Clinical characteristics and risk factors associated with ICU-acquired infections in sepsis: A retrospective cohort study

Comparison of different scoring systems for predicting 28-day mortality in critically ill patients with acute pancreatitis: a retrospective cohort study

BACKGROUND

This study compared eight scoring systems for predicting 28-day and 1-year all-cause mortality in critically ill patients with acute pancreatitis (AP).

METHODS

Data from the Medical Information Mart for Intensive Care IV were used to conduct a comparative analysis of several predictive scoring systems. Predictive performance for 28-day and 1-year mortality was assessed using receiver operating characteristic (ROC) curves (area under the curve [AUC]), restricted cubic splines (RCS) for nonlinearity testing, and multivariable logistic regression for independent predictor analysis.

RESULTS

A total of 694 patients were included (28-day mortality: 15.56%; 1-year mortality: 24.78%). Acute Physiology Score III (APSIII) demonstrated the highest accuracy for 28-day mortality (AUC: 0.847, 95% confidence interval (CI): 0.808-0.886), followed by Bedside Index for Severity in Acute Pancreatitis (BISAP) (AUC: 0.835, 95% CI: 0.794-0.875). Linear relationships between scores and 28-day mortality were confirmed (all p for nonlinear > 0.05). Multivariable regression identified APSIII and BISAP as independent 28-day mortality predictors. For 1-year mortality, APSIII, BISAP, and Simplified Acute Physiology Score II (SAPS II) were independent predictors.

CONCLUSIONS

Both APSIII and BISAP were identified as independent predictors of 28-day mortality, while APSIII, BISAP, and SAPSII were associated with 1-year mortality. Among them, APSIII showed the best overall discriminative ability for both short- and long-term outcomes. However, BISAP remains an attractive alternative for its simplicity and comparable performance.

Sepsis Recognition by Electronic Health Record Screening in the Pediatric ICU

BACKGROUND

Early recognition and intervention of sepsis in the pediatric population have been shown to decrease hospital length of stay and mortality rates. Stakeholders within a pediatric intensive care unit (PICU) identified a need to improve sepsis recognition in compliance with the Improving Pediatric Sepsis Outcomes Collaborative recommendations.

PURPOSE

The purpose of this study was to identify appropriate screening variables in an electronic health record (EHR)-embedded sepsis screening tool to improve sepsis recognition in the PICU setting.

METHODS

A retrospective data analysis was conducted to test 3 versions of an EHR sepsis screen including triggers based on vital signs and/or laboratory results.

RESULTS

Of the 3 tested versions, the sepsis screen version that triggered based on both vital signs and laboratory findings showed the most promising results with a sensitivity of 83.3% and a specificity of 76%.

CONCLUSIONS

EHR-embedded sepsis screens that monitor documented variables can identify potential sepsis while avoiding over-triggers.

Critical Management of Septic Orthopedic Patients: The Impact of Intensive Care on Survival and Recovery

The management of septic orthopedic patients, particularly those with periprosthetic joint infections (PJIs) and trauma-related sepsis, remains a significant clinical challenge. This retrospective cohort study evaluated 27 patients admitted to the Intensive Care Unit (ICU) at the Emergency University Hospital in Bucharest between 2021 and 2024. Patients presented with either PJIs or polytrauma-related infections requiring critical care interventions. The PJI-TNM classification system was employed to assess infection complexity, comorbidities, and implant stability. Therapeutic strategies included one- or two-stage revision surgeries and targeted antimicrobial therapy, including the use of antibiotic-impregnated calcium sulfate beads. Infection resolution was achieved in 85.2% of patients, with a mean ICU stay of 13 days. The overall ICU mortality rate was 11%, with two deaths occurring within the first 30 days of admission. Elevated SOFA scores (≥10) and poor glycemic control (HbA1c > 8.5%) were significantly associated with prolonged ICU stays and higher complication rates. Statistical analysis revealed significant differences in CRP normalization and bone healing times across glycemic control groups (p < 0.001). Patients requiring mechanical ventilation exhibited longer ICU stays and increased mortality (25%). The PJI-TNM classification showed potential utility for risk stratification and guiding personalized treatment strategies. These findings underscore the importance of multidisciplinary ICU-level care and metabolic control in improving outcomes for septic orthopedic patients. Future multicenter studies are needed to validate these preliminary observations and refine prognostic models for this high-risk population.

Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database

Background

Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, early clinical diagnosis remains challenging. This study aimed to develop a predictive model for SAE in elderly ICU patients.

Methods

The data of elderly sepsis patients were extracted from the MIMIC IV database (version 3.1) and divided into training and test sets in a 7:3 ratio. Feature variables were selected using the LASSO-Boruta combined algorithm, and five machine learning (ML) models, including Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost),Light Gradient Boosting Machine(LGBM), Multilayer Perceptron (MLP), and Support Vector Machines (SVM), were subsequently developed using these variables. A comprehensive set of performance metrics was used to assess the predictive accuracy, calibration, and clinical applicability of these models. For the machine learning model with the best performance, we employed the SHapley Additive Explanations(SHAP) method to visualize the model.

