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

Abstract

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.

Breakdown of Diabetic Foot Ulcer Care during the First Year of the Pandemic in Poland: A Retrospective National Cohort Study

“…In Poland, we observed a non-significant decrease in the number of major amputations with a simultaneous non-significant increase in the number of all (major and minor) amputations, which can be related to a two times higher risk of amputation in the pre-pandemic period in comparison with western countries, such as Italy [ 29 ]. The same increasing tendency was documented in China and India [ 46 , 47 ]. Reports from the United States also showed a 10.8 times increase in the risk of any amputations in patients with this diabetes complication ( p < 0.0001; 95%, CI:6.5–17.8) [ 40 ].…”

Design of an artificial neural network to predict mortality among COVID-19 patients

“…According to the literature, the ANN model [ [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] ] has the greatest performance in predicting COVID-19 mortality. The results of other reviewed studies also showed that ensemble ML (hybrid) models [ [65] , [66] , [67] , [68] , [69] ] and RF [ 58 , 61 , 70 , 71 ] algorithms are the most widely used and effective models for predicting COVID-19 mortality. So far, most efforts have targeted the application of ANNs and their comparison with other techniques for mortality prediction in patients with COVID-19.…”

“…In previous studies, different ML methods were trained to predict COVID-19 outcomes such as disease progression and deterioration [ 45 , 46 ], ICU hospitalization [ [46] , [47] , [48] , [49] , [50] ], and mortality [ 47 , 48 , [51] , [52] , [53] , [54] , [55] , [56] ]. The most important of these algorithms can be listed as ANN [ [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] ], ensemble models (boosting algorithms) [ [65] , [66] , [67] , [68] , [69] ], decision trees, in particular random forests (RF) [ 6 , 58 , 61 , 70 , 71 ], support vector machine (SVM) [ 58 , 61 ], and Naive Bayes (NB) [ 72 ]. According to the literature, the ANN model [ [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] ] has the greatest performance in predicting COVID-19 mortality.…”

Impact of acute hyperglycemic crisis episode on survival in individuals with diabetic foot ulcer using a machine learning approach

“…Extreme gradient boosting (XGBoost) algorithm is an efficient and flexible machine learning method with excellent scalability and a high running speed based on gradient tree boosting, which can learn nonlinear, high dimensional relationships from data ( 19 , 20 ). XGBoost often outperforms other machine learning algorithms for prediction with tabular data, and is currently considered as the state-of-the-art method for predicting tabular data ( 21 , 22 ). Therefore, XGBoost was used to predict mortality and assess the relative importance of potential risk factors in the study population.…”