Inappropriate Requesting of Glycated Hemoglobin (Hb A1c) Is Widespread: Assessment of Prevalence, Impact of National Guidance, and Practice-to-Practice Variability

Development and multinational validation of an algorithmic strategy for high Lp(a) screening

Elevated lipoprotein (a) (Lp(a)) is associated with premature atherosclerotic cardiovascular disease. However, fewer than 0.5% of individuals undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Here we describe the development of a machine learning model for targeted screening for elevated Lp(a) (≥150 nmol l-1) in the UK Biobank (N = 456,815), the largest cohort with protocolized Lp(a) testing. We externally validated the model in 3 large cohort studies, ARIC (N = 14,484), CARDIA (N = 4,124) and MESA (N = 4,672). The model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), reduced the number needed to test to find one individual with elevated Lp(a) by up to 67.3%, based on the probability threshold, with consistent performance across external validation cohorts. ARISE could be used to optimize screening for elevated Lp(a) using commonly available clinical features, with the potential for its deployment in electronic health records to enhance the yield of Lp(a) testing in real-world settings.

Diabetic control and compliance using glycated haemoglobin (HbA1C) testing guidelines in public healthcare facilities of Gauteng province, South Africa

OBJECTIVE

This study aimed at evaluating diabetic control and compliance with testing guidelines, across healthcare facilities of Gauteng Province, South Africa, as well as factors associated with time to achieve control. South Africa's estimated total unmet need for care for patients with type 2 diabetes mellitus is 80%.

RESEARCH DESIGN, METHODS AND FINDINGS

The data of 511 781 patients were longitudinally evaluated. Results were reported by year, age category, race, sex, facility and test types. HbA1C of ≤7% was reported as normal, >7 - ≤9% as poor control and >9% as very poor control. The chi-squared test was used to assess the association between a first-ever HbA1C status and variables listed above. The Kaplan-Meier analysis was used to assess probability of attaining control among those who started with out-of-control HbA1C. The extended Cox regression model assessed the association between time to attaining HbA1C control from date of treatment initiation and several covariates. We reported hazard ratios, 95% confidence intervals and p-values. Data is reported for 511 781 patients with 705 597 laboratory results. Poorly controlled patients constituted 51.5%, with 29.6% classified as very poor control. Most poorly controlled patients had only one test over the entire study period. Amongst those who started with poor control status and had at least two follow-up measurements, the likelihood of achieving good control was higher in males (adjusted Hazard Ratio (aHR) = 1.16; 95% CI:1.12-1.20; p<0.001) and in those attending care at hospitals (aHR = 1.99; 95% CI:1.92-2.06; p<0.001).

CONCLUSION

This study highlights poor adherence to guidelines for diabetes monitoring.

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.

Diabetes detection in women with gestational diabetes and polycystic ovarian syndrome

Gestational diabetes mellitus (GDM) and polycystic ovarian syndrome (PCOS) represent two of the highest risk factors for development of type 2 diabetes mellitus in young women. As these increasingly common conditions generally affect younger women, early detection of dysglycemia is key if preventative measures are to be effective. While international guidance recommends screening for type 2 diabetes, current screening strategies suffer from significant challenges.First, guidance lacks consensus in defining which tests to use and frequency of monitoring, thereby sending mixed messages to healthcare professionals.Second, conformity to guidance is poor, with only a minority of women having tests at the recommended frequency (where specified). Approaches to improve conformity have focused on healthcare related factors (largely technology driven reminder systems), but patient factors such as convenience and clear messaging around risk have been neglected.Third, and most critically, current screening strategies are too generic and rely on tests that become abnormal far too late in the trajectory towards dysglycemia to offer opportunities for effective preventative measures. Risk factors show wide interindividual variation, and insulin sensitivity and β cell function are often abnormal during pre-diabetes stage, well before frank diabetes.New, consistent, targeted screening strategies are required that incorporate early, prevention focused testing and personalised risk stratification.

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.

Assessment of the effect of the COVID-19 pandemic on UK HbA1c testing: implications for diabetes management and diagnosis

AIMS

The COVID-19 pandemic, and the focus on mitigating its effects, has disrupted diabetes healthcare services worldwide. We aimed to quantify the effect of the pandemic on diabetes diagnosis/management, using glycated haemoglobin (HbA1c) as surrogate, across six UK centres.

METHODS

Using routinely collected laboratory data, we estimated the number of missed HbA1c tests for 'diagnostic'/'screening'/'management' purposes during the COVID-19 impact period (CIP; 23 March 2020 to 30 September 2020). We examined potential impact in terms of: (1) diabetes control in people with diabetes and (2) detection of new diabetes and prediabetes cases.

RESULTS

In April 2020, HbA1c test numbers fell by ~80%. Overall, across six centres, 369 871 tests were missed during the 6.28 months of the CIP, equivalent to >6.6 million tests nationwide. We identified 79 131 missed 'monitoring' tests in people with diabetes. In those 28 564 people with suboptimal control, this delayed monitoring was associated with a 2-3 mmol/mol HbA1c increase. Overall, 149 455 'screening' and 141 285 'diagnostic' tests were also missed. Across the UK, our findings equate to 1.41 million missed/delayed diabetes monitoring tests (including 0.51 million in people with suboptimal control), 2.67 million screening tests in high-risk groups (0.48 million within the prediabetes range) and 2.52 million tests for diagnosis (0.21 million in the pre-diabetes range; ~70 000 in the diabetes range).

CONCLUSIONS

Our findings illustrate the widespread collateral impact of implementing measures to mitigate the impact of COVID-19 in people with, or being investigated for, diabetes. For people with diabetes, missed tests will result in further deterioration in diabetes control, especially in those whose HbA1c levels are already high.

Potential Overtreatment and Overtesting Among Older Adults With Type 2 Diabetes Across Canada: An Observational, Retrospective Cohort Study

OBJECTIVE

Our aim in this study was to assess potential overtreatment and overtesting among older adults with type 2 diabetes across Canada.

METHODS

An observational, population-based cohort study was conducted using data available through the Canadian Primary Care Sentinel Surveillance Network. All patients included in the study were seen by a primary care provider between 2010 and 2017, ≥65 years of age with type 2 diabetes and had at least one glycated hemoglobin (A1C) measurement. Potential overtreatment was defined as an index A1C of <7% and being prescribed antidiabetes medications other than metformin within 1 year of the index A1C. Testing ≥3 times/year in patients with A1C <7% was considered potential overtesting. Analyses were performed/compared within 2 cross-sectional cohorts (2012 and 2016). A subcohort analysis was performed on those with advanced age and dementia.

RESULTS

An overall cohort of 41,032 patients (mean age, 76.6 years) was identified. Proportions of potential overtreatment were 7.0% (2012) and 6.9% (2016) (difference in rate in %: 0.1; 95% confidence interval [CI], -0.32 to 0.52]). Overall, 19.2% (2012) and 19.0% (2016) of patients were potentially overtested (difference in rate in %: 0.2; 95% CI, -0.45 to 0.85), whereas 2.4% (2012) and 2.3% (2016) were potentially undertested (difference in rate in %: 0.1; 95% CI, -0.15 to 0.35). Among patients with dementia and advanced age, proportions of patients potentially overtreated were 14.5% and 12.1%, and those overtested were 29.2% and 25.0% in 2012 and 2016, respectively.

