Consecutive Test Results Research Articles

Interpreting changes in consecutive laboratory results: clinician’s perspectives on clinically significant change

BackgroundClinical laboratory tests are inevitably affected by various factors. Therefore, when comparing consecutive test results, it is crucial to consider the inherent uncertainty of the test. Clinical laboratories use reference change value (RCV) to determine a significant change between 2 results. Whereas the criteria for the interpretation of consecutive results by clinicians are not well known. We investigated the clinician’s interpretation of a clinically significant change in consecutive laboratory test results and compared them to RCV. MethodsWe performed a questionnaire survey on clinicians, which comprised 2 scenarios with 22 laboratory test items suggesting initial test results. Clinicians were asked to choose a result showing clinically significant change. RCV of the analytes from EFLM database were collected. ResultsWe received 290 valid questionnaire responses. Clinicians’ opinions on clinically significant change was inconsistent between clinicians and scenarios, and was generally larger than RCV. Clinicians commented that they were not familiar with the variability of the laboratory tests. ConclusionsClinicians' opinions on clinically significant changes were more prominent than RCV. Meanwhile, they tended to neglect the analytical and biological variation. Laboratories should properly guide clinicians on the RCV of tests for better decision-making on patients’ clinical states.

COVID-19 in India: Epidemiological reflections from initial 170 million consecutive test results.

The Indian Council of Medical Research (ICMR) played a crucial role in streamlining testing and diagnosis, formulating guidelines, and devising management strategies during the COVID-19 pandemic. Additionally, ICMR designed and developed a comprehensive data management tool for collecting testing data in a standardized format from all laboratories across the country. The current report is a retrospective analysis of the testing data generated by the ICMR. The study's main objectives are to understand the probability of a person testing negative based on their age after an initial positive test and to assess the varied impact and duration of the disease in people of different age groups and genders. Anonymized data on the testing for COVID were analyzed. The P-to-P is the longest time interval between two consecutive positive tests for a patient without any negative test in between the positives. P-to-Plast is the time between the first positive and last positive test, as opposed to P-to-P, here we are looking at the first and last positive tests that might or might not be consecutive. P-to-N intervals is the time between the first positive and first negative test of a patient. India conducted 170,914,170 tests during the study-period (until December 29, 2020). After excluding invalid test results and duplicates, there were 11,101,603 (6.5%) positive and 156,542,352 (93.5%) negative test-results performed upon 150,086,257 unique individuals. A negative-report following a positive-test was available in 12.69%. Nearly three-fourths of the cases (78.29%) belonged to the working-age group (18-60 years). The proportion of patients >50 years old has risen from 26.06 to 35.03%, with a steep rise beyond September 2020. Gender-ratio among the positives was 1.73:1 which was neutral in neonates < 7-days (age). The gender ratio was skewed in-favor-of males in the initial months with a reverse trend thereafter and with increasing age of patients. The mean P-to-P, P-to-Plast, and P-to-N durations were 12.7 + 4.3, 13.3 + 4.6, and 14.2 + 4.9 days for individuals with P-to-P duration of 1-4 weeks. The probability of testing negative was 82 & 85% at 14 & 21 days after the first-positive-test respectively with no gender bias. The current study has highlighted some vital aspects of COVID-19 epidemiology in India. This study will add to the current understanding of the virus in the absence of pre- existing information on the novel virus and the disease per se.

Evaluation of biological variations in glucose and glycated hemoglobin levels in healthy individuals

Abstract Objective In this study, we firstly aimed to determine components of biological variations (BVCs) in levels of glucose and glycated hemoglobin (HbA1c) in detail based on guidance from relevant organizations and experts. We also investigated whether reference intervals for both analytes were useful for evaluations, particularly consecutive test results. Methods The study group consisted of 36 healthy volunteers. Samples were collected from each individual 4 times every 2 weeks for 45 days. All samples were assayed in duplicate within a single run. Finally, we estimated BVCs and the analytical performance specifications of both analytes. Results Our results were fairly compatible with current biological variations (BVs) in both analytes reported in a database. It was calculated as within biological variation (CVI)=4.2% and between-subject variation (CVG)=5.3% for glucose while calculating as CVI=1.7% and CVG=4.5% for HbA1c. According to these results, the index of individuality (II) of glucose was higher than 0.6 while HbA1c’s II was lower than this value. Conclusion We thought that guidelines from relevant international organizations should be followed to standardize the study design and to appropriately calculate BVCs for any analyte in BV studies. Finally, reference change value should be used to evaluate meaningful differences in HbA1c levels instead of reference interval.

