Counting the lifetime cost of obesity: Analysis based on national England data.

AIM: Obesity has a significant impact on all-cause mortality rate and overall health care resource use (HCRU). These outcomes are also strongly linked to age, sex and local deprivation of the population. We aimed to establish the lifetime costs of obesity by demographic group/geographic area using published mortality rates and HCRU use for integrated care boards (ICB) in England in the context of costs of therapeutic intervention. METHODS: Population and expected mortality rates by age, sex and deprivation were obtained from national data. Obesity class prevalence was taken from the health of the nation study. The published impact of obesity by age, group, sex and deprivation on mortality and HCRU were applied to estimate life years lost and lifetime HCRU [by sex, age band and body mass index (BMI) class for each ICB]. The year 2019 was chosen as the study basis data to avoid influences of COVID-19 pandemic on obesity rates with application of 2022/23 HCRU values. Outcomes including prevalence, deaths, life years lost, HCRU and lifetime HCRU were compared by age and sex groups across four BMI classes normal/underweight (BMI <25 kg/m2 ), overweight (25-29.9 kg/m2 ), obese class I and II (30-39.9 kg/m2 ), and obese class III (≥40), with benchmarking being set against all population being BMI <25 kg/m2 overall and by each of the 42 ICBs. We also associated future life with deaths to provide an estimate of 'future life years lost' occurring each year. RESULTS: Total population aged >16 years was 45.4 million (51% female). PREVALENCE: 13.7 million (28% of the total adult population) had a BMI ≥30 mg/m2 and BMI ≥40 kg/m2 were 1.50 million (12%) of these 1.0 million (68%) were female and of these 0.6 million 40% were women aged 16-49 years. In addition, 35% of those with a BMI ≥40 kg/m2 were in the top deprivation quintile (i.e. overall 20%). Mortality was based on expected deaths of 518K/year, and modelling suggested that if a BMI <25 kg/m2 was achieved in all individuals, the death rate would fall by 63K to 455K/year for the English population (12% reduction). For those with a BMI ≥40 kg/m2 the predicted reduction was 12K deaths (54% lower); while in those aged 16-49 years with a BMI ≥40 kg/m2 72% of deaths were linked to obesity. For future life years lost, we estimated 2.5 years were lost in people with BMI 30-39.9 kg/m2 6.7 years when BMI ≥40 kg/m2 . However, for those aged 16-49 years with a BMI ≥40 kg/m2 , 8.3 years were lost. HCRU, for weight reduction, the annual HCRU decrease from BMI ≥40 kg/m2 to BMI 30-39.9 kg/m2 was £342 per person and from BMI 30-39.9 to 25-29.9 kg/m2 the reduction was £316/person. However, lifetime costs were similar because of reduced life expectancy for obese individuals. In quality adjusted life years (QALY), overall, 791 689 future life years were lost (13.1% of all) in people with BMI ≥25 kg/m2 and were related to excess weight. When the NICE £30 000 per QALY value was applied to the estimated total 791 689 future life years lost then the potential QALY value reduction lost was equivalent to £24 billion/year or £522/person in the obese population. For morbidly obese men and women the potential QALY value lost was £2864/person/year. Regarding geography, across the 42 ICBs, we observed significant variation in the prevalence of BMI ≥40 (1.8%-4.3%), excess mortality (11.6%-15.4%) and HCRU linked to higher BMI (7.2%-8.8%). The areas with the greatest impact on HCRU were in the north-west, north-east and Midlands of England, while the south shows less impact. CONCLUSION: The expected increases in annual HCRU because of obesity, when considered over a lifetime, are being mitigated by the increased mortality of obese individuals. Our data suggest that simple short-term HCRU reduction brought about through BMI reduction will be insufficient to fund additional specialist weight reduction interventions. The HRCUs associated with BMI are not in most cases related to short-term health conditions. They are a cumulative result over a number of years, so for age 16-49 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £325/year for women and £80/year for men but this might not have immediately occurred within that year. For those aged >70 years reducing BMI from ≥40 to 30-39.9 kg/m2 might show an annual decrease in HCRU/person by £777/year for women and £796/year for men but also may not be manifest within that year. However, for the morbidly obese men and women, the potential QALY value lost was £2864 per person per year with the potential for these funds to be applied to intensive weight management programmes, including pharmacotherapy.

Is the Current Cut Point for Glycated Haemoglobin (HbA1c) Correct for Diagnosing Diabetes Mellitus in Premenopausal Women? Evidence to Inform Discussion.

