Retrospective machine learning models predict amputation risk in diabetic arterial disease

A separate competing-risk model accounted for patients who died before amputation, while both systems were combined in a public web calculator. The retrospective multicenter study, conducted at four Chinese hospitals from January 2018 through September 2024 with follow-up ending in September 2025, found an overall 12-month amputation rate of 14.0%. The findings indicate that machine-learning estimates can complement conventional risk analysis, but the calculator remains an auxiliary research tool that has not been prospectively tested in clinical practice.

Patients in the study had both lower extremity arterial disease, involving stenosis or occlusion of arteries supplying the legs, and diabetes. Their mean age was 72.7 years, and 65.7% were men. The investigators divided the cohort into 967 training participants, 414 internal-validation participants and 538 external-validation participants.

During the standardized 12-month observation period, 269 patients underwent major or minor amputation, 118 died before amputation without limb loss, and 1,532 were censored. Amputation occurred in 14.6%, 13.3% and 13.6% of the training, internal-validation and external-validation groups, respectively. A competing event matters because a conventional binary model may treat death before amputation as simply a non-amputation outcome, potentially distorting the estimated probability that a patient actually undergoes limb loss while remaining at risk.

To reduce information leakage, candidate predictors were screened using only the training cohort. A systematic literature review initially identified 75 potential amputation-related factors, after which clinical accessibility and data completeness reduced the set to 48 variables. Three complementary methods—LASSO logistic regression, a random forest classifier and the Boruta algorithm—then ranked or selected those candidates.

A variable was retained when at least two methods supported it, yielding 14 predictors: Rutherford classification, financial burden, age, Barthel Index, diabetic foot, statin use, fibrinogen, neutrophil count, residence, prealbumin, C-reactive protein, activated partial thromboplastin time, body-mass-index classification and the presence of a wound. The same predictor set was used for both the machine-learning and competing-risk models, allowing their performance to be compared under a common population and 12-month prediction window. Ten algorithms were then trained with hyperparameter tuning and evaluated on discrimination, calibration and related classification measures.

The candidates were random forest, logistic regression, decision tree, naive Bayes, artificial neural network, k-nearest neighbors, XGBoost, gradient boosting, CatBoost and light gradient boosting machine. The selected GraBoost model, a gradient-boosting algorithm, performed best in internal validation: its AUC was 0.860, accuracy was 0.787, F1 score was 0.782 and Brier score was 0.156. In the geographically separate external cohort, it retained an AUC of 0.828, accuracy of 0.760, F1 score of 0.695 and Brier score of 0.178.

Eight algorithms had external AUC values above 0.70, while decision tree and k-nearest neighbors performed less consistently. DeLong tests found statistically significant AUC differences between GraBoost and most competing models in internal validation, although some external differences were not significant. These results describe discrimination and calibration in the studied cohorts; they do not establish that the model can improve clinical decisions or patient outcomes.

SHAP analysis, which decomposes each prediction into the directional contribution of its inputs, showed that Rutherford classification had the strongest influence, followed by financial burden and age. Barthel Index, diabetic foot, statin use, fibrinogen, neutrophil count, residence and prealbumin had moderate effects, while C-reactive protein, activated partial thromboplastin time, body-mass-index classification and wounds had comparatively weaker associations. Higher Rutherford grades, lower Barthel Index scores, younger age, wounds and diabetic foot were associated with higher predicted amputation risk.

Higher fibrinogen, activated partial thromboplastin time, C-reactive protein and neutrophil values also contributed to higher predictions, whereas higher prealbumin was associated with lower risk. Urban residence, lower financial burden, normal body-mass-index classification and statin use contributed in the protective direction. These are predictive associations in an observational cohort, not evidence that changing any one factor would cause a reduction in amputation.

The Fine-Gray competing-risk regression used the same 14 inputs and treated amputation as the index event and all-cause death before amputation as the competing event. Its 12-month time-dependent AUC was 0.762 in the training cohort, 0.711 in internal validation and 0.811 in external validation. Corresponding Brier scores were 0.092, 0.106 and 0.085.

Calibration curves for 3-, 6- and 12-month endpoints indicated agreement between predicted and observed cumulative incidence across risk strata, while Gray’s test found no significant difference in 12-month cumulative amputation incidence among the three cohorts, with p = 0.722. The relatively small internal-validation group produced wide confidence intervals for the time-dependent AUC, adding uncertainty to the estimate of discrimination. The authors also generated a nomogram that assigns points to the 14 predictors and translates total points into estimated 3-, 6- and 12-month cumulative incidence under the competing-risk framework.