Results

Based on strict inclusion and exclusion criteria, a total of 3,156 elderly sepsis patients were enrolled in the study, with an SAE incidence rate of 48.7%. The mortality rate of elderly sepsis patients who developed SAE was significantly higher than that of patients in the non-SAE group (28.78% vs. 12.59%, P < 0.001). A total of 18 feature variables were selected for the construction of the ML model using the LASSO-Boruta combined algorithm. Compared to the other four models and traditional scoring systems, the XGBoost model demonstrated the best overall predictive performance, with Area Under the Curve(AUC)=0.898, accuracy=0.830, recall=0.819, F1-Score=0.820, specificity=0.840, and Precision=0.821. Furthermore, the results from the Decision Curve Analysis (DCA) and calibration curves demonstrated that the XGBoost model has significant clinical value and stable predictive performance. The ten-fold cross-validation method further confirmed the robustness and generalizability of the model. In addition, we simplified the model based on the SHAP feature importance ranking, and the results indicated that the simplified XGBoost model retains excellent predictive ability (AUC=0.858).

Conclusions

The XGBoost model effectively predicts SAE in elderly ICU patients and may serve as a reliable tool for clinicians to identify high-risk patients.

Optimizing Antibiotic Treatment Duration for ESBL-Producing Enterobacteriaceae Bacteremia in ICU: A Multicentric Retrospective Cohort Study

BACKGROUND

The optimal duration of antibiotic treatment for extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-E) bloodstream infections (BSI) in intensive care unit (ICU) is not established. We aim to evaluate the frequency and clinical outcomesof a short appropriate antibiotic treatment (≤7 days) (SAT) for ESBL-E BSI acquired in the ICU. We specifically assessed the rate of ESBL-E BSI relapse, and in-ICU mortality.

METHOD

All patients who acquired ESBL-E BSI in three ICU in Northern France between January 2011 and June 2022 were included in a multicenter retrospective cohort study. The factors associated with prescribing short (SAT, ≤7 days) versus long (LAT, >7 days) antibiotic treatment were analyzed. To evaluate the impact of SAT on mortality in the ICU, an estimation was applied using a Cox model with a time-dependent co-variable adjusted by inverse weighting of the propensity score.

RESULTS

In total, 379 patients were included. The proportion of patients receiving a SAT was 40% in the entire cohort and 25% in survivors beyond 7 days. In bivariate analysis, the factors associated with prescribing a SAT in survivors were shorter pre-bacteremia ICU stay (p = 0.005), lower proportion of chronic renal failure history (p = 0.034), cancer (p = 0.042), or transplantation (p = 0.025), less frequent exposure to carbapenem within 3 months (p = 0.015). There was a higher proportion of septic shock (p = 0.017) or bacteremia secondary to pneumonia (p = 0.003) in the group of survivors receiving a LAT. After adjustment, no difference in survival was found between the two groups (HR: 1.65, 95%CI: 0.91-3.00, p = 0.10).

CONCLUSION

In our cohort, one quarter of patients with ESBL-E bacteremia acquired in the ICU surviving beyond 7 days were treated with a SAT. SAT did not appear to affect survival. Patients who could benefit from a SAT need to be better identified.

Restrictive fluid resuscitation versus liberal fluid resuscitation in patients with septic shock: comparison of outcomes

OBJECTIVE

To compare the prognosis of restrictive fluid resuscitation (RFR) versus liberal fluid resuscitation (LFR) in patients with septic shock.

METHODS

A retrospective analysis was conducted using clinical data from 82 septic shock patients treated in the Intensive Care Unit of Aviation General Hospital from January 2021 to December 2023. Patients were divided into two groups: the LFR group (n=41) and the RFR group (n=41), based on the resuscitation strategy used.

RESULTS

Both groups demonstrated significant reductions in heart rate (HR) and significant increases in mean arterial pressure (MAP) and central venous pressure (CVP) post-treatment (all P < 0.05). After treatment, the ejection fraction (EF) and cardiac index (CI) were significantly higher in the RFR group compared to the LFR group, while levels of troponin I (cTnI) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were significantly lower in the RFR group (all P < 0.05). After treatment, the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores exhibited a marked decrease in both groups, with the RFR group exhibiting greater reductions in both scales compared to the LFR group (both P < 0.05). The incidence of complications was significantly lower in the RFR group than in the LFR group (P < 0.05). Multivariable analysis identified age and fluid resuscitation modality as risk factors for complications in septic shock.

