Myeloid-derived suppressor cells (MDSCs) are known for their immunosuppressive effects on both innate and adaptive immunity, particularly targeting T cells, and they undergo continuous expansion during sepsis. However, the pathophysiological significance of MDSCs in sepsis-induced immunosuppression remains to be fully elucidated. In this study, we investigated the dynamic changes in MDSCs during sepsis and their contribution to sepsis-induced immunosuppression using a clinically relevant "two-hit" sepsis model. Our findings revealed that mice surviving cecal ligation and puncture (CLP) exhibited a significant accumulation and enhanced activity of MDSCs, which correlated with sepsis-related immune paralysis, impaired bacterial clearance, and heightened susceptibility to secondary infections. Importantly, administration of the liver X receptor (LXR) agonist GW3965 at the late stage of sepsis significantly restored immune function, decreased susceptibility to secondary infections, enhanced bacterial clearance, and improved prognosis by eliminating MDSCs. These results highlight the pivotal role of MDSCs in the development of sepsis-associated immunosuppression and indicate that targeting MDSCs could be a promising therapeutic approach to mitigate immunosuppression in sepsis.

Purpose: This study aims to establish and validate machine learning-based models to predict death in hospital among critical orthopedic trauma patients with sepsis or respiratory failure. Methods: This study collected 523 patients from the Medical Information Mart for Intensive Care database. All patients were randomly classified into a training cohort and a validation cohort. Six algorithms, including logistic regression (LR), extreme gradient boosting machine (eXGBM), support vector machine (SVM), random forest (RF), neural network (NN), and decision tree (DT), were used to develop and optimize models in the training cohort, and internal validation of these models were conducted in the validation cohort. Based on a comprehensive scoring system, which incorporated 10 evaluation metrics, the optimal model was obtained with the highest scores. An artificial intelligence (AI) application was deployed based on the optimal model in the study. Results: The in-hospital mortality was 19.69%. Among all developed models, the eXGBM had the highest area under the curve (AUC) value (0.951, 95% CI: 0.934-0.967), and it also showed the highest accuracy (0.902), precise (0.893), recall (0.915), and F1 score (0.904). Based on the scoring system, the eXGBM had the highest score of 53, followed by the RF model (43) and the NN model (39). The scores for the LR, SVM, and DT were 22, 36, and 17, respectively. The decision curve analysis confirmed that both the eXGBM and RF models provided substantial clinical net benefits. However, the eXGBM model consistently outperformed the RF model across multiple evaluation metrics, establishing itself as the superior option for predictive modeling in this scenario, with the RF model as a strong secondary choice. The Shapley Additive Explanation analysis revealed that Simplified Acute Physiology Score II, age, respiratory rate, Oxford Acute Severity of Illness Score, and temperature were the most important five features contributing to the outcome. Conclusions: This study develops an artificial intelligence application to predict in-hospital mortality among critical orthopedic trauma patients with sepsis or respiratory failure.

The correct and early diagnosis of an infection is pivotal for patients, especially if the patients are immunocompromised. Various infection markers are used in clinics with different advantages and disadvantages. The neutrophil-to-lymphocyte ratio (NLR) is a cost effective parameter easily obtained without further investments. The aim of this study is to elucidate the value of the NLR in comparison to other established inflammation markers in patients in the intensive care unit who underwent liver or lung transplantation for the detection of bacterial and fungal specimens.

In this retrospective single centre study infection marker and microbiology data of 543 intensive care cases of liver or lung transplanted patients in the intensive care unit after transplantation were analysed.

In total 5,072 lab work results and 1,104 positive microbiology results were analysed. Results of an area under curve analysis were better for the NLR (0.631; p < 0.001) than for CRP (0.522; p = 0.152) or IL-6 (0.579; p < 0.001). The NLR was independent of type of organ which was transplanted and gender of patients, whereas IL-6 values differed significantly between liver and lung transplanted patients and between male and female.

All analysed inflammation markers are far from being perfect. The NLR is a sensitive marker with reasonable threshold for the detection of microbiological specimens independent of gender or type of organ transplanted. The use allows a more differentiated approach to face the challenge of bacteria and fungus in patients who underwent liver or lung transplantation.

Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate prognostic tools. Machine learning methods such as logistic regression, random forests, and CatBoost, have shown potential in early sepsis prediction. The study aimed to create and verify a machine learning model capable of early prognostic identification of patients with sepsis in intensive care units (ICUs).

Patients adhering to inclusion and exclusion criteria from the MIMIC-IV v2.2 database were divided into a training set and a validation set in a 7:3 ratio. Initially, we employed difference analysis to assess the significance of each variable and subsequently screened relevant features with multinomial logistic regression analysis. Logistic regression, random forest, and CatBoost algorithms were used to construct machine learning models to predict rapid recovery, chronic critical illness, and mortality in sepsis. The models were compared through several evaluation indexes including precision, accuracy, recall, F1 score, and the area under the receiver-operating-characteristic curve(AUC) in the validation set to select the optimal model. The best model was visualized and interpreted utilizing the Shapley Additive explanations method.

13174 sepsis patients were included. Post the screening process,26 clinical features were obtained to develop three distinct machine learning models. CatBoost exhibited superior performance among the three models with a weighted AUC of 0.771. The prognosis with the highest predictive performance was mortality (AUC = 0.804), followed by the prognoses of rapid recovery (AUC = 0.773) and chronic critical illness(AUC = 0.737). Urine output, respiratory rate, and temperature were the top three important features for the whole model prediction.

The machine learning model developed leveraging the CatBoost algorithm demonstrates the latent capacity to identify sepsis prognosis early. It also suggests that interventions targeting factors such as urine output, respiratory status, and temperature in the early stage may potentially alter the adverse prognosis of sepsis patients. However, the model will still require further external validation in the future.

At present, there is insufficient evidence to evaluate the prognosis of patients with sepsis. This study anazed the clinical data of 822 sepsis patients in the ICU of a tertiary Grade A hospital to construct and validate a nomogram model for predicting the 28-day mortality risk in sepsis patients. The model was constructed using multivariate logistic regression analysis to screen for independent risk factors affecting prognosis, and a mortality risk prediction model was built based on these independent risk factors. The performance of the model was evaluated using the Hosmer-Lemeshow test, receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA). Multivariate logistic regression identified five independent risk factors for 28-day mortality in sepsis patients: Age, SOFA score, CRP, Mechanical ventilation, and the use of Vasoactive drugs. The odds ratios (OR) and 95% confidence intervals (95% CI) for these factors were 1.037 (1.024-1.050), 1.093 (1.044-1.145), 1.034 (1.026-1.042), 1.967 (1.176-3.328), and 2.515 (1.611-3.941), respectively, with all P-values < 0.05. Based on these five independent risk factors, a nomogram model was constructed, with the area under the ROC curve (AUC) in the training set and external validation set being 0.849 (95% CI 0.818-0.880) and 0.837 (95% CI 0.887-0.886), respectively. Both the DCA curve and calibration plot confirmed that the model has good clinical efficacy. The nomogram prediction model established in this study has excellent predictive ability, which can help clinicians identify high-risk patients early and provide guidance for clinical decision-making.

The neutrophil-to-lymphocyte ratio (NLR) is a cost-effective indicator of inflammation, which may impact decisions regarding therapy for patients undergoing continuous renal replacement therapy (CRRT), even with ongoing clinical arguments. This study aimed to examine the correlation between NLR and the prognosis of critically ill patients undergoing CRRT, specifically about mortality and morbidity. Additionally, the study sought to assess NLR's potential as a prognostic indicator for CRRT initiation.

Data were retrospectively analyzed from 175 critically ill patients who received CRRT. Clinical factors and biochemical markers were compared between survivors and non-survivors at admission, before CRRT, and at 24 and 72 h post-CRRT initiation.

Elevated NLR levels were significantly associated with increased in-hospital mortality. Neutrophil counts showed statistical significance across all measurement points, while NLR and lymphocyte counts were significant only on the third day of CRRT (p 0.001 and 0.011, respectively). Non-survivors had higher NLR values than survivors and experienced shorter hospital stays (median 22 vs. 44 days for survivors, p < 0.001). Patients with higher baseline NLR values also had more complications.

