Risk prediction models for diabetic foot ulcer development or amputation: a review of reviews

In 2019, an estimated 37 million individuals, or 11.3% of the United States (U.S.) population, has diabetes [1]. Many persons with diabetes have additional complications, including neuropathy and peripheral arterial disease, which increase the risk of developing foot ulcers [2, 3]. Between 15–25% of persons with diabetes develop a diabetic foot ulcer (DFU) during their lifetime [4]. Furthermore, a DFU is the greatest risk factor for lower extremity amputation; persons with type 2 diabetes (compared to those without) have a ten-fold higher rate of amputation [5]. The development of a DFU or amputation results in a significant decrease in patients’ quality of life and productivity due to a reduction in physical, social and employment activities [6]. Treatment costs range between $18,600 to $35,100 per DFU, with the U.S. spending $60 billion annually on lower extremity-related care for patients with diabetes [7, 8]. Hence, DFU development or amputation is associated with significant morbidity, mortality, decreased quality of life and productivity, and substantial health care costs.

Since the 1990s, a large body of research has focused on developing tools that predict risk of DFU or amputation with the goal of identifying persons at high-risk who may benefit from early prevention and intervention [8]. Multiple recent systematic reviews (SRs) have described these prediction tools and their performance characteristics [9‐11]. This review of reviews was conducted (as part of a larger review) by the Department of Veterans Affairs (VA) Evidence Synthesis Program at the request of the VA National Clinical Orthotic and Prosthetic Program Office. The goal was to identify tools that predict DFU development or amputation and summarize their performance (prognostic accuracy) to help inform clinical practice and policy in the VA Health Care System. Hence, we also evaluated the usability of the tool in busy outpatient clinics. Given the high prevalence of DFUs and amputations in the U.S. and worldwide, risk stratification and prevention would be of great benefit to individuals, health care systems, and society.

Of 1,495 unique citations, 131 articles underwent full-text review of which 30 reviews were deemed eligible for the larger report (Fig. 1) [9‐11]. Of these, 3 SRs were eligible for this review of reviews (tools that predict risk of DFU or amputation). Two SRs were rated low ROB [9, 11] and one was rated moderate ROB [10]. Detailed characteristics and conclusions of the 3 SRs are provided in Table 1. Across these three SRs, there was heterogeneity in the populations, models, and outcomes of the included studies, and the SRs reached different conclusions. We first provide results from eligible SRs, then describe performance characteristics of the top performing prediction models (distinguishing models used for risk classification versus risk prediction) and finally we reviewed usability characteristics.

Development of a DFU or amputation has severe consequences for the individual and healthcare system [21]. The identification of persons at a high absolute risk for DFU or amputation over a specified time horizon can aid in targeted monitoring and focused prevention efforts, whilst reducing unnecessary resource expenditure on low-risk persons. In this review of reviews, we found 3 SRs describing the performance characteristics of tools predicting DFU development or amputation [9‐11]. The SR by Monteiro-Soares et al., published prior to the development of 3 of the 4 top-performing models, did not identify any well-performing tools [11]. The 2 more recent SRs (2021 and 2020), included studies published in PubMed and EMBASE databases over similar time frames. However, these 2 SRs differed in their search criteria, included study populations, and necessity for tool/model validation and likely due to these differences, the 2 SRs identified different tools and reached different conclusions. Thus, in contrast to the low ROB SR by Beulens et al. which identified risk prediction models with a specified time horizon for prediction [9], the moderate ROB SR by Fernandez-Torres et al. mostly identified risk classification systems without a prediction time horizon [10]. Since tools that do not provide a time horizon for risk prediction are less useful for shared clinical decision making, we excluded these from further consideration.

Based on the results of the SRs, we investigated the performance of 4 models predicting DFU development (Boyko, Martins-Mendes [original and simplified], PODUS 2020), and 2 models predicting amputation (Martins-Mendes [original and simplified]) over a specified time horizon [8, 15, 17]. For the models by Boyko and Martins-Mendes, the time frame for risk prediction varied not only between models, but also for the same model between internal and external validation studies [9]. Additionally, categorization of risk, and thresholds used to define levels of risk varied across models without clear rationale. However, all 4 models had good prognostic accuracy, with PODUS 2020 performing best [8, 9].

Clinical usability is a critical consideration for successful widespread adoption of a risk prediction model. Given the time constraints in primary care clinics, the ideal prediction model includes evaluation of a few readily obtainable variables that permit accurate, reproducible, and understandable risk calculation to clinicians and patients. The 4 models included 2 to 7 variables. The models by Boyko and Martins-Mendes include many variables, or variables that take time or resources to obtain. In addition, these tools also require a calculator for risk calculation. These factors decrease the likelihood of widespread use of the Boyko and Martins-Mendes models. In contrast, PODUS 2020 consists of 3 binary variables (neuropathy [1 point], absence of any pedal pulse [1 point], and prior history of DFU or amputation [2 points]) that can be easily measured in the clinic and the scoring is summative [8]. In the PODUS 2020 developmental cohort, 8.5% of all persons had a prior history of DFU or amputation. The risk of DFU at 2 years with scores of 0, 1, 2, 3, and 4 were 2.4%, 6%, 14%, 29.2%, and 51.1%, respectively, with 5.2% of all persons developing a DFU at 2 years. PODUS 2020 has also been externally validated in an independent cohort, in whom 3.9% of persons developed DFU at 2 years. Based on their findings, PODUS 2020 authors concluded that individuals with a score of ≥ 1 (predicted 2-year rate of DFU ≥ 6%) would benefit from preventative treatment [8]. Based on our review, the PODUS 2020 model has the most favorable prognostic accuracy and feasibility characteristics to predict primary or subsequent DFU development. Furthermore, the absolute quantification of DFU risk provided by PODUS 2020 is more easily understood by clinicians and patients, and potentially more informative for shared clinical decision making than models that do not provide a time horizon or categorize individuals by subjective risk categories.

We identified several limitations in the models. For PODUS 2020, prognostic accuracy depends on the ability of clinicians to accurately use a 10 g monofilament and assess for palpable pedal pulses; skills which may vary based on clinicians’ specialty and experience [18]. All studies assessed one-time tool use to predict DFU development or amputation at specified time horizons. In the absence of studies on how risks for DFU change over time, appropriate rescreening intervals for any model are unknown. Most models did not assess if race or ethnicity predicted DFU development, hence the accuracy of these models in predicting DFU in different populations is unknown. Despite the relative simplicity of PODUS 2020, use of this tool in the primary care clinic setting may be challenging as patients and clinicians have competing health care priorities and there are limited time and resources to address them. Lastly, whether tool deployment will better guide referral, subsequent monitoring, or initiation or continuation of treatment to reduce risk of DFU or amputation is unknown.

A limitation of our review is that our search only includedstudies published in English which may have excluded potentially relevant SRs, however, we did not have geographical or date limitations on the search strategy or study eligibility. A strength of our review was that we were inclusive with search, abstraction, analysis, and critique criteria of SRs and we additionally reviewed primary studies of tool development to qualitatively describe clinical feasibility of prognostically accurate models.

Four well-performing models discriminate the risk of developing primary or subsequent DFU or amputation in adults with diabetes who are ulcer-free at baseline. Of these, PODUS 2020 has the most favorable prognostic accuracy and is feasible to use in the primary care clinic setting. The PODUS 2020 score predicts average absolute rate of DFU development at 2-years. The rescreening interval and the type or effectiveness of interventions in response to prediction scores to decrease DFU or amputation are unknown.