Unraveling the Intricate Web: Complement Activation Shapes the Pathogenesis of Sepsis-Induced Coagulopathy.

BACKGROUND: Sepsis-associated coagulopathy specifically refers to widespread systemic coagulation activation accompanied by a high risk of hemorrhage and organ damage, which in severe cases manifests as disseminated intravascular coagulation (DIC), or even develops into multiple organ dysfunction syndrome (MODS). The complement system and the coagulation system as the main columns of innate immunity and hemostasis, respectively, undergo substantial activation after sepsis. SUMMARY: Dysfunction of the complement, coagulation/fibrinolytic cascades caused by sepsis leads to "thromboinflammation," which ultimately amplifies the systemic inflammatory response and accelerates the development of MODS. Recent studies have revealed that massive activation of the complement system exacerbates sepsis-induced coagulation and even results in DIC, which suggests that inhibition of complement activation may have therapeutic potential in the treatment of septic coagulopathy. KEY MESSAGES: Sepsis-associated thrombosis involves the upregulation or activation of procoagulant factors, down-regulation or inactivation of anticoagulant factors, and impairment of the fibrinolytic mechanism. This review aims to summarize the latest literature and analyze the underlying molecular mechanisms of the activation of the complement system on the abnormal coagulation cascades in sepsis.

The effect of Shengmai injection in patients with coronary heart disease in real world and its personalized medicine research using machine learning techniques.

Objective: Shengmai injection is a common treatment for coronary heart disease. The accurate dose regimen is important to maximize effectiveness and minimize adverse reactions. We aim to explore the effect of Shengmai injection in patients with coronary heart disease based on real-world data and establish a personalized medicine model using machine learning and deep learning techniques. Methods: 211 patients were enrolled. The length of hospital stay was used to explore the effect of Shengmai injection in a case-control study. We applied propensity score matching to reduce bias and Wilcoxon rank sum test to compare results between the experimental group and the control group. Important variables influencing the dose regimen of Shengmai injection were screened by XGBoost. A personalized medicine model of Shengmai injection was established by XGBoost selected from nine algorithm models. SHapley Additive exPlanations and confusion matrix were used to interpret the results clinically. Results: Patients using Shengmai injection had shorter length of hospital stay than those not using Shengmai injection (median 10.00 days vs. 11.00 days, p = 0.006). The personalized medicine model established via XGBoost shows accuracy = 0.81 and AUC = 0.87 in test cohort and accuracy = 0.84 and AUC = 0.84 in external verification. The important variables influencing the dose regimen of Shengmai injection include lipid-lowering drugs, platelet-lowering drugs, levels of GGT, hemoglobin, prealbumin, and cholesterol at admission. Finally, the personalized model shows precision = 75%, recall rate = 83% and F1-score = 79% for predicting 40 mg of Shengmai injection; and precision = 86%, recall rate = 79% and F1-score = 83% for predicting 60 mg of Shengmai injection. Conclusion: This study provides evidence supporting the clinical effectiveness of Shengmai injection, and established its personalized medicine model, which may help clinicians make better decisions.

Vancomycin population pharmacokinetics analysis in Chinese paediatric patients with varying degrees of renal function and ages: development of new practical dosing recommendations.

OBJECTIVES: To describe the pharmacokinetics of vancomycin in a large Chinese paediatric cohort with varying degrees of renal function and ages and to develop practical dosing guidelines. PATIENTS AND METHODS: We conducted a retrospective population pharmacokinetic study using data from paediatric patients who received vancomycin between June 2013 and June 2022. A non-linear mixed-effect modelling approach with a one-compartment model structure was applied. Monte Carlo simulations were used to stimulate an optimal dosage regimen to achieve the target of AUC24/MIC between 400 and 650. RESULTS: We analysed a total of 673 paediatric patients and 1547 vancomycin serum concentrations. Covariate analysis revealed that physiological maturation, renal function, albumin and cardiothoracic surgery (CTS) significantly affected vancomycin pharmacokinetics. The typical clearance and volume of distribution, standardized to 70 kg, were 7.75 L/h (2.3% relative standard error, RSE) and 36.2 L (1.7% RSE), respectively. Based on the model, we proposed an optimal dosing regimen that considers the patient's age and estimate glomerular filtration rate (eGFR) to achieve a target AUC24/MIC for CTS and non-CTS patients. We also found that a loading dose of 20 mg/kg can help patients with an eGFR of <60 mL/min/1.73 m2 achieve the target AUC on the first day of treatment. CONCLUSIONS: We established vancomycin pharmacokinetic parameters in Chinese paediatric patients and proposed a dosing guideline integrating eGFR, age and CTS status, potentially improving clinical outcomes and reducing nephrotoxicity risk.

The relationship between vancomycin AUC/MIC and trough concentration, age, dose, renal function in Chinese critically ill pediatric patients.