The NLR shows potential as a prognostic predictor for mortality in CRRT patients. Its integration into clinical practice could enhance patient care and treatment timing, and further studies should validate its clinical utility.

The goal of this study was to develop and validate an online dynamic nomogram system for early differential diagnosis of influenza A and B.

Patients with severe influenza A and B admitted to Henan Children's Hospital from January 2019 to January 2022 were used as the modeling group (n = 161), and patients admitted from January to September 2023 were used as the validation group (n = 52). Univariate logistic regression and multivariate logistic regression were used to identify the risk variables of severe influenza A and B in children in the modeling group. The selected variables were used to build the nomogram, and the C-index, decision curve analysis, calibration curves, and receiver operating characteristic curves were used to assess the differentiation, calibration of the models, and external validation of the above models with validation group data.

Fever for >3 days, vomiting, lymphocyte count (LY), and duration from onset to hospitalization were independent factors for the identification of severe influenza A and B. We created a dynamic nomogram (https://ertong.shinyapps.io/influenza/) that can be accessed online. The C-index was 0.92. In the modeling group, the AUC of the prediction model was 0.92 (95% CI, 0.87-0.98), the calibration curve showed a good fit between the predicted probability and the actual probability, with high comparability, and the decision curve analysis showed that the nomogram model had significant clinical benefits. The application of this model in external verification predicts that the AUC of the verification group is 0.749 (95% CI, 0.61-0.88), and the validation results were in good agreement with reality.

Fever for >3 days, vomiting, lymphocyte count, and duration from onset to hospitalization have an impact on the differentiation of severe influenza A from severe influenza B. The prediction value and clinical benefit of the nomogram model are satisfactory.

Aims/Background The role of heparin in sepsis therapy has been widely debated. The controversy surrounding heparin's use as an anticoagulant in sepsis may stem from differences in sepsis definitions, study designs, timing and dosage of drug administration, treatment duration, complications, and patient severity. In this study, we aimed to determine the optimal timing and dosage of heparin in patients with sepsis, identify specific subgroups that could benefit from heparin therapy, and explore laboratory markers to assess its efficacy. Methods This retrospective cohort study was conducted using the Medical Information Mart for Intensive Care-IV dataset. Data from patients with sepsis were extracted based on the Sepsis 3.0 criteria. Patients were categorized according to heparin use. The effectiveness of early and appropriate heparin administration was assessed, and a subgroup analysis was performed to identify patients most likely to benefit from heparin therapy. Additionally, factors mediating the improvement in sepsis prognosis following heparin treatment were analyzed. Results We recruited 4149 participants who met the inclusion criteria, with an overall 28-day mortality rate of 19.5%. There were 2192 individuals in the heparin group and 1957 in the non-heparin group. After propensity score matching, heparin therapy demonstrated a significantly greater effect on reducing both 28-day and 90-day mortality compared to the non-heparin treatment (18.1% vs. 10.7%, p < 0.001; 18.8% vs. 12.6%, p < 0.001). However, the heparin group had a higher incidence of major bleeding (10.9% vs. 6.3%, p = 0.001), increased use of mechanical ventilation (54.3% vs. 45.1%, p < 0.001), and a longer intensive care unit stay (3.6 vs. 2.5 days, p < 0.001) compared to the non-heparin group. Early administration of high-dose heparin improved 28-day survival. Early and adequate heparin administration was more effective than late and insufficient dosing (p < 0.01), except in patients with sepsis who had low white blood cell counts, alkalosis, or reduced platelet counts. The increase in platelet count had a significant mediating effect on the entire cohort (p < 0.001 for the causal mediation effect), with a mediation proportion of 14%. Conclusion Early and adequate heparin administration can significantly improve the prognosis of sepsis. An increase in platelet count may serve as a potential indicator of the effectiveness of heparin therapy in sepsis.

Sepsis is a significant contributor to both intensive care unit (ICU) admissions and mortality among patients in ICU, with a rising prevalence of obesity. There is a lack of extensive research on the correlation between TyGI and findings in patients with sepsis, especially in obese patients.

This study used a retrospective cohort design and included patients with sepsis (≥18 years) from the Medical Information Mart for Intensive Care IV database. The association between TyGI and outcome was examined using multivariable logistic regression analysis.

