Can we predict when to start renal replacement therapy in patients with chronic kidney disease using 6 months of clinical data?

We aimed to develop a model of chronic kidney disease (CKD) progression for predicting the probability and time to progression from various CKD stages to renal replacement therapy (RRT), using 6 months of clinical data variables routinely measured at healthcare centers. Data were derived from the el...

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Veröffentlicht in:	PloS one 2018-10, Vol.13 (10), p.e0204586-e0204586
Hauptverfasser:	Lee, Min-Jeong, Park, Joo-Han, Moon, Yeo Rae, Jo, Soo-Yeon, Yoon, Dukyong, Park, Rae Woong, Jeong, Jong Cheol, Park, Inwhee, Shin, Gyu-Tae, Kim, Heungsoo
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Aged Aged, 80 and over Biology and Life Sciences Calcium Calcium (blood) Chronic kidney failure Comorbidity Development and progression Diabetes Diabetes mellitus Diagnosis Diagnostic systems Disease Progression Electronic Health Records Electronic medical records Epidemiology Epidermal growth factor receptors Female Glaucoma Glomerular filtration rate Health care Health informatics Hemoglobin Hospitals Humans Integrals Kidney diseases Kidneys Laboratories Male Mathematical models Medical records Medicine Medicine and Health Sciences Middle Aged Missing data Models, Biological Nephrology Patients Peritoneal dialysis Phosphorus Physical Sciences Polycystic kidney Population Population studies Potassium Prediction models Prognosis Proteins Random Allocation Renal Insufficiency, Chronic - diagnosis Renal Insufficiency, Chronic - physiopathology Renal Insufficiency, Chronic - therapy Renal Replacement Therapy Research and Analysis Methods Retrospective Studies Risk Risk Assessment - methods Serum albumin Sex Test sets Therapy Time Factors Training Urea Urine Young Adult
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