CONCLUSIONS

In patients with septic shock, RFR, compared to LFR, appears to better maintain hemodynamic stability and reduce myocardial injury. It also enhances cardiac function, mitigates organ failure, and lowers complication rates, possibly facilitating faster recovery.

Development and validation of a nomogram to predict survival in septic patients with heart failure in the intensive care unit

Heart failure is a common complication in patients with sepsis, and individuals who experience both sepsis and heart failure are at a heightened risk for adverse outcomes. This study aims to develop an effective nomogram model to predict the 7-day, 15-day, and 30-day survival probabilities of septic patients with heart failure in the intensive care unit (ICU). This study extracted the pertinent clinical data of septic patients with heart failure from the Critical Medical Information Mart for Intensive Care (MIMIC-IV) database. Patients were then randomly allocated into a training set and a test set at a ratio of 7:3. Cox proportional hazards regression analysis was used to determine independent risk factors influencing patient prognosis and to develop a nomogram model. The model's efficacy and clinical significance were assessed through metrics such as the concordance index (C-index), time-dependent receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). A total of 5,490 septic patients with heart failure were included in the study. A nomogram model was developed to predict short-term survival probabilities, using 13 variables: age, pneumonia, endotracheal intubation, mechanical ventilation, potassium (K), anion gap (AG), lactate (Lac), activated partial thromboplastin time (APTT), white blood cell count (WBC), red cell distribution width (RDW), hemoglobin-to-red cell distribution width ratio (HRR), Sequential Organ Failure Assessment (SOFA) score, and Charlson Comorbidity Index (CCI). The C-index was 0.730 (95% CI 0.719-0.742) for the training set and 0.761 (95% CI 0.745-0.776) for the test set, indicating strong model accuracy, indicating good model accuracy. Evaluations via the ROC curve, calibration curve, and decision curve analyses further confirmed the model's reliability and utility. This study effectively developed a straightforward and efficient nomogram model to predict the 7-day, 15-day, and 30-day survival probabilities of septic patients with heart failure in the ICU. The implementation of treatment strategies that address the risk factors identified in the model can enhance patient outcomes and increase survival rates.

Can an increase in nursing care complexity raise the risk of intra-hospital and intensive care unit transfers in children? A retrospective observational study

INTRODUCTION

Intra-hospital patient transfers (IPTs) and transfers to intensive care units (ICUs) are high-risk events in pediatric care. Nursing care complexity, reflected by nursing diagnoses (NDs) and nursing actions (NAs), may influence the frequency of these transfers. This study explores the association between nursing care complexity and IPTs, including ICU transfers, in hospitalized children.

MATERIALS AND METHODS

A retrospective observational study was conducted at a tertiary care university hospital in Italy. Data from 1013 children aged 2 to 12 years were collected from electronic health records. Sociodemographic, clinical, and nursing data, including NDs and NAs, were analyzed. Latent Class Analysis classified nursing care complexity, while backward elimination regression and binary logistic regression identified predictors of IPTs and ICU transfers.

RESULTS

Significant positive correlations were found between IPTs and both NDs (rs = 0.326, p < 0.001) and NAs (rs = 0.428, p < 0.001). Key predictors of IPTs included Diagnosis Related Groups (DRG) weight, total comorbidities, surgical DRG, the number of medications used, and high nursing care complexity. ICU-transferred patients had significantly higher nursing care complexity (6.54 vs. 3.46 NDs, p < 0.001; 31 vs. 16 NAs, p < 0.001). High nursing care complexity increased the likelihood of ICU transfer by 18 times (OR = 18.413, p < 0.001).

CONCLUSION

Nursing care complexity strongly influences IPTs and ICU transfers. Close monitoring of patients with high nursing care complexity is essential to anticipate transfers and reduce clinical risks.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease

BACKGROUND

The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).

METHODS

After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.

RESULTS

This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions.

CONCLUSIONS

In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

CISepsis: a causal inference framework for early sepsis detection

Introduction

The early prediction of sepsis based on machine learning or deep learning has achieved good results.Most of the methods use structured data stored in electronic medical records, but the pathological characteristics of sepsis involve complex interactions between multiple physiological systems and signaling pathways, resulting in mixed structured data. Some researchers will introduce unstructured data when also introduce confounders. These confounders mask the direct causality of sepsis, leading the model to learn misleading correlations. Finally, it affects the generalization ability, robustness, and interpretability of the model.

Methods

To address this challenge, we propose an early sepsis prediction approach based on causal inference which can remove confounding effects and capture causal relationships. First, we analyze the relationship between each type of observation, confounder, and label to create a causal structure diagram. To eliminate the effects of different confounders separately, the methods of back-door adjustment and instrumental variable are used. Specifically, we learn the confounder and an instrumental variable based on mutual information from various observed data and eliminate the influence of the confounder by optimizing mutual information. We use back-door adjustment to eliminate the influence of confounders in clinical notes and static indicators on the true causal effect.