8,840 patients with sepsis were included in the analysis. The in-ICU mortality rate was 9.7%. Non-survivors exhibited significantly greater TyGI levels than survivors [9.19(8.76-9.71) vs. 9.10(8.67-9.54), p < 0.001]. The adjusted multivariate regression model showed that elevated TyGI values were linked to a greater likelihood of death in ICU (odds ratio [OR] range 1.072-1.793, p < 0.001) and hospital (OR range 1.068-1.445, p = 0.005). Restricted Cubic Spline analysis revealed a nonlinear association between TyGI and in-ICU and in-hospital mortality risks within specified ranges. Subgroup analysis revealed interaction effects in the general obesity, abdominal obesity, and impaired fasting glucose subgroups (p = 0.014, 0.016, and < 0.001, respectively).

TyGI was associated with an increased sepsis-related short-term mortality risk and adverse outcomes after ICU admission.

To identify key risk factors for postoperative pulmonary infections (PPIs) in lung cancer (LC), patients undergoing radical surgery and construct a multiparametric nomogram model to improve PPI risk prediction accuracy, guiding individualized interventions.

A retrospective analysis was conducted on LC patients treated at Yidu Central Hospital of Weifang from March 2020 to May 2023. Among the 1,084 LC cases reviewed, patients were divided into an infected group (n = 131) and an uninfected group (n = 953) based on infection status. Key factors for PPIs were screened using machine learning techniques, including least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). A nomogram prediction model was developed, and its stability and clinical utility were evaluated using calibration curves and decision curve analysis, with internal validation through random case selection.

Thirteen factors - including tumor stage, diabetes history, chronic obstructive pulmonary disease (COPD), operation duration, mechanical ventilation duration, age, C-reactive protein, procalcitonin, high-mobility group box 1, interleukin-6, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune-inflammation index - were identified as significantly associated with PPIs. The nomogram model demonstrated high predictive accuracy in internal validation (C-index = 0.935), strong calibration, and substantial clinical benefit. For two randomly selected cases, the model predicted a 63% infection probability for the infected patient and a 32% probability for the uninfected patient, affirming the model's predictive effectiveness.

The multiparametric nomogram model developed in this study provides a reliable method for PPI risk prediction in LC patients, supporting clinical decision-making and improving postoperative management.

Objective: To construct and validate a predictive nomogram model for the survival of patients with ventilator-associated pneumonia (VAP) to enhance prediction of 28-day survival rate in critically ill patients with VAP. Methods: A total of 1,438 intensive care unit (ICU) patients with VAP were screened through Medical Information Mart for Intensive Care (MIMIC)-IV. On the basis of multi-variable Cox regression analysis data, nomogram performance in predicting survival status of patients with VAP at ICU admission for 7, 14, and 28 days was evaluated using the C-index and area under the curve (AUC). Calibration and decision curve analysis curves were generated to assess clinical value and effectiveness of model, and risk stratification was performed for patients with VAP. Result: Through stepwise regression screening of uni-variable and multi-variable Cox regression models, independent prognostic factors for predicting nomogram were determined, including age, race, body temperature, Sequential Organ Failure Assessment score, anion gap, bicarbonate concentration, partial pressure of carbon dioxide, mean corpuscular hemoglobin, and liver disease. The model had C-index values of 0.748 and 0.628 in the train and test sets, respectively. The receiver operating characteristic curve showed that nomogram had better performance in predicting 28-day survival status in the train set (AUC = 0.74), whereas it decreased in the test set (AUC = 0.66). Calibration and decision curve analysis curve results suggested that nomogram had favorable predictive performance and clinical efficacy. Kaplan-Meier curves showed significant differences in survival between low, medium, and high-risk groups in the total set and training set (log-rank p < 0.05), further validating the effectiveness of the model. Conclusion: The VAP patient admission ICU 7, 14, and 28-day survival prediction nomogram was constructed, contributing to risk stratification and decision-making for such patients. The model is expected to play a positive role in supporting personalized treatment and management of VAP.

