Machine Learning Algorithm-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa During the COVID-19 Pandemic.

Abstract

Background and Objectives: Diabetic foot sepsis (DFS) accounts for approximately 60% of hospital admissions in patients with diabetes mellitus (DM). Individuals with DM are at risk of severe COVID-19. This study investigated factors associated with major amputation and mortality in patients admitted with DFS during the COVID-19 pandemic. Materials and Methods: Demographic information, COVID-19 and HIV status, clinical findings, laboratory results, treatment and outcome from records of patients with diabetic foot sepsis, were collected and analysed. Supervised machine learning algorithms were used to compare their ability to predict mortality due to diabetic foot sepsis. Results: Overall, 114 records were found and 57.9% (66/114) were of male patients. The mean age of the patients was 55.7 (14) years and 47.4% (54/114) and 36% (41/114) tested positive for COVID-19 and HIV, respectively. The median c-reactive protein was 168 mg/dl, urea 7.8 mmol/L and creatinine 92 µmol/L. The mean potassium level was 4.8 ± 0.9 mmol, and glycosylated haemoglobin 11.2 ± 3%. The main outcomes included major amputation in 69.3% (79/114) and mortality of 37.7% (43/114) died. AI. The levels of potassium, urea, creatinine and HbA1c were significantly higher in the deceased. Conclusions: The COVID-19 pandemic led to an increase in the rate of major amputation and mortality in patients with DFS. The in-hospital mortality was higher in patients above 60 years of age who tested positive for COVID-19. The Random Forest algorithm of ML can be highly effective in predicting major amputation and death in patients with DFS.