Diabetes screening intervals based on risk stratification

Cost-effectiveness analysis for HbA1c test intervals to screen patients with type 2 diabetes based on risk stratification

BACKGROUND

The best HbA1c test interval strategy for detecting new type 2 diabetes mellitus (T2DM) cases in healthy individuals should be determined with consideration of HbA1c test characteristics, risk stratification towards T2DM and cost effectiveness.

METHODS

State transition models were constructed to investigate the optimal screening interval for new cases of T2DM among each age- and BMI-stratified health individuals. Age was stratified into 30-44-, 45-59-, and 60-74-year-old age groups, and BMI was also stratified into underweight, normal, overweight and obesity. In each model, different HbA1c test intervals were evaluated with respect to the incremental cost-effectiveness ratio (ICER) and costs per quality-adjusted life year (QALY). Annual intervals (Japanese current strategy), every 3 years (recommendations in US and UK) and intervals which are tailored to each risk stratification group were compared. All model parameters, including costs for screening and treatment, rates for complications and mortality and utilities, were taken from published studies. The willingness-to-pay threshold in the cost-effectiveness analysis was set to US $50,000/QALY.

RESULTS

The HbA1c test interval for detecting T2DM in healthy individuals varies by age and BMI. Three-year intervals were the most cost effective in obesity at all ages-30-44: $15,034/QALY, 45-59: $11,849/QALY, 60-74: $8685/QALY-compared with the other two interval strategies. The three-year interval was also the most cost effective in the 60-74-year-old age groups-underweight: $11,377/QALY, normal: $18,123/QALY, overweight: $12,537/QALY-and in the overweight 45-59-year-old group; $18,918/QALY. In other groups, the screening interval for detecting T2DM was found to be longer than 3 years, as previously reported. Annual screenings were dominated in many groups with low BMI and in younger age groups. Based on the probability distribution of the ICER, results were consistent among any groups.

CONCLUSIONS

The three-year screening interval was optimal among elderly at all ages, the obesity at all ages and the overweight in 45-59-year-old group. For those sin the low-BMI and younger age groups, the optimal HbA1c test interval could be longer than 3 years. Annual screening to detect T2DM was not cost effective and should not be applied in any population.

The potential for overdiagnosis and underdiagnosis because of blood pressure variability: a comparison of the 2017 ACC/AHA, 2018 ESC/ESH and 2019 NICE hypertension guidelines

OBJECTIVE

To estimate the extent that BP measurement variability may drive over- and underdiagnosis of 'hypertension' when measurements are made according to current guidelines.

METHODS

Using data from the National Health and Nutrition Examination Survey and empirical estimates of within-person variability, we simulated annual SBP measurement sets for 1 000 000 patients over 5 years. For each measurement set, we used an average of multiple readings, as recommended by guidelines.

RESULTS

The mean true SBP for the simulated population was 118.8 mmHg with a standard deviation of 17.5 mmHg. The proportion overdiagnosed with 'hypertension' after five sets of office or nonoffice measurements using the 2017 American College of Cardiology guideline was 3-5% for people with a true SBP less than 120 mmHg, and 65-72% for people with a true SBP 120-130 mmHg. These proportions were less than 1% and 14-33% using the 2018 European Society of Hypertension and 2019 National Institute for Health and Care Excellence guidelines (true SBP <120 and 120-130 mmHg, respectively). The proportion underdiagnosed with 'hypertension' was less than 3% for people with true SBP at least 140 mmHg after one set of office or nonoffice measurements using the 2017 American College of Cardiology guideline, and less than 18% using the other two guidelines.

CONCLUSION

More people are at risk of overdiagnosis under the 2017 American College of Cardiology guideline than the other two guidelines, even if nonoffice measurements are used. Making clinical decisions about cardiovascular prediction based primarily on absolute risk, minimizes the impact of blood pressure variability on overdiagnosis.

The minimal informative monitoring interval of N-terminal pro-B-type natriuretic peptide in patients with stable heart failure

Background

N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a potential biomarker for monitoring the status of heart failure. However, the optimal monitoring interval of NT-proBNP is unknown. This study sought to investigate the minimal informative monitoring interval of NT-proBNP in patients with stable chronic heart failure.

Methods

This retrospective cohort study included patients who were admitted due to heart failure and subsequently followed with serial NT-proBNP measurements in a tertiary hospital. We analyzed NT-proBNP measured between six months after discharge and the earliest timepoint of: an alteration of medication regimen, readmission due to worsening of heart failure, or all-cause death. To distinguish progression of the disease from biological variability and measurement error, the signal-to-noise ratio method was applied with a random-effects model.

Results

In the 368 patients included, NT-proBNP was measured for a median 6 times. In the random-effects model, signal (progression of disease) exceeded noise (biological variability and measurement error) at 7.9 months (95% confidence interval [CI]: 5.1–9.6), while noise corresponded to a 61% increase from baseline. In stratified analysis using the AHEAD risk score, the minimal informative monitoring interval shortened as the risk score increased (0–1 point: 12.2 months [95%CI: 10.3–14.4]; 2–3 points: 8.0 months [95%CI: 6.8–9.7]; 4–5 points: 3.3 months [95%CI: 3.0–3.8]).

Conclusions

In patients with stable chronic heart failure, the minimal informative monitoring interval of NT-proBNP measurement was 7.9 months in the current population, which varied with underlying risks. The optimal monitoring interval could be lengthened for patients at lower risks.

HbA1c monitoring interval in patients on treatment for stable type 2 diabetes. A ten-year retrospective, open cohort study

[Aims] This study aims to suggest an informative interval for HbA1c in DM patients with stable glycemic control, based on test characteristics of the HbA1C assay using the signal-to-noise ratio method. [Methods] This was a retrospective, open cohort study. Data were collected between January 2005 to December 2014 at a tertiary-level community hospital in Japan. All adult patients aged under 75 years, with stable glycemic control on a first pharmaceutical regimen for Type II diabetes, and at least two HbA1c measurements after they achieved glycemic stability, were included in the analysis. We defined stable glycemic control as HbA1c <7.0% (52 mmol/mol) and requiring no change in the medication regimen after three consecutive measurements. We adapted a signal-to-noise method for distinguishing true change from measurement error by constructing a linear random effects model to calculate signal and noise for HbA1c. The screening interval for HbA1c was defined as informative when the signal-to-noise ratio exceeded 1. [Results] Among 1066 adults with diabetes, 639 patients (18.5%) were identified as achieving stable glycemic control (511 male (67.3%)), with a mean HbA1c (SD) of 6.4 (0.4)% (46 mmol/mol). Patients with stable glycemic control increase their HbA1c 0.27% (3 mmol/mol) every year while HbA1c has 0.32% (3.5 mmol/mol) noise, as testing characteristics. Signal exceeds noise after 1.2 years (95%CI: 0.9-1.6). [Conclusion] Once patients achieve stable glycemic control at their HbA1c goal, an informative interval for HbA1c monitoring is once every year. Current guidelines, which suggest testing every six months, may contribute to substantial over-testing.