Development of a nomogram to predict 30-day mortality of patients with sepsis-associated encephalopathy: a retrospective cohort study

Biomarkers Improve Diagnostics of Sepsis in Adult Patients With Suspected Organ Dysfunction Based on the Quick Sepsis-Related Organ Failure Assessment (qSOFA) Score in the Emergency Department

OBJECTIVES

Consensus regarding biomarkers for detection of infection-related organ dysfunction in the emergency department is lacking. We aimed to identify and validate biomarkers that could improve risk prediction for overt or incipient organ dysfunction when added to quick Sepsis-related Organ Failure Assessment (qSOFA) as a screening tool.

DESIGN

In a large prospective multicenter cohort of adult patients presenting to the emergency department with a qSOFA score greater than or equal to 1, admission plasma levels of C-reactive protein, procalcitonin, adrenomedullin (either bioavailable adrenomedullin or midregional fragment of proadrenomedullin), proenkephalin, and dipeptidyl peptidase 3 were assessed. Least absolute shrinkage and selection operator regression was applied to assess the impact of these biomarkers alone or in combination to detect the primary endpoint of prediction of sepsis within 96 hours of admission.

SETTING

Three tertiary emergency departments at German University Hospitals (Jena University Hospital and two sites of the Charité University Hospital, Berlin).

PATIENTS

One thousand four hundred seventy-seven adult patients presenting with suspected organ dysfunction based on qSOFA score greater than or equal to 1.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The cohort was of moderate severity with 81% presenting with qSOFA = 1; 29.2% of these patients developed sepsis. Procalcitonin outperformed all other biomarkers regarding the primary endpoint (area under the curve for receiver operating characteristic [AUC-ROC], 0.86 [0.79-0.93]). Adding other biomarkers failed to further improve the AUC-ROC for the primary endpoint; however, they improved the model regarding several secondary endpoints, such as mortality, need for vasopressors, or dialysis. Addition of procalcitonin with a cutoff level of 0.25 ng/mL improved net (re)classification by 35.2% compared with qSOFA alone, with positive and negative predictive values of 60.7% and 88.7%, respectively.

CONCLUSIONS

Biomarkers of infection and organ dysfunction, most notably procalcitonin, substantially improve early prediction of sepsis with added value to qSOFA alone as a simple screening tool on emergency department admission.

Lipid peroxidation-induced ferroptosis as a therapeutic target for mitigating neuronal injury and inflammation in sepsis-associated encephalopathy: insights into the hippocampal PEBP-1/15-LOX/GPX4 pathway

BACKGROUND

Sepsis-associated encephalopathy (SAE) refers to the widespread impairment of brain function caused by noncentral nervous system infection mediated by sepsis. Lipid peroxidation-induced ferroptosis contributes to the occurrence and course of SAE. This study aimed to investigate the relationship between neuronal injury and lipid peroxidation-induced ferroptosis in SAE.

METHODS

Baseline data were collected from pediatric patients upon admission, and the expression levels of various markers related to lipid peroxidation and ferroptosis were monitored in the serum and peripheral blood mononuclear cells (PBMCs) of patients with SAE as well as SAE model mice. The hippocampal phosphatidylethanolamine-binding protein (PEBP)-1/15-lysine oxidase (LOX)/ glutathione peroxidase 4 (GPX4) pathway was assessed for its role on the inhibitory effect of ferroptosis in SAE treatment.

RESULTS

The results showed elevated levels of S100 calcium-binding protein beta (S-100β), glial fibrillary acidic protein, and malondialdehyde in the serum of SAE patients, while superoxide dismutase levels were reduced. Furthermore, analysis of PBMCs revealed increased transcription levels of PEBP1, LOX, and long-chain fatty acyl-CoA synthetase family member 4 (ACSL4) in SAE patients, while the transcription levels of GPX4 and cystine/glutamate transporter xCT (SLC7A11) were decreased. In comparison to the control group, the SAE mice exhibited increased expression of S-100β and neuron-specific enolase (NSE) in the hippocampus, whereas the expression of S-100β and NSE were reduced in deferoxamine (DFO) mice. Additionally, iron accumulation was observed in the hippocampus of SAE mice, while the iron ion levels were reduced in the DFO mice. Inhibition of ferroptosis alleviated the mitochondrial damage (as assessed by transmission electron microscopy, hippocampal mitochondrial ATP detection, and the JC-1 polymer-to-monomer ratio in the hippocampus) and the oxidative stress response induced by SAE as well as attenuated neuroinflammatory reactions. Further investigations revealed that the mechanism underlying the inhibitory effect of ferroptosis in SAE treatment is associated with the hippocampal PEBP-1/15-LOX/GPX4 pathway.

CONCLUSION

These results offer potential therapeutic targets for the management of neuronal injury in SAE and valuable insights into the potential mechanisms of ferroptosis in neurological disorders.

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke

BACKGROUND/AIM

We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations.

MATERIALS AND METHODS

We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions.

RESULTS

A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit.

CONCLUSIONS

Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

Cholinesterase activities and sepsis-associated encephalopathy in viral versus nonviral sepsis

PURPOSE

There is evidence that cholinergic imbalance secondary to neuroinflammation plays a role in the pathophysiology of sepsis-associated encephalopathy (SAE). Blood acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) activities have been proposed as surrogate parameters for the cholinergic function of the central nervous system. Viral sepsis is associated with systemic inflammation and BChE has been reported to be of prognostic value in a small cohort of COVID-19 patients. Nevertheless, the prognostic value of AChE in patients with viral sepsis remains unclear.

METHODS

We investigated the role of AChE and BChE activities as prognostic biomarkers of SAE and mortality in patients with viral vs nonviral sepsis enrolled in two prospective cohort studies. We quantified the AChE and BChE activities in whole blood of patients at two time points in the acute phase of viral sepsis (N = 108) and compared them with the activities in patients with nonviral sepsis (N = 117) and healthy volunteers (N = 81). Patients were observed until discharge from the intensive care unit (ICU).

RESULTS

Three days after sepsis onset, the median [interquartile range] levels of AChE and BChE were reduced in both patients with viral sepsis (AChE, 5,105 [4,010-6,250] U·L-1; BChE, 1,943 [1,393-2,468] U·L-1) and nonviral sepsis (AChE, 4,424 [3,630-5,055] U·L-1; BChE, 1,095 [834-1,526] U·L-1) compared with healthy volunteers (AChE, 6,693 [5,401-8,020] U·L-1; BChE, 2,645 [2,198-3,478] U·L-1). Patients with viral sepsis with SAE during their ICU stay had lower AChE activity three days after sepsis onset than patients without SAE (4,249 [3,798-5,351] U·L-1 vs 5,544 [4,124-6,461] U·L-1). Butyrylcholinesterase activity seven days after sepsis onset was lower in patients with viral sepsis who died in the ICU than in surviving patients (1,427 [865-2,181] U·L-1 vs 2,122 [1,571-2,787] U·L-1).