INTRODUCTION: Women are on average diagnosed with diabetes mellitus at later age than men but have higher mortality. As the diagnosis of diabetes mellitus is primarily based on HbA1c, the use of a non-specific reference range and cut point for diabetes mellitus that does not account for gender differences in diabetes could potentially lead to underdiagnosis of diabetes mellitus in women and missed opportunities for intervention. We investigated whether a contributing factor to the later diagnosis in women may be a difference in distribution of HbA1c in premenopausal women versus men of the same age by comparing HbA1c values in men and women across multiple sites in the UK. METHODS: We analysed the HbA1c levels of 146,907 individuals who underwent single testing only and had HbA1c ≤ 50 mmol/mol between 2012 and 2019 in one laboratory (cohort 1). This was replicated in six laboratories with 938,678 individuals tested between 2019 and 2021 (cohort 2). RESULTS: In cohort 1, women < 50 years old had an HbA1c distribution markedly lower than that in men by a mean of 1.6 mmol/mol (p < 0.0001), while the difference in the distribution of HbA1c for individuals aged ≥ 50 years was less pronounced (mean difference 0.9 mmol/mol, p < 0.0001). For individuals under the age of 50, HbA1c in women lagged by up to 10 years compared to men. Similar findings were found in cohort 2. We estimated an additional 17% (n = 34,953) of undiagnosed women aged < 50 years in England and Wales could be reclassified to have diabetes mellitus, which may contribute to up to 64% of the difference in mortality rates between men/women with diabetes mellitus aged 16-50 years. CONCLUSION: The HbA1c cut point for diagnosis of diabetes mellitus may need to be re-evaluated in women under the age of 50 years. Early identification of diabetes mellitus in women has the potential to improve women's health outcomes in the longer term.

The INSIGHT project: reflections on the co-production of a quality recognition programme to showcase excellence in public involvement in health and care research.

BACKGROUND: The quality of Patient and Public Involvement (PPI) in healthcare research varies considerably and is frequently tokenistic. We aimed to co-produce the Insight | Public Involvement Quality Recognition and Awards programme, based on the UK Standards for Public Involvement (UKSPI) alongside an incremental scale designed by Expert Citizens (a lived experience-led community group), to incentivise and celebrate continuous improvement in PPI. METHODS: We used Task and Finish Groups (19/44 [43%] public contributor membership) to co-produce the programme which we piloted in three organisations with different healthcare research models. We used surveys and review sessions to capture learning and reflections. RESULTS: We co-created: (1) A Quality descriptor matrix comprising four incremental quality levels (Welcoming, Listening, Learning, Leading) for each UKSPI standard. (2) An assessment framework including guidance materials, self-assessment form and final report template. (3) An assessor training package. (4) The quality awards event format and nomination form. These materials were modified based on pilot-site feedback. Of survey respondents: 94.4% felt they had made at least 'Some' personal contribution (half said 'Quite a lot'/'A great deal'), 88.9% said they were 'Always'/'Often' able to express their views freely and, 100% stated the programme would have 'A lot of impact'/'Quite a bit of impact'. During the project, we identified the importance of taking time to explain project aims and contributor roles, adapting to the needs of individual contributors and, using smaller bespoke sessions outside the main Task and Finish Groups. CONCLUSIONS: We co-produced and piloted a quality recognition programme to incentivise and celebrate continuous quality improvement in PPI. One public contributor stated, "I feel strongly that the Insight framework and awards will raise awareness of the [public involvement] work going on in many community settings. [It] is likely to result in better sharing of positive practice, incentivising research groups of any size to start work or to improve the quality of [PPI] could be one of the main benefits. I'm excited that if this initiative takes off, regionally and then in the longer term nationally, it could be a significant step in advancing the [public] voice."

How researchers involve members of the public in health research varies widely. We developed a scheme that encourages researchers from any health research organisation to improve the quality of public involvement. We used joint workshops with researchers, health workers and members of the public to design the scheme. We then tested it in three research organisations. We recorded the experience of people taking part in the project to learn what went well and what could be improved. We looked at the six areas covered in the UK Standards for Public Involvement. For each area, we worked together to define four levels (Welcoming, Listening, Learning, Leading) of increasing quality. We designed the materials needed for organisations to take part in the scheme. We also created a training pack for assessors and the format of a celebration event. We modified the materials after testing them. We asked those who took part in the project, half of whom were lay members, what they thought. 94% felt they had made at least some personal contribution. 89% said they were often or always able to express their views freely. Everyone thought the project would have some degree of impact. Overall, those members of the public who took part said they enjoyed the process and felt that their views were listened to. Along the way, we learned that it was important to carefully explain the project's aims, be clear about roles and have 1to1 discussions outside the main workshops.

