The amputation and mortality of inpatients with diabetic foot ulceration in the COVID‐19 pandemic and postpandemic era: A machine learning study

• Published in: International wound journal Vol. 19; no. 6; pp. 1289 - 1297
• Main Authors: Du, Chenzhen, Li, Yuyao, Xie, Puguang, Zhang, Xi, Deng, Bo, Wang, Guixue, Hu, Youqiang, Wang, Min, Deng, Wu, Armstrong, David G., Ma, Yu, Deng, Wuquan
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.10.2022; John Wiley & Sons, Inc
• Subjects: amputation; Amputation, Surgical; C-Reactive Protein; Chronic illnesses; Communicable Disease Control; COVID-19; COVID‐19 pandemic; Diabetes Mellitus; Diabetic Foot - epidemiology; diabetic foot ulceration; Foot Ulcer; Hemoglobins; Humans; Inpatients; Machine Learning; Medical imaging; Mortality; Pandemics; Ulcer; mortality; COVID-19 pandemic; amputation; machine learning; diabetic foot ulceration
• ISSN: 1742-4801; 1742-481X
• DOI: 10.1111/iwj.13723

This study aimed to explore the clinical characteristic and outcomes of inpatients with diabetic foot ulceration (DFU) in 2019 (prelockdown) and 2020 (postlockdown) due to the COVID‐19 pandemic, at an emergency medical service unit. Prediction models for mortality and amputation were developed to describe the risk factors using a machine learning‐based approach. Hospitalized DFU patients (N = 23) were recruited after the lockdown in 2020 and matched with corresponding inpatients (N = 23) before lockdown in 2019. Six widely used machine learning models were built and internally validated using 3‐fold cross‐validation to predict the risk of amputation and death in DFU inpatients under the COVID‐19 pandemic. Previous DF ulcers, prehospital delay, and mortality were significantly higher in 2020 compared to 2019. Diabetic foot patients in 2020 had higher hs‐CRP levels (P = .037) but lower hemoglobin levels (P = .017). The extreme gradient boosting (XGBoost) performed best in all models for predicting amputation and mortality with the highest area under the curve (0.86 and 0.94), accuracy (0.80 and 0.90), sensitivity (0.67 and 1.00), and negative predictive value (0.86 and 1.00). A long delay in admission and a higher risk of mortality was observed in patients with DFU who attended the emergency center during the COVID‐19 post lockdown. The XGBoost model can provide evidence‐based risk information for patients with DFU regarding their amputation and mortality. The prediction models would benefit DFU patients during the COVID‐19 pandemic.