Challenges in predictive modelling of chronic kidney disease: A narrative review

Table 1 Hypothetical data format for the use of logistic regression model for chronic kidney disease

ID	Age (year)	Gender	eGFR1 (mL/min/1.73 m²)	eGFR_grade11	eGFR2 (mL/min/1.73 m²)	eGFR_grade21	Chronic kidney disease2
1	58	0	88.08	2	103.68	1	0
2	48	1	107.51	1	88.90	2	0
3	37	1	94.28	1	88.12	2	0
4	58	0	93.17	1	87.06	2	0
5	53	0	58.42	3	51.35	3	1
6	37	0	95.73	1	108.90	1	0
7	43	1	84.51	2	97.25	1	0
8	49	0	100.02	1	97.84	1	0
9	33	1	105.80	1	98.74	1	0
10	53	0	108.04	1	104.39	1	0
11	46	1	106.05	1	89.04	2	0
12	59	0	114.62	1	106.81	1	0
13	60	0	121.17	1	88.75	2	0
14	40	0	101.23	1	103.60	1	0
15	55	1	114.35	1	90.59	1	0
16	55	1	90.07	1	119.00	1	0
17	42	1	86.74	2	157.50	1	0
28	43	0	47.93	3	55.74	3	1
29	41	0	97.79	1	102.74	1	0
30	30	1	117.68	1	77.65	1	0

¹> 2 ordered categories – ordinal logistic regression.

²2 categories – binary logistic regression.

1 coded for male and 0 coded for female. eGFR: Estimated glomerular filtration rate.