Failure to control conventional cardiovascular risk factors in women with type 2 diabetes might explain worse mortality.

INTRODUCTION: The standardised mortality rate (SMR) for people with diabetes in England is 1.5-1.7, with differences in outcomes between sexes. There has been little work examining the factors that could have an impact on this or on what may determine sex differences in outcome. METHODS: Data were extracted for patients with type 2 diabetes (T2D) in Salford (England) in 2010 for the years up to 2020, including any deaths recorded. Expected deaths were calculated from annual Office of National Statistics mortality rate and life expectancy by age and gender, adjusted for the local Index of Multiple Deprivation (IMD). This provided the SMR deprivation (SMRd), and life expectancy years lost per death (LEYLD). The effects of treatment type, and clinical features on SMRd relative to sex were examined by univariable and multivariable analysis. RESULTS: Data from n = 11,806 (F = 5184; M = 6622) patients were included. Of these, n = 5540 were newly diagnosed and n = 3921 died (F = 1841; M = 2080). In total, n = 78,930 patient years. The expected deaths numbered n = 2596 (adjusted for age, sex, and IMD). Excess deaths were n = 1325 (F = 689; M = 636). Life expectancy years lost (LEYL) 18,989 (F = 9714; M = 9275). SMRd 1.51 (F = 1.60; M = 1.44) and LEYLD 4.84 years (F = 5.28; M = 4.46). The impact of risk factors was not different by sex. However, women had higher prevalence of % diagnosed >65 years of age; % last eGFR <60 mLs/min/1.73 m2 , and lower prevalence of % prescribed ACE-inhibitor/ARB, DPP4-inhibitor and SGLT2-inhibitor. Applying the male prevalence rate to the female population and expected mortality suggested n = 437 (55%) of excess T2D female deaths were attributed to sex difference in the prevalence of these risk and protective factors. CONCLUSIONS: Outcomes in women with T2DM are worse than in men, contributed to by greater prevalence of adverse factors and less prescribing of cardioprotective medication.

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 ß 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 Utility of Salivary Cortisone in the Overnight Dexamethasone Suppression Test in Adrenal Incidentalomas.

CONTEXT: Guidelines recommend the assessment of cortisol secretion in patients with adrenal incidentalomas (AI) using the overnight dexamethasone suppression test (ONDST). This requires attendance at a health care facility and venepuncture. Alternatively, the ONDST can be done by measuring salivary cortisol and cortisone, which can be collected at home. OBJECTIVE: We aimed to assess the utility of these measurements in patients with AI. METHODS: A retrospective analysis of data from 173 patients with AI who underwent an ONDST and salivary cortisol/cortisone diurnal studies. Serum and salivary cortisol and salivary cortisone were collected at 09:00, late night, and at 09:00 the following morning after dexamethasone. Dexamethasone levels were measured in the postdexamethasone samples. Serum and salivary samples were analyzed with liquid chromatography-tandem mass spectrometry. RESULTS: We identified a strong correlation between salivary cortisone and serum cortisol after 1 mg of dexamethasone (r = 0.95). Stepwise multivariate regression showed that postdexamethasone salivary cortisone, baseline serum cortisol, salivary cortisone suppression (predexamethasone/postdexamethasone ratio), and sex were the only significant or near-significant independent variables. Performance of predictive indices using these 4 parameters (sensitivity = 88.5%, specificity = 91.2%; kappa 0.80) and postdexamethasone salivary cortisone alone (sensitivity = 85.3%, specificity = 91.7%; kappa 0.77) were comparable when used to predict an ONDST serum cortisol of ≤50 nmol/L. No correlation was observed with any of the other measured parameters. CONCLUSION: In AI patients, after dexamethasone, salivary cortisone correlates very strongly with serum cortisol in the ONDST and could therefore be used as an alternative sampling method which does not require venepuncture or attendance at hospital.

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.

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.

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.

Adrenal Incidentaloma: Prevalence and Referral Patterns From Routine Practice in a Large UK University Teaching Hospital.

