1. DIAFORA: a new Diabetic Foot Risk Assessment Tool
Matilde Monteiro Soares

2. Background: DM and Diabetic Foot
DM one of the most frequent metabolic disorders (IDF 2015)
Most common complications (Young BA et al, 2008)
Metabolic, retinopathy, nephropathy, cerebrovascular, cardiovascular, neuropathy and PAD
DPN and PAD increase risk of DFU and LEA (Frykberg R et al, 2006)
DFU increases risk of short-time mortality (Brownrigg J et al, 2012)
Background: DM and Diabetic Foot

3. Background: LEA impact
25% will re-ulcerate
25% of DFU require LEA
50% will require other LEA
Armstrong D et al, 2007
LEA subjects report lower QoL when compared to those with ESRD or blindness

4. Background: Screening
Diabetic foot screening is considered one of the pillar stones for complications’ prevention
Risk stratification of subjects by their risk of DFU or LEA allows a more rational resource allocation
DFU prognostic assessment is crucial to help decision-making, inter- professionals’ communication, treatment selection and efficacy control and centres audit
Background: Screening

5. Background: DFU risk classifications review

6. Background: DFU risk classifications review
5 classifications described in 13 studies
Number of variables and risk groups variable
DFU prevalence varied from 5 to 34%
Lack of validation studies and diagnostic accuracy measures
Background: DFU risk classifications review

7. Background: DFU risk classifications validation
Multicentre prospective cohort study
Subjects with DM and without active DFU, undergoing diabetic foot screening in
CHVNG Diabetic Foot Clinic (December 2010 to December 2012)
USF Aquae Flaviae (July 2013 to September 2014)
USF Santo André de Canidelo (March to September 2014)
Sample size calculation: 223 per setting, total 446
Validated all systems described SR + PODUS
Follow up for 1 year, until DFU or death
Background: DFU risk classifications validation

8. Background: DFU risk classifications validation
1st study prospectively validating all systems in multicentre context
comparing the classifications’ accuracy among them and
comparing between the primary care and the hospital setting
Systems are equally and highly valid
One should select which one to apply according to practicability and characteristics of each setting
Differences in subjects’ characteristics and systems’ accuracy when comparing settings
Background: DFU risk classifications validation

9. Background: LEA risk classifications review

10. Background: LEA risk classifications review
15 classifications in 25 studies
1 to 9 variables included
Most validated Meggit-Wagner, S(AD)SAD and TUC
11 validated once or never
Background: LEA risk classifications review

11. Background: LEA risk classifications review
LEA prevalence varied from 6 to 43%, major LEA from 0 to 25%
Only 3 studies reported for major LEA separately
The most validated variables were PAD, DFU’s depth and infection
Prognostic accuracy were reported in 5 and calculable in 8 studies
Values considerably variable according to study
LR with small impact on clinical decision
20 year between first and last system created
Not possible to find important differences between systems
Background: LEA risk classifications review

12. Background: LEA risk classifications validation

13. Background: LEA risk classifications validation
Prospective cohort study
Subjects with DM and active DFU
Attending CHVNG Diabetic Foot Clinic, January 2010 to March 2013
Data collected at 1st appointment
Subject, foot and DFU characterization variables
Systems identified in SR were applied
Follow up for at least 3 months, complete healing, LEA or death
Background: LEA risk classifications validation

14. Background: LEA risk classifications validation
1st study prospectively validating all systems
Data collection methods simplified
PAD and previous DFU associated with LEA
Only gangrene associated with major LEA (in multivariate analysis)
Comparing to values reported in literature our accuracy measures were frequently lower
Systems’ accuracy usually similar
All systems’ stages, grades or overall prognostic were associated with LEA
Background: LEA risk classifications validation

15. Background: LEA risk classifications validation
LR to 5.9
LR- superior to 0.3
PPV 21 to 65%
NPV superior to 80%
AUC superior to 0.72
Except for SEWSS and SIGN (significantly lower)
Background: LEA risk classifications validation

16. To create a classification that is composed by 2 parts
DFU prediction First part = validated DFU risk classification
LEA prediction
Second part = DFU characterization variables
Easier to remember, clinically sensible, easy to collect variables…
Breakthrough idea

17. DIAFORA

18. DIAFORA: Material and Methods
Same cohort used previous study
For the model’s creation we have selected to include
Foot characteristization variables: DPN, PAD, foot deformity and previous DFU or LEA = IWGDF
DFU characterization variables: Logistic regression and AUC analysis
3 risk groups by creating 2 cut-offs points
100% sensitivity, 90% specificity
DIAFORA: Material and Methods

19. DIAFORA: Results

20. DIAFORA: Results

21. DIAFORA: Results

22. DIAFORA: Results

23. This study integrates the results of our previous studies
It was not possible select the “best” system for DFU or LEA prediction
DPN, PAD, foot deformity and previous DFU or LEA frequently used and linked with both outcomes’ prediction
Possible create system that
first part for DFU development in subjects without DFU
full version for LEA prediction in subjects with DFU
DIAFORA: Conclusions

24. Potential improvement of clinical use due to selected variables
Easy to collect and reasonable
Transformed predictive model into a point system
DIAFORA showed a similar (81%) or significantly higher (17%) accuracy measures when compared to the existing systems
So…
Potentially useful in the majority of the clinical settings
Low risk  primary health care centres
High risk  specialized diabetic foot clinics
Conclusions

25. Thank you for yours! mat.monteirosoares@gmail.com