Image-based diagnosis has become a crucial tool in the identification and management of various cancers, particularly lung and colon cancer. This review delves into the latest advancements and ongoing challenges in the field, with a focus on deep learning, machine learning, and image processing techniques applied to X-rays, CT scans, and histopathological images. Significant progress has been made in imaging technologies like computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), which, when combined with machine learning and artificial intelligence (AI) methodologies, have greatly enhanced the accuracy of cancer detection and characterization. These advances have enabled early detection, more precise tumor localization, personalized treatment plans, and overall improved patient outcomes. However, despite these improvements, challenges persist. Variability in image interpretation, the lack of standardized diagnostic protocols, unequal access to advanced imaging technologies, and concerns over data privacy and security within AI-based systems remain major obstacles. Furthermore, integrating imaging data with broader clinical information is crucial to achieving a more comprehensive approach to cancer diagnosis and treatment. This review provides valuable insights into the recent developments and challenges in image-based diagnosis for lung and colon cancers, underscoring both the remarkable progress and the hurdles that still need to be overcome to optimize cancer care.

Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database.

A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction.

Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.

In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

Sepsis continues to be a critical issue in intensive care, characterized by significant morbidity and mortality. This study explores the association between Vasoactive Inotrope Score (VIS) trajectories and 28-day mortality in ICU patients with sepsis, employing VIS trajectories as a marker for assessing severity and guiding therapy.

We conducted a retrospective analysis of the MIMIC-IV database, which included sepsis patients admitted to the ICU between 2008 and 2019. VIS calculations were performed bi-hourly during the first 72 hours of ICU admission. Using latent growth mixture modeling, we identified distinct VIS trajectory patterns, and multivariate Cox proportional hazards models were employed to evaluate their association with 28-day mortality.

Among 6,802 sepsis patients who met the inclusion criteria, four distinct VIS trajectory patterns were identified: "Low-Decreasing" (52.1%), "Mild-Ascending" (13.2%), "Moderate-Decreasing" (23.0%), and "High-Stable" (11.6%). The 28-day survival analysis demonstrated that, compared to the "Low-Decreasing" group, the "Mild-Ascending" group had a hazard ratio (HR) for mortality of 2.55 (95% CI: 2.19-2.97, P < 0.001), the "Moderate-Decreasing" group had an HR of 1.20 (95% CI: 1.03-1.41, P = 0.021), and the "High-Stable" group presented the highest risk with an HR of 4.19 (95% CI: 3.43-5.12, P < 0.001).

This study offers significant insights into the prognostic value of VIS trajectories in sepsis patients. The identification of distinct trajectory patterns not only underscores the heterogeneity in sepsis but also emphasizes the importance of personalized management strategies. The findings underscore the potential of VIS trajectory monitoring in predicting 28-day outcomes and in guiding clinical decision-making in ICU settings.

This study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit (ICU).

We retrospectively analyzed data from 331 patients with sepsis admitted to the ICU as a training set and collected a validation set of 120 patients. Both groups were followed for 28 days. Logistic regression analyses were performed to identify the potential prognostic factors for sepsis-related 28-day mortality. A nomogram model was generated to predict 28-day mortality in patients with sepsis in the ICU. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to evaluate the model's prediction performance and clinical application. In addition, we used ROC curve analysis and DCA to compare this model with the sequential organ failure assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE II) scores and further assessed the clinical value of our model.

Logistic multivariate regression analysis revealed that mechanical ventilation, oxygenation index, and lactate and blood urea nitrogen (BUN) levels were independent predictors of 28-day mortality in patients with sepsis in the ICU. We developed a nomogram model based on these results to further predict 28-day mortality. The model demonstrated satisfactory calibration curves for both training and validation sets. Additionally, in the training set, the area under the ROC curve (AUC) for this model was 0.80. In the validation set, the AUC was 0.82. DCA showed that the high-risk thresholds ranged between 0 and 0.86 in the training set and between 0 and 0.75 in the validation set. We compared the ROC curve and DCA of this model with those of SOFA and APACHE II scores in both the training and validation sets. In the training set, the AUC of this model was significantly higher than those of the SOFA (P = 0.032) and APACHE II (P = 0.004) scores. Although the validation set showed a similar trend, the differences were not statistically significant for the SOFA (P = 0.273) and APACHE II (P = 0.320) scores. Additionally, the DCA showed comparable clinical utility in all three assessments.