CONCLUSION

Cholinesterase activities may be relevant prognostic markers for the occurrence of SAE and mortality in the ICU in patients with viral sepsis.

STUDY REGISTRATION

This study constitutes an analysis of data from the ongoing studies ICROS (NCT03620409, first submitted 15 May 2018) and ICROVID (DRKS00024162, first submitted 9 February 2021).

Improved prediction of sepsis-associated encephalopathy in intensive care unit sepsis patients with an innovative nomogram tool

Background

Sepsis-associated encephalopathy (SAE) occurs as a result of systemic inflammation caused by sepsis. It has been observed that the majority of sepsis patients experience SAE while being treated in the intensive care unit (ICU), and a significant number of survivors continue suffering from cognitive impairment even after recovering from the illness. The objective of this study was to create a predictive nomogram that could be used to identify SAE risk factors in patients with ICU sepsis.

Methods

We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We defined SAE as a Glasgow Coma Scale (GCS) score of 15 or less, or delirium. The patients were randomly divided into training and validation cohorts. We used least absolute shrinkage and selection operator (LASSO) regression modeling to optimize feature selection. Independent risk factors were determined through a multivariable logistic regression analysis, and a prediction model was built. The performance of the nomogram was evaluated using various metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

Among the 4,476 sepsis patients screened, 2,781 (62.1%) developed SAE. In-hospital mortality was higher in the SAE group compared to the non-SAE group (9.5% vs. 3.7%, p < 0.001). Several variables were analyzed, including the patient's age, gender, BMI on admission, mean arterial pressure, body temperature, platelet count, sodium level, and use of midazolam. These variables were used to create and validate a nomogram. The nomogram's performance, assessed by AUC, NRI, IDI, and DCA, was found to be superior to the conventional SOFA score combined with delirium. Calibration plots and the Hosmer-Lemeshow test confirmed the accuracy of the nomogram. The enhanced NRI and IDI values demonstrated that our scoring system outperformed traditional diagnostic approaches. Additionally, the DCA curve indicated the practicality of the nomogram in clinical settings.

Conclusion

This study successfully identified autonomous risk factors associated with the emergence of SAE in sepsis patients and utilized them to formulate a predictive model. The outcomes of this investigation have the potential to serve as a valuable clinical resource for the timely detection of SAE in patients.

Development and validation of a nomogram to predict the risk of sepsis-associated encephalopathy for septic patients in PICU: a multicenter retrospective cohort study

BACKGROUND

Patients with sepsis-associated encephalopathy (SAE) have higher mortality rates and longer ICU stays. Predictors of SAE are yet to be identified. We aimed to establish an effective and simple-to-use nomogram for the individual prediction of SAE in patients with sepsis admitted to pediatric intensive care unit (PICU) in order to prevent early onset of SAE.

METHODS

In this retrospective multicenter study, we screened 790 patients with sepsis admitted to the PICU of three hospitals in Shandong, China. Least absolute shrinkage and selection operator regression was used for variable selection and regularization in the training cohort. The selected variables were used to construct a nomogram to predict the risk of SAE in patients with sepsis in the PICU. The nomogram performance was assessed using discrimination and calibration.

RESULTS

From January 2017 to May 2022, 613 patients with sepsis from three centers were eligible for inclusion in the final study. The training cohort consisted of 251 patients, and the two independent validation cohorts consisted of 193 and 169 patients. Overall, 237 (38.7%) patients developed SAE. The morbidity of SAE in patients with sepsis is associated with the respiratory rate, blood urea nitrogen, activated partial thromboplastin time, arterial partial pressure of carbon dioxide, and pediatric critical illness score. We generated a nomogram for the early identification of SAE in the training cohort (area under curve [AUC] 0.82, 95% confidence interval [CI] 0.76-0.88, sensitivity 65.6%, specificity 88.8%) and validation cohort (validation cohort 1: AUC 0.80, 95% CI 0.74-0.86, sensitivity 75.0%, specificity 74.3%; validation cohort 2: AUC 0.81, 95% CI 0.73-0.88, sensitivity 69.1%, specificity 83.3%). Calibration plots for the nomogram showed excellent agreement between SAE probabilities of the observed and predicted values. Decision curve analysis indicated that the nomogram conferred a high net clinical benefit.

CONCLUSIONS

The novel nomogram and online calculator showed performance in predicting the morbidity of SAE in patients with sepsis admitted to the PICU, thereby potentially assisting clinicians in the early detection and intervention of SAE.

Naples score: a novel prognostic biomarker for breast cancer patients undergoing neoadjuvant chemotherapy

BACKGROUND AND PURPOSE

The Naples Score (NPS) is a novel prognostic indicator that has been used in various cancers, but its potential in breast malignant tumor patients receiving neoadjuvant chemotherapy (NAC) has not been discovered. This study aimed to investigate the relationship between NPS and overall survival (OS) and disease-free survival (DFS) in breast cancer patients.

METHODS

A total of 217 breast cancer patients undergoing NAC were incorporated into this retrospectively research. K-M survival curves and log-rank tests are used to determine OS and DFS. Cox regression model was used to evaluate the relationship between NPS and OS and DFS. Nomogram was developed based on the results of multivariate Cox regression analysis. Prognostic models were internally validated using bootstrapping and the consistency index (C-index).

RESULTS

Age group was correlated with NPS (p < 0.05). Low and moderate Naples risk patients had higher 5-year OS and DFS rates than high risk Naples patients (93.8% vs. 75.4% vs. 60.0%; X2 = 9.2, P = 0.01; 82.4% vs 64.5% vs 43.7%; X2 = 7.4, P = 0.024; respectively). The nomogram based on demonstrated good performance in predicting OS and DFS (AUC = 0.728, 0.630; respectively).

CONCLUSIONS

In breast cancer patients who have undergone NAC, NPS is a novel prognostic indicator. NPS combined with clinicopathological features showed good predictive ability, and its performance was better than that of traditional pathological TNM staging.

Relationship between Nonhepatic Serum Ammonia Levels and Sepsis-Associated Encephalopathy: A Retrospective Cohort Study

Objectives

Nonhepatic hyperammonemia often occurs in patients with sepsis. Ammonia plays an essential role in the occurrence of hepatic encephalopathy. However, the relationship between nonhepatic serum ammonia levels and sepsis-associated encephalopathy (SAE) remains unclear. Thus, we aimed to evaluate the association between serum ammonia levels and patients with SAE.

Methods

Data of critically ill adults with sepsis who were admitted to the intensive care unit were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC IV) between 2008 and 2019 and retrospectively analyzed. Data of patients with sepsis patients and serum ammonia not related to acute or chronic liver disease were not included.

