Differences in Clinical Outcomes between Patients with and without Hypoglycemia during Hospitalization: A Retrospective Study Using Real-World Evidence

Acute kidney injury: a strong risk factor for hypoglycaemia in hospitalized patients with type 2 diabetes

AIMS

Acute kidney injury (AKI) is highly prevalent during hospitalization of patients with type 2 diabetes (T2D). We aimed to assess the impact of AKI and its severity and duration on the risk of hypoglycaemia in hospitalized patients with T2D.

METHODS

Retrospective cohort analysis of patients with T2D, admitted at a University Hospital in 2018-2019. AKI was defined as an increase in serum creatinine by ≥ 0.3 mg/dl (48 h) or ≥ 1.5 times baseline (7 days), and hypoglycaemia as blood glucose concentration < 70 mg/dl. Patients with chronic kidney disease stage ≥ 4 were excluded. We registered 239 hospitalizations with AKI and randomly selected 239 without AKI (control). Multiple logistic regression was used to adjust for confounding factors and ROC curve analysis to determine a cutoff for AKI duration.

RESULTS

The risk of hypoglycaemia was higher in the AKI group (crude OR 3.6, 95%CI 1.8-9.6), even after adjusting for covariates (OR 4.2, 95%CI 1.8-9.6). Each day of AKI duration was associated with a 14% increase in the risk of hypoglycaemia (95%CI 1.1-1.2), and a cutoff of 5.5 days of AKI duration was obtained for increased risk of hypoglycaemia and mortality. AKI severity was also associated with mortality, but showed no significant association with hypoglycaemia. Patients with hypoglycaemia had 4.4 times greater risk of mortality (95%CI 2.4-8.2).

CONCLUSIONS

AKI increased the risk of hypoglycaemia during hospitalization of patients with T2D, and its duration was the main risk factor. These results highlight the need for specific protocols to avoid hypoglycaemia and its burden in patients with AKI.

Adherence to healthy lifestyle behaviors as a preventable risk factor for severe hypoglycemia in people with type 2 diabetes: A longitudinal nationwide cohort study

AIMS/INTRODUCTION

We investigated the associations between a combination of lifestyle factors and changes to these factors and the subsequent risk of severe hypoglycemia (SH) in type 2 diabetes patients.

MATERIALS AND METHODS

Individuals with adult type 2 diabetes who underwent consecutive 2-year interval health screening programs from 2009 to 2012 from the Korean National Health Insurance Service database were included and followed up until 2018. Information on history of smoking status, alcohol consumption and physical activity, as well as changes to these factors, was obtained. The primary outcome was incident SH.

RESULTS

Of the 1,490,233 type 2 diabetes patients, 30,539 (2.1%) patients developed SH. Current smokers and heavy drinkers had increased risk of SH, compared with non-smokers and non-drinkers, respectively (hazard ratio 1.28, 95% confidence interval 1.23-1.34; hazard ratio 1.22, 95% confidence interval 1.15-1.30). However, regular physical activity was associated with reduced SH risk (hazard ratio 0.79, 95% confidence interval 0.77-0.82). A combination of unhealthy lifestyle habits was associated with increased SH risk in a dose-dependent fashion (P for trend <0.001). Compared with participants without changes in their unhealthy lifestyles, participants who improved lifestyles had decreased risk of SH.

CONCLUSIONS

Greater adherence to healthy lifestyle factors and any improvement in unhealthy lifestyle habits were associated with a substantially lower risk of SH in individuals with type 2 diabetes.

Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians

Personal medical information is an essential resource for research; however, there are laws that regulate its use, and it typically has to be pseudonymized or anonymized. When data are anonymized, the quantity and quality of extractable information decrease significantly. From the perspective of a clinical researcher, a method of achieving pseudonymized data without degrading data quality while also preventing data loss is proposed herein. As the level of pseudonymization varies according to the research purpose, the pseudonymization method applied should be carefully chosen. Therefore, the active participation of clinicians is crucial to transform the data according to the research purpose. This can contribute to data security by simply transforming the data through secondary data processing. Case studies demonstrated that, compared with the initial baseline data, there was a clinically significant difference in the number of datapoints added with the participation of a clinician (from 267,979 to 280,127 points, P < 0.001). Thus, depending on the degree of clinician participation, data anonymization may not affect data quality and quantity, and proper data quality management along with data security are emphasized. Although the pseudonymization level and clinical use of data have a trade-off relationship, it is possible to create pseudonymized data while maintaining the data quality required for a given research purpose. Therefore, rather than relying solely on security guidelines, the active participation of clinicians is important.

