Use of disease embedding technique to predict the risk of progression to end-stage renal disease

Prediction models used in the progression of chronic kidney disease: A scoping review

“…It was generally observed that regardless of the model used, the predicted outcome included measurement of risk towards ESKD which were defined as [ 41 , 43 , 44 , 48 – 50 , 53 , 56 – 60 , 64 , 69 , 70 , 72 ]:…”

“…Seven studies used machine learning (ML) methods [ 9 , 67 – 71 ], and one compared the performance among a number of ML techniques [ 70 ]. One study developed a model using Random Forest regression [ 68 ], and another study implemented a disease2disease model by first learning the International Classification of Diseases and then clustering the data into groups by considering the variables within the dataset [ 69 ]. A multistate marginal structural model (MS-MSM) was also developed in one study that considers an estimated effect of time-dependent variables towards the predicted outcome [ 72 ].…”

“…The eGFR formula was also not consistent across studies, 13 studies used the CKD Epidemiology Collaboration (CKD-EPI) equation [ 9 , 41 , 42 , 44 , 46 , 51 , 54 – 56 , 58 , 63 , 65 , 66 ] and 9 studies used the Modification of Diet in Renal Disease (MDRD) equation [ 43 , 45 , 47 , 49 , 53 , 60 , 62 , 64 , 71 ]. Two studies used unique equations customised for their specific cohort [ 48 , 69 ]. There were also 9 studies that did not specify the formula that they used to calculate the eGFR.…”

“…The smallest study [ 50 ] had 43 participants and the largest study included over 300,000 patient records [ 54 ]. Included study populations were from the United States [ 45 , 52 – 56 , 60 , 68 , 69 , 72 ], Canada [ 41 , 46 , 58 , 59 ], Taiwan [ 9 , 42 , 70 ], Germany [ 64 , 66 ], Japan [ 48 , 50 , 61 , 67 ], France [ 43 ], Croatia [ 44 ], Korea [ 49 , 51 ], United Kingdom [ 62 ], Iran [ 71 ], Romania [ 65 ], Spain [ 63 ], Netherlands [ 47 ] and China [ 57 ].…”

“…These studies that investigated on CKD progression used data records that were sourced from all levels of healthcare. Data records ranged from single medical facilities at a local level [ 58 ], to tertiary hospitals [ 50 , 58 , 59 , 62 , 67 , 71 ], and to databases that were linked nationwide [ 42 , 60 , 69 , 72 ]. The populations were also selected based on a particular comorbidity of interest, for example, polycystic kidney disease [ 41 , 43 , 63 ], ANCA associated vasculitis (AAV) [ 44 ], diabetes [ 46 ] or other cardiovascular conditions [ 53 , 56 , 67 ].…”