Concordance analysis of paired cancer antigen (CA) 15-3 and 27.29 testing.

Cancer antigens (CA) 15-3 and 27.29 are used in the clinical management of many breast cancer patients. Given that immunoassays for CA 15-3 and CA 27.29 target epitopes on the same glycoprotein-Mucin 1 (MUC1)-the present analysis was conducted to evaluate the potential concordance of tumor marker results when both tests were ordered by providers on the same specimens. A retrospective limited dataset of paired CA 15-3 (Roche Diagnostics) and CA 27.29 (Siemens Diagnostics) test results was obtained from a national clinical reference laboratory. Concordance according to reference interval (RI) status and percent (%) change between consecutive test results was analyzed. 37,652 paired results from 12,470 distinct patients were obtained. The correlation between CA 15-3 and CA 27.29 results was high (correlation coefficient: Pearson, 0.967), although across the dataset a significant difference between CA 15-3 and CA 27.29 results was observed (P<0.05). RI concordance between CA 15-3 and CA 27.29 results was observed in 93.7% of pairs (35,280 of 37,652). Correlation was also observed in the % change of CA 15-3 and CA 27.29 results between consecutive specimens for individual patients. Using doubling or halving thresholds (i.e., 100% increase or 50% decrease), concordance in % change was observed between CA 15-3 and CA 27.29 in approximately 90% of cases. Individual patient results trended similarly across both markers over time. While generally concordant, CA 15-3 and CA 27.29 results should not be used interchangeably. The present report provides no evidence for added value in performing both tests routinely for individual patients.

Presence of viral and bacterial organisms in milk and their association with somatic cell counts

About 20 to 35% of milk samples from cows with intramammary infection or high somatic cell count (SCC) are negative on bacteriological culture analysis. However, little is known about SCC in milk of cows infected with viruses. In the first part of our study, we developed a real-time PCR assay for detection of bovine herpesvirus (BHV) 1, BHV2, and BHV4, and bovine viral diarrhea virus (BVDV) in composite quarter milk samples. A total of 1,479 lactating cows of 1,964 cows in the dairy herd were initially selected because these cows had complete SCC data for at least 3 consecutive test results, of which 139 lactating cows from different lactation age groups were selected randomly and studied extensively. Composite quarter milk samples were collected on 3 alternate days and examined for viruses, SCC, and bacteriological analysis. In total, 10, 28, and 0.7% of the composite quarter milk samples from cows were positive for BHV1, BHV2, and BHV4, respectively; BVDV was not detected in composite quarter milk samples. Bovine herpesvirus was not associated with a particular bacterial species. Our study results indicate that cows positive for BHV in composite quarter milk samples alone are less likely to have elevated SCC compared with cows with bacterial intramammary infection; BHV1, BHV2, and BHV4 are probably not major udder pathogens.

Probabilistic evaluation of conformity criteria for concrete families

When only a limited number of test results are available, compressive strength results of concrete family members can be combined in order to enable conformity control on a sufficient number of test results. The principles behind the conformity control of concrete families are explained and an original probabilistic evaluation of conformity criteria for concrete families is introduced. For the calculation of the corresponding operating characteristics, some approximate formulae are presented. Additionally, Monte Carlo simulation techniques are used for more exact calculations. In this way, also autocorrelation between consecutive test results can be taken into account, using an autoregressive process. The current guidelines and conformity criteria for concrete families in EN 206-1 are evaluated and discussed, based on the AOQL concept. Different conformity criteria and transformation methods are described, evaluated and discussed.

A feasible development of computerized perimetry.

A procedure is suggested to also utilize the computer of the automatic perimeter for the evaluation of the visual field recordings. Parameters are constructed describing the general performance level of a test, size of a defect and its location etcetera. Comparisons with normal values, with the contralateral eye and, when possible, with previous test results are carried through. Trends towards decay or improvement in series of consecutive test results are analyzed.--The information of the field test is printed out in a concise form, verbally and numerically.