Several patterns challenge simple assumptions about risk. Younger age was associated with greater predicted amputation despite the general tendency for arterial disease to become more prevalent with age. The investigators proposed possible explanations, including more aggressive surgical decisions among younger patients with severe limb complications and a more aggressive disease phenotype in some younger patients, but emphasized that these ideas are speculative.

They also noted that residual confounding could not be excluded. Similarly, the observed association between rural or town residence, greater financial burden and amputation may reflect differences in access to care, delayed intervention or adherence, although the retrospective data cannot establish those mechanisms. The model also identified a U-shaped pattern in which both underweight and higher body-mass-index classifications were associated with greater predicted risk.

Underweight may reflect malnutrition, sarcopenia or underlying disease burden, while higher body mass index may accompany metabolic disturbance and inflammation, but the study did not test those pathways as causes of amputation. Wounds and diabetic foot were positively associated with predicted risk, but their SHAP effects were weaker than those of Rutherford classification, financial burden and age. Their presence can be consistent with tissue ischemia, impaired healing and infection, but the model’s outputs do not determine why an individual patient developed either condition.

Statin use was associated with lower predicted risk, while higher fibrinogen and prolonged activated partial thromboplastin time were associated with higher risk. The authors interpreted these laboratory patterns in relation to inflammation, coagulation and tissue healing. They also emphasized the role of limited self-care reflected by a lower Barthel Index: reduced mobility and poorer self-management might delay recognition or treatment of foot injury, although the study did not directly test that pathway.

These biological and social interpretations remain explanations for model associations rather than demonstrated causal effects. The researchers deployed both models in a Shiny-based web calculator that accepts patient information and displays synchronized 12-month risk estimates from the binary GraBoost and Fine-Gray frameworks. Dropdown menus are provided for categorical predictors and sliders for continuous variables, allowing a user to enter clinical data and calculate the two outputs.

The 14 inputs were described by the authors as routinely measurable across different hospital tiers, supporting the practical feasibility of collecting the required data. The paired outputs are intended to present two perspectives: a binary machine-learning estimate and a competing-risk estimate that explicitly accounts for pre-amputation death. Nevertheless, the web implementation has not undergone prospective validation in routine care.

It cannot replace comprehensive clinical assessment, and the study did not test whether using it changes surveillance, revascularization, medication prescribing, limb outcomes or survival. Important limitations constrain how the findings should be interpreted. The study was retrospective and observational, so selection bias, incomplete records and unmeasured confounding remain possible.

Baseline characteristics differed among the three cohorts, including differences in Rutherford classification, Barthel Index, residence, statin use and other variables. All participants were Chinese patients treated in Chinese hospitals, limiting extrapolation to other ethnic populations and healthcare systems. Multiple imputation was performed on the pooled dataset before partitioning, which may have introduced some data leakage even though feature selection was restricted to the training cohort.

Patients lost to follow-up also differed significantly from the overall cohort on most baseline variables, indicating non-random attrition. The models were designed specifically for 12-month amputation and cannot be assumed to predict longer-term outcomes. The observed 14.0% amputation rate was also within the 13%–48% range reported for comparable populations, although variations among studies may reflect differences in inclusion criteria, disease severity, healthcare systems and follow-up definitions.

Taken together, the study demonstrates promising external discrimination in a defined clinical population, while prospective multicenter testing remains necessary before the tools can support routine clinical use.

Subject of Research: Development and external validation of 12-month amputation risk models for patients with lower extremity arterial disease and diabetes

Article Title: Development and validation of a machine learning-based model for predicting amputation risk in patients with lower extremity arterial disease and diabetes

Article References: Tang, L., Wang, L., Li, J., Liu, X., Chen, Q., Song, P., Quan, X., Li, Q., Liu, G., Liu, Y., Cui, C., & Shi, M. (2026). Development and validation of a machine learning-based model for predicting amputation risk in patients with lower extremity arterial disease and diabetes. Frontiers in Cardiovascular Medicine, 13, Article 1893875. https://doi.org/10.3389/fcvm.2026.1893875

Image Credits: AI Generated

DOI: 10.3389/fcvm.2026.1893875

Keywords: amputation risk, lower extremity arterial disease, diabetes, machine learning, gradient boosting, competing risks, external validation, clinical prediction, SHAP interpretability, web calculator, Development, validation