CONCLUSIONS

Potential overtreatment and overtesting exists among older adults with diabetes in Canadian primary care practices with minimal change over time. Higher proportions of potentially unnecessary care were observed in those with advanced age and dementia. Our study highlights an opportunity for primary care clinicians to improve testing and treatment practices considering the individual patient, context and potential for net benefit.

High-frequency HbA1c testing among older patients with diabetes in Japan: a longitudinal analysis using medical claims data

Aims

We aimed to determine the rate of high-frequency HbA1c testing among older Japanese patients (≥ 75 years) with type 2 diabetes who did not have diabetic complications and were not using insulin, and to explore its influencing factors and association with the number of hospital admissions.

Methods

A retrospective, longitudinal observation study design based on medical claims data between 2015 and 2020 was adopted. We tracked patients for 3 years to describe the annual rate and determine factors associated with high-frequency HbA1c testing using a hierarchical logistical model. We employed a zero-inflated Poisson model to examine the association between frequency of HbA1c testing and the number of hospitalizations.

Results

Among 6594 patients included (mean age 80.4 years), the rates of high-frequency HbA1c testing in the first, second, and third year were 10.2%, 4.8%, and 4.7%, respectively. Follow-up year [odds ratio (OR) = 0.54, 95% confidence interval (CI) = 0.49-0.59];insulin use (OR = 1.63, 95% CI = 1.26-2.11); multi facility for HbA1c testing (OR = 5.51, 95% CI = 4.66-6.51); more than 48 outpatient days per year (OR = 2.13, 95% CI = 1.81-2.51); hospitalizations (OR = 0.66, 95% CI = 0.55-0.79); two or more classes of antidiabetic agents at baseline (OR = 1.32, 95% CI = 1.11-1.56); and dementia at baseline (OR = 0.70, 95% CI = 0.51-0.97) were significantly associated with high-frequency HbA1c testing. No significant association between frequent HbA1c testing and the number of all-cause hospitalizations was found.

Conclusions

We revealed a high rate of HbA1c testing among older Japanese patients with type 2 diabetes in ambulatory care and indicated that high-frequency HbA1c testing was more likely to result from the decentralized healthcare system and its structure.

The experience of blood glucose monitoring in people with type 2 diabetes mellitus (T2DM)

BACKGROUND

Finger prick blood glucose (BG) monitoring remains a mainstay of management in people with type 2 diabetes (T2DM) who take sulphonylurea (SU) drugs or insulin. We recently examined patient experience of BG monitoring in people with type 1 diabetes (T1DM). There has not been any recent comprehensive assessment of the performance of BG monitoring strips or the patient experience of BG strips in people with T2DM in the UK.

METHODS

An online self-reported questionnaire containing 44 questions, prepared following consultation with clinicians and patients, was circulated to people with T2DM. 186 responders provided completed responses (25.5% return rate). Fixed responses were coded numerically (eg not confident = 0 fairly confident = 1).

RESULTS

Of responders, 84% were treated with insulin in addition to other agents. 75% reported having had an HbA1c check in the previous 6 months. For those with reported HbA1c ≥ 65 mmol/mol, a majority of people (70%) were concerned or really concerned about the shorter term consequences of running a high HbA1c This contrasted with those who did not know their recent HbA1c, of whom only 33% were concerned/really concerned and those with HbA1c <65 mmol/mol of whom 35% were concerned. Regarding BG monitoring/insulin adjustment, only 25% of responders reported having sufficient information with 13% believing that the accuracy and precision of their BG metre was being independently checked. Only 9% recalled discussing BG metre accuracy when their latest metre was provided and only 7% were aware of the International Standardisation Organisation (ISO) standards for BG metres. 77% did not recall discussing BG metre performance with a healthcare professional.

CONCLUSION

The group surveyed comprised engaged people with T2DM but even within this group there was significant variation in (a) awareness of shorter term risks, (b) confidence in their ability to implement appropriate insulin dosage (c) awareness of the limitations of BG monitoring technology. There is clearly an area where changes in education/support would benefit many.

Variability in Test Interval Is Linked to Glycated Haemoglobin (HbA1c) Trajectory over Time

AIMS

We previously showed that the glycated haemoglobin (HbA1c) testing frequency links to diabetes control. Here, we examine the effect of variability in test interval, adjusted for the frequency, on change in HbA1c (ΔHbA1c). Materials & Methods. HbA1c results were collected on 83,872 people with HbA1c results at baseline and 5 years (±3 months) later and ≥6 tests during this period. We calculated the standard deviation (SD) of test interval for each individual and examined the link between deciles of SD of the test interval and ΔHbA1c level, stratified by baseline HbA1c.

RESULTS

In general, less variability in testing frequency (more consistent monitoring) was associated with better diabetes control. This was most evident with moderately raised baseline HbA1c levels (7.0-9.0% (54-75 mmol/mol)). For example, in those with a starting HbA1c of 7.0-7.5% (54-58 mmol/mol), the lowest SD decile was associated with little change in HbA1c over 5 years, while for those with the highest decile, HbA1c rose by 0.4-0.6% (4-6 mmol/mol; p < 0.0001). Multivariate analysis showed that the association was independent of the age/sex/hospital site. Subanalysis suggested that the effect was most pronounced in those aged <65 years with baseline HbA1c of 7.0-7.5% (54-58 mmol/mol). We observed a 6.7-fold variation in the proportion of people in the top-three SD deciles across general practices.

CONCLUSIONS

These findings indicate that the consistency of testing interval, not the just number of tests/year, is important in maintaining diabetes control, especially in those with moderately raised HbA1c levels. Systems to improve regularity of HbA1c testing are therefore needed, especially given the impact of COVID-19 on diabetes monitoring.

Can we check serum lithium levels less often without compromising patient safety?

BACKGROUND

Lithium is viewed as the first-line long-term treatment for prevention of relapse in people with bipolar disorder.

AIMS

This study examined factors associated with the likelihood of maintaining serum lithium levels within the recommended range and explored whether the monitoring interval could be extended in some cases.

METHOD

We included 46 555 lithium rest requests in 3371 individuals over 7 years from three UK centres. Using lithium results in four categories (<0.4 mmol/L; 0.40-0.79 mmol/L; 0.80-0.99 mmol/L; ≥1.0 mmol/L), we determined the proportion of instances where lithium results remained stable or switched category on subsequent testing, considering the effects of age, duration of lithium therapy and testing history.

RESULTS

For tests within the recommended range (0.40-0.99 mmol/L categories), 84.5% of subsequent tests remained within this range. Overall, 3 monthly testing was associated with 90% of lithium results remaining within range, compared with 85% at 6 monthly intervals. In cases where the lithium level in the previous 12 months was on target (0.40-0.79 mmol/L; British National Formulary/National Institute for Health and Care Excellence criteria), 90% remained within the target range at 6 months. Neither age nor duration of lithium therapy had any significant effect on lithium level stability. Levels within the 0.80-0.99 mmol/L category were linked to a higher probability of moving to the ≥1.0 mmol/L category (10%) compared with those in the 0.4-0.79 mmol/L group (2%), irrespective of testing frequency.

CONCLUSION

We propose that for those who achieve 12 months of lithium tests within the 0.40-0.79 mmol/L range, the interval between tests could increase to 6 months, irrespective of age. Where lithium levels are 0.80-0.99 mmol/L, the test interval should remain at 3 months. This could reduce lithium test numbers by 15% and costs by ~$0.4 m p.a.