Results

Data from 720 patients with sepsis were included. SAE was found to have a high incidence (64.6%). After adjusting for other risk factors, a serum ammonia level of ≥45 μmol/L (odds ratio (OR): 3.508, 95% confidence interval (CI): 2.336-5.269, p < 0.001) was found to be an independent risk factor for patients with SAE; moreover, as the serum ammonia level increased, the hospital mortality of SAE gradually increased in a certain range (serum ammonia <150 μmol/L). Serum ammonia levels of ≥45 μmol/L were associated with higher Simplified Acute Physiology Score II and Sequential Organ Failure Assessment (SOFA) scores in patients with SAE. Besides, our study found that patients with SAE used opioid analgesics (OR:3.433, 95% CI: 1.360-8.669, p = 0.009) and the SOFA scores of patients with SAE (OR: 1.126, 95% CI: 1.062-1.194, p < 0.001) were significantly higher than those without SAE.

Conclusions

Nonhepatic serum ammonia levels of ≥45 μmol/L evidently increased the incidence of SAE. Serum ammonia levels should be closely monitored in patients with sepsis.

Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit

Septic patients in the intensive care unit (ICU) often develop sepsis-associated delirium (SAD), which is strongly associated with poor prognosis. The aim of this study is to develop a machine learning-based model for the early prediction of SAD. Patient data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV data were divided into a training set and an internal validation set, while the eICU-CRD data served as an external validation set. Feature variables were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, support vector machines, decision trees, random forests, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. The performance of the models was evaluated in the validation set. The model was also applied to a group of patients who were not assessed or could not be assessed for delirium. The MIMIC-IV and eICU-CRD databases included 14,620 and 1723 patients, respectively, with a median time to diagnosis of SAD of 24 and 30 h. Compared with Non-SAD patients, SAD patients had higher 28-days ICU mortality rates and longer ICU stays. Among the models compared, the XGBoost model had the best performance and was selected as the final model (internal validation area under the receiver operating characteristic curves (AUROC) = 0.793, external validation AUROC = 0.701). The XGBoost model outperformed other models in predicting SAD. The establishment of this predictive model allows for earlier prediction of SAD compared to traditional delirium assessments and is applicable to patients who are difficult to assess with traditional methods.

Development and verification of a nomogram for predicting short-term mortality in elderly ischemic stroke populations

Stroke is a major healthcare problem worldwide, particularly in the elderly population. Despite limited research on the development of prediction models for mortality in elderly individuals with ischemic stroke, our study aimed to address this knowledge gap. By leveraging data from the Medical Information Mart for Intensive Care IV database, we collected comprehensive raw data pertaining to elderly patients diagnosed with ischemic stroke. Through meticulous screening of clinical variables associated with 28-day mortality, we successfully established a robust nomogram. To assess the performance and clinical utility of our nomogram, various statistical analyses were conducted, including the concordance index, integrated discrimination improvement (IDI), net reclassification index (NRI), calibration curves and decision curve analysis (DCA). Our study comprised a total of 1259 individuals, who were further divided into training (n = 894) and validation (n = 365) cohorts. By identifying several common clinical features, we developed a nomogram that exhibited a concordance index of 0.809 in the training dataset. Notably, our findings demonstrated positive improvements in predictive performance through the IDI and NRI analyses in both cohorts. Furthermore, calibration curves indicated favorable agreement between the predicted and actual incidence of mortality (P > 0.05). DCA curves highlighted the substantial net clinical benefit of our nomogram compared to existing scoring systems used in routine clinical practice. In conclusion, our study successfully constructed and validated a prognostic nomogram, which enables accurate short-term mortality prediction in elderly individuals with ischemic stroke.

Exploring Neuroprotective Agents for Sepsis-Associated Encephalopathy: A Comprehensive Review

Sepsis is a life-threatening condition resulting from an inflammatory overreaction that is induced by an infectious factor, which leads to multi-organ failure. Sepsis-associated encephalopathy (SAE) is a common complication of sepsis that can lead to acute cognitive and consciousness disorders, and no strict diagnostic criteria have been created for the complication thus far. The etiopathology of SAE is not fully understood, but plausible mechanisms include neuroinflammation, blood-brain barrier disruption, altered cerebral microcirculation, alterations in neurotransmission, changes in calcium homeostasis, and oxidative stress. SAE may also lead to long-term consequences such as dementia and post-traumatic stress disorder. This review aims to provide a comprehensive summary of substances with neuroprotective properties that have the potential to offer neuroprotection in the treatment of SAE. An extensive literature search was conducted, extracting 71 articles that cover a range of substances, including plant-derived drugs, peptides, monoclonal antibodies, and other commonly used drugs. This review may provide valuable insights for clinicians and researchers working in the field of sepsis and SAE and contribute to the development of new treatment options for this challenging condition.

Development of a nomogram for predicting 90-day mortality in patients with sepsis-associated liver injury

The high mortality rate in sepsis patients is related to sepsis-associated liver injury (SALI). We sought to develop an accurate forecasting nomogram to estimate individual 90-day mortality in SALI patients. Data from 34,329 patients were extracted from the public Medical Information Mart for Intensive Care (MIMIC-IV) database. SALI was defined by total bilirubin (TBIL) > 2 mg/dL and the occurrence of an international normalized ratio (INR) > 1.5 in the presence of sepsis. Logistic regression analysis was performed to establish a prediction model called the nomogram based on the training set (n = 727), which was subsequently subjected to internal validation. Multivariate logistic regression analysis showed that SALI was an independent risk factor for mortality in patients with sepsis. The Kaplan‒Meier curves for 90-day survival were different between the SALI and non-SALI groups after propensity score matching (PSM) (log rank: P < 0.001 versus P = 0.038), regardless of PSM balance. The nomogram demonstrated better discrimination than the sequential organ failure assessment (SOFA) score, logistic organ dysfunction system (LODS) score, simplified acute physiology II (SAPS II) score, and Albumin-Bilirubin (ALBI) score in the training and validation sets, with areas under the receiver operating characteristic curve (AUROC) of 0.778 (95% CI 0.730-0.799, P < 0.001) and 0.804 (95% CI 0.713-0.820, P < 0.001), respectively. The calibration plot showed that the nomogram was sufficiently successful to predict the probability of 90-day mortality in both groups. The DCA of the nomogram demonstrated a higher net benefit regarding clinical usefulness than SOFA, LODS, SAPSII, and ALBI scores in the two groups. The nomogram performs exceptionally well in predicting the 90-day mortality rate in SALI patients, which can be used to assess the prognosis of patients with SALI and may assist in guiding clinical practice to enhance patient outcomes.

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.

