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Table 2 Patient characteristics (N = 135)

From: Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease

Characteristics

n (%) or median (IQR)

Age (years)

71 (60–79)

Sex

 

Male

87 (64%)

Female

48 (36%)

Observation period (days)

496 (160–1207)

CKD etiology

 

Diabetic nephropathy (DN)

52 (38%)

Nephrosclerosis (NS)

42 (31%)

Chronic glomerulonephritis (CGN)

17 (12%)

Polycystic kidney disease (PKD)

5 (3%)

Other

19 (16%)

Comorbidities

 

Diabetes mellitus

60 (44%)

Hypertension

130 (96%)

Dyslipidemia

97 (72%)

Hyperuricemia

70 (52%)

Heart failure with reduced ejection fraction

9 (7%)

Ischemic heart disease

12 (9%)

Cerebrovascular disease

16 (12%)

Peripheral vascular disease

5 (4%)

Medication at initial visit

 

Renin-angiotensin system inhibitors

95 (70%)

Mineralocorticoid-receptor antagonists

9 (7%)

Sodium-glucose cotransporter 2 inhibitors

4 (3%)

Glucagon-like peptide-1 receptor agonist

8 (6%)

Statin

81 (22%)

Uric acid-lowering drug

69 (51%)

AST-120

30 (22%)

Erythropoiesis stimulating agent

18 (13%)

Sodium bicarbonate

7 (5%)

Cr at RRT initiation (mg/dL)

7.3 (6.0–8.7)

eGFR at RRT initiation (ml/min/1.73 m2)

6.2 (4.7–7.6)

  1. Categorical variables are presented as n (%) and continuous variables as median (IQR).
  2. N: total number of people, n: number of items, IQR: interquartile range