Results

Our method, named CISepsis, was validated on the MIMIC-IV dataset. Compared to existing state-of-the-art early sepsis prediction models such as XGBoost, LSTM, and MGP-AttTCN, our method demonstrated a significant improvement in AUC. Specifically, our model achieved AUC values of 0.921, 0.920, 0.919, 0.923, 0.924, 0.926, and 0.926 at the 6, 5, 4, 3, 2, 1, and 0 time points, respectively. Furthermore, the effectiveness of our method was confirmed through ablation experiments.

Discussion

Our method, based on causal inference, effectively removes the influence of confounding factors, significantly improving the predictive accuracy of the model. Compared to traditional methods, this adjustment allows for a more accurate capture of the true causal effects of sepsis, thereby enhancing the model's generalizability, robustness, and interpretability. Future research will explore the impact of specific indicators or treatment interventions on sepsis using counterfactual adjustments in causal inference, as well as investigate the potential clinical application of our method.

Single-center nomogram model for sepsis complicated by acute lung injury

OBJECTIVE

To construct and validate a nomogram model for predicting sepsis complicated by acute lung injury (ALI).

METHODS

The healthcare records of 193 sepsis patients hospitalized at The Affiliated Tai'an City Central Hospital of Qingdao University from January 2022 to December 2023 were retrospectively reviewed. Among these patients, 69 were in the ALI group and 124 in the non-ALI group. A nomogram prediction model was constructed using logistic regression analysis. Its predictive performance was evaluated through various measures, including the area under the curve (AUC), calibration curve, decision curve, sensitivity, specificity, accuracy, recall rate, and precision rate.

RESULTS

The predictive factors included the neutrophil/lymphocyte ratio (NLR), oxygenation index (PaO2/FiO2), tumor necrosis factor-α (TNF-α), and acute physiology and chronic health evaluation II (APACHE II). The nomogram training set achieved an AUC of 0.959 (95% CI: 0.924-0.995), an accuracy of 92.59%, a recall of 96.70%, and a precision of 92.63%. In the validation set, the AUC was 0.938 (95% CI: 0.880-0.996), with an accuracy of 89.66%, a recall of 93.94%, and a precision of 88.57%. The calibration curve demonstrated that the prediction results were consistent with the actual findings. The decision curve indicated that the model has clinical applicability.

CONCLUSION

NLR, PaO2/FiO2, TNF-α, and APACHE II are closely associated with ALI in sepsis patients. A nomogram model based on these four variables shows strong predictive performance and may be used as a clinical decision-support tool to help physicians better identify high-risk groups.

Association between serum osmolality and deteriorating renal function in patients with acute myocardial infarction: analysis of the MIMIC- IV database

BACKGROUND

To investigate the association between serum osmolality and deteriorating renal function in patients with acute myocardial infarction (AMI).

METHODS

Three thousand eight hundred eighty-five AMI patients from the Medical Information Mart for Intensive Care IV were enrolled for this study. The primary outcome was deteriorating renal function. Secondary outcomes included the new-onset of acute kidney injury (AKI) and progress of AKI. < 293.2725 mmol/L was defined as low serum osmolality, and ≥ 293.2725 mmol/L as high serum osmolality based on upper quartile. Univariate and multivariate logistic regression models were used to explore the associations between serum osmolality and the development of deteriorating renal function, the new-onset of AKI and progress of AKI among AMI patients. Subgroup analysis was also conducted.

RESULTS

One thousand three hundred ninety-three AMI patients developed deteriorating renal function. After adjusting all confounding factors, high serum osmolality was associated with increased risk of deteriorating renal function [odds ratio (OR) = 1.47, 95% confidence interval (CI): 1.22-1.78], new-onset of AKI (OR = 1.31, 95% CI: 1.01-1.69), and progress of AKI risk (OR = 1.26, 95% CI: 1.01-1.59) among AMI patients. In addition, when the stratified analysis was performed for age, AMI type, cardiogenic shock, and estimated glomerular filtration rate (eGFR), high serum osmolality was risk factor for the risk of deteriorating renal function among patients aged 65 years or older, without cardiogenic shock, and with an eGFR ≥ 60 mL/min/1.73m2.

CONCLUSION

Higher serum osmolality increased the risk of deteriorating renal function among AMI patients.