The nomogram to predict the occurrence of sepsis-associated encephalopathy in elderly patients in the intensive care units: A retrospective cohort study

Background

Sepsis-associated encephalopathy (SAE) is a critical and common problem in elderly patients with sepsis, which is still short of efficient predictive tools. Therefore, this study aims to screen the risk factors and establish a useful predictive nomogram for SAE in elderly patients with sepsis in the intensive care unit (ICU).

Patients and methods

Elderly patients (age ≥ 65 years) with sepsis were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Data from demographics and laboratory examinations were collected on the first day of admission to the ICU. SAE was defined by two criteria in the presence of sepsis: ① a Glasgow Coma Scale (GCS) score of < 15 or ② delirium. Differences in demographics and laboratory tests were calculated between SAE and non-SAE groups. Participants were randomly divided into a training set and a validation set without replacement at a ratio of 6:4. A predictive nomogram was constructed in the training set by logistic regression analysis and then validated. The predictive capability of the nomogram was demonstrated by receiver operating characteristic (ROC) analysis and calibration curve analysis.

Results

A total of 22,361 patients were selected, of which 2,809 patients (12.7%) died in the hospital and 8,290 patients (37.1%) had SAE. In-hospital mortality in the SAE group was higher than that in the non-SAE group (18.8 vs. 8.9%, p < 0.001). Based on the results of logistic regression analysis, a nomogram integrating age, Na⁺, Sequential Organ Failure Assessment (SOFA) score, heart rate, and body temperature were constructed. The area under the curve (AUC) of the nomogram was 80.2% in the training set and 80.9% in the validation set. Calibration curve analysis showed a good predictive capacity of the nomogram.

Conclusion

SAE is an independent risk of in-hospital mortality in elderly patients in the intensive care unit. The nomogram has an excellent predictive capability of SAE and helps in clinical practice.

HSF1 Alleviates Brain Injury by Inhibiting NLRP3-Induced Pyroptosis in a Sepsis Model

Background

Sepsis, which could cause a systemic inflammatory response, is a life-threatening disease with a high morbidity and mortality rate. There is evidence that brain injury may be related to severe systemic infection induced by sepsis. The brain injury caused by sepsis could increase the risk of mortality in septic patients, which seriously affects the septic patient's prognosis of survival. Although there remains a focus on sepsis research, clinical measures to prevent and treat brain injury in sepsis are not yet available, and the high mortality rate is still a big health burden. Therefore, it is necessary to investigate the new molecules or regulated pathways that can effectively inhibit the progress of sepsis.

Objective

NLR family pyrin domain-containing 3 (NLRP3) increased in the procession of sepsis and functioned as the key regulator of pyroptosis. Heat shock factor 1 (HSF1) can protect organs from multiorgan dysfunction syndrome induced by lipopolysaccharides in mice, and NLRP3 could be inhibited by HSF1 in many organs. However, whether HSF1 regulated NLRP3 in sepsis-induced brain injury, as well as the detailed mechanism of HSF1 in brain injury, remains unknown in the sepsis model. In this research, we try to explore the relationship between HSF1 and NLRP3 in a sepsis model and try to reveal the mechanism of HSF1 inhibiting the process of brain injury.

Methods

In this study, we used wild-type mice and hsf1 ^-/- mice for in vivo research and PC12 cells for in vitro research. Real-time PCR and Western blot were used to analyze the expression of HSF1, NLRP3, cytokines, and pyrolytic proteins. EthD-III staining was chosen to detect the pyroptosis of the hippocampus and PC12 cells.

Results

The results showed that HSF1 is negatively related to pyroptosis. The pyroptosis in cells of brain tissue was significantly increased in the hsf1 ^-/- mouse model compared to hsf1 ^+/+ mice. In PC12 cells, hsf1 siRNA can upregulate pyroptosis while HSF1-transfected plasmid could inhibit the pyroptosis. HSF1 could negatively regulate the NLRP3 pathway in PC12 cells, while hsf1 siRNA enhanced the pyroptosis in PC12 cells, which could be reversed by nlrp3 siRNA.

Conclusion

These results imply that HSF1 could alleviate sepsis-induced brain injury by inhibiting pyroptosis through the NLRP3-dependent pathway in brain tissue and PC12 cells, suggesting HSF1 as a potential molecular target for treating brain injury in sepsis clinical studies.

Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

Sepsis-associated encephalopathy (SAE) is a major complication of sepsis and is associated with high mortality and poor long-term prognosis. The purpose of this study is to develop interpretable machine learning models to predict the occurrence of SAE after ICU admission and implement the individual prediction and analysis. Patients with sepsis admitted to ICU were included. SAE was diagnosed as glasgow coma score (GCS) less than 15. Statistical analysis at baseline was performed between SAE and non-SAE. Six machine learning classifiers were employed to predict the occurrence of SAE, and the adjustment of model super parameters was performed by using Bayesian optimization method. Finally, the optimal algorithm was selected according to the prediction efficiency. In addition, professional physicians were invited to evaluate our model prediction results for further quantitative assessment of the model interpretability. The preliminary analysis of variance showed significant differences in the incidence of SAE among patients with pathogen infection. There were significant differences in physical indicators like respiratory rate, temperature, SpO₂ and mean arterial pressure (P < 0.001). In addition, the laboratory results were also significantly different. The optimal classification model (XGBoost) indicated that the best risk factors (cut-off points) were creatinine (1.1 mg/dl), mean respiratory rate (18), pH (7.38), age (72), chlorine (101 mmol/L), sodium (138.5 k/ul), SAPSII score (23), platelet count (160), and phosphorus (2.4 and 5.0 mg/dL). The ranked features derived from the best model (AUC is 0.8837) were mechanical ventilation, duration of mechanical ventilation, phosphorus, SOFA score, and vasopressin usage. The SAE risk prediction model based on XGBoost created here can make very accurate predictions using simple indicators and support the visual explanation. The interpretable model was effectively evaluated by professional physicians and can help them predict the occurrence of SAE more intuitively.

Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model

OBJECTIVE

Early identifying sepsis patients who had higher risk of poor prognosis was extremely important. The aim of this study was to develop an artificial neural networks (ANN) model for early predicting clinical outcomes in sepsis.

METHODS

This study was a retrospective design. Sepsis patients from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were enrolled. A predictive model for predicting 30-day morality in sepsis was performed based on the ANN approach.

RESULTS

A total of 2874 patients with sepsis were included and 30-day mortality was 29.8%. The study population was categorized into the training set (n = 1698) and validation set (n = 1176) based on the ratio of 6:4. 11 variables which showed significant differences between survivor group and nonsurvivor group in training set were selected for constructing the ANN model. In training set, the predictive performance based on the area under the receiver-operating characteristic curve (AUC) were 0.873 for ANN model, 0.720 for logistic regression, 0.629 for APACHEII score and 0.619 for SOFA score. In validation set, the AUCs of ANN, logistic regression, APAHCEII score, and SOFA score were 0.811, 0.752, 0.607, and 0.628, respectively.

