Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study

ROLE OF MICROGLIA IN SEPSIS-ASSOCIATED ENCEPHALOPATHY PATHOGENESIS: AN UPDATE

ABSTRACT

Sepsis-associated encephalopathy (SAE) is a serious complication of sepsis, which is characterized by cognitive dysfunction, a poor prognosis, and high incidences of morbidity and mortality. Substantial levels of systemic inflammatory factors induce neuroinflammatory responses during sepsis, ultimately disrupting the central nervous system's (CNS) homeostasis. This disruption results in brain dysfunction through various underlying mechanisms, contributing further to SAE's development. Microglia, the most important macrophage in the CNS, can induce neuroinflammatory responses, brain tissue injury, and neuronal dysregulation, resulting in brain dysfunction. They serve an important regulatory role in CNS homeostasis and can be activated through multiple pathways. Consequently, activated microglia are involved in several pathogenic mechanisms related to SAE and play a crucial role in its development. This article discusses the role of microglia in neuroinflammation, dysfunction of neurotransmitters, disruption of the blood-brain barrier, abnormal control of cerebral blood flow, mitochondrial dysfunction, and reduction in the number of good bacteria in the gut as main pathogenic mechanisms of SAE and focuses on studies targeting microglia to ameliorate SAE to provide a theoretical basis for targeted microglial therapy for SAE.

Combining biomarkers of BNIP3 L, S100B, NSE, and accessible measures to predict sepsis-associated encephalopathy: a prospective observational study

BACKGROUND

Accurate identification of delirium in sepsis patients is crucial for guiding clinical diagnosis and treatment. However, there are no accurate biomarkers and indicators at present. We aimed to identify which combinations of cognitive impairment-related biomarkers and other easily accessible assessments best predict delirium in sepsis patients.

METHODS

One hundred and one sepsis patients were enrolled in a prospective study cohort. S100B, NSE, and BNIP3 L biomarkers were detected in plasma and cerebrospinal fluid and patients' optic nerve sheath diameter (ONSD). The optimal biomarkers identified by Logistic regression are combined with other factors such as ONSD to filter out the perfect model to predict delirium in sepsis patients through Logistic regression, Naïve Bayes, decision tree, and neural network models.

MAIN RESULTS

Among all biomarkers, compared with BNIP3 L (AUC = .706, 95% CI = .597-.815) and NSE (AUC = .711, 95% CI = .609-.813) in cerebrospinal fluid, plasma S100B (AUC = .729, 95% CI = .626-.832) had the best discrimination performance for delirium in sepsis patients. Logistic regression analysis showed that the combination of cerebrospinal fluid BNIP3 L with plasma S100B, ONSD, neutrophils, and age provided the best discrimination to cognitive impairment in sepsis patients (accuracy = .901, specificity = .923, sensitivity = .911), which was better than Naïve Bayes, decision tree, and neural network models. Neutrophils, ONSD, and cerebrospinal fluid BNIP3 L were consistently the major contributors in a few models.

CONCLUSIONS

The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

Establishment and Validation of Models for the Risk of Multi-Drug Resistant Bacteria Infection and Prognosis in Elderly Patients with Pulmonary Infection: A Multicenter Retrospective Study

Purpose

The aim of this study was to establish risk prediction and prognosis models for multidrug-resistant bacterial infections (MDRB) in elderly patients with pulmonary infections in a multicenter setting.

Patients and Methods

This study is a retrospective cohort analysis in Anhui province of China. Data dimension reduction and feature selection were performed using the lasso regression model. Multifactorial regression analysis to identify risk factors associated with MDRB infection and prognosis. The relevant risks of each patient in the prognostic training cohort were scored based on prognostic independent risk factors. Subsequently, patients were classified into high-risk and low-risk groups, and survival differences were compared between them. Finally, models were established based on independent risk factors for infection, risk groups, and independent prognostic factors, and were presented on nomograms. The predictive accuracy of the model was assessed using corresponding external validation set data.