Hypoglycaemia and Cardiovascular Disease Risk in Patients with Diabetes

Hypoglycaemia represents an important side effect of insulin therapy and insulin secretagogues. It can occur in both type 1 and type 2 diabetes mellitus patients. Also, some associations between hypoglycaemia and cardiovascular (CV) risk have been reported. Several mechanisms may be involved, including the sympathoadrenal system, hypokalaemia, endothelial dysfunction, coagulation, platelets, inflammation, atherothrombosis and impaired autonomic cardiac reflexes. This narrative review discusses the associations of hypoglycaemia with CV diseases, including coronary heart disease (CHD), cardiac arrhythmias, stroke, carotid disease and peripheral artery disease (PAD), as well as with dementia. Severe hypoglycaemia has been related to CHD, CV and all-cause mortality. Furthermore, there is evidence supporting an association between hypoglycaemia and cardiac arrhythmias, potentially predisposing to sudden death. The data linking hypoglycaemia with stroke, carotid disease and PAD is limited. Several factors may affect the hypoglycaemia-CV relationships, such as the definition of hypoglycaemia, patient characteristics, co-morbidities (including chronic kidney disease) and antidiabetic drug therapy. However, the association between hypoglycaemia and dementia is bilateral. Both the disorders are more common in the elderly; thus, glycaemic goals should be carefully selected in older patients. Further research is needed to elucidate the impact of hypoglycaemia on CV disease.

Hospital care: improving outcomes in type 1 diabetes

PURPOSE OF REVIEW

Caring for patients with type 1 diabetes (T1D) in the hospital presents unique challenges. This review provides an update on significant issues relevant to the inpatient management of T1D. Topics include trends in diabetic ketoacidosis (DKA), hypoglycemia, and adapting ambulatory technologies for inpatient use.

RECENT FINDINGS

Rates of DKA in the United States are rising. Although socioeconomic status, health insurance coverage, and hemoglobin A1c are persistently associated with DKA in individuals with T1D, newer risk factors have also emerged. These include the off-label use of sodium-glucose cotransporter inhibitor medications, immune checkpoint inhibitor-induced diabetes, and infection with severe acute respiratory syndrome coronavirus 2. Hypoglycemia is common among hospitalized patients with T1D. Use of validated hypoglycemia risk prediction models and multidisciplinary care initiatives can reduce the risk of inpatient hypoglycemia. Finally, continuous glucose monitoring is being adapted for use in the hospital setting and has shown promise during the coronavirus disease 2019 (COVID-19) pandemic.

SUMMARY

Evidence-based treatment algorithms, risk prediction calculators, multidisciplinary interventions, and wearable technology hold promise for improved outcomes in hospitalized patients with T1D.

Predicting hypoglycemia in hospitalized patients with diabetes: A derivation and validation study

AIMS

Develop and validate a model for predicting hypoglycemia in inpatients.

METHODS

Derivation cohort: patients treated with hypoglycemic drugs and admitted to the departments of medicine of a university hospital during 2016.

VALIDATION

patients admitted to a community hospital, and patients admitted to a university hospital in the north of Israel, 2017-2018. Data available in the electronic patient record (EPR) during the first hours of hospital stay were used to develop a logistic model to predict the probability of hypoglycemia. The performance of the model was measured in the validation cohorts.

RESULTS

In the derivation cohort, hypoglycemia was measured in 474 out of 3605 patients, 13.1%. The logistic model to predict hypoglycemia included age, nasogastric or percutaneous gastrostomy tube, Charlson score, vomiting, chest pain, acute renal failure, insulin, hemoglobin and diastolic blood pressure. The area under the ROC curve (AUROC) was 0.71 (95% CI 0.69-0.73). In the highest probability group the percentage of hypoglycemia was 24.3% (258/1061). In the two validation groups hypoglycemia was measured in 269/2592 patients (11.1%); and 393/3635 (10.8%). AUROC was 0.72 (95% CI 0.68-0.76); and 0.71 (95% CI 0.68-0.74). In the highest probability groups hypoglycemia was measured in 28.1% (111/395); and 23.0% (211/909) of patients.

CONCLUSIONS

The derived model performed well in the validation cohorts. Assuming that most of the hypoglycemia episodes could be prevented we would need to invest efforts to avoid hypoglycemia in 4-5 patients to prevent one episode of hypoglycemia.