Skip to main content

Table 1 Details of studies developing CKD prediction models that were included for external validation

From: An external validation of models to predict the onset of chronic kidney disease using population-based electronic health records from Salford, UK

Authors [ref]
Publication year
Study design/Study context
Study period
Ethnicity Age range Population size Number (%) of CKD cases Type of models
Time horizon
Handling of missing values
Method of internal validation
Definition of CKD Predictors in model
Bang et al. [54]
2007
Cross-sectional population-based survey/Screening programme
1999-2002
US, mixed
20–85 years
8530
601 (7.5 %)
Logistic
2 years
Excluded
Random split-sample
At least one eGFR measurement < 60a Age, sex, anaemia, proteinuriaa, hypertension, diabetes mellitus, history of cardiovascular disease, history of heart failure, peripheral vascular disease
Chien et al. [51]
2010
Prospective cohort study/ Secondary care
2003
Taiwan, Chinese
51.2 years (mean)
5168
190 (3.7)
Cox
4 years
NR
NR
At least one eGFR measurement < 60a Age, BMI, diastolic blood pressure, type 2 diabetes, history of stroke
Hippisley-Cox and Coupland (QKidney®) [36]
2010
Prospective cohort population based/Primary care
2002-2008
UK, mixed
35–74 years
1,591,884
23,786 (1.5 %)
Cox
5 years
Multiple imputation
Random split-sample
At least one eGFR measurement < 45a, kidney transplant; dialysis; nephropathy diagnosis; proteinuria Age, ethnicity, deprivation, smoking, BMI, systolic blood pressure, diabetes mellitus, rheumatoid arthritis, cardiovascular disease, treated hypertension, congestive cardiac failure, peripheral vascular disease, NSAID use, and family history of kidney disease
Kshirsagar et al. [53]
2008
Prospective cohort study/
Community-based
1987-1989
US, white and black
45–64 years
9470
1605 (16.9 %)
Logistic
9 years
NR
Random split sample
At least one eGFR measurement < 60a Age, sex, anaemia, hypertension, type 2 diabetes mellitus, history of cardiovascular disease, history of heart failure, peripheral vascular disease
Kwon et al. [55]
2012
Cross-sectional survey/ Population-based
2007-2009
Korean, Asian
≥19 years
6565
100 (1.5 %)
Logistic
1 year
Excluded
Split sample
At least one eGFR measurement < 60a Age, sex, anaemia, proteinuriaa, hypertension, type 2 diabetes mellitus, history of cardiovascular disease
O’Seaghdha et al. [52]
2011
Prospective cohort study/ Population-based
1995-2008
US white
45–64 years
2490
229 (9.2 %)
Logistic
10 years
Excluded
Bootstrap
At least one eGFR measurement < 60a Age, hypertension, diabetes mellitus
Thakkinstian et al. [56]
2011
Cross-sectional survey/
Community-based
NR
Thailand-Asian
≥ 18 years
3459
606 (17.5 %)
Logistic
1 year
NR
Bootstrap
At least one eGFR measurement < 90a Age, hypertension, diabetes mellitus, kidney stones
  1. BMI, body mass index; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate, NR, not reported; NSAID, non-steroidal inflammatory drugs; US, United States
  2. aPredictor not included in external validation due to missing data in our dataset
\