Hemoglobin A1C testing frequency among patients with type 2 diabetes within a US payer system: a retrospective observational study

OBJECTIVE

The American Diabetes Association (ADA) guidelines recommend A1C testing schedules for patients with type 2 diabetes; however, level of real-world guideline adherence remains unclear. The current study evaluated A1C testing frequency and its association with glycemic control and cardiovascular outcomes.

METHODS

A retrospective study was conducted utilizing Aetna's Enterprise Data Warehouse. Adult patients with a medical claim for type 2 diabetes in 2017 (index date) were included. Patients had continuous enrollment through December 2019 and ≥1 reported A1C measurement from 2017 to 2019. Follow-up was up to 36 months post-index date.

RESULTS

Of the 112,572 eligible patients, 50.0% were female and median age was 70 years; 32.9% of patients with controlled baseline A1C (<8%, 64 mmol/mol) received less than the 2 tests/year recommended by the ADA, while 60.6% of patients with uncontrolled baseline A1C received less than the quarterly testing recommended by the ADA. More frequent testing was associated with age (65-75 years), uncontrolled baseline A1C and presence of comorbidities. In separate multivariable models, 2-3 A1C tests/year were associated with greater likelihood of A1C < 8% (64 mmol/mol) vs. <2 tests/year (OR = 1.07, 95% confidence interval [CI] 1.02-1.12), while >3 tests/year was associated with a modestly increased risk of cardiovascular events vs. <2 tests/year (OR = 1.08, 95% CI 1.01-1.15).

CONCLUSIONS

A large proportion of type 2 diabetes patients were not tested per guideline recommendations. The relationship between A1C testing frequency and glycemic control was inconsistent, though there was a significant association between more frequent testing and experiencing a CV event.

Inadequate postpartum screening for type 2 diabetes in women with previous gestation diabetes mellitus: A retrospective audit of practice over 17 years

INTRODUCTION

Women with gestational diabetes (GDM) are at greatly increased risk of type 2 diabetes (T2DM). The UK guidance recommends screening for T2DM at around 6-week postpartum and annually thereafter. We evaluated conformity to this guidance in two separate time periods.

METHODS

The proportion of tests performed within guidance was assessed using longitudinal plasma glucose and glycated haemoglobin data in two cohorts (1999-2007, n = 251; 2015-2016, n = 260) from hospital records on women previously diagnosed with GDM.

RESULTS

In the 1999-2007 and 2015-2016 cohorts, 59.8% and 35.0% of women had the recommended postpartum testing, respectively (P < .001); just 13.5% and 14.2%, respectively, underwent the first annual test on time. During long-term follow-up of the 1999-2007 cohort (median follow-up: 12.3 years), the proportion of women tested in any given year averaged 34.2% over a 17-year period; there was a progressive decline in the proportion of women receiving a yearly test with time since delivery (P = .002). Over the follow-up period, 85 women from the 1999-2007 cohort developed blood test results in the diabetic range with a median time to presumed DM diagnosis of 5.2 years (range 0.11-15.95 years). Kaplan-Meier analysis showed that 18.8% of women had blood test results in the diabetes range by 5-year postpartum and 37.8% by 10-year postpartum.

CONCLUSIONS

Despite high profile guidelines and a clear clinical rationale to screen women with a past diagnosis of GDM, many women did not receive adequate screening for T2DM both in the short term and long term. This suggests that alternative approaches are needed to ensure effective follow-up of this high-risk group. To have an impact, interventions need to be tailored to a young, generally healthy group in which traditional approaches to follow-up may not be best suited.

Serum lithium test requesting across three UK regions: an evaluation of adherence to monitoring guidelines

BACKGROUND

Bipolar disorder is the fourth most common mental health condition, affecting ~ 1% of UK adults. Lithium is an effective treatment for prevention of relapse and hospital admission, and is widely recommended as a first-line treatment. We previously showed in other areas that laboratory testing patterns are variable with sub-optimal conformity to guidance. We therefore examined lithium results and requesting patterns relative to monitoring recommendations.

METHODS

Data on serum lithium levels and intervals between requests were extracted from Clinical Biochemistry laboratory information systems at the University Hospitals of North Midlands, Salford Royal Foundation Trust and Pennine Acute Hospitals from 2012 to 2018 (46,555 requests; 3371 individuals). Data were examined with respect to region/source of request, age and sex.

RESULTS

Across all sites, lithium levels on many requests were outside the recommended UK therapeutic range (0.4-0.99 mmol/L); 19.2% below the range and 6.1% above the range (median [Li]: 0.60 mmol/L). A small percentage were found at the extremes (3.2% at < 0.1 mmol/L, 1.0% at ≥1.4 mmol/L). Most requests were from general practice (56.3%) or mental health units (34.4%), though those in the toxic range (≥1.4 mmol/L) were more likely to be from secondary care (63.9%). For requesting intervals, there was a distinct peak at 12 weeks, consistent with guidance for those stabilised on lithium therapy. There was no peak at 6 months, as recommended for those aged < 65 years on unchanging therapy, though re-test intervals in this age group were more likely to be longer. There was a peak at 0-7 days, reflecting those requiring closer monitoring (e.g. treatment initiation, toxicity). However, for those with initial lithium concentrations within the BNF range (0.4-0.99 mmol/L), 69.4% of tests were requested outside expected testing frequencies.

CONCLUSIONS

Our data showed: (a) lithium levels are often maintained at the lower end of the recommended therapeutic range, (b) patterns of lithium results and testing frequency were comparable across three UK sites with differing models of care and, (c) re-test intervals demonstrate a noticeable peak at the recommended 3-monthly, but not at 6-monthly intervals. Many tests were repeated outside expected frequencies, indicating the need for measures to minimise inappropriate testing.

Development of an Artificial Intelligence-Based Automated Recommendation System for Clinical Laboratory Tests: Retrospective Analysis of the National Health Insurance Database

BACKGROUND

Laboratory tests are considered an essential part of patient safety as patients' screening, diagnosis, and follow-up are solely based on laboratory tests. Diagnosis of patients could be wrong, missed, or delayed if laboratory tests are performed erroneously. However, recognizing the value of correct laboratory test ordering remains underestimated by policymakers and clinicians. Nowadays, artificial intelligence methods such as machine learning and deep learning (DL) have been extensively used as powerful tools for pattern recognition in large data sets. Therefore, developing an automated laboratory test recommendation tool using available data from electronic health records (EHRs) could support current clinical practice.

OBJECTIVE

The objective of this study was to develop an artificial intelligence-based automated model that can provide laboratory tests recommendation based on simple variables available in EHRs.

METHODS

A retrospective analysis of the National Health Insurance database between January 1, 2013, and December 31, 2013, was performed. We reviewed the record of all patients who visited the cardiology department at least once and were prescribed laboratory tests. The data set was split into training and testing sets (80:20) to develop the DL model. In the internal validation, 25% of data were randomly selected from the training set to evaluate the performance of this model.

RESULTS

We used the area under the receiver operating characteristic curve, precision, recall, and hamming loss as comparative measures. A total of 129,938 prescriptions were used in our model. The DL-based automated recommendation system for laboratory tests achieved a significantly higher area under the receiver operating characteristic curve (AUROCmacro and AUROCmicro of 0.76 and 0.87, respectively). Using a low cutoff, the model identified appropriate laboratory tests with 99% sensitivity.

