Using an artificial neural network to predict healing times and risk factors for venous leg ulcers.
OBJECTIVE
This study aimed to identify the risk factors that influence the healing process of venous leg ulcers treated with compression bandaging, with a view to predicting healing time.


METHODS
A retrospective cohort study was performed on data collected prospectively on 325 consecutive patients presenting with 345 venous ulcers at the Salford Primary Care Trust leg ulcer clinic between January 1997 and December 1999. Use of an artificial neural network (ANN) technique accurately predicted the healing times for 68% of the patients.


RESULTS
The ANN demonstrated that healing was significantly related to a history of previous leg ulceration, 'quite wet' ulcer exudate, high body mass index, large initial total ulcer area, increasing age and male gender.


CONCLUSIONS
The ability to identify at presentation ulcers that might be resistant to standard therapy would allow early consideration of more radical treatments such as hospitalisation, wound debridement or venous surgery.
Taylor
R J
RJ
Department of Medical Physics, Salford Royal Hospitals NHS Trust, Salford, UK. RTaylor@FSI.ho.man.ac.uk
Taylor
A D
AD
Smyth
J V
JV
D000328:Adult; D000367:Age Factors; D000368:Aged; D001458:Bandages; D002908:Chronic Disease; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D012737:Sex Factors; D013997:Time Factors; D014647:Varicose Ulcer / Q000175:diagnosis / Q000453:epidemiology / Q000451:nursing / Q000628:therapy*; D014945:Wound Healing / Q000502:physiology