Development and validation of a nomogram to predict mortality of patients with DIC in ICU.

Background: Disseminated intravascular coagulation (DIC) is a devastating condition, which always cause poor outcome of critically ill patients in intensive care unit. Studies concerning short-term mortality prediction in DIC patients is scarce. This study aimed to identify risk factors contributing to DIC mortality and construct a predictive nomogram. Methods: A total of 676 overt DIC patients were included. A Cox proportional hazards regression model was developed based on covariates identified using least absolute shrinkage and selection operator (LASSO) regression. The prediction performance was independently evaluated in the MIMIC-III and MIMIC-IV Clinical Database, as well as the 908th Hospital Database (908thH). Model performance was independently assessed using MIMIC-III, MIMIC-IV, and the 908th Hospital Clinical Database. Results: The Cox model incorporated variables identified by Lasso regression including heart failure, sepsis, height, SBP, lactate levels, HCT, PLT, INR, AST, and norepinephrine use. The model effectively stratified patients into different mortality risk groups, with a C-index of >0.65 across the MIMIC-III, MIMIC-IV, and 908th Hospital databases. The calibration curves of the model at 7 and 28 days demonstrated that the prediction performance was good. And then, a nomogram was developed to facilitate result visualization. Decision curve analysis indicated superior net benefits of the nomogram. Conclusion: This study provides a predictive nomogram for short-term overt DIC mortality risk based on a Lasso-Cox regression model, offering individualized and reliable mortality risk predictions.

Nomogram for predicting disseminated intravascular coagulation in heatstroke patients: A 10 years retrospective study.

Background: Disseminated intravascular coagulation (DIC) can lead to multiple organ failure and death in patients with heatstroke. This study aimed to identify independent risk factors of DIC and construct a predictive model for clinical application. Methods: This retrospective study included 87 patients with heatstroke who were treated in the intensive care unit of our hospital from May 2012 to October 2022. Patients were divided into those with DIC (n = 23) or without DIC (n = 64). Clinical and hematological factors associated with DIC were identified using a random forest model, least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). Overlapping factors were used to develop a nomogram model, which was diagnostically validated. Survival at 30 days after admission was compared between patients with or without DIC using Kaplan-Meier analysis. Results: Random forest, LASSO, and SVM-RFE identified a low maximum amplitude, decreased albumin level, high creatinine level, increased total bilirubin, and aspartate transaminase (AST) level as risk factors for DIC. Principal component analysis confirmed that these independent variables differentiated between patients who experienced DIC or not, so they were used to construct a nomogram. The nomogram showed good predictive power, with an area under the receiver operating characteristic curve of 0.976 (95% CI 0.948-1.000) and 0.971 (95% CI, 0.914-0.989) in the internal validation. Decision curve analysis indicated clinical utility for the nomogram. DIC was associated with significantly lower 30 days survival for heatstroke patients. Conclusion: A nomogram incorporating coagulation-related risk factors can predict DIC in patients with heatstroke and may be useful in clinical decision-making.

Prediction of 90-Day Mortality among Sepsis Patients Based on a Nomogram Integrating Diverse Clinical Indices.

BACKGROUND: Sepsis is prevalent among intensive care units and is a frequent cause of death. Several studies have identified individual risk factors or potential predictors of sepsis-associated mortality, without defining an integrated predictive model. The present work was aimed at defining a nomogram for reliably predicting mortality. METHODS: We carried out a retrospective, single-center study based on 231 patients with sepsis who were admitted to our intensive care unit between May 2018 and October 2020. Patients were randomly split into training and validation cohorts. In the training cohort, multivariate logistic regression and a stepwise algorithm were performed to identify risk factors, which were then integrated into a predictive nomogram. Nomogram performance was assessed against the training and validation cohorts based on the area under receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis. RESULTS: Among the 161 patients in the training cohort and 70 patients in the validation cohort, 90-day mortality was 31.6%. Older age and higher values for the international normalized ratio, lactate level, and thrombomodulin level were associated with greater risk of 90-day mortality. The nomogram showed an AUC of 0.810 (95% CI 0.739 to 0.881) in the training cohort and 0.813 (95% CI 0.708 to 0.917) in the validation cohort. The nomogram also performed well based on the calibration curve and decision curve analysis. CONCLUSION: This nomogram may help identify sepsis patients at elevated risk of 90-day mortality, which may help clinicians allocate resources appropriately to improve patient outcomes.

Pre-expansion of posterior gastric fascia during laparoscopic radical gastrectomy for gastric cancer prevents injuries to upper short gastric vessels.

Splenic injuries during laparoscopic gastrectomy include splenic vascular injury, splenic parenchymal injury and splenic capsule injury. Sometimes, it can lead to lethal hemorrhage. The avoidance of splenic injuries in the laparoscopic radical gastrectomy for gastric cancer is important, especially for the obese patients. Establishing a safe method to avoid splenic injuries in laparoscopic total gastrectomy (LTG) is an urgent issue in the surgical procedure. We developed a novel approach by extended separation of the superficial plane of the posterior gastric fascia to avoid the splenic injury effectively.

Magnetically guided delivery of DHA and Fe ions for enhanced cancer therapy based on pH-responsive degradation of DHA-loaded Fe3O4@C@MIL-100(Fe) nanoparticles.

Dihydroartemisinin (DHA) has been investigated in cancer therapy for its reactive oxygen species (ROS) based cytotoxicity originated from interacting with ferrous ions that may reduce or eliminate the multidrug resistance commonly associated with conventional chemotherapy agents. However, synchronously delivery of hydrophobic DHA and Fe (Ⅲ) ions into tumor cells remains a major challenge. In this work, we develop novel Fe3O4@C@MIL-100(Fe) (FCM) nanoparticles for synchronously delivery of DHA and Fe (Ⅲ) for cancer therapy. The MOFs structure based on Fe (Ⅲ) carboxylate materials MIL-100 (Fe) holds great potential for storage/delivery of hydrophobic drug DHA. As a unique nanoplatform, the hybrid inorganic-organic drug delivery vehicles show pH-responsive biodegradation and synchronous releasing of DHA and Fe (Ⅲ) upon reaching tumor sites. The intracellular Fe (Ⅲ) will be reduced further to ferrous ion and interact with DHA to increase its cytotoxicity. The potential of this alternative anti-tumor modality is demonstrated in vivo due to an increased intracellular accumulation of DHA in tumor and activated mechanism via co-release of DHA and Fe (Ⅲ), especially under the guidance of an external applied magnetic field.