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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