Introduction
Diabetic foot ulcer (DFU), a severe chronic complication of diabetes mellitus, represents a critical global health challenge.1 Lipsky2 reported amputation rates up to 23% among patients with diabetic foot. In China, the 5-year post-amputation mortality rate in diabetic populations exceeds 40%,3 highlighting the dual role of DFU-related amputations as a public health challenge and healthcare quality indicator. Beyond mortality, this condition inflicts profound physical disability and psychological trauma, while generating substantial socioeconomic burdens.4 A review reported that annual hospitalization costs associated with diabetic amputations reached £43.8 million in the UK,5 a financial strain amplified in low-resource settings where delayed presentations and fragmented multidisciplinary care exacerbate preventable complications.6
A prediction model combines various risk factors to calculate the incidence of specific end-point events.7 Risk prediction models use quantitative research methods, providing more objective results than clinical judgment alone.8 Predictive models for amputation risk among people with DFU can help medical staff identify high-risk patients, design customized programs for patients with different risk stratifications, and reduce amputation incidence. These tailored programs can be adjusted according to different needs, reducing both the risk of under-screening and the cost of over-screening, especially in areas where health resources are scarce.9
While numerous amputation prediction models exist for DFU patients, their methodological quality remains uncertain. Previous systematic reviews,10,11 including Beulens et al’s comprehensive analysis,12 have examined prognostic models for diabetic foot ulcers. We aimed to specifically analyze risk prediction models for DFU progressing to amputation, using PROBAST criteria to assess methodological quality and provide recommendations for future model development.
Methods
Study Design
We conducted this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines,13 the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines,14 and the Cochrane guidance for prognostic model reviews.15 The study selection process is illustrated in Figure 1, and the PRISMA checklist is provided in Supplementary Table 1.
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Figure 1 PRISMA flow diagram of study selection process.
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Study Selection
Two researchers (XXR and YMF) independently searched Medline, Embase, Cochrane Library, and Clinicaltrials.gov from inception to January 29, 2025, to collect studies on risk prediction models for DFU progressing to amputation. According to the Cochrane guidance,15 our search strategy was based on Geersing et al16 and included terms associated with diabetic foot, prognostic model, and amputation. We then conducted a manual search of the references of included studies to obtain additional eligible articles. The complete search strategy is provided in Supplementary File 1.
Inclusion and Exclusion Criteria
We included all studies that developed or validated risk prediction models of amputation in DFU patients. Inclusion criteria were: (1) studies involving adult participants (aged ≥18 years) with DFU; (2) amputation as the primary outcome; (3) cohort or case-control study design.
Exclusion criteria were: (1) studies not focused on model development or validation; (2) models targeting specific disease subgroups (eg, limited to one DFU subtype); (3) conference abstracts, review articles, or letters; (4) basic science studies (eg, cellular/molecular level research); (5) studies with unavailable full text; (6) models containing only a single predictor; (7) non-English language studies.
When development studies did not meet the criteria, we still considered their corresponding external validation studies if they met the inclusion criteria.17 Two researchers (XXR and YMF) independently selected literature according to the above criteria, with a third referee (ZJ) resolving disagreements.
Data Extraction
We extracted data using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist.18 From development studies, we extracted: first author, year, study type, study population, predicted outcome, candidate predictors, sample size, missing data, modeling method, variable selection, model performance, method of internal validation, number of predictors in final model, and model presentation. From external validation articles, we extracted: first author, year, original model, study population, predicted outcome, sample size, missing data, and model performance.
Risk of Bias and Applicability Assessment
Two researchers (XXR and ZJ) assessed risk of bias and applicability concerns using the Prediction model Risk of Bias Assessment Tool (PROBAST).19 Developed by the Cochrane Prognosis Methods Group in 2019, PROBAST evaluates bias across four domains and applicability across three domains.
Results
Study Selection
Of 7,219 records screened, 18 papers met inclusion criteria (Figure 1). These comprised 15 development studies that reported 17 prediction models and 3 validation studies that externally validated 12 models. Notably, one validation study prospectively validated a model originally developed in one of the included development studies. Overall, we analyzed 28 models across the 18 studies.
Development Studies of Risk Prediction Models for DFU Progressing to Amputation
Characteristics and Predicted Outcome of Development Studies
We included 15 development studies, six published within the past five years. Most were conducted in Western countries (n=7), followed by China (n=4). Fourteen studies adopted cohort designs, and one used a case-control design, with five being multicenter and 10 single-center. Table 1 presents the basic characteristics and predictive outcomes of these development studies.
