Systematic review and critical appraisal of predictive models for diabetic peripheral neuropathy: Existing challenges and proposed enhancements

Table 2 Characteristics of studies included in the development and validation of the model

Ref.	Modeling method	Variable selection methods	Methods for handling continuous variables	Missing data		Predictors in the final model	Model performance		Model presentation	Internal validation	External validation
				Quantity	Processing method		Discrimination	Calibration
Ning et al[11]	Logistic regression	Monofactor analysis	Maintaining continuity	-	-	Duration of DM/FPG/FINS/HbA1c/HOMA-IR/Vaspin/Omentin-1	AUC = 0.789 (0.741-0.873)	Calibration curve	Nomogram	Bootstrap	None
Metsker et al[22]	Artificial neural network/support vector machine/decision tree/linear regression/logistic regression	-	-	-	Delete, replace	Unsatisfactory control of glycemia/systemic inflammation/renal dyslipidemia/dyslipidemia/macroangiopathy	(1) AUC = 0.8922; (2) AUC = 0.8644; (3) AUC = 0.8988; (4) AUC = 0.8926; and (5) AUC = 0.8941	None	LIME explanation	5-fold cross-validation	None
Wu et al[17]	Logistic regression	LASSO regression	Maintaining continuity	19	-	FBG/PBG/LDL-C/age/TC/BMI/HbA1c	D: (1) AUC = 0.656; (2) AUC = 0.724; (3) AUC = 0.731; and (4) AUC = 0.713. V: (1) AUC = 0.629; (2) AUC = 0.712; (3) AUC = 0.813; and (4) AUC = 0.830	Hosmer-Lemeshow test/Calibration Plot	Nomogram	None	Geographical
Fan et al[21]	Machine learning	Monofactor analysis	Categorizing continuous variables	-	-	Age/duration of DM/duration of unadjusted hypoglycemic treatment (≥ 1 year)/number of insulin species/total cost of hypoglycemic drugs/number of hypoglycemic drugs/gender/genetic history of diabetes/dyslipidemia	(1) XF: AUC = 0.847 ± 0.081; (2) CHAID: AUC = 0.787 ± 0.081; (3) QUEST: AUC = 0.720 ± 0.06; and (4) D: AUC = 0.859 ± 0.05	None	Variable Importance	Bootstrap	None
Zhang et al[18]	Logistic regression	Monofactor analysis	Maintaining continuity	-	-	Age/gender/duration of DM/BMI/uric acid/HbA1c/FT3	D: AUC = 0.763; V: AUC = 0.755	Calibration curve	Nomogram	Bootstrap	None
Li et al[10]	Logistic regression	LASSO regression	Maintaining continuity	-	-	Sex/age/DR/duration of DM/WBC/eosinophil fraction/lymphocyte count/HbA1c/GSP/TC/TG/HDL/LDL/ApoA1/ApoB	D: AUC = 0.858 (0.851-0.865); V: AUC = 0.852 (0.840-0.865)	Hosmer-Lemeshow Test/Calibration curve	Nomogram	Bootstrap	None
Tian et al[20]	Logistic regression	LASSO regression	Categorizing continuous variables	-	-	Advanced age of grading/smoking/insomnia/sweating/loose teeth/dry skin/purple tongue	D: AUC = 0.727; V: AUC = 0.744	Calibration curve	Nomogram	5-fold cross-validation	None
Li et al[15]	Logistic regression	LASSO regression	Maintaining continuity	-	-	Age/25(OH)D3/duration of T2DM/HDL/HbA1c/FBG	D: AUC = 0.8256 (0.8104-0.8408); V: AUC = 0.8608 (0.8376-0.8840)	Hosmer-lemeshow test/Calibration curve	Nomogram	Bootstrap	Geographical
Lian et al[16]	Logistic regression machine learning	-	Maintaining continuity	10	Delete, multiple imputation, or leave unprocessed	Age/ALT/ALB/TBIL/UREA/TC/HbA1c/APTT/24-hUTP/urine protein concentration/duration of DM/neutrophil-to-lymphocyte Ratio/HOMA-IR	AUC = 0.818	None	The Shapley additive explanations	10-fold cross-validation	None
Liu et al[19]	Β coefficient	-	Maintaining continuity	-	-	Age/smoking/BMI/duration of DM/HbA1c/low HDL-c/high TG/hypertension/DR/DKD/CVD	AUC = 0.831 (0.794-0.868)	None	-	None	Geographical
Wang et al[12]	Logistic regression	Monofactor analysis	Maintaining continuity	-	-	Age/duration of DM/HbA1c/TG/2 hours CP/T3	AUC = 0.938 (0.918-0.958)	Hosmer-lemeshow test/Calibration curve	Nomogram	Bootstrap	None
Zhang et al[13]	Logistic regression	LASSO regression	Maintaining continuity	-	-	Age/smoking/dyslipidemia/HbA1c/glucose variability parameters	AUC = 0.647 (0.585-0.708)	Hosmer-lemeshow test/Calibration curve	Nomogram	Bootstrap	None
Gelaw et al[14]	Logistic regression/machine learning	LASSO regression	Categorizing continuous variables	-	Multiple Imputation	Hypertension/FBG/other comorbidities/Alcohol consumption/Physical activity/type of DM treatment/WBC/RBC	(1) AUC = 0.732 (0.69-0.773); and (2) AUC = 0.702 (0.658-0.746)	Hosmer-lemeshow test	Nomogram	Bootstrap	None
Baskozos et al[23]	Machine learning	-	-	-	Multiple imputation	Quality of life (EQ5D)/lifestyle (smoking, alcohol consumption)/demographics (age, gender)/personality and psychology traits (anxiety, depression, personality traits)/biochemical (HbA1c)/clinical variables (BMI, hospital stay and trauma at young age)	(1) AUC = 0.8184 (0.8167-0.8201); (2) AUC = 0.8188 (0.8171-0.8205); and (3) AUC = 0.8123 (0.8107-0.8140)	Calibration curve	The adaptive regression splines classifier	10-fold cross-validation	Geographical

D: Development; V: Validation; DM: Diabetes mellitus; FPG: Fasting plasma glucose; FINS: Fasting blood insulin; HbA1c: Glycated hemoglobin; AUC: The receiver operating characteristic curve; FBG: Fasting blood glucose; PBG: Postprandial blood glucose; HOMA-IR: Homeostasis model assessment of insulin resistance; LDL-C: Low-density lipoprotein cholesterol; BMI: Body mass index; HbA1c: Glycated hemoglobin; CHAID: χ² automatic interaction detector; QUEST: Quick, unbiased, and efficient statistical tree; FT3: Free triiodothyronine; RBC: Red blood cell; WBC: White blood cell; TC: Total cholesterol; HDL: High-density lipoprotein; T2DM: Type 2 diabetes mellitus; ALT: Alanine aminotransferase; ALB: Albumin; TBIL: Total bilirubin; APTT: Activated partial thromboplastin time; 24-hUTP: 24- hour urine total protein; DKD: Diabetic kidney disease; CVD: Cardiovascular disease; EQ5D: European Quality of Life Five Dimension Scale; UREA: Urea nitrogen; DR: Diabetic retinopathy; GSP: Glycosylated serum protein.