Comparison of bacterial species and clinical outcomes in patients with diabetic hand infection in tropical and nontropical regions.

Hand infection is a rare complication in patients with diabetes. Its clinical outcomes depend on the severity of hand infection caused by bacteria, but the difference in bacterial species in the regional disparity is unknown. The purpose of this study was to explore the influence of tropical and nontropical regions on bacterial species and clinical outcomes for diabetic hand. A systematic literature review was conducted using PubMed, EMBASE, Web of Science, and Google Scholar. Moreover, the bacterial species and clinical outcomes were analyzed with respect to multicenter wound care in China (nontropical regions). Both mixed bacteria (31.2% vs. 16.6%, p = 0.014) and fungi (7.5% vs. 0.8%, p = 0.017) in the nontropical region were significantly more prevalent than those in the tropical region. Staphylococcus and Streptococcus spp. were dominant in gram-positive bacteria, and Klebsiella, Escherichia coli, Proteus and Pseudomonas in gram-negative bacteria occupied the next majority in the two regions. The rate of surgical treatment in the patients was 31.2% in the nontropical region, which was significantly higher than the 11.4% in the tropical region (p = 0.001). Although the overall mortality was not significantly different, there was a tendency to be increased in tropical regions (6.3%) compared with nontropical regions (0.9%). However, amputation (32.9% vs. 31.3%, p = 0.762) and disability (6.3% vs. 12.2%, p = 0.138) were not significantly different between the two regions. Similar numbers of cases were reported, and the most common bacteria were similar in tropical and nontropical regions in patients with diabetic hand. There were more species of bacteria in the nontropical region, and their distribution was basically similar, except for fungi, which had differences between the two regions. The present study also showed that surgical treatment and mortality were inversely correlated because delays in debridement and surgery can deteriorate deep infections, eventually leading to amputation and even death.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. OBJECTIVE: This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. METHODS: In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. RESULTS: A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. CONCLUSIONS: The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Comparison of bacterial species and clinical outcomes in patients with diabetic hand infection in tropical and nontropical regions.

Purpose: Hand infection is a rare complication in patients with diabetes. Its clinical outcomes depend on the severity of hand infection caused by bacteria, but the difference in bacterial species in the regional disparity is unknown. The purpose of this study was to explore the influence of tropical and nontropical regions on bacterial species and clinical outcomes for diabetic hand. Patients and Methods: A systematic literature review was conducted using PubMed, EMBASE, Web of Science, and Google Scholar. Moreover, the bacterial species and clinical outcomes were analyzed with respect to multicenter wound care in China (nontropical regions). Results: Both mixed bacteria (31.2% vs. 16.6%, p=0.014) and fungi (7.5% vs. 0.8%, p=0.017) in the nontropical region were significantly more prevalent than those in the tropical region. Staphylococcus and Streptococcus spp. were dominant in gram-positive bacteria, and Klebsiella, Escherichia coli, Proteus and Pseudomonas in gram-negative bacteria occupied the next majority in the two regions. The rate of surgical treatment in the patients was 31.2% in the nontropical region, which was significantly higher than the 11.4% in the tropical region (p=0.001). Although the overall mortality was not significantly different, there was a tendency to be increased in tropical regions (6.3%) compared with nontropical regions (0.9%). However, amputation (32.9% vs. 31.3%, p=0.762) and disability (6.3% vs. 12.2%, p=0.138) were not significantly differentbetween the two regions. Conclusion: Similar numbers of cases were reported, and the most common bacteria were similar in tropical and nontropical regions in patients with diabetic hand. There were more species of bacteria in the nontropical region, and their distribution was basically similar, except for fungi, which had differences between the two regions. The present study also showed that surgical treatment and mortality were inversely correlated because delays in debridement and surgery can deteriorate deep infections, eventually leading to amputation and even death.

Association between gut microbiota and influenza: a bidirectional two-sample mendelian randomization study.