CONCLUSION

An ANN model for predicting 30-day mortality in sepsis was performed. Our predictive model can be beneficial for early detection of patients with higher risk of poor prognosis.

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Background

It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning.

Methods

This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test.

Results

After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance (AUC = 0.865 and 0.771, respectively). For overall survival (OS, cutoff value = 0.3917) and disease-free survival (cutoff value = 0.4896), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P < 0.0001; and 59.6% vs. 32.1%, P < 0.0001, respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables (AUC = 0.615, P = 0.0003; AUC = 0.596, P = 0.0003, respectively).

Conclusion

The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression.

Prognostic value of a modified systemic inflammation score in breast cancer patients who underwent neoadjuvant chemotherapy

BACKGROUND AND PURPOSE

The modified systemic inflammation score (mSIS) system, which is constructed based on the neutrophil to lymphocyte ratio (NLR) and albumin (Alb), has not been applied to evaluate the prognosis of malignant breast cancer patients who underwent neoadjuvant chemotherapy (NAC). The present study aimed to explore the relationship between the mSIS and overall survival (OS), disease-free survival (DFS) and pathological complete response (pCR).

METHODS

A total of 305 malignant breast tumor patients who underwent NAC were incorporated into this retrospective analysis. We determined OS and DFS using K-M survival curves and the log-rank test. The relationship between the mSIS and OS and DFS was evaluated by a Cox regression model. A nomogram was constructed based on Cox regression analysis.

RESULTS

Patients in the mSIS low-risk group had better 5- and 8-year OS rates than those in the mSIS high-risk group (59.8% vs. 77.0%; 50.1% vs. 67.7%; X² = 8.5, P = 0.0035, respectively). Patients in the mSIS (1 + 2 score) + pCR subgroup had the highest 5- and 8-year OS and disease-free survival (DFS) rates (OS: 55.0% vs. 75.7% vs. 84.8, 42.8% vs. 65.7% vs. 79.8%, X² = 16.6, P = 0.00025; DFS: 38.8% vs. 54.7% vs. 76.3%, 33.3% vs. 42.3 vs. 72.1%, X2 = 12.4, P = 0.002, respectively). Based on the mSIS, clinical T stage and pCR results, the nomogram had better predictive ability than the clinical TNM stage, NLR and Alb.

CONCLUSIONS

mSIS is a promising prognostic tool for malignant breast tumor patients who underwent NAC, and the combination of mSIS and pCR is helpful in enhancing the ability to predict a pCR.

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.

Effects of hydrogen-rich saline in neuroinflammation and mitochondrial dysfunction in rat model of sepsis-associated encephalopathy

Background

Sepsis-associated encephalopathy (SAE) is one of the most common types of sepsis-related organ dysfunction without overt central nervous system (CNS) infection. It is associated with higher mortality, low quality of life, and long-term neurological sequelae in suspected patients. At present there is no specific treatment for SAE rather than supportive therapy and judicious use of antibiotics, which are sometimes associated with adverse effects. Molecular hydrogen (H2) has been reported to play crucial role in regulating inflammatory responses, neuronal injury, apoptosis and mitochondrial dysfunction in adult models of SAE. Here we report the protective effect of hydrogen-rich saline in juvenile SAE rat model and its possible underling mechanism(s).

Materials and methods

Rats were challenged with lipopolysaccharide (LPS) at a dose of 8 mg/kg injected intraperitoneally to induce sepsis and hydrogen-rich saline (HRS) administered 1 h following LPS induction at a dose of 5 ml/kg. Rats were divided into: sham, sham + HRS, LPS and LPS + HRS. At 48 h, rats were sacrificed and Nissl staining for neuronal injury, TUNEL assay for apoptotic cells detection, immunohistochemistry, and ELISA protocol for inflammatory cytokines determination, mitochondrial dysfunction parameters, electron microscopy and western blot analysis were studied to examine the effect of HRS in LPS-induced septic rats.

Results

Rats treated with HRS improved neuronal injury, improvement in rats’ survival rate. ELISA analysis showed decreased TNF-α and IL-1β and increased IL-10 expression levels in the HRS-treated group. Apoptotic cells were decreased after HRS administration in septic rats. The numbers of GFAP and IBA-1positive cells were attenuated in the HRS-treated group when compared to the LPS group. Subsequently, GFAP and IBA-1 immunoreactivity were decreased after HRS treatment. Mitochondrial membrane potential detected by JC-1 dye and ATP content were decreased in septic rats, which were improved after HRS treatment, while release of ROS was increased in the LPS group reverted by HRS treatment, ameliorating mitochondrial dysfunction. Further analysis by transmission electron microscopy showed decreased number of mitochondria and synapses, and disrupted mitochondrial membrane ultrastructure in the LPS group, while HRS administration increased mitochondria and synapses number.

Conclusion

These data demonstrated that HRS can improve survival rate, attenuate neuroinflammation, astrocyte and microglial activation, neuronal injury and mitochondrial dysfunction in juvenile SAE rat model, making it a potential therapeutic candidate in treating paediatric SAE.

The emerging roles and therapeutic potential of B cells in sepsis

Sepsis is a life-threatening syndrome caused by anomalous host response to infection. The pathogenesis of sepsis is complex, and immune dysfunction is the central link in its occurrence and development. The sepsis immune response is not a local and transient process but a complex and continuous process involving all major cell types of innate and adaptive immunity. B cells are traditionally studied for their ability to produce antibodies in the context of mediating humoral immunity. However, over the past few years, B cells have been increasingly recognized as key modulators of adaptive and innate immunity, and they can participate in immune responses by presenting antigens, producing cytokines, and modulating other immune cells. Recently, increasing evidence links B-cell dysfunction to mechanisms of immune derangement in sepsis, which has drawn attention to the powerful properties of this unique immune cell type in sepsis. Here, we reviewed the dynamic alterations of B cells and their novel roles in animal models and patients with sepsis, and provided new perspectives for therapeutic strategies targeting B cells in sepsis.

Conservative oxygen therapy in critically ill and perioperative period of patients with sepsis-associated encephalopathy

OBJECTIVES

Sepsis-associated encephalopathy (SAE) patients in the intensive care unit (ICU) and perioperative period are administrated supplemental oxygen. However, the correlation between oxygenation status with SAE and the target for oxygen therapy remains unclear. This study aimed to examine the relationship between oxygen therapy and SAE patients.

METHODS

Patients diagnosed with sepsis 3.0 in the intensive care unit (ICU) were enrolled. The data were collected from the Medical Information Mart for Intensive Care IV (MIMIC IV) database and the eICU Collaborative Research Database (eICU-CRD) database. The generalized additive models were adopted to estimate the oxygen therapy targets in SAE patients. The results were confirmed by multivariate Logistic, propensity score analysis, inversion probability-weighting, doubly robust model, and multivariate COX analyses. Survival was analyzed by the Kaplan-Meier method.