Characteristics and Clinical Prognosis of Septic Patients With Persistent Lymphopenia

Background: Septic patients with persistent lymphopenia may be in an immunosuppressed state. Therefore, we evaluated and compared the clinical characteristics and outcomes of septic patients with persistent lymphopenia (≥2d) and those with nonpersistent lymphopenia. Methods: A retrospective cohort study was designed. A total of 1306 patients with sepsis who were attended to the First Affiliated Hospital of Dalian Medical University from March 2016 to August 2022 were included. The primary clinical outcome was 90d mortality. The secondary clinical outcomes were the length of stay, hospital mortality, 28d mortality, the incidence of secondary infection, and differences in clinical characteristics. Results: Among 1306 patients with sepsis, 913 (69.9%) patients developed persistent lymphopenia. Compared with patients with nonpersistent lymphopenia, patients with persistent lymphocytopenia were admitted to intensive care unit (75.7% vs 52.7%, P < .05), treated with mechanical ventilation (67.6% vs 39.2%, P < .05), positive rate of microbial culture pathogens (86.7% vs 71.2%, P < .05), SOFA [8.0 (6.0-10.0) vs 6.0 (4.0-8.0), P < .05], length of stay [17.0d (12.0-27.0) vs 13.0d (10.0-21.0), P < .05], hospital mortality (37.7% vs 24.2%, P < .05), 28d mortality (38.0% vs 22.9%, P < .05), and 90d mortality (51.2% vs 31.3%, P < .05) were higher. As the duration of lymphocytopenia increased, so did the mortality rate in hospital. In addition, the onset time of persistent lymphopenia was not associated with SOFA. But we found that the frequency of persistent lymphopenia during hospitalization was positively associated with SOFA. Conclusion: Septic patients with persistent lymphopenia have higher mortality, worse conditions, increased risk of secondary infection, and poor prognosis regardless of shock.

The value of right ventricular pulmonary artery coupling in determining the prognosis of patients with sepsis

The outcomes of patients with sepsis are influenced by the contractile function of the right ventricle (RV), but the impact of cardiopulmonary interaction in ICU-mortality of sepsis patients remains unclear. This study aims to investigate the ICU-mortality impact of right ventricular-pulmonary artery (RV-PA) coupling in patients with sepsis. We employed echocardiography to assess patients with sepsis within the initial 24 h of their admission to the ICU. RV-PA coupling was evaluated using the tricuspid annular plane systolic excursion (TAPSE) to pulmonary artery systolic pressure (PASP) ratio. A total of 92 subjects were enrolled, with 55 survivors and 37 non-survivors. TAPSE/PASP ratio assessed mortality with an area under the curve (AUC) of 0.766 (95% CI 0.670-0.862) and the optimal cutoff value was 0.495 mm/mmHg. We constructed a nomogram depicting the TAPSE/PASP in conjunction with IL-6 and Lac for the joint prediction of sepsis prognosis, and demonstrated the highest predictive capability (AUC = 0.878, 95% CI 0.809-0.948). In conclusion, the TAPSE/PASP ratio demonstrated prognostic value for ICU mortality in sepsis patients. The nomogram, which combines the TAPSE/PASP, IL-6, and LAC, demonstrated enhanced predictive efficacy for the prognosis of sepsis patients.

Sepsis death risk factor score based on systemic inflammatory response syndrome, quick sequential organ failure assessment, and comorbidities

OBJECTIVE

In this study, we aimed to evaluate the death risk factors of patients included in the sepsis protocol bundle, using clinical data from qSOFA, SIRS, and comorbidities, as well as development of a mortality risk score.

DESIGN

This retrospective cohort study was conducted between 2016 and 2021.

SETTING

Two university hospitals in Brazil.

PARTICIPANTS

Patients with sepsis.

INTERVENTIONS

Several clinical and laboratory data were collected focused on SIRS, qSOFA, and comorbidities.

MAIN VARIABLE OF INTEREST

In-hospital mortality was the primary outcome variable. A mortality risk score was developed after logistic regression analysis.

RESULTS

A total of 1,808 patients were included with a death rate of 36%. Ten variables remained independent factors related to death in multivariate analysis: temperature ≥38 °C (odds ratio [OR] = 0.65), previous sepsis (OR = 1.42), qSOFA ≥ 2 (OR = 1.43), leukocytes >12,000 or <4,000 cells/mm3 (OR = 1.61), encephalic vascular accident (OR = 1.88), age >60 years (OR = 1.93), cancer (OR = 2.2), length of hospital stay before sepsis >7 days (OR = 2.22,), dialysis (OR = 2.51), and cirrhosis (OR = 3.97). Considering the equation of the binary regression logistic analysis, the score presented an area under curve of 0.668, is not a potential model for death prediction.

CONCLUSIONS

Several risk factors are independently associated with mortality, allowing the development of a prediction score based on qSOFA, SIRS, and comorbidities data, however, the performance of this score is low.