The present study used four common clinical variables, including mechanical ventilation, oxygenation index and lactate and BUN levels, to develop a nomogram model to predict 28-day mortality in patients with sepsis in the ICU. Our model demonstrated robust prediction performance and clinical application after validation and comparison.

The early detection of diabetic ketoacidosis (DKA) in patients with type 2 diabetes (T2D) plays a crucial role in enhancing outcomes. We developed a nomogram prediction model for screening DKA in T2D patients. At the same time, the input variables were adjusted to reduce misdiagnosis.

We obtained data on T2D patients from Mimic-IV V0.4 and Mimic-III V1.4 databases. A nomogram model was developed using the training data set, internally validated, subjected to sensitivity analysis, and further externally validated with data from T2D patients in Aviation General Hospital.

Based on the established model, we analyzed 1885 type 2 diabetes patients, among whom 614 with DKA. We further additionally identified risk factors for DKA based on literature reports and multivariate analysis. We identified age, glucose, chloride, calcium, and urea nitrogen as predictors in our model. The logistic regression model demonstrated an area under the curve (AUC) of 0.86 (95%CI: 0.85-0.90]. To validate the model, we collected data from 91 T2D patients, including 15 with DKA, at our hospital. The external validation of the model yielded an AUC of 0.68 (95%CI: 0.67-0.70). The calibration plot confirmed that our model was adequate for predicting patients with DKA. The decision-curve analysis revealed that our model offered net benefits for clinical use.

Our model offers a convenient and accurate tool for predicting whether DKA is present. Excluding input variables that may potentially hinder patient compliance increases the practical application significance of our model.

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤  - 0.50%), Q2 (- 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan-Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89-3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29-1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45-2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08-1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

Sepsis is a life-threatening condition but predicting its development and progression remains a challenge.

This study aimed to assess the impact of infection site on sepsis development among emergency department (ED) patients.

Data were collected from a single-center ED between January 2016 and December 2019. Patient encounters with documented infections, as defined by the Systematized Nomenclature of Medicine-Clinical Terms for upper respiratory tract (URI), lower respiratory tract (LRI), urinary tract (UTI), or skin or soft-tissue infections were included. Primary outcome was the development of sepsis or septic shock, as defined by Sepsis-1/2 criteria. Secondary outcomes included hospital disposition and length of stay, blood and urine culture positivity, antibiotic administration, vasopressor use, in-hospital mortality, and 30-day mortality. Analysis of variance and various different logistic regression approaches were used for analysis with URI used as the reference variable.

LRI was most associated with sepsis (relative risk ratio [RRR] 5.63; 95% CI 5.07-6.24) and septic shock (RRR 21.2; 95% CI 17.99-24.98) development, as well as hospital admission rates (odds ratio [OR] 8.23; 95% CI 7.41-9.14), intensive care unit admission (OR 4.27; 95% CI 3.84-4.74), in-hospital mortality (OR 6.93; 95% CI 5.60-8.57), and 30-day mortality (OR 7.34; 95% CI 5.86-9.19). UTIs were also associated with sepsis and septic shock development, but to a lesser degree than LRI.

Primary infection sites including LRI and UTI were significantly associated with sepsis development, hospitalization, length of stay, and mortality among patients presenting with infections in the ED.

To develop a nomogram for predicting occurrence of secondary pulmonary infection in patients with critically traumatic brain injury (TBI) during their stay in the intensive care unit, to further optimise personalised treatment for patients and support the development of effective, evidence-based prevention and intervention strategies.

This study used patient data from the publicly available MIMIC-IV (Medical Information Mart for Intensive Care IV) database.

A population-based retrospective cohort study.

In this retrospective cohort study, 1780 patients with TBI were included and randomly divided into a training set (n=1246) and a development set (n=534). The impact of pulmonary infection on survival was analysed using Kaplan-Meier curves. A univariate logistic regression model was built in training set to identify potential factors for pulmonary infection, and independent risk factors were determined in a multivariate logistic regression model to build nomogram model. Nomogram performance was assessed with receiver operating characteristic (ROC) curves, calibration curves and Hosmer-Lemeshow test, and predictive value was assessed by decision curve analysis (DCA).

