Risk prediction models for re-fractures following hip fracture in older adults: A methodological evaluation

Table 2 Overview of the included prediction models

Ref.	Cases/sample	Methods	Number of predictors	Predictors	Type of validation	Performance		Presentation
						Development	Validation
Wen et al[19], 2024	A: 35/421 (8.31%); B: 16/180 (8.89%)	LR	4	Age, female, OP, comorbidity	IV	Sensitivity: 0.826; specificity: 0.804; AUC: 0.876; C-index: 0.810; 95%CI: 0.711-0.912	Sensitivity: 0.788; specificity: 0.781; AUC: 0.830; C-index: 0.832; 95%CI: 0.720-0.928	Nomogram
Guo[20], 2022	A: 90/1097 (8.2%); B: 40/540 (7.3%)	LR	7	Age, Harris score, AD, PD, visual impairment, heart disease, exercise	IV	Sensitivity: 0.722; specificity: 0.736; AUC: 0.776; 95%CI: 0.727-0.825	Sensitivity: 0.750; specificity: 0.701; AUC: 0.815; 95%CI: 0.743-0.887	Nomogram
Wu et al[9], 2025	NR	LR	5	Age, DM, OP, exercise, preoperative total protein	EV	Precision: 0.906-0.926; accuracy: 0.806-0.877; AUC: 0.912-0.976	Precision: 0.901-0.921; accuracy: 0.818-0.903; AUC: 0.893-0.976	Weighted Ensemble-L2, XGBoost, NeuralNetTorch, LightGBM, CatBoost, LightGBMXT, RandomForestEntr, RandomForestGini, LightGBMLarge, NeuralNetFastAl
Kim et al[15], 2024	A: 113/1012 (11.17%); B: 22/468 (4.7%)	XGBoost Algorithm	3	Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs	IV	C-index: 0.59-0.73; AUC: 0.55-0.77		Deep learning-based convolutional neural network model
Liang et al[16], 2023	A: 31/513 (6%); B: 21/221 (9.5%)	LR	8	Age, hemoglobin, heart disease, neurovascular disease, PD, AD, COPD, CKD	IV	AUC: 0.906; 95%CI: 0.845-0.967	AUC: 0.956; 95%CI: 0.927-0.985	Nomogram
Huang et al[17], 2024	NR	Machine learning	19	Age, male, height, weight, LOS, smoking, CKD, arteriosclerosis, epilepsy, DM, liver disease, dyslipidaemia, pisphosphonates, PD, raloxifene and alendronate intake, single, activities, incomes	IV + EV	Sensitivity: 0.83-0.95; specificity: 0.82-0.95; AUC: 0.91-0.98		Generalised linear models, random forests, stochastic gradient boosting machines, generalized additive models, support vector machines, Naive Bayes
Larrainzar-Garijo et al[10], 2024	NR	NR	16	Age, female, OP, dementia, heart disease, COPD, asthma, renal failure, hypothyroidism, stroke, malnutrition, visual deficit, anaemia, walking assistance, pertrochanteric fracture	IV	AUC: 0.69; C-index: 0.58		Fine and Gray sub-distribution hazard competing risk model
Wang et al[18], 2025	NR	LR	5	Harris score, sunshine time, exercise, AD, LOS	EV	Sensitivity: 0.670; specificity: 0.774; AUC: 0.778	C-index: 0.761; accuracy: 0.789	Nomogram

A: Re-fracture; AD: Alzheimer’s disease; AUC: Area under the curve; B: Normal; CKD: Chronic kidney disease; COPD: Chronic obstructive pulmonary disease; DM: Diabetes mellitus; EV: External validation; IV: Internal validation; LOS: Length of hospital stay; LR: Logistic regression; NR: Not report; OP: Osteoporosis; PD: Parkinson’s disease.