DEVELOPMENT AND VALIDATION OF A NOMOGRAM FOR PREDICTING 28-DAY IN-HOSPITAL MORTALITY IN SEPSIS PATIENTS BASED ON AN OPTIMIZED ACUTE PHYSIOLOGY AND CHRONIC HEALTH EVALUATION II SCORE

ABSTRACT

Purpose : The objective of this study is to establish a nomogram that correlates optimized Acute Physiology and Chronic Health Evaluation II (APACHE II) score with sepsis-related indicators, aiming to provide a robust model for early prediction of sepsis prognosis in clinical practice and serve as a valuable reference for improved diagnosis and treatment strategies. Methods : This retrospective study extracted sepsis patients meeting the inclusion criteria from the MIMIC-IV database to form the training group. An optimized APACHE II score integrated with relevant indicators was developed using a nomogram for predicting the prognosis of sepsis patients. External validation was conducted using data from the intensive care unit at Lanzhou University Second Hospital. Results : The study enrolled 1805 patients in the training cohort and 203 patients in the validation cohort. A multifactor analysis was conducted to identify factors affecting patient mortality within 28 days, resulting in the development of an optimized score by simplifying evaluation indicators from APACHE II score. The results showed that the optimized score (area under the ROC curve [AUC] = 0.715) had a higher area under receiver operating characteristic curve than Sequential Organ Failure Assessment score (AUC = 0.637) but slightly lower than APACHE II score (AUC = 0.720). Significant indicators identified through multifactor analysis included platelet count, total bilirubin level, albumin level, prothrombin time, activated partial thromboplastin time, mechanical ventilation use and renal replacement therapy use. These seven indicators were combined with optimized score to construct a nomogram based on these seven indicators. The nomogram demonstrated good clinical predictive value in both training cohort (AUC = 0.803) and validation cohort (AUC = 0.750). Calibration curves and decision curve analyses also confirmed its good predictive ability, surpassing the APACHE II score and Sequential Organ Failure Assessment score in identifying high-risk patients. Conclusions : The nomogram was established in this study using the MIMIC-IV database and validated with external data, demonstrating its robust discriminability, calibration, and clinical practicability for predicting 28-day mortality in sepsis patients. These findings aim to provide substantial support for clinicians' decision making.

The Association between the rs3747406 Polymorphism in the Glucocorticoid-Induced Leucine Zipper Gene and Sepsis Survivals Depends on the SOFA Score

The variability in mortality in sepsis could be a consequence of genetic variability. The glucocorticoid system and the intermediate TSC22D3 gene product-glucocorticoid-induced leucine zipper-are clinically relevant in sepsis, which is why this study aimed to clarify whether TSC22D3 gene polymorphisms contribute to the variance in sepsis mortality. Blood samples for DNA extraction were obtained from 455 patients with a sepsis diagnosis according to the Sepsis-III criteria and from 73 control subjects. A SNP TaqMan assay was used to detect single-nucleotide polymorphisms (SNPs) in the TSC22D3 gene. Statistical and graphical analyses were performed using the SPSS Statistics and GraphPad Prism software. C-allele carriers of rs3747406 have a 2.07-fold higher mortality rate when the sequential organ failure assessment (SOFA) score is higher than eight. In a multivariate COX regression model, the SNP rs3747406 with a SOFA score ≥ 8 was found to be an independent risk factor for 30-day survival in sepsis. The HR was calculated to be 2.12, with a p-value of 0.011. The wild-type allele was present in four out of six SNPs in our cohort. The promoter of TSC22D3 was found to be highly conserved. However, we discovered that the C-allele of rs3747406 poses a risk for sepsis mortality for SOFA Scores higher than 6.

A nomogram for predicting the necessity of tracheostomy after severe acute brain injury in patients within the neurosurgery intensive care unit: A retrospective cohort study

Objective

This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU).

Method

We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility.

Results

The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822-0.886) for the training cohort and 0.865 (95% CI: 0.817-0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit.

Conclusion

We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.

Prognostic impacts of repeated sepsis in intensive care unit on autoimmune disease patients: a retrospective cohort study

BACKGROUND

Autoimmune diseases (ADs) may be complicated by sepsis when intensive care unit (ICU) admission. But repeated sepsis among AD patients has not been studied yet. The aim of this study is to investigate the impact of repeated in-ICU sepsis on the 1-year overall-cause mortality, septic shock and in-ICU death of AD patients.

METHODS

Data of AD patients with sepsis retrieved from Medical Information Mart for Intensive Care IV (MIMIC-IV) database were divided into the single group and the repeated group according to the frequency of in-ICU sepsis. Propensity score matching was used to balance inter-group bias. Cox proportional hazard regression and sensitivity analysis were utilized to assess the variables on mortality.

RESULTS

The incidence of repeated in-ICU sepsis in baseline was 19.8%. The repeated in-ICU sepsis was a risk factor for 1-year overall-cause mortality among AD patients (adjusted hazard ratio [HR] = 1.50, 95% CI: 1.16-1.93, P = 0.002), with robust adjusted HRs by the adjustment for confounders in the sensitivity analysis (all P < 0.01). Maximum Sequential Organ Failure Assessment (Max SOFA), Charlson comorbidity index (CCI) and Simplified Acute Physiology Score-II (SAPS-II) were risk factors for 1-year overall-cause mortality among AD with repeated sepsis (Max SOFA: HR = 1.09, P = 0.002; CCI: HR = 1.08, P = 0.039; SAPS-II: HR = 1.03, P < 0.001).