CONCLUSIONS

The developed artificial intelligence model based on DL exhibited good discriminative capability for predicting laboratory tests using routinely collected EHR data. Utilization of DL approaches can facilitate optimal laboratory test selection for patients, which may in turn improve patient safety. However, future study is recommended to assess the cost-effectiveness for implementing this model in real-world clinical settings.

Minimum retesting intervals in practice: 10 years experience

Background

Minimum retesting intervals (MRI) are a popular demand management solution for the identification and reduction of over-utilized tests. In 2011 Association of Clinical Biochemistry and Laboratory Medicines (ACB) published evidence-based recommendations for the use of MRI.

Aim

The aim of the paper was to review the use of MRI over the period since the introduction of these recommendations in 2011 to 2020 and compare it to previous published data between 2000-2010.

Methods

A multi-source literature search was performed to identify studies that reported the use of a MRI in the management or identification of inappropriate testing between the years prior to (2000–2010) and after implementation (2011–2020) of these recommendations.

Results

31 studies were identified which met the acceptance criteria (2000–2010 n=4, 2011–2020 n=27). Between 2000 and 2010 4.6% of tests (203,104/4,425,311) were identified as failing a defined MRI which rose to 11.8% of tests (2,691,591/22,777,288) in the 2011–2020 period. For those studies between 2011 and 2020 reporting predicted savings (n=20), 14.3% of tests (1,079,972/750,580) were cancelled, representing a total saving of 2.9 M Euros or 2.77 Euro/test. The most popular rejected test was Haemoglobin A1c which accounted for nearly a quarter of the total number of rejected tests. 13 out 27 studies used the ACB recommendations.

Conclusions

MRI are now an established, safe and sustainable demand management tool for the identification and management of inappropriate testing. Evidence based consensus recommendations have supported the adoption of this demand management tool into practice across multiple healthcare settings globally and harmonizing laboratory practice.

Evaluation and cost estimation of laboratory test overuse in 43 commonly ordered parameters through a Computerized Clinical Decision Support System (CCDSS) in a large university hospital

BACKGROUND

Computerized Clinical Decision Support Systems (CCDSS) have become increasingly important in ensuring patient safety and supporting all phases of clinical decision making. The aim of this study is to evaluate, through a CCDSS, the rate of the laboratory tests overuse and to estimate the cost of the inappropriate requests in a large university hospital.

METHOD

In this observational study, hospital physicians submitted the examination requests for the inpatients through a Computerized Physician Order Entry. Violations of the rules in tests requests were intercepted and counted by a CCDSS, over a period of 20 months. Descriptive and inferential statistics (Student's t-test and ANOVA) were made. Finally, the monthly comprehensive cost of the laboratory tests was calculated.

RESULTS

During the observation period a total of 5,716,370 requests were analyzed and 809,245 violations were counted. The global rate of overuse was 14.2% ± 3.0%. The most inappropriate exams were Alpha Fetoprotein (85.8% ± 30.5%), Chlamydia trachomatis Nucleic Acid Amplification (48.7% ± 8.8%) and Alkaline Phosphatase (20.3% ± 6.5%). The monthly cost of over-utilization was 56,534€ for basic panel, 14,421€ for coagulation, 4,758€ for microbiology, 432€ for immunology exams. All the exams, generated an estimated avoidable cost of 1,719,337€ (85,967€ per month) for the hospital.

CONCLUSIONS

The study confirms the wide variability in over-utilization rates of laboratory tests. For these reasons, the real impact of inappropriateness is difficult to assess, but the generated costs for patients, hospitals and health systems are certainly high and not negligible. It would be desirable for international medical communities to produce a complete panel of prescriptive rules for all the most common laboratory exams that is useful not only to reduce costs, but also to ensure standardization and high-quality care.

Reducing the probability of falsely elevated HbA1c results in diabetic patients by applying automated and educative HbA1c re-testing intervals

INTRODUCTION

Too frequent HbA1c measurements may lead to unnecessary treatment modifications of diabetic patients. The aim of this study was to estimate the percentage of falsely elevated HbA1c results in two hospitals, Landeskrankenhaus/Uniklinikum Salzburg (LKH) and Landesklinik St. Veit (STV), as well as to retrospectively investigate the effect of an automated and an educative 60-day re-testing interval (RTI).

METHODS

The amount of estimated falsely elevated results (eFER), based on odds calculated using the baseline and the follow-up values and the time between these measurements, the number of HbA1c re-testings within 60 days as well as the overall number of ordered and performed HbA1c analyses were calculated. In LKH, an automated algorithm cancelling inappropriate HbA1c testing was applied, and in STV, educational actions were taken.

RESULTS

Before RTI-implementation, eFER were 0.9% and 2.1% and within-60-days-re-testing were 15.0% and 7.4% of cases in LKH and STV, respectively. After RTI-implementation, these numbers decreased to 0.2% (p < .001) and 1.8% (p = .869) and within-60-days-re-testing decreased to 1.1% (p < .001) and 3.6% (p = .003) in LKH and STV, respectively. Median monthly HbA1c measurements decreased by 15.8% (p < .001) and 21.1% (p = .002) in LKH and STV, respectively.

CONCLUSION

Both the educational and the automated 60-day-RTI were proven to be efficient in reducing overall HbA1c measurements, re-testing within 60 days and eFER.

Screening for thyroid disease in patients with type 2 diabetes mellitus: An evaluation of current practice

BACKGROUND

United Kingdom national guidelines do not recommend routine testing for thyroid disease in people with type 2 diabetes mellitus (T2DM). However, some studies suggest an increased risk of thyroid dysfunction in T2DM. The aim of this study was to evaluate the current practice of screening for thyroid disease in patients with T2DM.

METHODS

Patients with pre-existing T2DM (n = 339) were selected from records for routine glycated haemoglobin testing performed in December 2008. Using routinely collected primary/secondary care data from 2009 to 2017, we examined longitudinal thyroid-stimulating hormone and free thyroxine requests to determine the overall proportion of patients screened for thyroid dysfunction and the time interval between thyroid tests requested.

RESULTS

Thirty-three patients (9.7%) had pre-existing thyroid disease. Of the remaining 306 patients, 96.4% had at least one thyroid test during the follow-up period. When the time interval between tests was evaluated in these patients, there was a discrete peak in thyroid function test requests at 12 months, consistent with routine annual testing. Most requests (77%) originated from a general practice setting.

CONCLUSIONS

Contrary to current guidelines, we have provided evidence suggestive of regular screening for thyroid dysfunction in patients with T2DM, particularly in general practice. The appropriateness of this practice remains unclear, but may warrant further examination to assess the clinical benefits of screening, balanced against cost.

The frequency of testing for glycated haemoglobin, HbA1c, is linked to the probability of achieving target levels in patients with suboptimally controlled diabetes mellitus

Background We previously showed, in patients with diabetes, that >50% of monitoring tests for glycated haemoglobin (HbA1c) are outside recommended intervals and that this is linked to diabetes control. Here, we examined the effect of tests/year on achievement of commonly utilised HbA1c targets and on HbA1c changes over time. Methods Data on 20,690 adults with diabetes with a baseline HbA1c of >53 mmol/mol (7%) were extracted from Clinical Biochemistry Laboratory records at three UK hospitals. We examined the effect of HbA1c tests/year on (i) the probability of achieving targets of ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) in a year using multi-state modelling and (ii) the changes in mean HbA1c using a linear mixed-effects model. Results The probabilities of achieving ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) targets within 1 year were 0.20 (95% confidence interval: 0.19-0.21) and 0.10 (0.09-0.10), respectively. Compared with four tests/year, having one test or more than four tests/year were associated with lower likelihoods of achieving either target; two to three tests/year gave similar likelihoods to four tests/year. Mean HbA1c levels were higher in patients who had one test/year compared to those with four tests/year (mean difference: 2.64 mmol/mol [0.24%], p<0.001). Conclusions We showed that ≥80% of patients with suboptimal control are not achieving commonly recommended HbA1c targets within 1 year, highlighting the major challenge facing healthcare services. We also demonstrated that, although appropriate monitoring frequency is important, testing every 6 months is as effective as quarterly testing, supporting international recommendations. We suggest that the importance HbA1c monitoring frequency is being insufficiently recognised in diabetes management.