BACKGROUND: Previous observational studies have indicated a correlation between the gut microbiota and influenza; however, the exact nature of the bidirectional causal connection remains uncertain. METHOD: A two-way, two-sample Mendelian randomization (MR) study was conducted to evaluate the possible causal connection between the gut microbiota and the two outcomes of influenza (pneumonia without influenza and influenza pneumonia). The statistical analysis of gut microbiota is derived from the information of the most extensive meta-analysis (GWAS) conducted by the MiBioGen Alliance, encompassing a sample size of 18,340.The summary statistical data for influenza (not pneumonia, n = 291,090) and influenza pneumonia (n = 342,499) are from GWAS data published by FinnGen consortium R8.Estimate and summarize Single-nucleotide polymorphisms (SNPs) using Inverse variance weighted (IVW), MR Egger, and Weighted median (WM) in bidirectional MR analysis. To assess the heterogeneity, horizontal pleiotropy, and stability of SNPs, we employed Cochran's Q test, MR Egger intercept test, and sensitivity analysis. RESULT: The IVW analysis indicated that there was a significant association between influenza infection and five bacterial taxa. Additionally, the abundance changes of seven gut microbiota were found to be causally related to influenza infection. In addition, seven bacterial taxa showed a significant association with the occurrence of influenza pneumonia. The findings from the WM analysis largely support the outcomes of IVW, however, the results of MR egger analysis do not align with IVW. Furthermore, there is no proof to substantiate the cause-and-effect relationship between influenza pneumonia and the composition of gut microbiota. CONCLUSION: This analysis demonstrates a possible bidirectional causal connection between the prevalence of particular gut microbiota and the occurrence of influenza infection. The presence of certain gut microbiota may potentially contribute to the development of pneumonia caused by influenza. Additional investigation into the interaction between particular bacterial communities and influenza can enhance efforts in preventing, monitoring, and treating influenza.

Mortality prediction in patients with hyperglycaemic crisis using explainable machine learning: a prospective, multicentre study based on tertiary hospitals.

BACKGROUND: Experiencing a hyperglycaemic crisis is associated with a short- and long-term increased risk of mortality. We aimed to develop an explainable machine learning model for predicting 3-year mortality and providing individualized risk factor assessment of patients with hyperglycaemic crisis after admission. METHODS: Based on five representative machine learning algorithms, we trained prediction models on data from patients with hyperglycaemic crisis admitted to two tertiary hospitals between 2016 and 2020. The models were internally validated by tenfold cross-validation and externally validated using previously unseen data from two other tertiary hospitals. A SHapley Additive exPlanations algorithm was used to interpret the predictions of the best performing model, and the relative importance of the features in the model was compared with the traditional statistical test results. RESULTS: A total of 337 patients with hyperglycaemic crisis were enrolled in the study, 3-year mortality was 13.6% (46 patients). 257 patients were used to train the models, and 80 patients were used for model validation. The Light Gradient Boosting Machine model performed best across testing cohorts (area under the ROC curve 0.89 [95% CI 0.77-0.97]). Advanced age, higher blood glucose and blood urea nitrogen were the three most important predictors for increased mortality. CONCLUSION: The developed explainable model can provide estimates of the mortality and visual contribution of the features to the prediction for an individual patient with hyperglycaemic crisis. Advanced age, metabolic disorders, and impaired renal and cardiac function were important factors that predicted non-survival. TRIAL REGISTRATION NUMBER: ChiCTR1800015981, 2018/05/04.

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

Objective: The outcome of DFUs concomitant with HCE remains unknown. This study aimed to investigate mortality rates and identify risk factors of mortality in patients with DFUs-HCE. Methods: 27 inpatients with DFUs-HCE were retrospectively enrolled in a cohort design, they were compared to 93 inpatients with DFUs in a city designated emergency center, between January 2016 and January 2021. After a 6-year followed-up, clinical characteristic, amputation and survival rates were compared. Extreme gradient boosting was further used to explore the relative importance of HCE and other risk factors to all-cause mortality in DFUs. Results: Patients with DFUs-HCE were more likely to havedementia, acute kidney injury and septic shock, whereas DFUs were more likely to have diabetic peripheral neuropathy and ulcer recurrence (P<0.05). No significant difference was observed on the amputation rate and diabetes duration. Both Kaplan-Meier curves and adjusted Cox proportional model revealed that DFUs-HCE was associated with a higher mortality compared with DFUs (P<0.05). HCE significantly increased the risk of mortality in patients with DFUs (hazard ratio, 1.941; 95% CI 1.018-3.700; P = 0.044) and was independent from other confounding factors (age, sex, diabetes duration, Wagner grades and Charlson Comorbidity Index). The XGBoost model also revealed that HCE was one of the most important risk factors associated with all-cause mortality in patients with DFUs. Conclusions: DFUs-HCE had significantly lower immediate survival rates (first 1-6 month) than DFUs alone. HCE is an important risk factor for death in DFUs patients.

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

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.

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

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.

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

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.