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

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm

BACKGRUOUND

Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea.

METHODS

To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary's Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset.

RESULTS

The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036).

CONCLUSION

GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.

Long-Term Risk of Cardiovascular Disease Among Type 2 Diabetes Patients According to Average and Visit-to-Visit Variations of HbA1c Levels During the First 3 Years of Diabetes Diagnosis

BACKGROUND

It remains unclear whether a combination of glycemic variability and glycated hemoglobin (HbA1c) status leads to a higher incidence of cardiovascular disease (CVD). Therefore, to investigate CVD risk according to the glucose control status during early diabetes, we examined visit-to-visit HbA1c variability among patients with type 2 diabetes (T2DM).

METHODS

In this 9-year retrospective study, we measured HbA1c levels at each visit and tracked the change in HbA1c levels for 3 years after the first presentation (observation window) in newly diagnosed T2DM patients. We later assessed the occurrence of CVD in the last 3 years (target outcome window) of the study period after allowing a 3-year buffering window. The HbA1c variability score (HVS; divided into quartiles, HVS_Q1-4) was used to determine visit-to-visit HbA1c variability.

RESULTS

Among 4,817 enrolled T2DM patients, the mean HbA1c level was < 7% for the first 3 years. The group with the lowest HVS had the lowest rate of CVD (9.4%; 104/1,109 patients). The highest incidence of CVD of 26.7% (8/30 patients) was found in HVS [≥ 9.0%]_Q3, which was significantly higher than that in HVS [6.0-6.9%]_Q1 (P = 0.006), HVS [6.0-6.9%]_Q2 (P = 0.013), HVS [6.0-6.9%]_Q3 (P = 0.018), and HVS [7.0-7.9%]_Q3 (P = 0.040).

CONCLUSION

To our knowledge, this is the first long-term study to analyze the importance of both HbA1c change and visit-to-visit HbA1c variability during outpatient visits within the first 3 years. Lowering glucose levels during early diabetes may be more critical than reducing visit-to-visit HbA1c variability.

Long-Term Changes in HbA1c According to Blood Glucose Control Status During the First 3 Months After Visiting a Tertiary University Hospital

BACKGROUND

We evaluated patients visiting a tertiary university hospital due to a diagnosis of diabetes with a goal of achieving blood glucose control and evaluated blood glucose persistence over 7 years according to the change in blood glucose evident at 3 months after the first visit.

METHODS

Patients treated from 2009 to 2013 were categorized into four groups according to the change in HbA1c levels during the first 3 months of follow-up (Best_group, ≥ 1.6% decrease; Better_group, 0.5-1.5% decrease; Neutral_group, maintained at -0.4% to +0.4%; Worse_group, ≥ 0.5% increase). Each patient's blood glucose control status was then monitored for 7 years. The incidence of stroke and acute coronary syndrome during this period was confirmed.

RESULTS

Overall, 9,776 patients were included. HbA1c values were lower in the Best_group than in the other groups at all time points (all P < 0.001). The rate of reaching targets of < 6.5% or < 7.0% HbA1c decreased over time; the rate at which the estimated glomerular filtration rate decreased to < 30 or < 60 mL/min/1.73m² increased over time (all trends, P < 0.01).

CONCLUSION

Blood glucose control status in the first 3 months after initiating hospital care enabled estimation of the patient's glycemic control status for the next 7 years. In cases with poor initial blood glucose control, a new or more active method of blood glucose control should be sought.

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.