Managing inappropriate utilization of laboratory resources

Background The inappropriate use of laboratory resources, due to excessive number of tests not really necessary for patient care or by failure to order the appropriate diagnostic test, may lead to wrong, missed or delayed diagnosis, thus potentially jeopardizing patient safety. It is estimated that 5-95% of tests are currently used inappropriately, depending on the appropriateness criteria, thus significantly contributing to the potential of generating medical errors, the third leading cause of death in the US. Content In this review, we discuss the reasons as well as the medical and financial consequences of inappropriate utilization of laboratory tests. We then provide demand management (DM) tools as a means for overcoming this issue and also discuss their benefits, challenges, limitations and requirements for successful implementation. Summary and outlook When based on current evidence, adapted to local conditions and developed in close collaboration with clinicians, DM is a reasonable strategy for progressing toward better management of over- and underuse of laboratory resources.

Effectiveness of monthly and bimonthly follow-up of patients with well-controlled type 2 diabetes: a propensity score matched cohort study

BACKGROUND

On average, patients in Japan with type 2 diabetes mellitus have a clinical consultation every month, although evidence for a favorable follow-up interval is lacking. This study investigated whether the follow-up interval can be extended by comparing the clinical outcomes and cost for monthly versus bimonthly follow-up of patients with well-controlled diabetes mellitus.

METHODS

We combined administrative claims data from the National Health Insurance and the Health Checkups Program data of Tsu city, Japan between 2011 and 2014 to conduct a retrospective cohort study of patients with well-controlled type 2 diabetes mellitus. Propensity scores were used to assemble a matched-pairs cohort from patients who had monthly and bimonthly follow-up. Equivalence between two groups was assessed by designating the proportion of patients who maintained good control of their diabetes in the subsequent year as a primary outcome. The proportion achieving target blood pressure and lipid levels, favorable lifestyle, and annual cost were compared as secondary outcomes.

RESULTS

Of 12,145 participants, 693 with monthly follow-up and 693 with bimonthly follow-up were matched using propensity scores. In the monthly follow-up group 654 (94.4%) remained under good diabetic control, versus 658 (95.0%) in the bimonthly group (difference: 0.6%; 95% confidence interval: - 1.8 to 2.9%). All secondary outcomes were equivalent for the monthly and bimonthly follow-up groups except the proportion achieving target blood pressure, the proportion engaging in regular exercise, and annual cost.

CONCLUSIONS

For patients with well-controlled diabetes mellitus, although frequent follow-up by a physician does not affect the control of blood glucose level in the subsequent year, the annual treatment cost becomes much higher. We suggest that patients with well-controlled diabetes can be followed up less often.

Timings for HbA1c testing in people with diabetes are associated with incentive payments: an analysis of UK primary care data

AIMS

Guidelines recommend testing HbA1c every 3-6 months in people with diabetes. In the United Kingdom (UK), primary care clinics are financially incentivized to monitor HbA1c at least annually and report proportions of patients meeting targets on 31 March. We explored the hypothesis that this reporting deadline may be associated with over-frequent or delayed HbA1c testing.

METHODS

This analysis used HbA1c results from 100 000 people with diabetes during 2005-2014 in the Clinical Practice Research Datalink UK primary care database. Logistic regression was used to explore whether the four months prior to the deadline for quality reporting (December to March) or individual's previous HbA1c were aligned with retesting HbA1c within 60 days or > 1 year from the previous test, and identify other factors associated with the timing of HbA1c testing.

RESULTS

Retesting HbA1c within 60 days or > 1 year was more common in December to March compared with other months of the year (odds ratio 1.06, 95% confidence interval 1.04-1.08 for retesting within 60 days). Those with higher HbA1c were more likely to have a repeat test within 60 days and less likely to have a repeat test > 1 year from the previous test.

CONCLUSIONS

We have found that retesting HbA1c within 60 days and > 1 year from the previous test was more common in December to March compared with the other months of the year. This work suggests that both practice-centred administrative factors and patient-centred considerations may be influencing diabetes care in the UK.

Improving the quality of point-of-care testing

BACKGROUND

It is suggested that new models of primary care should have better access to test results through the use of point-of-care testing (POCT).

OBJECTIVE

To determine whether quality management of POCT leads to better results.

METHODS

A comprehensive search of the literature on quality management of POCT in primary care, where the impact of participation in quality management programmes had been investigated with relevant outcome measures.

RESULTS

Three databases were systematically searched using key words relevant to POCT and quality management, covering from 1945 to January 2017. Titles and abstracts were reviewed for relevance and papers selected for review and data extraction. Five observational studies were found in which the performance of POCT for specific analytes in external quality assurance (EQA) programmes was used to assess improvement over a period of time, varying from 3.5 to 15 years. The tests monitored were HbA1c, urine albumin, C-reactive protein, glucose and haemoglobin. In each case, the performance of the test against defined analytical criteria was used to judge improvement in performance. Different summary performance criteria were used, including the imprecision of results over a period of time (two studies) and meeting defined target values for bias and imprecision of measurement (three studies). Performance improved with time and was associated with regular participation in EQA schemes and with the use of internal quality control (IQC) procedures.

CONCLUSIONS

These findings indicate that adoption of quality management for POCT, including participation in IQC and EQA, with the support of laboratory medicine professionals, will improve the quality of the results 'produced'.

Electronic Laboratory Medicine ordering with evidence-based Order sets in primary care (ELMO study): protocol for a cluster randomised trial

BACKGROUND

Laboratory testing is an important clinical act with a valuable role in screening, diagnosis, management and monitoring of diseases or therapies. However, inappropriate laboratory test ordering is frequent, burdening health care spending and negatively influencing quality of care. Inappropriate tests may also result in false-positive results and potentially cause excessive downstream activities. Clinical decision support systems (CDSSs) have shown promising results to influence the test-ordering behaviour of physicians and to improve appropriateness. Order sets, a form of CDSS where a limited set of evidence-based tests are proposed for a series of indications, integrated in a computerised physician order entry (CPOE) have been shown to be effective in reducing the volume of ordered laboratory tests but convincing evidence that they influence appropriateness is lacking. The aim of this study is to evaluate the effect of order sets on the quality and quantity of laboratory test orders by physicians. We also aim to evaluate the effect of order sets on diagnostic error and explore the effect on downstream or cascade activities.

METHODS

We will conduct a cluster randomised controlled trial in Belgian primary care practices. The study is powered to measure two outcomes. We will primarily measure the influence of our CDSS on the appropriateness of laboratory test ordering. Additionally, we will also measure the influence on diagnostic error. We will also explore the effects of our intervention on cascade activities due to altered results of inappropriate tests.

