The Association of Coagulation Indicators and Coagulant Agents With 30-Day Mortality of Critical Diabetics

Huang, Yingxin; Zhong, Zhihua; Liu, Fanna

doi:10.1177/10760296211026385

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…The pathophysiological mechanism of DFU is still debated. Previous studies have identified diabetic foot as one of the serious complications caused by pathophysiological changes such as inflammation, endothelial dysfunction, and blood coagulation imbalance (33). The presence of immune cells in an environment of chronic hyperglycemia and hypertriglyceridemia caused chronic immune system disorders and impaired responses to acute infections and sepsis in patients with DFU.…”

Section: Discussionmentioning

confidence: 99%

Clinical value of neutrophil-to-lymphocyte ratio and prognostic nutritional index on prediction of occurrence and development of diabetic foot-induced sepsis

Sun,

Chen,

Man

et al. 2023

Front. Public Health

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BackgroundDiabetic foot-induced sepsis is a serious complication associated with increased disability and mortality in hospitalized patients. Early prediction of admission and detection effectively improve treatment options and prevent further deterioration. This study aims to evaluate the clinical value of the neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) to predict the risk of sepsis in patients with diabetic foot ulcers (DFU).MethodsRetrospective analysis was performed on 216 patients who were admitted to the Fujian Medical University Union Hospital between January 2015 and December 2022. Patients with DFU were divided into the non-sepsis (n = 166) and the DFU-induced sepsis (n = 50) groups. The independent factors of DFU-induced sepsis were determined by univariate and multivariate logistic regression analyses. A receiver operating characteristic (ROC) curve was performed to compare the area under the curves (AUC) of PNI and NLR.ResultsMultivariate logistic regression analysis revealed that the PNI, NLR, international normalized ratio (INR), thrombin time (PT), and C-reactive protein (CRP) were independent prognostic factors for DFU-induced sepsis. After adjusting for potential confounders, the adjusted odds ratios of NLR for DFU-induced sepsis were 1.121 (1.072–1.172), 1.132 (1.077–1.189), and 1.080 (1.022–1.142), while those of PNI were 0.912 (0.873–0.953), 0.902 (0.856–0.950), and 1.004 (1.001–1.006). Moreover, the AUC of NLR was significantly greater than that of CRP (0.790, 95% CI: 0.689–0.891, p < 0.001 vs. 0.780, 95% CI: 0.686–0.873, p < 0.001).ConclusionNLR and PNI have been regarded as readily and independently predictive markers in patients with DFU-induced sepsis. NLR is critical for the early detection and effective treatment of DFU-induced sepsis and is superior to CRP.

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Section: Discussionmentioning

confidence: 99%

Clinical value of neutrophil-to-lymphocyte ratio and prognostic nutritional index on prediction of occurrence and development of diabetic foot-induced sepsis

Sun,

Chen,

Man

et al. 2023

Front. Public Health

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Machine Learning-Based Predictions of Mortality and Readmission in Type 2 Diabetes Patients in the ICU

Hu,

Chao,

et al. 2024

Applied Sciences

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Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the potential of machine learning techniques for enhancing predictive accuracy has not been fully realized. This study seeks to develop and validate predictive models employing machine learning algorithms to forecast mortality and 30-day post-discharge readmission rates among ICU type 2 diabetes patients, thereby enhancing predictive accuracy and supporting clinical decision-making. Data were extracted and preprocessed from the MIMIC-III database, focusing on 14,222 patients with type 2 diabetes and their corresponding ICU admission records. Comprehensive information, including vital signs, laboratory results, and demographic characteristics, was utilized. Six machine learning algorithms—bagging, AdaBoost, GaussianNB, logistic regression, MLP, and SVC—were developed and evaluated using 10-fold cross-validation to predict mortality at 3 days, 30 days, and 365 days, as well as 30-day post-discharge readmission rates. The machine learning models demonstrated strong predictive performance for both mortality and readmission rates. Notably, the bagging and AdaBoost models showed superior performance in predicting mortality across various time intervals, achieving AUC values up to 0.8112 and an accuracy of 0.8832. In predicting 30-day readmission rates, the MLP and AdaBoost models yielded the highest performance, with AUC values reaching 0.8487 and accuracy rates of 0.9249. The integration of electronic health record data with advanced machine learning techniques significantly enhances the accuracy of mortality and readmission predictions in ICU type 2 diabetes patients. These models facilitate the identification of high-risk patients, enabling timely interventions, improving patient outcomes, and demonstrating the significant potential of machine learning in clinical prediction and decision support.

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The Association of Coagulation Indicators and Coagulant Agents With 30-Day Mortality of Critical Diabetics

Cited by 2 publications

References 25 publications

Clinical value of neutrophil-to-lymphocyte ratio and prognostic nutritional index on prediction of occurrence and development of diabetic foot-induced sepsis

Clinical value of neutrophil-to-lymphocyte ratio and prognostic nutritional index on prediction of occurrence and development of diabetic foot-induced sepsis

Machine Learning-Based Predictions of Mortality and Readmission in Type 2 Diabetes Patients in the ICU

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