To assess the pharmacokinetic parameters of vancomycin in Chinese critically ill pediatric patients, children treated with vancomycin, hospitalized in the intensive care unit were included. Samples to determine peak and trough serum concentrations were obtained on the third day of treatment. Half-life was significantly longer in neonates and showed a decreasing trend in infants and children. In patients aged ≥1 month, AUC24 /MIC ≥400 was achieved in 31.8% at the dose of 40 mg/kg/d, and in 48.7% at the dose of 60 mg/kg/d with an assumed MIC of 1 mg/L. Augmented renal clearance (ARC) was present in 27.3% of children, which was associated with higher vancomycin clearance and lower AUC values. A good correlation was observed between trough concentration and AUC24 , and the trough concentration that correlated with AUC24 of 400 were varied according to the dosage regimens, 8.42 mg/L for 6-hintervals, and 6.63 mg/L for 8-h intervals. To conclude, vancomycin trough concentration that related to the AUC24 of 400 was much lower in critically ill children than that in adults. The dosage of 60 mg/kg/day did not enough for producing AUC24 in the range of 400-600 mg h/L in critically ill children, especially in those with ARC.

Prediction of vancomycin dose on high-dimensional data using machine learning techniques.

OBJECTIVES: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods. METHODS: This study proposed a variable engineering process that automatically generates second-order variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables. RESULTS: Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups. CONCLUSION: The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.

An Ensemble Model for Prediction of Vancomycin Trough Concentrations in Pediatric Patients.

PURPOSE: This study aimed to establish an optimal model to predict vancomycin trough concentrations by using machine learning. PATIENTS AND METHODS: We enrolled 407 pediatric patients (age < 18 years) who received vancomycin intravenously and underwent therapeutic drug monitoring from June 2013 to April 2020 at Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine. The median (interquartile range) age and weight of the patients were 2 (0.63-5) years and 12 (7.8-19) kg. Vancomycin trough concentrations were considered as the target variable, and eight different algorithms were used for predictive performance comparison. The whole dataset (407 cases) was divided into training group and testing group at the ratio of 80%: 20%, which were 325 and 82 cases, respectively. RESULTS: Ultimately, five algorithms (XGBoost, GBRT, Bagging, ExtraTree and decision tree) with high R 2 (0.657, 0.514, 0.468, 0.425 and 0.450, respectively) were selected and further ensembled to establish the final model and achieve an optimal result. For missing data, through filling the missing values and model ensemble, we obtained R 2 =0.614, MAE=3.32, MSE=24.39, RMSE=4.94 and a prediction accuracy of 51.22% (predicted trough concentration within ±30% of the actual trough concentration). In comparison with the pharmacokinetic models (R 2 =0.3), the machine learning model works better in model fitting and has better prediction accuracy. CONCLUSION: Therefore, the ensemble model is useful for the vancomycin concentration prediction, especially in the population of children with great individual variation. As machine learning methods evolve, the clinical value of the ensemble model will be demonstrated in the clinical practice.

Prediction of Prognosis in Adult Patients With Carbapenem-Resistant Klebsiella pneumoniae Infection.

OBJECTIVE: Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections are associated with poor patient outcomes. We aimed to analyze the clinical information of adult patients with CRKP infection in order to establish a nomogram for mortality risk as well as to determine the treatment effectiveness of different antimicrobial regimens. METHODS: Adult patients diagnosed with CRKP infection in a tertiary hospital in Shanghai between September 2019 and March 2021 were included. The clinical characteristics and clinical outcomes of these patients were analyzed. RESULTS: A total of 199 cases of CRKP infection were examined. Five factors, namely age ≥65 years, respiratory failure, Sequential Organ Failure Assessment score, serum procalcitonin ≥5 ng/mL, and appropriate treatments in 3 days, were found to be associated with 30-day mortality. Upon incorporating these factors, the nomogram achieved good concordance indexes of 0.85 (95% confidence interval [CI]: 0.80-0.90) and well-fitted calibration curves. Receiver-operating characteristic curves for 7-, 15-, and 30-day survival had areas under the curve of 0.90, 0.87, and 0.88, respectively. Three-drug combination therapy was observed to be associated with lower mortality in the high-risk group (adjusted hazard ratio = 0.24, 95% CI: 0.06-0.99) but not in the low-risk group. Ceftazidime-avibactam, fosfomycin, and amikacin were effective against infections caused by CRKP. Tigecycline improved the treatment efficiency in 7 days, but a trend toward increased mortality was seen (HR, 1.69; 95% CI: 0.98-2.94; P = 0.061). CONCLUSION: The antimicrobial regimen efficacy data and the predictive nomogram established in this study can help clinicians in identifying high-risk adult patients with CRKP infection, improving the therapeutic effect, and reducing mortality.

Andrographolide attenuates microglia-mediated Aß neurotoxicity partially through inhibiting NF-κB and JNK MAPK signaling pathway.