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Table 1 Basic Characteristics and Predicted Outcomes of the Development Studies
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Establishment of the Models
The number of candidate predictors ranged from 3 to 39; sample sizes varied from 62 to 326,853; and outcome events ranged from 9 to 19,344. Nine studies did not report missing data. Most models employed logistic regression (n=8), while others used machine learning (n=5), Cox regression (n=1), or variable combination methods (n=1). Table 2 provides detailed information about model development.
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Table 2 Establishment of Prediction Models
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Model Performance and Predictors
Four studies assessed calibration, while 11 evaluated discriminations. The area under the curve (AUC) for 12 models ranged from 0.557 to 0.957. Internal validation methods included split-sample validation (n=4), bootstrap resampling (n=2), and cross-validation (n=2); however, seven studies performed no internal validation. Final models included 3 to 33 predictors, with peripheral arterial disease (PAD), glycated hemoglobin, infection, Wagner classification, and ulcer depth being most common. Eight models presented results as risk scores. Table 3 summarizes model performance and predictors.
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Table 3 Performance and Predictors of Prediction Models
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External Validation Studies
Three validation studies externally validated 12 models, all including the University of Texas system.35 Jeon’s36 study used a retrospective cohort design, while the other two were prospective. Sample sizes ranged from 101 to 293, with 24 to 68 outcome events. All three studies excluded participants with missing data. Carro’s37 validation of the Saint Elian Wound Score System38 reported the highest AUC (0.893). None assessed calibration. Table 4 summarizes the validation studies’ characteristics.
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Table 4 Characteristics of External Validation Studies
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Risk of Bias Assessment
All included studies demonstrated high overall risk of bias. While all studies showed low risk of bias for the participant domain, several issues emerged in other domains. For the predictor domain, bias primarily resulted from lack of blinding during predictor assessment (n=9). Similarly, nine studies showed potential outcome bias due to unblinded outcome assessment. Analysis domain concerns included insufficient sample size (n=14), inappropriate handling of censored data (n=14), failure to account for data complexity (n=11), incomplete model performance assessment (n=14), and absence of internal validation (n=11). All models demonstrated low applicability concerns across all domains. Table 5 presents the risk of bias assessment summary, with detailed signaling questions in Supplementary Tables 2 and 3.
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Table 5 Risk of Bias and Applicability Concerns Assessment
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Discussion
This systematic review analyzed studies on risk prediction models for DFU progression to amputation. We identified 18 eligible papers including 15 development studies and three external validation studies, totaling 28 models. Discrimination indices ranged from 0.557 to 0.957, with most models achieving AUCs > 0.8, indicating good discriminatory performance. However, only Lipsky’s29 study reported calibration results. All studies demonstrated high risk of bias, primarily due to insufficient events per variable, missing data, inadequate handling of data complexity, incomplete performance reporting, and lack of internal validation. Consequently, none of the 28 included prediction models can be recommended for clinical use without further validation.
Principal Findings and Future Suggestions
Although diabetic foot amputation incidence remains highest in developing and low-income countries,40 relatively few models have been developed or validated in Asian or African settings, with most studies conducted in Western countries. Among the 15 development studies, only five were multicenter investigations. Multicenter studies can recruit more participants and cover diverse populations, potentially enhancing generalizability.41 However, heterogeneity across research settings may introduce higher risk of bias.42
Most development models used logistic regression. Lin et al25 compared Cox regression, backpropagation neural network (BPNN), and genetic algorithm-optimized BPNN, finding that machine learning models exhibited higher AUCs than Cox regression. While machine learning methods offer high prediction accuracy, their lack of transparency may hinder clinical applicability.43 Whether machine learning consistently outperforms regression models remains contentious.
Predicting amputation among DFU patients is critical for targeting limb salvage interventions. The most frequently reported predictors across all models were PAD, glycated hemoglobin (HbA1c), infection, Wagner classification, and ulcer depth. PAD, present in approximately half of DFU cases, drives both amputation and mortality, making it central to lower limb ischemia management.44 The pathophysiology underlying these associations is complex and multifactorial. Recent studies have explored broader mechanistic links, including causal relationships between type 2 diabetes and neurological disorders,45 environmental endocrine disruptors that may induce mitochondrial dysfunction,46 and systemic metabolic pathways that could influence peripheral complications.47 These emerging insights suggest that future prediction models might benefit from incorporating biomarkers reflecting these diverse pathophysiological mechanisms.