Results

The study cohort comprised 994 elderly patients with pulmonary infection. Multivariate analysis revealed that endotracheal intubation, previous antibiotic use beyond 2 weeks, and concurrent respiratory failure or cerebrovascular disease were independent risk factors associated with the incidence of MDRB infection. Cox regression analysis identified respiratory failure, malnutrition, an APACHE II score of at least 20, and higher blood creatinine levels as independent prognostic risk factors. The models were validated using an external validation dataset from multiple centers, which demonstrated good diagnostic ability and a good fit with a fair benefit.

Conclusion

In conclusion, our study provides an appropriate and generalisable assessment of risk factors affecting infection and prognosis in patients with MDRB, contributing to improved early identification of patients at higher risk of infection and death, and appropriately guiding clinical management.

Paediatric sepsis-associated encephalopathy (SAE): a comprehensive review

Sepsis-associated encephalopathy (SAE) is one of the most common types of organ dysfunction without overt central nervous system (CNS) infection. It is associated with higher mortality, low quality of life, and long-term neurological sequelae, its mortality in patients diagnosed with sepsis, progressing to SAE, is 9% to 76%. The pathophysiology of SAE is still unknown, but its mechanisms are well elaborated, including oxidative stress, increased cytokines and proinflammatory factors levels, disturbances in the cerebral circulation, changes in blood-brain barrier permeability, injury to the brain's vascular endothelium, altered levels of neurotransmitters, changes in amino acid levels, dysfunction of cerebral microvascular cells, mitochondria dysfunction, activation of microglia and astrocytes, and neuronal death. The diagnosis of SAE involves excluding direct CNS infection or other types of encephalopathies, which might hinder its early detection and appropriate implementation of management protocols, especially in paediatric patients where only a few cases have been reported in the literature. The most commonly applied diagnostic tools include electroencephalography, neurological imaging, and biomarker detection. SAE treatment mainly focuses on managing underlying conditions and using antibiotics and supportive therapy. In contrast, sedative medication is used judiciously to treat those showing features such as agitation. The most widely used medication is dexmedetomidine which is neuroprotective by inhibiting neuronal apoptosis and reducing a sepsis-associated inflammatory response, resulting in improved short-term mortality and shorter time on a ventilator. Other agents, such as dexamethasone, melatonin, and magnesium, are also being explored in vivo and ex vivo with encouraging results. Managing modifiable factors associated with SAE is crucial in improving generalised neurological outcomes. From those mentioned above, there are still only a few experimentation models of paediatric SAE and its treatment strategies. Extrapolation of adult SAE models is challenging because of the evolving brain and technical complexity of the model being investigated. Here, we reviewed the current understanding of paediatric SAE, its pathophysiological mechanisms, diagnostic methods, therapeutic interventions, and potential emerging neuroprotective agents.

Prognostic role of serum ammonia in patients with sepsis-associated encephalopathy without hepatic failure

Objectives

Our previous study shows that serum ammonia in sepsis patients without hepatic failure is associated with a poor prognosis. The relationship between serum ammonia level and the prognosis of sepsis-associated encephalopathy (SAE) patients without hepatic failure remains unclear. We aimed to explore the relationship between serum ammonia levels and the prognosis of patients with SAE.

Materials and methods

This study is a retrospective cohort study. We collected 465 patients with SAE admitted to the intensive care unit (ICU) from Medical Information Mart for Intensive Care IV (MIMIC IV) from 2008 to 2019. Patients with SAE were divided into a survival group (369 patients) and a non-survival group (96 patients). We used the Wilcoxon signed-rank test and the multivariate logistic regression analysis to analyze the relationship between serum ammonia levels and the prognosis of patients with SAE. R software was used to analyze the dataset.

