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Pregnancy diabetes: A comparison of diagnostic protocols based on point-of-care, routine and optimized laboratory conditions.

ABSTRACT: In vitro glycolysis poses a problem during diabetes screening, especially in remote laboratories. Point-of-care analysis (POC) may provide an alternative. We compared POC, routine and STAT analysis and a feasible protocol during glucose tolerance test (GTT) for pregnancy diabetes (GDM) screening. In the routine protocol, heparin tubes were used and turn-around-time (TAT) was unsupervised. In the STAT protocol, tubes were processed immediately. The feasible protocol comprised of citrated tubes with a TAT of 1 hour. Outcome was defined as glucose concentration and clinical diagnosis. Glucose measured by POC was higher compared to routine analysis at t = 0 (0.25 mM) and t = 120 (1.17 mM) resulting in 17% more GDM diagnoses. Compared to STAT analysis, POC glucose was also higher, although less pronounced (0.06 and 0.9 mM at t = 0 and t = 120 minutes, respectively) and misclassification was only 2%. Glucose levels and clinical diagnosis were similar using the feasible protocol and STAT analysis (0.03 mM and -0.07 mM at t = 0 and t = 120, 100% identical diagnoses). POC is an viable alternative for STAT glucose analysis in GDM screening (sensitivity: 100%, specificity: 98%). A feasible protocol (citrated phlebotomy tubes with a TAT of 60 minutes) resulted in 100% identical outcome and provides the best alternative.

SUBMITTER: van den Berg SA

PROVIDER: S-EPMC4635356 | biostudies-other | 2015 Nov

REPOSITORIES: biostudies-other

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Project description:ObjectivesTo compare the validity of diagnosis of urinary tract infection (UTI) through urine culture between samples processed in routine health service laboratories and those processed in a research laboratory.Population and methodsWe conducted a prospective diagnostic cohort study in 4808 acutely ill children aged <5 years attending UK primary health care. UTI, defined as pure/predominant growth ≥105 CFU/mL of a uropathogen (the reference standard), was diagnosed at routine health service laboratories and a central research laboratory by culture of urine samples. We calculated areas under the receiver-operator curve (AUC) for UTI predicted by pre-specified symptoms, signs and dipstick test results (the "index test"), separately according to whether samples were obtained by clean catch or nappy (diaper) pads.Results251 (5.2%) and 88 (1.8%) children were classified as UTI positive by health service and research laboratories respectively. Agreement between laboratories was moderate (kappa = 0.36; 95% confidence interval [CI] 0.29, 0.43), and better for clean catch (0.54; 0.45, 0.63) than nappy pad samples (0.20; 0.12, 0.28). In clean catch samples, the AUC was lower for health service laboratories (AUC = 0.75; 95% CI 0.69, 0.80) than the research laboratory (0.86; 0.79, 0.92). Values of AUC were lower in nappy pad samples (0.65 [0.61, 0.70] and 0.79 [0.70, 0.88] for health service and research laboratory positivity, respectively) than clean catch samples.ConclusionsThe agreement of microbiological diagnosis of UTI comparing routine health service laboratories with a research laboratory was moderate for clean catch samples and poor for nappy pad samples and reliability is lower for nappy pad than for clean catch samples. Positive results from the research laboratory appear more likely to reflect real UTIs than those from routine health service laboratories, many of which (particularly from nappy pad samples) could be due to contamination. Health service laboratories should consider adopting procedures used in the research laboratory for paediatric urine samples. Primary care clinicians should try to obtain clean catch samples, even in very young children.

Project description:BackgroundLarge-scale evaluation of COVID-19 is likely to rely on the quality of ICD coding. However, little is known about the validity of ICD-coded COVID-19 diagnoses.ObjectivesTo evaluate the performance of diagnostic codes in detecting COVID-19 during pregnancy.MethodsWe used data from a national cohort of 78,283 individuals with a pregnancy ending between 11 March 2020 and 31 January 2021 in the OptumLabs® Data Warehouse (OLDW). OLDW is a longitudinal, real-world data asset with de-identified administrative claims and electronic health record data. We identified all services with an ICD-10-CM diagnostic code of U07.1 and all laboratory claims records for COVID-19 diagnostic testing. We compared ICD-coded diagnoses to testing results to estimate positive and negative predictive values (PPV and NPV). To evaluate impact on risk estimation, we estimated risk of adverse pregnancy outcomes by source of exposure information.ResultsOf 78,283 pregnancies, 5644 had a laboratory test result for COVID-19. Testing was most common among older individuals, Hispanic individuals, those with higher socioeconomic status and those with a diagnosed medical condition or pregnancy complication; 52% of COVID-19 cases was identified through ICD-coded diagnosis alone, 19% from laboratory test results alone and 29% from both sources. Agreement between ICD-coded diagnosis and laboratory testing records was high 91% (95% confidence interval [CI] 90, 92). However, the PPV of ICD-code diagnosis was low (36%; 95% CI 33, 39). We observed up to a 50% difference in risk estimates of adverse pregnancy outcomes when exposure was based on laboratory testing results or diagnostic coding alone.ConclusionsMore than one-in-five COVID-19 cases would be missed by using ICD-coded diagnoses alone to identify COVID-19 during pregnancy. Epidemiological studies exclusively relying on diagnostic coding or laboratory testing results are likely to be affected by exposure misclassification. Research and surveillance should draw upon multiple sources of COVID-19 diagnostic information.

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Pregnancy diabetes: A comparison of diagnostic protocols based on point-of-care, routine and optimized laboratory conditions.

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