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Table 1 Survey items (35 items)

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

Data types

Items

Patient background

age*, sex*, height*, weight*, CKD etiology*

Laboratory data

red blood cell (RBC)*, hemoglobin (Hb)*, hematocrit (Ht)*, mean corpuscular volume (MCV)*, mean corpuscular hemoglobin concentration (MCHC)*, total lymphocyte count (TLC), albumin(Alb)*, choline esterase (ChE), uric acid (UA), blood urea nitrogen (BUN)*, creatinine (Cr)*, estimated glomerular filtration rate (eGFR)*, sodium (Na)*, potassium (K)*, chlorine (Cl)*, calcium (Ca) *, phosphorus (P) *, glucose (Glu), hemoglobin A1c (HbA1c), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), ferritin, iron (Fe), unsaturated iron binding capacity (UIBC), intact parathyroid hormone (i-PTH), hydrogen carbonate ion (HCO3-), urinary occult blood (UOb), urinary protein to creatinine ratio (UP/UCr)*, estimated urinary salt excretion (UNaCl)

  1. *Final 20 items used for analysis