Prevalence of admission plasma glucose in 'diabetes' or 'at risk' ranges in hospital emergencies with no prior diagnosis of diabetes by gender, age and ethnicity

The potential for utilising in-hospital glucose measurements to detect individuals at high risk of previously undiagnosed diabetes: Retrospective cohort study

BACKGROUND

Many people with undiagnosed diabetes have hyperglycaemia when admitted to hospital. Inpatient hyperglycaemia can be an indication of diabetes mellitus but can also indicate a stress response. This study reports the extent to which an in-hospital maximum observed random glucose measurement is an indicator of the need for in-hospital (or subsequent) HbA1c measurement to look for undiagnosed diabetes.

METHODS

Blood glucose, HbA1c, age and sex were collected for all adults following admission to a UK NHS trust hospital from 1 January 2019 to 31 December 2020. We restricted the analysis to those participants who were registered with a GP practice that uses the trust laboratory and who had at least some tests requested by those practices since 2008. We stratified individuals according to their maximum in-hospital glucose measurement and report the number of these with HbA1c measurement ≥48 mmol/mol (6.5%) prior to the index admission, and during and after admission. We calculated an estimated proportion of individuals in each blood glucose stratum without a follow-up HbA1c who could have undiagnosed diabetes.

RESULTS

In toal, 764,241 glucose measurements were recorded for 81,763 individuals who were admitted to the Oxford University Hospitals Trust. The median (Q1, Q3) age was 70 (56, 81) years, and 53% were males. Of the population, 70.7% of individuals declared themselves to be of White ethnicity, 3.1% of Asian background, and 1.1% of Black background, with 23.1% unstated. Of those individuals, 22,375 (27.4%) had no previous HbA1c measurement recorded. A total of 1689 individuals had a diabetes-range HbA1c during or after their hospital admission (2.5%) while we estimate an additional 1496 (2.2%) may have undiagnosed diabetes, with the greatest proportion of these having an in-hospital glucose of ≥15 mmol/L. We estimate that the number needed to detect a possible new case of diabetes falls from 16 (in-hospital glucose 8 mmol/L to <9 mmol/L) to 4 (14 mmol/L to <15 mmol/L).

CONCLUSION

The number of people who need to be tested to identify an individual who may have diabetes decreases as a testing threshold based on maximum in-hospital glucose concentration increases. Among those with hyperglycaemia and no previous HbA1c measurement in the diabetes range, there appears to be a lack of subsequent HbA1c measurement. This work identifies the potential for integrating the testing and follow-up of people, with apparently unrecognised hospital hyperglycaemia across primary and secondary care.

Machine Learning Models for Inpatient Glucose Prediction

PURPOSE OF REVIEW

Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory.

RECENT FINDINGS

The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

Safety and Efficacy of Inpatient Diabetes Management with Non-insulin Agents: an Overview of International Practices

PURPOSE OF REVIEW

The field of inpatient diabetes has advanced significantly over the last 20 years, leading to the development of personalized treatment approaches. However, outdated guidelines still recommend the use of basal-bolus insulin therapy as the preferred treatment approach, and against the use of non-insulin anti-hyperglycemic agents.

RECENT FINDINGS

Several observational and prospective randomized controlled studies have demonstrated that oral anti-hyperglycemic agents are widely used in the hospital, including studies of DPP-4 agents and GLP-1 agonists. With advances in the field of inpatient diabetes management, a paradigm shift has occurred, from an approach of recommending "basal-bolus regimens" for all patients to a more precision medicine option for hospitalized non-critically ill patients with type 2 diabetes.

Assessment of glycaemic status in adult hospital patients for the detection of undiagnosed diabetes mellitus: A systematic review

AIM

In-hospital blood glucose testing is commonplace, particularly in acute care. In-hospital screening for hyperglycaemia may present a valuable opportunity for early diabetes diagnosis by identifying at-risk individuals. This systematic review investigates the extent to which random blood glucose testing in acute and inpatient hospital settings predicts undiagnosed diabetes.

METHODS

Two databases were systematically searched for studies in which adult patients received an in-hospital random blood glucose test, followed by a diagnostic HbA1c test. The primary outcome was the proportion of hyperglycaemic individuals diagnosed with diabetes by HbA1c.

RESULTS

A total of 3245 unique citations were identified, and 12 were eligible for inclusion. Ten different blood glucose thresholds, ranging from 5.5 to 11.1 mmol/L, were used to detect hyperglycaemia, indicating that there is no consistent clinical definition for hyperglycaemia. The proportion of participants with hyperglycaemia in each study ranged from 3.3% to 62.1%, with a median (Q₁ , Q₃ ) of 34.5% (5.95%, 61.1%). The proportion of hyperglycaemic participants found to have a diabetes-range HbA1c varied from 4.1% to 90%, with a median (Q₁ , Q₃ ) of 18.9% (11.5%, 61.1%). Meta-analysis was not possible due to substantial heterogeneity between study protocols.

CONCLUSIONS

All studies consistently identified a proportion of hyperglycaemic hospital patients as having a diabetes-range HbA1c, showing that in-hospital blood glucose screening can facilitate diabetes diagnosis. The proportion of hyperglycaemic participants with undiagnosed diabetes varied substantially, indicating a need for further research and consistency in defining in-hospital hyperglycaemia. This may aid the development of a standardised screening protocol to identify people with possible undiagnosed diabetes.

Basic types of the first-day glycemia in acute myocardial infarction: Prognostic, diagnostic, threshold and target glycemia

We described the importance of stress hyperglycemia (SH) in critical illnesses and their evaluation in the emergency department (ED) and coronary care unit (CCU). Hyperglycemia is found in over half of the patients with suspected acute myocardial infarction (AMI). SH can be used for several purposes in AMI. Receiver operating characteristic curves are needed to find optimal cut-offs to divide blood glucose levels associated with good from bad prognosis in AMI. There is a need for a consensus for pragmatic classification of first day glycemia in order to be useful in a busy ED and CCU.