CONTEXT: Adrenal incidentalomas (AIs) are increasingly being identified during unrelated imaging. Unlike AI clinical management, data on referral patterns in routine practice are lacking. OBJECTIVE: This work aimed to identify factors associated with AI referral. METHODS: We linked data from imaging reports and outpatient bookings from a large UK teaching hospital. We examined (i) AI prevalence and (ii) pattern of referral to endocrinology, stratified by age, imaging modality, scan anatomical site, requesting clinical specialty, and temporal trends. Using key radiology phrases to identify scans reporting potential AI, we identified 4097 individuals from 479 945 scan reports (2015-2019). Main outcome measures included prevalence of AI and referral rates. RESULTS: Overall, AI lesions were identified in 1.2% of scans. They were more prevalent in abdomen computed tomography and magnetic resonance imaging scans (3.0% and 0.6%, respectively). Scans performed increased 7.7% year-on-year from 2015 to 2019, with a more pronounced increase in the number with AI lesions (14.7% per year).Only 394 of 4097 patients (9.6%) had a documented endocrinology referral code within 90 days, with medical (11.8%) more likely to refer than surgical (7.2%) specialties (P < .001). Despite prevalence increasing with age, older patients were less likely to be referred (P < .001). CONCLUSION: While overall AI prevalence appeared low, scan numbers are large and rising; the number with identified AI are increasing still further. The poor AI referral rates, even in centers such as ours where dedicated AI multidisciplinary team meetings and digital management systems are used, highlights the need for new streamlined, clinically effective systems and processes to appropriately manage the AI workload.

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.

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.

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.

Investigation of polycystic ovarian syndrome: variation in practice and impact on the speed of diagnosis.

OBJECTIVE: Accurate diagnosis of polycystic ovarian syndrome (PCOS) enables clinical interventions/cardiometabolic risk factor management. Diagnosis can take over 2 years and multiple clinician contacts. We examined patterns of PCOS-associated biochemical investigations following initial consultation prior to pelvic ultrasound scan (USS). METHODS: We determined in 206 women (i) the range of different biochemical test panels used in the diagnosis of PCOS in primary/secondary care prior to USS relative to national guidance in the UK and (ii) the relation between testing patterns and time to USS to highlight potential delays introduced by inappropriate testing. RESULTS: In these 206 women, 47 different test combinations were requested at initial venepuncture; only 7 (3%) had the test panel suggested in UK guidance (follicle-stimulating hormone/luteinizing hormone/testosterone/sex hormone-binding globulin/prolactin). The number of tests performed prior to USS varied from one test to all seven tests. There was an inverse relation between the number of biochemistry tests requested at initial venepuncture episode and 'time to scan'. Those who had <3 tests had a significantly longer time from first request to USS (median 70 days) than those with 3-7 tests (median 40 days; P = 0.002). One venepuncture episode prior to USS was associated with shorter 'time to scan' (median 29 days) than those with 2-4 episodes (median 255 days; P < 0.001). CONCLUSION: There was no identifiable pattern to biochemical investigations requested as part of the initial diagnostic evaluation in women with suspected PCOS. We recommend standardization of the initial biochemical panel of analytes for PCOS workup, with incorporation into hospital/general practice ordering software systems.

Recalibration of thinking about adrenocortical function assessment: how the 'random' cortisol relates to the short synacthen test results.

BACKGROUND: The short synacthen test (SST) is the most commonly performed investigation to assess adrenal function. Appropriate criteria for when an SST is performed are subject to debate. We investigated how random serum cortisol levels relate to SST response. METHODS: We examined random cortisol measurements taken between 04.40-23.55 p.m. results of SST baseline and 30-/60-min cortisol performed over 12 months (225 SSTs) at Salford Royal Hospital. Serum cortisol was measured on the Siemens Centaur Analyser.A 30-60-min cortisol concentration of ≥450 nmol/L defined a pass; 350-449 nmol/L defined borderline. RESULTS: Patients only proceeded to SST if random cortisol was <400 nmol/L. For those not on corticosteroids for at least 2 weeks, 42/43 (97.7%) cases with random cortisol concentration of ≥200 nmol/L had an SST 'pass'. The relation was less clear with corticosteroid treatment (19/35 cases; 54%).For those not taking glucocorticoid treatment (including inhaled/topical corticosteroids) in the previous 2 weeks, 91.8% of SSTs were pass/2.7% borderline/5.5% fail. For those on steroids, 51.9% of SSTs were a pass/11.4% were borderline.In relation to the postsynacthen cortisol pass cut-off of ≥450 nmol/L, in 15/207 (7.2%) of cases, the 60-min cortisol was ≥450 nmol/L (adequate adrenocortical function), but 30-min cortisol was below this. In all cases where the 30-min cortisol did indicate a pass (i.e. was ≥450 nmol/L) the 60-min cortisol was also ≥450 nmol/L. CONCLUSION: Our findings suggest that if the random cortisol level is ≥200 nmol/L, regardless of the time of day and the person was not taking corticosteroid treatment in the previous 2 weeks, SST may not be needed. Our data also suggests that 60-min cortisol retains utility.