Prognostic models for predicting the risk of foot ulcer or amputation in people with type 2 diabetes: a systematic review and external validation study

Abstract

Aims/hypothesis Approximately 25% of people with type 2 diabetes experience a foot ulcer and their risk of amputation is 10–20 times higher than that of people without type 2 diabetes. Prognostic models can aid in targeted monitoring but an overview of their performance is lacking. This study aimed to systematically review prognostic models for the risk of foot ulcer or amputation and quantify their predictive performance in an independent cohort. Methods A systematic review identified studies developing prognostic models for foot ulcer or amputation over minimal 1 year follow-up applicable to people with type 2 diabetes. After data extraction and risk of bias assessment (both in duplicate), selected models were externally validated in a prospective cohort with a 5 year follow-up in terms of discrimination (C statistics) and calibration (calibration plots). Results We identified 21 studies with 34 models predicting polyneuropathy, foot ulcer or amputation. Eleven models were validated in 7624 participants, of whom 485 developed an ulcer and 70 underwent amputation. The models for foot ulcer showed C statistics (95% CI) ranging from 0.54 (0.54, 0.54) to 0.81 (0.75, 0.86) and models for amputation showed C statistics (95% CI) ranging from 0.63 (0.55, 0.71) to 0.86 (0.78, 0.94). Most models underestimated the ulcer or amputation risk in the highest risk quintiles. Three models performed well to predict a combined endpoint of amputation and foot ulcer (C statistics >0.75). Conclusions/interpretation Thirty-four prognostic models for the risk of foot ulcer or amputation were identified. Although the performance of the models varied considerably, three models performed well to predict foot ulcer or amputation and may be applicable to clinical practice. Graphical abstract

Risk Prediction Models for Diabetic Foot Ulcer Development or Amputation: A Review of Reviews

“…1) (10,12,13). Two SRs were rated low ROB (10,13) and one was rated moderate ROB (12). Detailed characteristics and conclusions of the 3 SRs are provided in Table 1.…”

“…All 4 risk prediction models have been externally validated. The Beulens et al SR externally validated the models by Boyko and Martins-Mendes (10). PODUS 2020 was externally validated by the study team (8).…”

Precision prognostics for the development of complications in diabetes

“…However, most did not allow estimation of absolute risks, thus limiting risk stratification and interpretation to the relative scale and ruling out the assessment of model calibration. The C statistic (95% CI) of 13 models in the DCS study sample [57] for the composite outcome (including foot ulcer and amputation) ranged from 0.53 (0.51, 0.55) to 0.84 (0.82, 0.86), with the model by Boyko et al [58] reaching the highest. One of the few models developed in individuals with type 1 diabetes to predict neuropathy-related outcomes showed good discriminatory ability in the type 1 diabetes subsample of the external validation cohort.…”

“…Neuropathy A recent systematic review summarised available models predicting polyneuropathy and foot ulcer or amputation as hard endpoints of neuropathy in individuals with diabetes and identified 34 prognostic models [57]. However, most did not allow estimation of absolute risks, thus limiting risk stratification and interpretation to the relative scale and ruling out the assessment of model calibration.…”

DNA Methylation Age Acceleration, Type 2 Diabetes, and its Complications: Cross-sectional and Longitudinal Data from the Berlin Aging Study II (BASE-II)

“…Although complications cannot be prevented completely by the currently available therapeutic options, early interventions can delay their onset [11] and prevent potentially severe disease progression. Although several risk scores for diabetic complications exist (reviewed in [12][13][14][15][16][17][18]), they are not used regularly in clinical practice [19] or are recommended by health organizations [2, 19,20]. Therefore, the demand for a risk score with high predictive power that is based on reliable and well-assessable data is still not met.…”