Measurements used as inputs for machine learning algorithms (MLAs) and for calculating CDC risk score.
| Measurements used as inputs to MLA | Measurements used as inputs to CDC risk score | |
| Demographics | Age | Age |
| Sex | Sex | |
| Clinical measurements | BMI | None |
| Blood pressure (systolic and diastolic) | ||
| Laboratory values | Blood urea nitrogen | None |
| Creatinine | ||
| eGFR | ||
| Cholesterol (HDL and LDL) | ||
| White cell count | ||
| Medical history | Presence of past acute kidney injury | Cardiovascular disease |
| History of chronic heart failure | Congestive heart failure | |
| Reported smoking history | Peripheral vascular disease | |
| Reported alcohol history | Proteinuria | |
Demographics (age, sex), clinical measurements (BMI, blood pressure (systolic and diastolic)), laboratory values (blood urea nitrogen, creatinine and eGFR, cholesterol (high-density lipoprotein and low-density lipoprotein), white cell count), and medical history (presence of past acute kidney injury, history of chronic heart failure, reported smoking history, reported alcohol history) served as input features for the MLA models. The clinical and laboratory measurement values were pooled using 5th and 95th percentiles, median, and last available result over 1 year prior to T2DM diagnosis.
BMI, body mass index; CDC, Centers for Disease Control and Prevention; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; T2DM, type 2 diabetes mellitus.