DISCUSSION

We have designed a study that should be able to demonstrate whether the CDSS aimed at diagnostic testing is not only able to influence appropriateness but also safe with respect to diagnostic error. These findings will influence a lager, nationwide implementation of this CDSS.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02950142 .

Translational health economics: The key to accountable adoption of in vitro diagnostic technologies

Adoption of new technologies, including diagnostic tests, is often considered not to deliver the expected return on investment. The reasons for this poor link between expectation and outcome include lack of evidence, variation in use of the technology, and an inability of the health system to manage the balance between investment and disinvestment associated with the change in care pathway. The challenges lie in the complex nature of healthcare provision where the investment is likely to be made in the jurisdiction of one stakeholder while the benefits (as well as dis-benefits) accrue to the other stakeholders. A prime example is found in the field of laboratory medicine and the use of diagnostic tests. The current economic tools employed in healthcare are primarily used to make policy and strategic decisions, particularly across health systems, and in purchaser and provider domains. These tools primarily involve cost effectiveness and budget impact analyses, both of which have been applied in health technology assessment of diagnostic technologies. However, they lack the granularity to translate findings down to the financial management and operational decision making at the provider department level. We propose an approach to translational health economics based on information derived from service line management and time-driven activity-based costing, identifying the resource utilisation for each of the units involved in the delivery of a care pathway, before and after adoption of new technology. This will inform investment and disinvestment decisions, along with identifying where the benefits, and dis-benefits, can be achieved for all stakeholders.

Essential and Nonessential Blood Testing in the Clinical Teaching Unit

OBJECTIVES

The aim of the study was to evaluate the essential and nonessential blood tests ordered on the internal medicine clinical teaching units (CTUs) at Kingston General Hospital. Our aim was to establish a baseline performance measure identifying appropriate use of laboratory tests that could be used to inform improvement over time.

METHODS

For an 8-week period, 14 CTU attending physicians at Kingston General Hospital were surveyed. They were asked for each of their patients, "What blood tests do you consider to be essential for tomorrow morning to maintain appropriate care for this patient?" The following day, blood tests that were ordered were compared with the "essential" list previously given by the attending physicians.

RESULTS

Of 291 processed blood tests, 148 (51%) had not been considered essential by attending physicians; of the 203 tests considered essential, 60 (30%) were not ordered. Total agreement between "essential" and processed tests was poor (κ = 0.51; confidence interval, 0.45-0.56).

CONCLUSIONS

Inadequate use of blood tests for CTU patients is common. Quality improvement initiatives should aim to address the lack of observed consensus between attending physicians' views and the ordered tests and to streamline decision-making and the ordering/communication processes. Clinical standards and guidelines regarding ordering of laboratory tests should be clearly defined.

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Automatic laboratory-based strategy to improve the diagnosis of type 2 diabetes in primary care

Introduction

To study the pre-design and success of a strategy based on the addition of hemoglobin A1c (HbA1c) in the blood samples of certain primary care patients to detect new cases of type 2 diabetes.

Materials and methods

In a first step, we retrospectively calculated the number of HbA1c that would have been measured in one year if HbA1c would have been processed, according to the guidelines of the American Diabetes Association (ADA). Based on those results we decided to prospectively measure HbA1c in every primary care patient above 45 years, with no HbA1c in the previous 3 years, and glucose concentration between 5.6-6.9 mmol/L, during an 18 months period. We calculated the number of HbA1c that were automatically added by the LIS based on our strategy, we evaluated the medical record of such subjects to confirm whether type 2 diabetes was finally confirmed, and we calculated the cost of our intervention.

Results

In a first stage, according to the guidelines, Hb1Ac should have been added to the blood samples of 13,085 patients, resulting in a cost of 14,973€. In the prospective study, the laboratory added Hb1Ac to 2092 patients, leading to an expense of 2393€. 314 patients had an HbA1c value ≥ 6.5% (48 mmol/mol). 82 were finally diagnosed as type 2 diabetes; 28 thanks to our strategy, with an individual cost of 85.4€; and 54 due to the request of HbA1c by the general practitioners (GPs), with a cost of 47.5€.

Conclusion

The automatic laboratory-based strategy detected patients with type 2 diabetes in primary care, at a cost of 85.4€ per new case.

Audit of Use and Overuse of Serum Protein Immunofixation Electrophoresis and Serum Free Light Chain Assay in Tertiary Health Care: A Case for Algorithmic Testing to Optimize Laboratory Utilization

OBJECTIVES

Overuse of laboratory tests is a persistent issue. We examined the use and overuse of serum immunofixation electrophoresis and serum free light chain assays to develop an algorithm for optimizing utilization.

METHODS

A retrospective review of all tests, for investigation of monoclonal gammopathies, for all patients who had any of these tests done from April 24, 2014, through July 25, 2014, was carried out. The test orders were categorized as warranted or not warranted according to criteria presented in the article.

RESULTS

A total of 237 patients were tested, and their historical records included 1,503 episodes of testing for one or more of serum protein electrophoresis, serum immunofixation electrophoresis, and serum free light chain assays. Only 46% of the serum immunofixation and 42% serum free light chain assays were warranted. Proper utilization, at our institution alone, would have obviated $64,182.95/year in health care costs, reduced laboratory cost of reagent alone by $26,436.04/year, and put $21,904.92/year of part B reimbursement at risk.

CONCLUSIONS

Fewer than half of the serum immunofixation and serum free light chain assays added value. The proposed algorithm for testing should improve utilization. Risk to part B billing may be a disincentive to reducing test utilization.

Adherence to NICE guidance on glucagon-like peptide-1 receptor agonists among patients with type 2 diabetes mellitus: an evaluation using the Clinical Practice Research Datalink

AIMS

To assess adherence to the UK's National Institute for Health and Care Excellence (NICE) guidelines for initiating and continuing glucagon-like peptide-1 (GLP-1) receptor agonists in patients with type 2 diabetes (T2DM).

RESEARCH DESIGN AND METHODS

A retrospective cohort study of 7133 primary care patients ≥40 years with a first prescription for a GLP-1 receptor agonist following publication of NICE guideline/guidance. Patient characteristics and levels of clinical monitoring were assessed using descriptive analyses.

MAIN OUTCOME MEASURES

Main outcomes were the proportion of patients initiating GLP-1 receptor agonists as part of NICE-recommended dual- or triple-therapy regimens; the proportions meeting NICE triple therapy initiation criteria (glycosylated hemoglobin [HbA1c] ≥7.5% and body mass index [BMI] ≥35 kg/m(2)) and the proportions continuing GLP-1 receptor agonist at 6 months according to NICE recommendations.

RESULTS

Mean age at initiating GLP-1 receptor agonists was 58.2 years (SD 9.4), BMI 38.4 kg/m(2) (SD 6.8) and HbA1c 9.2% (SD 3.2%). Overall, only 25% of patients initiated GLP-1 receptor agonists as part of a NICE-recommended regimen. Of patients initiated on a recommended triple-therapy regimen, 50% (646/1284) fulfilled both NICE HbA1c and BMI initiation criteria. Approximately 18% (32/174) of patients continuing NICE-recommended dual therapy at 6 months achieved a 1% reduction in HbA1c and 6.4% (33/515) continuing with NICE-recommended triple therapy achieved NICE's target reductions for both HbA1c and body weight. About 8% of patients continuing exenatide as triple therapy (N = 243) achieved both targets.