Poor glycemic control, as measured by HbA1c, represents another established risk factor. Pscherer et al48 showed that patients with mean HbA1c > 7.5% had 20% higher risk of limb loss compared to those with levels < 7.5%. Elevated white blood cell (WBC) counts also correlate with amputation risk.49 Eneroth50 found that WBC counts > 12 × 109/L were associated with increased amputation likelihood. Ulcer depth, a core component of the Wagner classification, strongly predicts outcomes. DFUs are classified by depth (skin, soft tissue, bone), with bone/joint involvement serving as a critical amputation risk indicator.51
Infection remains a major modifiable risk factor for amputation. Novel therapeutic approaches are being developed to address this challenge, including glucose-responsive gels that combine photodynamic therapy with hypoxia relief for treating diabetic abscesses52 and advanced drug delivery systems such as the regulation of selenoproteins.53 While these therapies are still under investigation, their potential to reduce infection-related amputations could influence future risk stratification models by introducing new modifiable factors.
These widely used predictors are readily measurable in primary care settings, making them practical for routine assessment. Clinicians should prioritize DFU education and proactive management of these risk factors to enhance foot care quality.54 These validated predictors should form the foundation for future model development.
To our knowledge, this represents the first systematic review specifically evaluating risk prediction models for DFU progression to amputation using PROBAST criteria. Our assessment revealed universal high risk of bias across included studies. For model development, overfitting risk increases when events per variable (EPV) fall below 10, while EPV > 20 enhances result reliability.55 Validation studies should include at least 100 outcome events to minimize bias in performance estimates,56 with machine learning models typically requiring larger samples.57 Although recent methodologies58,59 enable accurate sample size calculation for prognostic model studies, only four of 18 included studies met recommended sample size criteria.
Six studies reported missing data, with two showing proportions > 10%. Only one study applied multiple imputation, the gold standard for handling missing data, which generates multiple plausible values for each missing observation to appropriately reflect uncertainty.60,61 Prediction model performance encompasses both discrimination and calibration,62 yet only one study assessed calibration. Calibration—measuring accuracy of absolute risk estimates—is as important as discrimination for clinical decision-making.63 Calibration plots, rather than the Hosmer-Lemeshow test, represent the preferred assessment method.14 Proper internal validation is essential to correct for optimism bias; without it, model performance will be overestimated.64 While split-sample validation remains common, cross-validation or bootstrapping provides more robust internal validation. External validation in independent populations remains necessary to establish generalizability.65
Future Perspectives
Several priority areas should guide future research in DFU amputation prediction. First, developing artificial intelligence-enhanced models that integrate multimodal data—including clinical parameters, imaging findings, and molecular biomarkers—may substantially improve prediction accuracy.66 Second, implementation studies are urgently needed to evaluate real-world model performance and impact on patient outcomes. Third, dynamic prediction models that update risk estimates as patient conditions evolve could enable more personalized care delivery. Fourth, international collaborative efforts should establish standardized datasets and validation protocols to ensure model generalizability across diverse populations and healthcare settings. Finally, seamless integration of validated models into clinical decision support systems and electronic health records will be essential for translating research findings into improved patient care.67
Limitations of the Review
PROBAST, published by the Cochrane Group in 2019, was not available when most included studies were conducted. Consequently, our methodological quality assessment may appear stricter than if studies had been designed with PROBAST criteria in mind. Despite comprehensive literature searches, we may have missed relevant studies. Meta-analysis was not possible due to sparse calibration reporting and substantial heterogeneity across studies. Our review was restricted to English-language publications, potentially excluding relevant research published in other languages, particularly from non-English speaking countries where diabetic foot disease is highly prevalent. We also acknowledge that certain PRISMA guideline elements were not fully addressed, as our review focused on prediction models rather than interventions, which may have limited search comprehensiveness.
Conclusion
Our review included 18 articles that developed or externally validated 28 models, and summarized their characteristics. The results suggest that the studies on risk prediction models for DFU progressing to amputation are still in the development stage. At present, there is no model that can be applied directly. In the future, prediction models with good performance and low risk of bias should be developed, to identify patients at high risk for diabetic foot amputation as soon as possible and intervene to prevent or delay amputation.
Data Sharing Statement
All data used or generated in this research can be found during this article and its supplementary files.
Author Contributions
All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Specifically, XR and LY designed this study. XR, YMF, and ZJ searched the literature and extracted data. XXR and YMF analyzed data. XXR wrote the first draft of the manuscript. XR and LY supervised and revised the manuscript.
Funding
Natural Science Foundation of Hubei Province (2022CFB145) and Research Fund of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (2023D36).
Disclosure
Xiao-Ran Xie and Ming-Feng Yu are co-first authors for this study. Rong Xu and Yu Liu are co-correspondence authors for this study. The authors report no conflicts of interest in this work.
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