This study included a total of 1780 patients with TBI, of which 186 patients (approximately 10%) developed secondary lung infections, and 21 patients died during hospitalisation. Among the 1594 patients who did not develop lung infections, only 85 patients died (accounting for 5.3%). The survival curves indicated a significant survival disadvantage for patients with TBI with pulmonary infection at 7 and 14 days after intensive care unit admission (p<0.001). Both univariate and multivariate logistic regression analyses showed that factors such as race other than white or black, respiratory rate, temperature, mechanical ventilation, antibiotics and congestive heart failure were independent risk factors for pulmonary infection in patients with TBI (OR>1, p<0.05). Based on these factors, along with Glasgow Coma Scale and international normalised ratio variables, a training set model was constructed to predict the risk of pulmonary infection in patients with TBI, with an area under the ROC curve of 0.800 in the training set and 0.768 in the validation set. The calibration curve demonstrated the model's good calibration and consistency with actual observations, while DCA indicated the practical utility of the predictive model in clinical practice.

This study established a predictive model for pulmonary infections in patients with TBI, which may help clinical doctors identify high-risk patients early and prevent occurrence of pulmonary infections.

Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated.

Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis.

In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them.

This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.

Sepsis complicated by ARDS significantly increases morbidity and mortality, underscoring the need for robust predictive models to enhance patient management.

We collected data on 6390 patients with ARDS-complicated sepsis from the MIMIC IV database. Following rigorous data cleaning, including outlier management, handling missing values, and transforming variables, we conducted univariate analysis and logistic multivariate regression. We employed the LASSO machine learning algorithm to identify risk factors closely associated with patient outcomes. These factors were then used to develop a new clinical prediction model. The model underwent preliminary assessment and internal validation, and its performance was further tested through external validation using data from 225 patients at a major tertiary hospital in China. This validation assessed the model's discrimination, calibration, and net clinical benefits.

The model, illustrated by a concise nomogram, demonstrated significant discrimination with an area under the curve (AUC) of 0.711 in the internal validation set and 0.771 in the external validation set, outperforming conventional severity scores such as the SOFA and SAPS II. It also showed good calibration and net clinical benefits.

Our model serves as a valuable tool for identifying sepsis patients with ARDS at high risk of in-hospital mortality. This could enable the implementation of personalized treatment strategies, potentially improving patient outcomes.

It is unclear if premorbid use of beta-blockers affects sepsis outcomes. The present systematic review aimed to assess the impact of premorbid beta-blocker use on mortality and the need for mechanical ventilation in patients with sepsis. Embase, Scopus, PubMed and Web of Science were searched for studies comparing outcomes of patients with sepsis based on the premorbid use of beta-blockers. The primary outcome was mortality, and the secondary outcome was the need for mechanical ventilation. The results were reported as odds ratios (ORs) with 95% confidence intervals (CIs). A total of 17 studies including 64,586 patients with sepsis were included. Of them, 8,665 patients received premorbid beta-blockers and 55,921 patients were not treated with premorbid beta-blockers and served as a control group. Pooled analysis of mortality rates revealed that premorbid use of beta-blockers did not affect in-hospital mortality (OR: 0.96; 95% CI: 0.78, 1.18; and I2=63%) but significantly reduced one-month mortality rates (OR: 0.83; 95% CI: 0.72, 0.96; and I2=63%). Combined analysis of adjusted data showed that premorbid beta-blockers were associated with a significant survival advantage in patients with sepsis (OR: 0.81; 95% CI: 0.72, 0.92; and I2=70%). However, there was no effect of premorbid use of beta-blockers on the need for mechanical ventilation (OR: 0.93; 95% CI: 0.66, 1.30); and I2=72%). The results of the present study indicated that premorbid use of beta-blockers is associated with improved survival in patients with sepsis. However, it does not impact the need for mechanical ventilation. The results should be interpreted with caution as the data is observational and unadjusted.

To identify independent risk factors of pulmonary infection in intensive care unit (ICU) patients, and to construct a prediction model.

