Hospital inpatient setting with access to laboratory and imaging diagnostics
Key Highlights
DFU is the leading cause of non-traumatic lower extremity amputation worldwide, accounting for approximately 85% of such amputations.
Major amputation is defined as amputation above the ankle joint and is associated with high mortality and reduced quality of life.
There is currently no objective clinical tool to predict major amputation risk in DFU patients; this study aims to develop a predictive model using clinical and laboratory data.
Guideline-Based Recommendations
Diagnosis
Use ICD-10 code E14.500×050 to identify DFU patients.
Confirm major amputation by surgical records indicating amputation above the ankle.
Assess severe ischemia with ankle brachial index (ABI) < 0.4.
Diagnose coronary artery disease (CAD) and peripheral arterial disease (PAD) via Doppler ultrasonography or arteriography with >50% stenosis.
Identify multi-drug resistant bacterial infections based on resistance to three or more antibiotics.
Management
Prioritize limb salvage when possible; abandon limb salvage in extremely severe cases to preserve life and improve quality of life.
Use the predictive model to identify high-risk patients for major amputation to guide clinical decision-making and interventions.
Monitoring & Follow-up
Perform first laboratory tests and imaging examinations within 3 days of admission.
Follow up on major amputation outcomes within 1 month after admission.
Risks
Major amputation is associated with a 5-year mortality rate above 50%.
Patients with major amputation experience significant decline in limb function, quality of life, and increased psychological and economic burden.
Patient & Prescribing Data
Hospitalized patients with diabetic foot ulcers undergoing evaluation for major amputation risk
Early identification of high-risk patients via the predictive model can optimize intervention strategies and potentially reduce amputation rates.
Clinical Best Practices
Use a combination of demographic, laboratory, imaging, and clinical data to assess amputation risk.
Apply nested case-control matching by sex and age to improve predictive model accuracy.
Ensure complete and accurate clinical data collection within the first days of admission.
Consider patient and family preferences alongside clinical indicators when deciding on amputation.