Neuroinflammation plays a critical role in the pathogenesis of several neurodegenerative diseases, including Alzheimer's disease (AD). Microglial cells after activated play critical roles in development of neuroinflammation, and may accelerate the progression of AD. Andrographolide (ANDRO), a potent naturally extracted substance, has been demonstrated to exert suppressive effects on LPS-induced inflammation by modulating macrophage and microglia overactivation. Whereas in AD, ß-amyloid (Aß) peptides have been considered as a potent activator of neuroinflammation, the effect of ANDRO on Aß-induced neuroinflammation has not been examined. In this study, we investigated the effects of ANDRO on Aß(1-42)-induced neuroinflammation. We found that ANDRO significantly protected neuronal cells against microglia-mediated Aß(1-42) toxicity and attenuated the release of preinflammatory productions such as tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), nitric oxide (NO), and prostaglandin E2 (PGE2). It also downregulated the protein levels of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) in microglial cells. Further the involved mechanism study demonstrated that ANDRO inhibited the nuclear translocation of nuclear factor-κB (NF-κB) by affecting IκB phosphorylation, and attenuated Aß(1-42)-induced JNK-MAPK overactivation. In summary, this study, for the first time, revealed ANDRO reduced inflammation-mediated neuronal damage by blocking inflammatory responses of microglial cells to Aß(1-42), suggesting ANDRO may be an effective agent in modulating neuroinflammatory process in AD.

Retrospective Analysis of Vancomycin Nephrotoxicity in Elderly Chinese Patients.

This study explored nephrotoxicity in elderly Chinese patients after exposure to vancomycin and other nephrotoxic risk factors. This was a single-center retrospective study. The patient population included those who were ≥60 years of age, had normal baseline serum creatinine values, and received vancomycin for ≥48 h between January 1, 2013 and August 30, 2014. Nephrotoxicity occurred in 29% of 124 patients. A baseline creatinine clearance ≥63.5 ml/min was more common in the nephrotoxic group. Patients with high (≥15 mg/l) rather than low (<15 mg/l) average vancomycin troughs had elevated nephrotoxicity (47.2 vs. 27.3%, p = 0.0001). Of the comorbid conditions evaluated, there were more patients with shock (p = 0.001), hypertension (p = 0.020) and congestive heart failure (p = 0.04) in the nephrotoxic group. Drugs frequently given at the same time with vancomycin, such as angiotensin receptor blockers and furosemide, were also associated with increased nephrotoxic risk. In conclusion, nephrotoxicity was frequently observed in patients with concurrent vancomycin trough concentrations ≥15 µg/ml and hypertension, shock, congestive heart failure. In addition, drugs concurrently used with vancomycin may also increase its nephrotoxicity. Therefore, renal function and vancomycin serum troughs should be closely monitored, especially in patients with other renal injury risk factors.

Relationships between PON1 Q192R polymorphism and clinical outcome of antiplatelet treatment after percutaneous coronary intervention: a meta-analysis.

This meta-analysis was performed to assess the relationships between the PON1 Q192R (rs662 T>C) polymorphism and the clinical outcome of antiplatelet treatment after percutaneous coronary intervention (PCI). A range of electronic databases were searched: Web of Science (1945-2013), the Cochrane Library Database (Issue 12, 2013), PubMed (1966-2013), EMBASE (1980-2013), CINAHL (1982-2013) and the Chinese Biomedical Database (CBM) (1982-2013) without language restrictions. Meta-analysis was conducted using the STATA 12.0 software. The crude odds ratio (OR) with their 95 % confidence interval (CI) were calculated. Six clinical cohort studies with a total number of 5,189 patients undergoing PCI for coronary heart disease were included. Our meta-analysis revealed that the PON1 Q192R polymorphism was correlated with an increased risk of major adverse cardiovascular events (MACE) in patients receiving antiplatelet treatment after PCI (C allele vs. T allele: OR = 1.22, 95 % CI 1.04-1.43, P = 0.014; CT+CC vs. TT: OR = 1.38, 95 % CI 1.03-1.86, P = 0.029; CC vs. TT: OR = 1.45, 95 % CI 1.05-1.99, P = 0.024; respectively), especially among Asians. Furthermore, we found significantly positive correlations between the PON1 Q192R polymorphism and the incidence of stent thrombosis in patients receiving antiplatelet treatment after PCI (C allele vs. T allele: OR = 1.42, 95 % CI 1.08-1.87, P = 0.011; CT+CC vs. TT: OR = 1.93, 95 % CI 1.01-3.67, P = 0.046; CC vs. TT: OR = 2.18, 95 % CI 1.09-4.35, P = 0.027; respectively). Our meta-analysis of clinical cohort studies provides evidence that the PON1 Q192R polymorphism may increase the risk of MACE and stent thrombosis in patients receiving antiplatelet treatment after PCI.