Publications by authors named "Puguang Xie"

3 Publications

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

Int Wound J 2021 Nov 24. Epub 2021 Nov 24.

Department of Endocrinology, School of Medicine, Bioengineering College, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing University, Chongqing, China.

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.
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http://dx.doi.org/10.1111/iwj.13723DOI Listing
November 2021

Time in range in relation to amputation and all-cause mortality in hospitalised patients with diabetic foot ulcers.

Diabetes Metab Res Rev 2021 Sep 29:e3498. Epub 2021 Sep 29.

Department of Endocrinology, College of Medicine, College of Bioengineering, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing University, Chongqing, China.

Aims: The aim of this study was to evaluate the association of time in range (TIR) with amputation and all-cause mortality in hospitalised patients with diabetic foot ulcers (DFUs).

Materials And Methods: A retrospective analysis was performed on 303 hospitalised patients with DFUs. During hospitalisation, TIR, mean blood glucose (MBG), coefficient of variation (CV), time above range (TAR) and time below range (TBR) of patients were determined from seven-point blood glucose profiles. Participants were grouped based on their clinical outcomes (i.e., amputation and death). Logistic regression was employed to analyse the association of TIR with amputation and all-cause mortality of inpatients with DFUs.

Results: Among the 303 enrolled patients, 50 (16.5%) had undergone amputation whereas seven (2.3%) were deceased. Blood glucose was determined in 41,012 samples obtained from all participants. Patients who underwent amputation had significantly lower TIR and higher MBG, CV, level 2 TAR and level 1 TBR whereas deceased patients had significantly lower TIR and higher MBG and level 2 TAR. Both amputation and all-cause mortality rate declined with an increase in TIR quartiles. Logistic regression showed association of TIR with amputation (p = 0.034) and all-cause mortality (p = 0.013) after controlling for 15 confounders. This association was similarly significant in all-cause mortality after further adjustment for CV (p = 0.022) and level 1 TBR (p = 0.021), respectively.

Conclusions: TIR is inversely associated with amputation and all-cause mortality of hospitalised patients with DFUs. Further prospective studies are warranted to establish a causal relationship between TIR and clinical outcomes in patients with DFUs.
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http://dx.doi.org/10.1002/dmrr.3498DOI Listing
September 2021

An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer.

Int Wound J 2021 Sep 14. Epub 2021 Sep 14.

Department of Endocrinology and Metabolism, Chongqing Emergency Medical Center, Chongqing Key Laboratory of Emergency Medicine, Chongqing University Central Hospital, Chongqing University, Chongqing, China.

Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision-making. We aimed to develop an accurate and explainable prediction model to estimate the risk of in-hospital amputation in patients with DFU. A total of 618 hospitalised patients with DFU were included in this study. The patients were divided into non-amputation, minor amputation or major amputation group. Light Gradient Boosting Machine (LightGBM) and 5-fold cross-validation tools were used to construct a multi-class classification model to predict the three outcomes of interest. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the predictions of the model. Our area under the receiver-operating-characteristic curve (AUC) demonstrated a 0.90, 0.85 and 0.86 predictive ability for non-amputation, minor amputation and major amputation outcomes, respectively. Taken together, our data demonstrated that the developed explainable machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors.
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http://dx.doi.org/10.1111/iwj.13691DOI Listing
September 2021
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