Table 1:

The variables in the Wong et al. dataset [15], their type and their representation in the logistic regression and Cox-survival model. Numeric variables (age, BMI, Hba1c) were grouped and one-hot-encoded, while categorical variables (Gender, Ethnicity, and Race) were one-hot-encoded. To avoid collinearity, when one-hot-encoding a predictor variable, the binary predictor representing the largest group was left out for reference (marked with “used for reference” in the table). For each predictor group, the table also reports the percentage of missing cases, if any.

Predictor Group and predictor type	Predictor	percentage of missing values	all cases
Number of cases (%)			56123 (100%)
Gender One-hot-encoded categorical variable	Male		49%
	Female (used for reference)		51%
Age Grouped and one-hot-encoded numeric variable			61.88 ± 0.06 [18,89]
	age < 40		7%
	40 ≤ age < 50		11%
	50 ≤ age < 60		22%
	60 ≤ age < 70 (used for reference)		28%
	70 ≤ age < 80		22%
	age ≥ 80		10%
BMI Grouped and one-hot-encoded numeric variable		29%	33.25 ± 0.04 [12.13,79.73]
	BMI < 20		1%
	20 ≤ BMI < 25		8%
	25 ≤ BMI < 30		18%
	30 ≤ BMI < 35 (used for reference)		18%
	35 ≤ BMI < 40		12%
	BMI ≥ 40		13%
Race One-hot-encoded categorical variable	White (used for reference)	15%	55%
	Other		1%
	Black		26%
	Asian		3%
Ethnicity One-hot-encoded categoric variable	Hispanic	12%	16%
	Not hispanic (used for reference)		73%
Hba1c Grouped and one-hot-encoded numeric variable			7.58 ± 0.01 [4.1,19.3]
	Hba1c < 6		17%
	6 ≤ Hba1c < 7 (used for reference)		30%
	7 ≤ Hba1c < 8		21%
	8 ≤ Hba1c < 9		12%
	9 ≤ Hba1c < 10		07%
	Hba1c ≥ 10		12%
Comorbidities Binary variables; 1 = has comorbidity 0 = does not have comorbidity	MI		13%
	CHF		23%
	PVD		21%
	Stroke		17%
	Dementia		5%
	Pulmonary		31%
	liver mild		16%
	liver severe		3%
	Renal		30%
	Cancer		14%
	Hiv		1%
Treatments Binary variables; 1 = has comorbidity 0 = does not have comorbidity	Metformin		26%
	dpp4		5%
	sglt2		5%
	Glp		7%
	Tzd		1%
	Insulin		25%
	Sulfonylurea		9%