The Effect of the COVID-19 Pandemic on HbA1c Testing: Prioritization of High-Risk Cases and Impact of Social Deprivation.

INTRODUCTION: Studies show that the COVID-19 pandemic disproportionately affected people with diabetes and those from disadvantaged backgrounds. During the first 6 months of the UK lockdown, > 6.6 M glycated haemoglobin (HbA1c) tests were missed. We now report variability in the recovery of HbA1c testing, and its association with diabetes control and demographic characteristics. METHODS: In a service evaluation, we examined HbA1c testing across ten UK sites (representing 9.9% of England's population) from January 2019 to December 2021. We compared monthly requests from April 2020 to those in the equivalent 2019 months. We examined effects of (i) HbA1c level, (ii) between-practice variability, and (iii) practice demographics. RESULTS: In April 2020, monthly requests dropped to 7.9-18.1% of 2019 volumes. By July 2020, testing had recovered to 61.7-86.9% of 2019 levels. During April-June 2020, we observed a 5.1-fold variation in the reduction of HbA1c testing between general practices (12.4-63.8% of 2019 levels). There was evidence of limited prioritization of testing for patients with HbA1c > 86 mmol/mol during April-June 2020 (4.6% of total tests vs. 2.6% during 2019). Testing in areas with the highest social disadvantage was lower during the first lockdown (April-June 2020; trend test p < 0.001) and two subsequent periods (July-September and October-December 2020; both p < 0.001). By February 2021, testing in the highest deprivation group had a cumulative fall in testing of 34.9% of 2019 levels versus 24.6% in those in the lowest group. CONCLUSION: Our findings highlight that the pandemic response had a major impact on diabetes monitoring and screening. Despite limited test prioritization in the > 86 mmol/mol group, this failed to acknowledge that those in the 59-86 mmol/mol group require consistent monitoring to achieve the best outcomes. Our findings provide additional evidence that those from poorer backgrounds were disproportionately disadvantaged. Healthcare services should redress this health inequality.

Evaluation of thyroid function monitoring in people treated with lithium: Advice based on real-world data.

INTRODUCTION: Blood test monitoring is essential for the management of lithium treatment and National Institute for Health and Care Excellence guidance recommends 6-monthly serum testing of thyroid function. We examined conformity to these guidelines and the impact of monitoring outside these intervals. METHODS: We extracted serum lithium and thyroid hormone results at one centre between January 2009 and December 2020. We identified 266 patients who started lithium during this period with no history of thyroid abnormality within the previous 2 years and were at risk of developing thyroid abnormalities. We examined the interval between tests, time between onset of lithium testing and first thyroid-stimulating hormone (TSH) outside the laboratory reference range and assessed impact of testing outside recommended 6-monthly intervals. RESULTS: The most common testing frequency was 3 months (±1 month), accounting for 17.3% of test intervals. Kaplan-Meier analysis showed that most thyroid dysfunction manifests within 3 years (proportion with abnormal TSH at 3 years = 91.4%, 19.9% of total patients). In the first 3 months after commencing lithium therapy, eight patients developed subclinical hypothyroidism and had clinical follow-up data available. Of these, half spontaneously normalized without clinical intervention. In the remaining patients, thyroxine replacement was only initiated after multiple occasions of subclinical hypothyroidism (median = 2 years after initiating lithium, range: 6 months to 3 years). CONCLUSION: The peak interval at 3 months suggests that thyroid function is frequently checked at the same time as serum lithium, indicating too frequent testing. Our data support the recommended 6-monthly testing interval and highlight poor adherence to it.

Interlaboratory comparison study of immunosuppressant analysis using a fully automated LC-MS/MS system.

OBJECTIVES: All guidelines recommend LC-MS/MS as the analytical method of choice for the quantification of immunosuppressants in whole blood. Until now, the lack of harmonization of methods and the complexity of the analytical technique have prevented its widespread use in clinical laboratories. This can be seen in international proficiency schemes, where more than half of the participants used immunoassays. With the Cascadion SM Clinical analyzer (Thermo Fisher Scientific, Oy, Vantaa, FI) a fully automated LC-MS/MS system has been introduced, which enables the use of LC-MS/MS without being an expert in mass spectrometry. METHODS: To verify the interlaboratory comparison of the immunosuppressant assay on this type of instrument, three centers across Europe compared 1097 routine whole blood samples, each site sharing its own samples with the other two. In other experiments, the effects of freezing and thawing of whole blood samples was studied, and the use of secondary cups instead of primary tubes was assessed. RESULTS: In the Bland-Altman plot, the comparison of the results of tacrolimus in fresh and frozen samples had an average bias of only 0.36%. The respective data for the comparison between the primary and secondary tubes had an average bias of 1.14%. The correlation coefficients for patient samples with cyclosporine A (n=411), everolimus (n=139), sirolimus (n=114) and tacrolimus (n=433) were 0.993, 0.993, 0.993 and 0.990, respectively. CONCLUSIONS: The outcome of this study demonstrates a new level of result harmonization for LC-MS/MS based immunosuppressant analysis with a commercially available fully automated platform for routine clinical application.