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Table 2 Key characteristics for constructing amputation prediction models in diabetic foot patients

From: Prediction models for amputation after diabetic foot: systematic review and critical appraisal

First author year

Candidate variables

Missing data

Variable selection methods

None

Type of validation

Model evaluation

Calibration method

No

Continuous variables processing method

EPV

No

Processing methods

Chen 2023

14

Remain unaltered

1.786

Missing were excluded, analysis with complete data

CR

Internal

Random split validation

None

Li 2023

31

Remain unaltered

0.484

Missing were excluded, analysis with complete data

VIMP

Internal

Bootstrap

H–L test

Yang 2023

32

Remain unaltered

6.656

< 10% of quantitative data

Missing values > 40% were deleted, Median was used for missing quantitative data

FSR, Pruning algorithm

Internal

Tenfold cross validation

H–L test

Stefanopoulos 2022

36

Some converted to categorical variables

53.556

Missing were excluded, analysis with complete data

Lasso regression

Internal

Random split validation

None

Wang 2022

21

All converted to categorical variables

3.571

Missing were excluded, analysis with complete data

LR

Internal

Tenfold cross validation

None

Xie 2022

37

Remain unaltered

1.919

0.270

Model automatically handle

None

Internal

Fivefold cross validation

H–L test, calibration curve

Du 2021

31

Some converted to categorical variables

0.194

None

Internal

Threefold cross validation

None

Li 2021

44

Z-score standardization

2.682

70

Delete of features with > 60% missing values. The rest were imputed using median, mean, mode, fixed value, or KNN

RF-RFE, mRMR, JMI, JMIM, original

Internal

Fivefold cross validation

None

Peng 2021

21

Remain unaltered

2.762

Sever missing values were excluded

FSR

Internal

Bootstrap

H–L test, Calibration curve

Hüsers 2020

7

Remain unaltered

10.714

4.143

LTFU:16

Analysis with complete data

None

None

None

Lin 2020

33

Remain unaltered

Analysis with complete data

CR

Internal

Random split validation

None

Vera-Cruz 2020

NA

Some converted to categorical variables

NA

None

NA

External

Spatial validation

None

Chetpet 2018

13

All converted to categorical variables

3.384

LTFU:21

Analysis with complete data

None

None

None

Chen 2018

33

Some converted to categorical variables

1.121

CR

Internal

Random split validation

None

Jeon 2017

NA

Remain unaltered

NA

LTFU:21

Analysis with complete data

NA

External

Spatial validation

None

Kasbekar 2017

17

Remain unaltered

4.882

Missing were excluded, analysis with complete data

Internal

Random split validation

None

Monteiro-Soares 2015

NA

Remain unaltered

NA

LTFU: 9

Miss: 223

Missing were excluded, analysis with complete data

NA

External

Spatial validation

None

Pickwell 2015

20

Some converted to categorical variables

7.950

5.150

FSR

None

None

None

Lipsky 2011

33

Some converted to categorical variables

19.606

SR

Internal

Random split validation

H–L test

Barberan 2010

19

Remain unaltered

1.368

UA

None

None

None

  1. “–” Indicated not reported
  2. EPV events per variable, CR Cox regression, VIMP variable importance, H–L test Hosmer–Lemeshow goodness of fit test, FSR forward stepwise regression, LR logistic regression, KNN K-Nearest Neighbor, RF-RFE random forest recursive feature elimination, LTFU lost to follow-up, mRMR minimum redundancy maximum relevance, JMI joint mutual information, JMIM joint mutual information maximization, SR stepwise regression, UA univariate analysis