CONCLUSIONS

Adherence to NICE guidance for initiating and continuing GLP-1 receptor agonists is low. However, lack of data on ethnicity (for assessing NICE's BMI criteria) and on contraindications and/or hypersensitivity to other diabetes medication in the treatment pathway have limited our ability to fully assess adherence to GLP-1 prescribing. Further research is warranted to better understand general practitioners' prescribing decisions given the cost of prescribing GLP-1 receptor agonists.

HbA1c overtesting and overtreatment among US adults with controlled type 2 diabetes, 2001-13: observational population based study

STUDY QUESTION

What is the extent and effect of excessive testing for glycated hemoglobin (HbA1c) among adults with controlled type 2 diabetes?

METHODS

A retrospective analysis of data from a national administrative claims database included commercially insured individuals in the USA, 2001-13. Study patients were aged 18 years or older, had type 2 diabetes with stable glycemic control (two consecutive tests showing HbA1c<7.0% within 24 months), did not use insulin, had no history of severe hypoglycemia or hyperglycemia, and were not pregnant. HbA1c testing frequency was measured within 24 months after the second (index) HbA1c test, and classified as guideline recommended (≤ 2 times/year), frequent (3-4 times/year), and excessive (≥ 5 times/year). Changes in treatment regimen were ascertained within three months of the index test.

STUDY ANSWER AND LIMITATIONS

Of 31,545 patients in the study cohort (mean age 58 years; mean index HbA1c 6.2%), HbA1c testing frequency was excessive in 6% and frequent in 55%. Despite good glycemic control at baseline, treatment was further intensified by addition of glucose lowering drugs or insulin in 8.4% of patients (comprising 13%, 9%, and 7% of those tested excessively, frequently, and per guidelines, respectively; P<0.001). Compared with guideline recommended testing, excessive testing was associated with treatment intensification (odds ratio 1.35 (95% confidence interval 1.22 to 1.50)). Excessive testing rates remained unchanged in 2001-08, but fell significantly after 2009. The odds of excessive testing was 46% lower in 2011 than in 2001-02. The study population is not representative of all US patients with type 2 diabetes because it was restricted to commercially insured adults with stable and controlled diabetes not receiving insulin treatment. The study design did not capture the underuse of HbA1c testing.

WHAT THIS STUDY ADDS

In this US cohort of adults with stable and controlled type 2 diabetes, more than 60% received too many HbA1c tests, a practice associated with potential overtreatment with hypoglycemic drugs. Excessive testing contributes to the growing problem of waste in healthcare and increased patient burden in diabetes management.

FUNDING, COMPETING INTERESTS, DATA SHARING

NDS and RGM are funded partly by the Agency for Healthcare Research and Quality (R18HS18339) and AcademyHealth Delivery System Science Fellowship (2013), respectively. No competing interests declared. Additional data are available from mccoy.rozalina@mayo.edu.

The relationship between competition and quality in procedural cardiac care

BACKGROUND

Anesthesiologists are frequently involved in efforts to meet perioperative quality metrics. The degree to which hospitals compete on publicly reported quality measures, however, is unclear. We hypothesized that hospitals in more competitive environments would be more likely to compete on quality and thus perform better on such measures. To test our hypothesis, we studied the relationship between competition and quality in hospitals providing procedural cardiac care and participating in a national quality database.

METHODS

For hospitals performing heart valve surgery (HVS) and delivering acute myocardial infarction (AMI) care in the Hospital Compare database, we assessed the degree of intrahospital competition using both geographical radius and federally defined metropolitan statistical area (MSA) to determine the degree of intrahospital competition. For each hospital, we then correlated the degree of competition with quality measure performance, mortality, patient volume, and per-patient Medicare costs for both HVS and AMI.

RESULTS

Six hundred fifty-three hospitals met inclusion criteria for HVS and 1898 hospitals for AMI care. We found that for both definitions of competition, hospitals facing greater competition did not demonstrate better quality measure performance for either HVS or AMI. For both diagnoses, competition by number of hospitals correlated positively with cost: partial correlation coefficients = 0.40 (0.42 for MSA) (P < 0.001) for HVS and 0.52 (0.47 for MSA) (P < 0.001) for AMI.

CONCLUSIONS

An analysis of the Hospital Compare database found that competition among hospitals correlated overall with increased Medicare costs but did not predict better scores on publicly reported quality metrics. Our results suggest that hospitals do not compete meaningfully on publicly reported quality metrics or costs.

Reduced testing frequency for glycated hemoglobin, HbA1c, is associated with deteriorating diabetes control

OBJECTIVE

We previously showed that in patients with diabetes mellitus, glycated hemoglobin (HbA1c) monitoring outside international guidance on testing frequency is widespread. Here we examined the relationship between testing frequency and diabetes control to test the hypothesis that retest interval is linked to change in HbA1c level.

RESEARCH DESIGN AND METHODS

We examined repeat HbA1c tests (400,497 tests in 79,409 patients, 2008-2011) processed by three U.K. clinical laboratories. We examined the relationship between retest interval and 1) percentage change in HbA1c and 2) proportion of cases showing a significant HbA1c rise. The effect of demographics factors on these findings was also explored.

RESULTS

Our data showed that the optimal testing frequency required to maximize the downward trajectory in HbA1c was four times per year, particularly in those with an initial HbA1c of ≥7% (≥53 mmol/mol), supporting international guidance. Testing 3-monthly was associated with a 3.8% reduction in HbA1c compared with a 1.5% increase observed with annual testing; testing more frequently provided no additional benefit. Compared with annual monitoring, 3-monthly testing was associated with a halving of the proportion showing a significant rise in HbA1c (7-10 vs. 15-20%).

CONCLUSIONS

These findings provide, in a large, multicenter data set, objective evidence that testing outside guidance on HbA1c monitoring frequency is associated with a significant detrimental effect on diabetes control. To achieve the optimum downward trajectory in HbA1c, monitoring frequency should be quarterly, particularly in cases with suboptimal HbA1c. While this impact appears small, optimizing monitoring frequency across the diabetes population may have major implications for diabetes control and comorbidity risk.

Frequency monitoring of hemoglobin A1c, low-density lipoprotein, and urine protein laboratory testing: a College of American Pathologists Q-Probes study

CONTEXT

Ensuring laboratory tests are performed at intervals consistent with established, evidence-based guidelines in diabetic patients is an important aspect of laboratory utilization.

OBJECTIVES

To measure how frequently diabetes mellitus patients are tested for hemoglobin A1c (HbA1c), low-density lipoprotein, and urine protein and to determine whether the frequencies with which these analytes are tested are consistent with recognized guidelines.

DESIGN

Participants prospectively identified up to 40 patients with a current HbA1c result and at least 2 previous measurements within the past 24 months. For each patient identified with at least 3 HbA1c measurements during a 24-month period, participants also determined the number of low-density lipoprotein and spot or random urine protein tests that were performed during those 24 months.

RESULTS

Participants from 49 institutions submitted a total of 1915 cases that met the study criteria of at least 3 HbA1c test results within the past 24 months. Approximately 95% of patients had 8 or fewer HbA1c tests in the past 24 months; 79% of patients with at least 3 HbA1c tests had at least 2 low-density lipoprotein tests and 27% had at least 2 urine protein tests reported during the 24-month study period.

CONCLUSION

Participating laboratories were generally successful in documenting appropriate utilization of HbA1c testing in diabetes mellitus monitoring, but had more difficulty documenting that diabetes mellitus patients had annual testing for low-density lipoprotein and urine protein.

