Q fever pneumonia present with hypophosphatemia and have prolonged length of hospital stay when compared to seropositive patients, suggesting an increased disease severity.

Abstract

Background: Query-fever (Q-fever) is a zoonotic infection caused by the intracellular Gram-negative coccobacillus Coxiella burnetii. A large ongoing outbreak of Q-fever has been reported in the Netherlands. We studied various markers of infection in inpatients (hospitalised) and outpatients (treated by a general physician) with acute Q-fever in relation to disease severity.

Methods: Leukocyte counts, C-reactive protein (CRP) and procalcitonin (PCT) concentrations were measured in 25 inpatients and 40 outpatients upon presentation with acute Q-fever. Chest X-rays, if available, were analysed and confusion, urea, respiratory rate, blood pressure-age 65 (CURB-65) scores, indicating severity of pneumonia, were calculated.

Results: CRP was the only marker that significantly differentiated between inpatients and outpatients. It was increased in all patients from both groups. Leukocyte counts and PCT concentrations did not differ between inpatients and outpatients. Overall, only 13/65 patients had an increased leukocyte count and only 11/65 patients presented with PCT concentrations indicative of possible bacterial respiratory tract infection. Infiltrative changes on the chest X-ray were observed in the majority of patients. CURB-65 score was 0±1 (mean±SD).

Conclusions: Acute Q-fever, a relatively mild pneumonia with low CURB-65 scores, specifically induces a response in CRP, while PCT concentrations and leukocytes are within the normal range or increased only marginally.

Clin Chem Lab Med 2009;47:1407–9.

Abstract

Intake of drugs may influence the interpretation of laboratory test results. Knowledge and correct interpretation of possible drug-laboratory test interactions (DLTIs) is important for physicians, pharmacists and laboratory specialists. Laboratory results may be affected by analytical or physiological effects of medication. Failure to take into account the possible unintended influence of drug use on a laboratory test result may lead to incorrect diagnosis, incorrect treatment and unnecessary follow-up. The aim of this review is to give an overview of the literature investigating the clinical impact and use of DLTI decision support systems on laboratory test interpretation. Particular interactions were reported in a large number of articles, but they were fragmentarily described and some papers even reported contradictory findings. To provide an overview of information that clinicians and laboratory staff need to interpret test results, DLTI databases have been made by several groups. In a literature search, only four relevant studies have been found on DLTI decision support applications for laboratory test interpretation in clinical practice. These studies show a potential benefit of automated DLTI messages to physicians for the correct interpretation of laboratory test results. Physicians reported 30–100% usefulness of DLTI messages. In one study 74% of physicians sometimes even refrained from further additional examination. The benefit of decision support increases when a refined set of clinical rules is determined in cooperation with health care professionals. The prevalence of DLTIs is high in a broad range of combinations of laboratory tests and drugs and these frequently remain unrecognized.

Abstract

Background

Knowledge of possible drug-laboratory test interactions (DLTIs) is important for the interpretation of laboratory test results. Test results may be affected by physiological or analytical drug effects. Failure to recognize these interactions may lead to misinterpretation of test results, a delayed or erroneous diagnosis or unnecessary extra tests or therapy, which may harm patients.

Content

Thousands of interactions have been reported in the literature, but are often fragmentarily described and some papers even reported contradictory findings. How can healthcare professionals become aware of all these possible interactions in their individual patients? DLTI decision support applications could be a good solution. In a literature search, only four relevant studies have been found on DLTI decision support applications in clinical practice. These studies show a potential benefit of automated DLTI messages to physicians for the interpretation of laboratory test results. All physicians reported that part of the DLTI messages were useful. In one study, 74% of physicians even sometimes refrained from further additional examination.

Summary and outlook

Unrecognized DLTIs potentially cause diagnostic errors in a large number of patients. Therefore, efforts to avoid these errors, for example with a DLTI decision support application, could tremendously improve patient outcome.