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Developing a foot ulcer risk model: what is needed to do this in a real-world primary care setting?
A Heald1,2, M Lunt1, M K Rutter3,4
1School of Medicine and Manchester Academic Health Sciences Centre, University of Manchester, Manchester.
Aim:
To determine how routinely collected data can inform a risk model to predict de novo foot ulcer presentation in the primary care setting.
Methods:
Data were available on 15 727 individuals without foot ulcers and 1125 individuals with new foot ulcers over a 12-year follow-up in UK primary care. We examined known risk factors and added putative risk factors in our logistic model.
Results:
People with foot ulcers were 4.2 years older (95% CI 3.1-5.2) than those without, and had higher HbA1c % (mean 7.9 ± 1.9 vs 7.5 ± 1.7) / HbA1c mmol/mol (63 ± 21 vs 59 ± 19) (p<0.0001) concentration [+0.45 (95% CI 0.33-0.56), creatinine level [+6.9 μmol/L (95% CI 4.1-9.8)] and Townsend score [+0.055 (95% CI 0.033-0.077)]. Absence of monofilament sensation was more common in people with foot ulcers (28% vs 21%; P<0.0001), as was absence of foot pulses (6.4% vs 4.8%; P=0.017). There was no difference between people with or without foot ulcers in smoking status, gender, history of stroke or foot deformity, although foot deformity was extremely rare (0.4% in people with foot ulcers, 0.6% in people without foot ulcers). Combining risk factors in a single logistic regression model gave modest predictive power, with an area under the receiver-operating characteristic curve of 0.65 (95% CI 0.62-0.67). The prevalence of ulceration in the bottom decile of risk was 1.8% and in the top decile it was 13.4% (compared with an overall prevalence of 6.5%); thus, the presence of all six risk factors gave a relative risk of 7.4 for development of a foot ulcer over 12 years.
Conclusion:
We have made some progress towards defining a variable set that can be used to create a foot ulcer prediction model. More accurate determination of foot deformity/pedal circulation in primary care may improve the predictive value of such a future risk model, as will identification of additional risk variables.
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