Results

The primary outcome was the relationship between serum ammonia level and hospital mortality of SAE. The secondary outcomes were the relationship between serum ammonia level and hospital stays, simplified acute physiology score (SAPS II), Charlson, Glasgow coma scale (GCS), sequential organ failure assessment (SOFA), and lactate level of SAE. The mortality of patients with SAE was 20.6%. The serum ammonia level was not significantly associated with hospital mortality, longer hospital stays, higher SAPS II and Charlson scores, and lower GCS of patients with SAE. The serum ammonia level was associated with higher SOFA scores and lactate levels in patients with SAE. The SAPS II and Charlson scores were independent risk factors for death in patients with SAE.

Conclusion

Serum ammonia level was associated with higher SOFA scores and lactate levels in patients with SAE. In addition, the SAPS II and Charlson scores can be used to assess the prognosis of patients with SAE. Therefore, we should closely monitor serum ammonia, SAPS II, and Charlson levels in patients with SAE.

New Strategies to Optimize Hemodynamics for Sepsis-Associated Encephalopathy

Background: Sepsis-associated encephalopathy (SAE) is associated with high morbidity and mortality. Hemodynamic dysfunction plays a significant role in the incidence and mortality of SAE. Therefore, this study aimed to explore the relationship between hemodynamic indicators and SAE. Methods: 9033 patients with sepsis 3.0 were selected in a prospective study cohort. The LASSO regression model was used to select characteristic variables and remove the collinearity between them. In addition, a generalized additive model was used to find the optimal hemodynamic index value for patients with SAE. Multivariate logistic regression models, propensity matching scores, inverse probability weighting, and doubly robust estimation confirmed the reliability of the study results (i.e., the optimal hemodynamic indicators targeting patients with SAE). Results: A mean arterial pressure ≥ 65 mmHg, systolic blood pressure ≥ 90 mmHg, and lactate levels ≤ 3.5 mmol/L decrease the incidence of SAE, whereas a mean arterial pressure ≥ 59 mmHg and lactate levels ≤ 4.5 mmol/L decrease the 28-day mortality in patients with SAE. Conclusions: The hemodynamic indices of patients with SAE should be maintained at certain levels to reduce the incidence and mortality in patients with SAE, such that the mean arterial pressure is ≥65 mmHg, lactate levels are ≤3.5 mmol/L, and systolic blood pressure is ≥90 mmHg. These hemodynamic indicators should be targeted in patients with SAE.

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated encephalopathy

Objective

Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE).

Materials and methods

ML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test.

Results

Of 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO₂, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively.

Conclusions

The ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01664-z.

A novel prognostic model for predicting the mortality risk of patients with sepsis-related acute respiratory failure: a cohort study using the MIMIC-IV database

OBJECTIVES

Acute respiratory failure increases short-term mortality in sepsis patients. Hence, in this study, we aimed to develop a novel model for predicting the risk of hospital mortality in sepsis patients with acute respiratory failure.

METHODS

From the Medical Information Mart for Intensive Care (MIMIC)-IV database, we developed a matched cohort of adult sepsis patients with acute respiratory failure. After applying a multivariate COX regression analysis, we developed a nomogram based on the identified risk factors of mortality. Further, we evaluated the ability of the nomogram in predicting individual hospital death by the area under a receiver operating characteristic (ROC) curve.

RESULTS

A total of 663 sepsis patients with acute respiratory failure were included in this study. Systolic blood pressure, neutrophil percentage, white blood cells count, mechanical ventilation, partial pressure of oxygen < 60 mmHg, abdominal cavity infection, Klebsiella pneumoniae and Acinetobacter baumannii infection, and immunosuppressive diseases were the independent risk factors of mortality in sepsis patients with acute respiratory failure. The area under the ROC curve of the nomogram was 0.880 (95% CI: 0.851-0.908), which provided significantly higher discrimination compared to that of the simplified acute physiology score II [0.656 (95% CI: 0.612-0.701)].

CONCLUSION

The model shows a good performance in predicting the mortality risk of patients with sepsis-related acute respiratory failure. Hence, this model can be used to evaluate the short-term prognosis of critically ill patients with sepsis and acute respiratory failure.