RESULTS

A total of 10055 patients from eICU-CRD and 1685 from MIMIC IV were included. The incidence of SAE patients was 58.43%. The range of PaO2 (97-339) mmHg, PaO2/FiO2 (189-619), and SPO2≥93% may reduce the incidence of SAE, which were verified by multivariable Logistic regression, propensity score analysis, inversion probability-weighting, and doubly robust model estimation in MIMIC IV database and eICU database. The range of PaO2/FiO2 (189-619) and SPO2≥93% may reduce the hospital mortality of SAE were verified by multivariable COX regression.

CONCLUSIONS

SAE patients in ICU, including perioperative period, require conservative oxygen therapy. We should maintain SPO2≥93%, PaO2 (97-339) mmHg and PaO2/FiO2 (189-619) in SAE patients.

Construction and validation of a nomogram for predicting survival in elderly patients with cardiac surgery

Background

In recent years, the number of elderly patients undergoing cardiac surgery has rapidly increased and is associated with poor outcomes. However, there is still a lack of adequate models for predicting the risk of death after cardiac surgery in elderly patients. This study sought to identify independent risk factors for 1-year all-cause mortality in elderly patients after cardiac surgery and to develop a predictive model.

Methods

A total of 3,752 elderly patients with cardiac surgery were enrolled from the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and randomly divided into training and validation sets. The primary outcome was the all-cause mortality at 1 year. The Least absolute shrinkage and selection operator (LASSO) regression was used to decrease data dimensionality and select features. Multivariate logistic regression was used to establish the prediction model. The concordance index (C-index), receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.

Results

Our results demonstrated that age, sex, Sequential Organ Failure Assessment (SOFA), respiratory rate (RR), creatinine, glucose, and RBC transfusion (red blood cell) were independent factors for elderly patient mortality after cardiac surgery. The C-index of the training and validation sets was 0.744 (95%CI: 0.707-0.781) and 0.751 (95%CI: 0.709-0.794), respectively. The area under the curve (AUC) and decision curve analysis (DCA) results substantiated that the nomogram yielded an excellent performance predicting the 1-year all-cause mortality after cardiac surgery.

Conclusions

We developed a novel nomogram model for predicting the 1-year all-cause mortality for elderly patients after cardiac surgery, which could be an effective and useful clinical tool for clinicians for tailored therapy and prognosis prediction.

Non-hepatic Hyperammonemia: A Potential Therapeutic Target for Sepsis-associated Encephalopathy

Sepsis-Associated Encephalopathy (SAE) is a common complication in the acute phase of sepsis, and patients who develop SAE have a higher mortality rate, longer hospital stay, and worse quality of life than other sepsis patients. Although the incidence of SAE is as high as 70% in sepsis patients, no effective treatment is available for this condition. To develop an effective treatment for SAE, it is vital to explore its pathogenesis. It is known that hyperammonemia is a possible factor in the pathogenesis of hepatic encephalopathy as ammonia is a potent neurotoxin. Furthermore, our previous studies indicate that non-hepatic hyperammonemia seems to occur more often in sepsis patients; it was also found that >50% of sepsis patients with non-hepatic hyperammonemia exhibited encephalopathy and delirium. Substatistical analyses indicate that non-hepatic hyperammonemia is an independent risk factor for SAE. This study updates the definition, clinical manifestations, and diagnosis of SAE; it also investigates the possible treatment options available for non-hepatic hyperammonemia in patients with sepsis and the mechanisms by which non-hepatic hyperammonemia causes encephalopathy.

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.

Development and Validation of a Prognostic Nomogram to Predict 30-Day Mortality Risk in Patients with Sepsis-Induced Cardiorenal Syndrome

INTRODUCTION

Sepsis-induced cardiorenal syndrome (sepsis-induced CRS) is a devastating medical condition that is frequently associated with a high fatality rate. In this study, we aimed to develop an individualized nomogram that may help clinicians assess 30-day mortality risk in patients diagnosed with sepsis-induced CRS.

METHODS

A total of 340 patients with sepsis-induced CRS admitted from January 2015 to May 2019 in Shanghai Tongji Hospital were used as a training cohort to develop a nomogram prognostic model. The model was constructed using multivariable logistic analyses and was then externally validated by an independent cohort of 103 patients diagnosed with sepsis-induced CRS from June 2019 to December 2020. The prognostic ability of the nomogram was assessed through discrimination, calibration, and accuracy.

RESULTS

Five prognostic factors were determined and included in the nomogram: age, Sequential (sepsis-related) Organ Failure Assessment (SOFA) score, vasopressors, baseline serum creatinine, and the rate of change in myoglobin. Our prognostic nomogram showed well-fitted calibration curves and yielded strong discrimination power with the area under the curve of 0.879 and 0.912 in model development and validation, respectively. In addition, the nomogram prognostic model exhibited an evidently higher predictive accuracy than the SOFA score.

CONCLUSIONS

We developed a prognostic nomogram model for patients with sepsis-induced CRS and externally validated the model in another independent cohort. The nomogram exhibited greater strength in predicting 30-day mortality risk than the SOFA score, which may help clinicians estimate short-term prognosis and modulate therapeutic strategies.

Biomarkers for sepsis: more than just fever and leukocytosis-a narrative review

A biomarker describes a measurable indicator of a patient's clinical condition that can be measured accurately and reproducibly. Biomarkers offer utility for diagnosis, prognosis, early disease recognition, risk stratification, appropriate treatment (theranostics), and trial enrichment for patients with sepsis or suspected sepsis. In this narrative review, we aim to answer the question, "Do biomarkers in patients with sepsis or septic shock predict mortality, multiple organ dysfunction syndrome (MODS), or organ dysfunction?" We also discuss the role of pro- and anti-inflammatory biomarkers and biomarkers associated with intestinal permeability, endothelial injury, organ dysfunction, blood–brain barrier (BBB) breakdown, brain injury, and short and long-term mortality. For sepsis, a range of biomarkers is identified, including fluid phase pattern recognition molecules (PRMs), complement system, cytokines, chemokines, damage-associated molecular patterns (DAMPs), non-coding RNAs, miRNAs, cell membrane receptors, cell proteins, metabolites, and soluble receptors. We also provide an overview of immune response biomarkers that can help identify or differentiate between systemic inflammatory response syndrome (SIRS), sepsis, septic shock, and sepsis-associated encephalopathy. However, significant work is needed to identify the optimal combinations of biomarkers that can augment diagnosis, treatment, and good patient outcomes.