CONCLUSIONS

Compared to single hit, repeated in-ICU sepsis was independently related to a higher risk of 1-year overall-cause mortality among AD patients. Assessment tools (Higher SOFA, CCI and SAPS-II scores) were closely linked to poor prognosis of AD with repeated sepsis and helped to reflect ill physical conditions for the patients.

Analysis of postoperative pulmonary complications after gastrectomy for gastric cancer: development and validation of a nomogram

Background

Postoperative pulmonary complications (PPCs) are common in gastric cancer patients after gastrectomy. The aim of our study was to investigate the perioperative risk factors and to develop a nomogram to identify patients who are at significant risk of PPCs.

Methods

The clinical data of gastric cancer patients who underwent elective gastrectomy in the First Affiliated Hospital of Nanjing Medical University from 2017 to 2021 were retrospectively collected. All patients were randomly divided into a training and a validation cohort at a ratio of 7:3. Univariate and multivariate analysis were applied to identify the independent risk factors that might predict PPCs, and a nomogram was constructed. Both discrimination and calibration abilities were estimated by the area under a receiver operating characteristic curve (AUC) and calibration curves. The clinical effectiveness of the nomogram was further quantified with the decision curve analysis (DCA).

Results

Of 2,124 included patients, one hundred and fifty patients (7.1%) developed PPCs. Binary logistic analysis showed that age > 65 years, higher total cholesterol level, longer duration of surgery, total gastrectomy, and the dose of oxycodone > 5.5 mg were independent risk factors for the occurrence of PPCs, which were contained in the nomogram. The predictive nomogram showed good discrimination and calibration [an AUC of 0.735 (95% CI: 0.687-0.783) in a training cohort and 0.781 (95% CI: 0.715-0.847) in a validation cohort]. The calibration curve and decision curve analysis showed a good agreement between nomogram predictions and actual observations.

Conclusion

We developed a nomogram model based on age, total cholesterol, extent of resection, duration of surgery, and the dose of oxycodone to predict the risk of PPCs in gastric cancer patients after elective gastrectomy.

Mollugin prevents CLP-induced sepsis in mice by inhibiting TAK1-NF-κB/MAPKs pathways and activating Keap1-Nrf2 pathway in macrophages

Sepsis is a life-threatening organ dysfunction associated with macrophage overactivation. Targeted therapy against macrophages is considered a promising strategy for sepsis treatment. Mollugin (MLG), a compound extracted from traditional Chinese medicine Rubia cordifolia L., possesses anti-tumor and anti-inflammatory activities. This study aimed to investigate the anti-inflammatory effects and mechanisms of MLG in macrophages and its therapeutic role in CLP-induced sepsis in mice. The results demonstrated that MLG downregulated the inflammatory response induced by LPS or tumor necrosis factor α (TNF-α) in macrophages. Mechanistically, MLG suppressed the phosphorylation of TAK1, the upstream modulator of IKKα/β and MAPKs, thereby inhibiting the pro-inflammatory signaling transduction of NF-κB and MAPKs. Additionally, MLG also activated the Nrf2 antioxidant pathway, reducing intracellular reactive oxygen species. CETSA and molecular docking analyses revealed that MLG could effectively bind to TAK1 and Keap1, which may be involved in the inhibition of TAK1- NF-κB/MAPKs and activation of Nrf2 mediated by MLG. Animal study demonstrated that MLG ameliorated inflammatory injury of lung and liver in CLP-induced sepsis mice probably by reducing the levels of pro-inflammatory cytokines. Therefore, our study suggests that bi-directional roles of MLG in improving sepsis via blocking the TAK1-NF-κB/MAPKs and activating Nrf2 pathways, indicating its potential as a promising candidate drug for sepsis treatment.

A hierarchical prognostic model for Co-diabetes pancreatic adenocarcinoma

Background

Co-diabetes pancreatic adenocarcinoma has a poorer prognosis than pancreatic adenocarcinoma without diabetes. This study aimed to develop a reliable prognostic model for patients with co-diabetes pancreatic adenocarcinoma.

Method

Overall, 169 patients with co-diabetes pancreatic adenocarcinoma were included in our study. First, the independent risk factors affecting the prognosis of patients with co-diabetes pancreatic adenocarcinoma were determined by univariate and multivariate Cox regression analyses. Based on these identified risk factors, we developed a nomogram and evaluated its predictive ability using the concordance index, receiver operating characteristic curve, calibration plot, decision curve, and net reclassification index.