Temporal trends of hemoglobin A1c testing

OBJECTIVE

The study of utilization patterns can quantify potential overuse of laboratory tests and find new ways to reduce healthcare costs. We demonstrate the use of distributional analytics for comparing electronic health record (EHR) laboratory test orders across time to diagnose and quantify overutilization.

MATERIALS AND METHODS

We looked at hemoglobin A1c (HbA1c) testing across 119,000 patients and 15 years of hospital records. We examined the patterns of HbA1c ordering before and after the publication of the 2002 American Diabetes Association guidelines for HbA1c testing. We conducted analyses to answer three questions. What are the patterns of HbA1c ordering? Do HbA1c orders follow the guidelines with respect to frequency of measurement? If not, how and why do they depart from the guidelines?

RESULTS

The raw number of HbA1c orderings has steadily increased over time, with a specific increase in low-measurement orderings (<6.5%). There is a change in ordering pattern following the 2002 guideline (p<0.001). However, by comparing ordering distributions, we found that the changes do not reflect the guidelines and rather exhibit a new practice of rapid-repeat testing. The rapid-retesting phenomenon does not follow the 2009 guidelines for diabetes diagnosis either, illustrated by a stratified HbA1c value analysis.

DISCUSSION

Results suggest HbA1c test overutilization, and contributing factors include lack of care coordination, unexpected values prompting retesting, and point-of-care tests followed by confirmatory laboratory tests.

CONCLUSIONS

We present a method of comparing ordering distributions in an EHR across time as a useful diagnostic approach for identifying and assessing the trend of inappropriate use over time.

An automated minimum retest interval rejection rule reduces repeat CRP workload and expenditure, and influences clinician-requesting behaviour

AIMS

Repeat serum C-reactive protein (CRP) measurements on the same day or on consecutive days are of limited clinical value. Minimum retesting intervals are recommended for managing unnecessary repeat testing. As not previously reported, we studied the effect of minimum retesting interval test rejection on laboratory workload and expenditure and on clinician-requesting behaviour.

METHODS

In a prospective study, we evaluated the effect of an automated 48 h CRP minimum retesting interval rule on inpatient and outpatient CRP workload and costs. Control data on inpatient and outpatient serum urea and electrolytes (UE) workload were collected during the study.

RESULTS

Over 1 year, there was a 7.0% and 12.3% decrease in CRP requests and CRP tests analysed, respectively, following the introduction of the minimum retesting interval rule when compared to the 1 year baseline period. This equated to an estimated annual reduction in revenue costs of £10 500, but cash savings in consumable costs of £3000. There was no significant change in UE requests.

CONCLUSIONS

We report, for the first time, that automated minimum retesting interval rejection rules as a stand-alone strategy are a cheap and sustainable method for reducing unnecessary repeat CRP tests, resulting in small laboratory cash savings, more efficient use of laboratory resources and standardisation of patient care pathways. The minimum retesting interval rejection rule also altered clinician test-requesting behaviour towards more appropriate requesting.

Changes in HbA1c level over a 12-week follow-up in patients with type 2 diabetes following a medication change

Background

Current guidance about the interval needed before retesting HbA1c when monitoring for glycaemic control is based on expert opinion rather than well-powered studies. The aim of our work was to explore how fast HbA1c changes after a change in glucose-lowering medication. This has implications for whether routine HbA1c testing intervals before 12 weeks could inform diabetes medication adjustments.

Methods

This 12-week cohort study recruited patients from 18 general practices in the United Kingdom with non-insulin treated diabetes who were initiating or changing dose of oral glucose-lowering medication. HbA1c was measured at baseline and 2, 4, 8 and 12 weeks after recruitment. HbA1c levels at earlier time intervals were correlated with 12-week HbA1c. A ROC curve analysis was used to identify the 8-week threshold above which medication adjustment may be clinically appropriate.

Results

Ninety-three patients were recruited to the study. Seventy-nine patients with no change in medication and full 12-week follow-up had the following baseline characteristics: mean±standard deviation age of 61.3±10.8 years, 34% were female and diabetes duration of 6.0±4.3 years. Mean HbA1c at baseline, 2, 4, 8 and 12 weeks was 8.7±1.5%, (72.0±16.8 mmol/mol) 8.6±1.6% (70.7±17.0 mmol/mol), 8.4±1.5% (68.7±15.9 mmol/mol), 8.2±1.4% (66.3±15.8 mmol/mol) and 8.1±1.4% (64.8±15.7 mmol/mol) respectively. At the end of the study 61% of patients had sub-optimal glycaemic control (HbA1c>7.5% or 59 mmol/mol). The 8-week change correlated significantly with the 12-week change in HbA1c and an HbA1c above 8.2% (66 mmol/mol) at 8 weeks correctly classified all 28 patients who had not achieved glycaemic control by 12 weeks.

Conclusions/interpretation

This is the first study designed with sufficient power to examine short-term changes in HbA1c. The 12-week change in HbA1c can be predicted 8 weeks after a medication change. Many participants who had not achieved glycaemic control after 12 weeks may have benefitted from an earlier review of their HbA1c and medication.

Cognitive Processes in Anesthesiology Decision Making

The quality and safety of health care are under increasing scrutiny. Recent studies suggest that medical errors, practice variability, and guideline noncompliance are common, and that cognitive error contributes significantly to delayed or incorrect diagnoses. These observations have increased interest in understanding decision-making psychology.

Many nonrational (i.e., not purely based in statistics) cognitive factors influence medical decisions and may lead to error. The most well-studied include heuristics, preferences for certainty, overconfidence, affective (emotional) influences, memory distortions, bias, and social forces such as fairness or blame.

Although the extent to which such cognitive processes play a role in anesthesia practice is unknown, anesthesia care frequently requires rapid, complex decisions that are most susceptible to decision errors. This review will examine current theories of human decision behavior, identify effects of nonrational cognitive processes on decision making, describe characteristic anesthesia decisions in this context, and suggest strategies to improve decision making.

Assessing physician utilization of laboratory practice guidelines: barriers and opportunities for improvement

OBJECTIVES

To assess physician utilization of laboratory practice guidelines (LPGs)³ to improve LPG uptake and use.

DESIGN AND METHODS

A statewide survey of 4987 primary care physicians (PCPs) and 75 infectious disease (ID) specialists was conducted in 2005-2006 to correlate guideline source with users' awareness, utilization, and perceived usefulness of LPGs. We compared LPGs developed by the Centers for Disease Control and Prevention (CDC) to LPGs developed by the Washington State Department of Health through its Clinical Laboratory Advisory Council (CLAC).

RESULTS

Physician awareness of LPGs was a major impediment to utilization of CLAC LPGs, and they were perceived as inaccessible, too numerous and unhelpful. However, once aware, respondents tended to use LPGs and there were no important differences in impediments or the ways CDC and CLAC LPGs were used. Of the PCPs who had a computerized physician order entry system (CPOE), a majority (92%) found, or expected that they would find, the integration of guidelines into their system helpful.

CONCLUSIONS

For both CDC and CLAC LPGs, the greatest impediments to uptake were awareness and familiarity, which depended upon LPG source, physician specialty, and practice setting. There was no apparent impediment to uptake of CLAC or CDC LPGs based upon their credibility. Because better promotion could increase uptake, CLAC LPGs are now promoted by the Washington State Medical Association. Integration of LPGs into CPOE and smart phone applications could address major impediments to clinician use. The Cabana paradigm would be useful for any organization seeking to improve LPG impact.