The Hemoglobin-to-Red Cell Distribution Width Ratio to Predict All-Cause Mortality in Patients with Sepsis-Associated Encephalopathy in the MIMIC-IV Database

OBJECTIVE

The hemoglobin-to-red cell distribution width ratio (HRR) is associated with the prognosis of sepsis-associated encephalopathy (SAE). This study aimed to determine the relationship between HRR and SAE and to clarify the possible mechanism of HRR as a prognostic factor for SAE.

METHODS

A multivariate Cox proportional-hazards regression model was used to assess the correlation between HRR and all-cause mortality. Piecewise linear regression and smooth-curve Cox proportional-hazards regression models were used to observe whether there was a nonlinear relationship between HRR and all-cause mortality in SAE.

RESULTS

This study included 8853 patients with SAE. A nonlinear relationship between HRR and SAE was observed through a two-segment regression model. The left inflection point for the HRR threshold was calculated to be 15.54, which was negatively correlated with all-cause mortality (HR = 0.83, 95% CI = 0.76-0.91, p < 0.001). Subgroup analyses revealed significant interactions between white blood cell count, glucose, and patients who received dialysis and HRR. The inverse correlation between HRR and SAE was more pronounced in patients who did not receive vasopressin (HR = 0.91, 95% CI = 0.87-0.96, p < 0.001) than in those who did receive vasopressin (HR = 0.94, 95% CI = 0.88-1.02, p=0.152) and was significantly more pronounced in patients without myocardial infarction (HR = 0.91, 95% CI = 0.88-0.96, p < 0.001) than in those with myocardial infarction (HR = 0.94, 95% CI = 0.87-1.02, p < 0.114).

CONCLUSION

This large retrospective study found a nonlinear relationship between all-cause mortality and HRR in patients with SAE in intensive care units, with low HRR being inversely associated with increased all-cause mortality in patients with SAE.

A Novel Risk-Prediction Scoring System for Sepsis among Patients with Acute Pancreatitis: A Retrospective Analysis of a Large Clinical Database

BACKGROUND

The prognosis is poor when acute pancreatitis (AP) progresses to sepsis; therefore, it is necessary to accurately predict the probability of sepsis and develop a personalized treatment plan to reduce the disease burden of AP patients.

METHODS

A total of 1295 patients with AP and 43 variables were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database. The included patients were randomly assigned to the training set and to the validation set at a ratio of 7 : 3. The chi-square test or Fisher's exact test was used to test the distribution of categorical variables, and Student's t-test was used for continuous variables. Multivariate logistic regression was used to establish a prognostic model for predicting the occurrence of sepsis in AP patients. The indicators to verify the overall performance of the model included the area under the receiver operating characteristic curve (AUC), calibration curves, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA).

RESULTS

The multifactor analysis results showed that temperature, phosphate, calcium, lactate, the mean blood pressure (MBP), urinary output, Glasgow Coma Scale (GCS), Charlson Comorbidity Index (CCI), sodium, platelet count, and albumin were independent risk factors. All of the indicators proved that the prediction performance and clinical profitability of the newly established nomogram were better than those of other common indicators (including SIRS, BISAP, SOFA, and qSOFA).

CONCLUSIONS

The new risk-prediction system that was established in this research can accurately predict the probability of sepsis in patients with acute pancreatitis, and this helps clinicians formulate personalized treatment plans for patients. The new model can reduce the disease burden of patients and can contribute to the reasonable allocation of medical resources, which is significant for tertiary prevention.

Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy

Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).

Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. The model was developed using multivariable logistic regression analysis. The performance of the nomogram has been evaluated in terms of calibration, discrimination, and clinical utility.

Results: There were nine particular features in septic patients that were significantly associated with SAE. Predictors of individualized prediction nomograms included age, rapid sequential evaluation of organ failure (qSOFA), and drugs including carbapenem antibiotics, quinolone antibiotics, steroids, midazolam, H₂-antagonist, diphenhydramine hydrochloride, and heparin sodium injection. The area under the curve (AUC) was 0.743, indicating good discrimination. The prediction model showed calibration curves with minor deviations from the ideal predictions. Decision curve analysis (DCA) suggested that the nomogram was clinically useful.

Conclusion: We propose a nomogram for the individualized prediction of SAE with satisfactory performance and clinical utility, which could aid the clinician in the early detection and management of SAE.

Fisetin ameliorates cognitive impairment by activating mitophagy and suppressing neuroinflammation in rats with sepsis-associated encephalopathy

BACKGROUND

Fisetin, the effective ingredient of the traditional Chinese medicine named Cotinus coggygria, is recommended to be active therapeutic in many disorders. However, its role in sepsis-associated encephalopathy (SAE) remains unclarified.

METHODS

Cecal ligation and puncture (CLP) operation was performed to establish a rat model of SAE. Rats were grouped according to the surgery operation and fisetin administration. Cognitive impairment was assessed by Morris water maze test. Disruption of blood-brain barrier (BBB) integrity was detected by Evan's blue staining. The mitophagy, reactive oxygen species (ROS) generation, NLRP3 inflammasome activation, and pro-inflammatory cytokines levels were measured through western blot and double immunofluorescence labeling. A transmission electron microscope was applied for the observation of mitochondrial autophagosomes.

RESULTS

Rats in the CLP group presented increased expression of IL-1R1, pNF-κB, TNF-α, and iNOS in microglial cells, indicating severe inflammation in the central nervous system (CNS). Nevertheless, there was no increase in BBB permeability. Meanwhile, NLRP3 inflammasome was activated in cerebral microvascular endothelial cells (CMECs), presented with an elevation of caspase-1 expression and IL-1β secretion into CNS. In addition, we found fisetin significantly improved cognitive dysfunction in rats with SAE. Neuroprotective effects of fisetin might be associated with inhibition of neuroinflammation, represented with decreased expression of IL-1R1, pNF-κB, TNF-α, and iNOS in microglia. Furthermore, fisetin induced mitophagy, scavenged ROS, blocked NLRP3 inflammasome activation of CMECs, as evidenced by decreased expression of caspase-1 and reduced release of IL-1β into CNS.

CONCLUSION

Collectively, fisetin-blocked NLRP3 inflammasome activation via promoting mitophagy in CMECs may suppress the secretion of IL-1β into CNS, reduce neuroinflammation, and contribute to the amelioration of cognitive impairment.

A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices

Introduction: Liver cirrhosis is caused by the development of various acute and chronic liver diseases. Esophageal varices is a common and serious complication of liver cirrhosis during decompensation. Despite the development of various treatments, the prognosis for liver cirrhosis with esophageal varices (LCEV) remains poor. We aimed to establish and validate a nomogram for predicting in-hospital death in LCEV patients.

Methods: Data on LCEV patients were extracted from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV) database. The patients from MIMIC-III were randomly divided into training and validation cohorts. Training cohort was used for establishing the model, validation and MIMIC-IV cohorts were used for validation. The independent prognostic factors for LCEV patients were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression. We then constructed a nomogram to predict the in-hospital death of LCEV patients. Multiple indicators were used to validate the nomogram, including the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA).