Results

In this study, prealbumin, transferrin, carcinoembryonic antigen, distant metastasis, tumor differentiation neutrophil count, lymphocyte count and fasting blood glucose were confirmed as significant prognostic factors. Based on these predictors, a new nomogram was developed. Compared with the American Joint Committee on Cancer 8 staging system and other models, the nomogram achieved a higher concordance index in the training (0.795) and validation (0.729) queues. The area under the nomogram's curve for predicting patient survival at 0.5, 1, and 1.5 years in the training queue was >0.8. Patients were risk-stratified using the nomogram, and Kaplan-Meier survival curves of subgroups were plotted. The Kaplan-Meier curve also showed better separation than the American Joint Committee on Cancer 8 staging system, indicating that our model has a better risk hierarchical ability.

Conclusions

Compared to the American Joint Committee on Cancer 8 staging system and other predictive models, our model showed better predictive ability for patients with co-diabetes pancreatic adenocarcinoma. Our model will help in patients' risk stratification and improves their prognosis.

Association between base excess and 28-day mortality in sepsis patients: A secondary analysis based on the MIMIC- IV database

Objective

The relationship between base excess (BE) and 28-day death in sepsis patients remains to be elucidated. The aim of our clinical study is to explore the association of BE with 28-day mortality in patients with sepsis by using a large sample, multicenter Medical Information Mart for Intensive Care IV (MIMIC-IV) database.

Methods

We extracted the data of 35,010 patients with sepsis from the MIMIC-IV database, in which we used BE as an exposure variable and the 28-day mortality as an outcome variable, respectively, so as to explore the impact of BE on the 28-day mortality of patients with sepsis after adjusting for covariates.

Results

BE and the 28-day mortality of patients with sepsis appeared to have a U-shaped relationship. The calculated inflection points were -2.5 mEq/L and 1.9 mEq/L, respectively. Our data demonstrated that BE was negatively associated with 28-day mortality in the range of -41.0 mEq/L to -2.5 mEq/L (odds ratio: 0.95; 95% confidence intervals (95%CI): 0.93 to 0.96), p < 0.0001. When BE was in the range of 1.9 mEq/L to 55.5 mEq/L, however, a positive association existed between BE and 28-day mortality of patients with sepsis (odds ratio: 1.03; 95% CI: 1.00 to 1.05; p < 0.05).

Conclusion

The BE levels have a U-shaped relationship with the 28-day mortality in patients with sepsis, in which the mortality of patients will gradually decrease with a BE value from -41.0 mEq/L to -2.5 mEq/L, while the mortality will increase with a BE value from 1.9 mEq/L to 55.5 mEq/L.

Development and validation of nomograms for predicting the risk probability of carbapenem resistance and 28-day all-cause mortality in gram-negative bacteremia among patients with hematological diseases

Objectives

Gram-negative bacteria (GNB) bloodstream infections (BSIs) are the most widespread and serious complications in hospitalized patients with hematological diseases. The emergence and prevalence of carbapenem-resistant (CR) pathogens has developed into a considerable challenge in clinical practice. Currently, nomograms have been extensively applied in the field of medicine to facilitate clinical diagnosis and treatment. The purpose of this study was to explore risk indicators predicting mortality and carbapenem resistance in hematological (HM) patients with GNB BSI and to construct two nomograms to achieve personalized prediction.

Methods

A single-center retrospective case-control study enrolled 244 hospitalized HM patients with GNB-BSI from January 2015 to December 2019. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were conducted to select potential characteristic predictors of plotting nomograms. Subsequently, to evaluate the prediction performance of the models, the prediction models were internally validated using the bootstrap approach (resampling = 1000) and 10-fold cross validation.

Results

Of all 244 eligible patients with BSI attributed to GNB in this study, 77 (31.6%) were resistant to carbapenems. The rate of carbapenem resistance exhibited a growing tendency year by year, from 20.4% in 2015 to 42.6% in 2019 (p = 0.004). The carbapenem resistance nomogram constructed with the parameters of hypoproteinemia, duration of neutropenia ≥ 6 days, previous exposure to carbapenems, and previous exposure to cephalosporin/β-lactamase inhibitors indicated a favorable discrimination ability with a modified concordance index (C-index) of 0.788 and 0.781 in both the bootstrapping and 10-fold cross validation procedures. The 28-day all-cause mortality was 28.3% (68/240). The prognosis nomogram plotted with the variables of hypoproteinemia, septic shock, isolation of CR-GNB, and the incomplete remission status of underlying diseases showed a superior discriminative ability of poorer clinical prognosis. The modified C-index of the prognosis nomogram was 0.873 with bootstrapping and 0.887 with 10-fold cross validation. The decision curve analysis (DCA) for two nomogram models both demonstrated better clinical practicality.

Conclusions

For clinicians, nomogram models were effective individualized risk prediction tools to facilitate the early identification of HM patients with GNB BSI at high risk of mortality and carbapenem resistance.