Results: Nine independent prognostic factors were identified by using LASSO and stepwise regressions: age, Elixhauser score, anion gap, sodium, albumin, bilirubin, international normalized ratio, vasopressor use, and bleeding. The nomogram was then constructed and validated. The AUC value of the nomogram was 0.867 (95% CI = 0.832–0.904) in the training cohort, 0.846 (95% CI = 0.790–0.896) in the validation cohort and 0.840 (95% CI = 0.807–0.872) in the MIMIC-IV cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and the Hosmer-Lemeshow test results demonstrated that the nomogram was well-calibrated. Improvements in NRI and IDI values suggested that our nomogram was superior to MELD-Na, CAGIB, and OASIS scoring system. DCA curves indicated that the nomogram had good value in clinical applications.

Conclusion: We have established the first prognostic nomogram for predicting the in-hospital death of LCEV patients. The nomogram is easy to use, performs well, and can be used to guide clinical practice, but further external prospective validation is still required.

Establishment of a prognostic model for patients with sepsis based on SOFA: a retrospective cohort study

Objective

To construct a nomogram based on the Sequential Organ Failure Assessment (SOFA) that is more accurate in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis.

Methods

Data from patients with sepsis were retrospectively collected from the Medical Information Mart for Intensive Care (MIMIC) database. Included patients were randomly divided into training and validation cohorts. Variables were selected using a backward stepwise selection method with Cox regression, then used to construct a prognostic nomogram. The nomogram was compared with the SOFA model using the concordance index (C-index), area under the time-dependent receiver operating characteristics curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA).

Results

A total of 5240 patients were included in the study. Patient’s age, SOFA score, metastatic cancer, SpO₂, lactate, body temperature, albumin, and red blood cell distribution width were included in the nomogram. The C-index, AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the SOFA alone.

Conclusion

A nomogram was established that performed better than the SOFA in predicting 30-, 60-, and 90-day mortality risk in patients with sepsis.

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

Background

Hospital mortality is high for patients with encephalopathy caused by microbial infection. Microbial infections often induce sepsis. The damage to the central nervous system (CNS) is defined as sepsis-associated encephalopathy (SAE). However, the relationship between pathogenic microorganisms and the prognosis of SAE patients is still unclear, especially gut microbiota, and there is no clinical tool to predict hospital mortality for SAE patients. The study aimed to explore the relationship between pathogenic microorganisms and the hospital mortality of SAE patients and develop a nomogram for the prediction of hospital mortality in SAE patients.

Methods

The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of SAE patients was developed by multivariable Cox regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) to evaluate the clinical utility of the model.

Results

Unfortunately, the results of our study did not find intestinal infection and microorganisms of the gastrointestinal (such as: Escherichia coli) that are related to the prognosis of SAE. Lasso regression and multivariate Cox regression indicated that factors including respiratory failure, lactate, international normalized ratio (INR), albumin, SpO₂, temperature, and renal replacement therapy were significantly correlated with hospital mortality. The AUC of 0.812 under the nomogram was more than that of the Simplified Acute Physiology Score (0.745), indicating excellent discrimination. DCA demonstrated that using the nomogram or including the prognostic signature score status was better than without the nomogram or using the SAPS II at predicting hospital mortality.

Conclusion

The prognosis of SAE patients has nothing to do with intestinal and microbial infections. We developed a nomogram that predicts hospital mortality in patients with SAE according to clinical data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

Construction and Evaluation of a Sepsis Risk Prediction Model for Urinary Tract Infection

Background: Urinary tract infection (UTI) is one of the common causes of sepsis. However, nomograms predicting the sepsis risk in UTI patients have not been comprehensively researched. The goal of this study was to establish and validate a nomogram to predict the probability of sepsis in UTI patients.

Methods: Patients diagnosed with UTI were extracted from the Medical Information Mart for Intensive Care III database. These patients were randomly divided into training and validation cohorts. Independent prognostic factors for UTI patients were determined using forward stepwise logistic regression. A nomogram containing these factors was established to predict the sepsis incidence in UTI patients. The validity of our nomogram model was determined using multiple indicators, including the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).

Results: This study included 6,551 UTI patients. Stepwise regression analysis revealed that the independent risk factors for sepsis in UTI patients were congestive heart failure, diabetes, liver disease, fluid electrolyte disorders, APSIII, neutrophils, lymphocytes, red blood cell distribution width, urinary protein, urinary blood, and microorganisms. The nomogram was then constructed and validated. The AUC, NRI, IDI and DCA of the nomogram all showed better performance than traditional APSIII score. The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. Improved NRI and IDI values indicate that our nomogram scoring system is superior to other commonly used ICU scoring systems. The DCA curve indicates that the DCA map of the nomogram has good clinical application ability.

Conclusion: This study identified the independent risk factors of sepsis in UTI patients and used them to construct a prediction model. The present findings may provide clinical reference information for preventing sepsis in UTI patients.

Body mass index linked to short-term and long-term all-cause mortality in patients with acute myocardial infarction

PURPOSES OF STUDY

This study aimed to elucidate the relationship between obesity and short-term and long-term mortality in patients with acute myocardial infarction (AMI) by analysing the body mass index (BMI).

STUDY DESIGN

A retrospective cohort study was performed on adult intensive care unit (ICU) patients with AMI in the Medical Information Mart for Intensive Care III database. The WHO BMI classification was used in the study. The Kaplan-Meier curve was used to show the likelihood of survival in patients with AMI. The relationships of the BMI classification with short-term and long-term mortality were assessed using Cox proportional hazard regression models.

RESULTS

This study included 1295 ICU patients with AMI, who were divided into four groups according to the WHO BMI classification. Our results suggest that obese patients with AMI tended to be younger (p<0.001), be men (p=0.001) and have higher blood glucose and creatine kinase (p<0.001) compared with normal weight patients. In the adjusted model, compared with normal weight AMI patients, those who were overweight and obese had lower ICU risks of death HR=0.64 (95% CI 0.46 to 0.89) and 0.55 (0.38 to 0.78), respectively, inhospital risks of death (0.77 (0.56 to 1.09) and 0.61 (0.43 to 0.87)) and long-term risks of death (0.78 0.64 to 0.94) and 0.72 (0.59 to 0.89). On the other hand, underweight patients had higher risks of short-term(ICU or inhospital mortality) and long-term mortality compared with normal weight patients (HR=1.39 (95% CI 0.58 to 3.30), 1.46 (0.62 to 3.42) and 1.99 (1.15 to 3.44), respectively).

CONCLUSIONS

Overweight and obesity were protective factors for the short-term and long-term risks of death in patients with AMI.