Medical data of 398 patients treated in the ICU of Jiaxing Hospital of Traditional Chinese Medicine from January 2019 to January 2023 were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for pulmonary infection in ICU patients. R software was used to construct a nomogram prediction model, and the prediction model was internally validated using computer simulation bootstrap method. Predictive value of the model was analyzed using the receiver operating characteristic (ROC) curve.

A total of 97 ICU patients (24.37%) developed pulmonary infection. Age, ICU stay time, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness were all identified as risk factors for pulmonary infection. The calibration curve of the constructed nomogram prediction model showed a good consistency between the predicted value of the model and the actual observed value. ROC curve analysis showed that the area under the curve (AUC) of the model was 0.784 (95% CI: 0.731-0.837), indicating a certain predictive value.

Age, length of stay in ICU, invasive operation, diabetes, duration of mechanical ventilation, and state of consciousness are risk factors for pulmonary infection in ICU patients. The nomogram prediction model constructed based on the above risk factors has shown a good predictive value.

The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk of premature death in patients receiving intensive care after pulmonary inflammation.

In this study, information from the China intensive care units (ICU) Open Source database was used to examine data from 2790 patients who had infections between January 2019 and December 2020. A 7:3 ratio was used to randomly assign the whole patient population to training and validation groups. This study used six machine learning techniques: logistic regression, random forest, gradient boosting tree, extreme gradient boosting tree (XGBoost), multilayer perceptron, and K-nearest neighbor. A cross-validation grid search method was used to search the parameters in each model. Eight metrics were used to assess the models' performance: accuracy, precision, recall, F1 score, area under the curve (AUC) value, Brier score, Jordon's index, and calibration slope. The machine methods were ranked based on how well they performed in each of these metrics. The best-performing models were selected for interpretation using both the Shapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME) interpretable techniques.

A subset of the study cohort's patients (120/1668, or 7.19%) died in the hospital following screening for inclusion and exclusion criteria. Using a cross-validated grid search to evaluate the six machine learning techniques, XGBoost showed good discriminative ability, achieving an accuracy score of 0.889 (0.874-0.904), precision score of 0.871 (0.849-0.893), recall score of 0.913 (0.890-0.936), F1 score of 0.891 (0.876-0.906), and AUC of 0.956 (0.939-0.973). Additionally, XGBoost exhibited excellent performance with a Brier score of 0.050, Jordon index of 0.947, and calibration slope of 1.074. It was also possible to create an interactive internet page using the XGBoost model.

By identifying patients at higher risk of early mortality, machine learning-based mortality risk prediction models have the potential to significantly improve patient care by directing clinical decision making and enabling early detection of survival and mortality issues in patients with pulmonary inflammation disease.

Acute liver injury (ALI) is a common complication of sepsis and is associated with adverse clinical outcomes. We aimed to develop a model to predict the risk of ALI in patients with sepsis after hospitalization.

Medical records of 3196 septic patients treated at the Lishui Central Hospital in Zhejiang Province from January 2015 to May 2023 were selected. Cohort 1 was divided into ALI and non-ALI groups for model training and internal validation. The initial laboratory test results of the study subjects were used as features for machine learning (ML), and models built using nine different ML algorithms were compared to select the best algorithm and model. The predictive performance of model stacking methods was then explored. The best model was externally validated in Cohort 2.

In Cohort 1, LightGBM demonstrated good stability and predictive performance with an area under the curve (AUC) of 0.841. The top five most important variables in the model were diabetes, congestive heart failure, prothrombin time, heart rate, and platelet count. The LightGBM model showed stable and good ALI risk prediction ability in the external validation of Cohort 2 with an AUC of 0.815. Furthermore, an online prediction website was developed to assist healthcare professionals in applying this model more effectively.

The Light GBM model can predict the risk of ALI in patients with sepsis after hospitalization.

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.

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.

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.

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.

The objective of this study was to utilize LASSO regression (Least Absolute Shrinkage and Selection Operator Regression) to identify key variables in septic patients and develop a predictive model for intensive care unit (ICU) mortality.

We conducted a cohort consisting of septic patients admitted to the ICU between December 2016 and July 2019. The disease severity and laboratory index were analyzed using LASSO regression. The selected variables were then used to develop a model for predicting ICU mortality. AUCs of ROCs were applied to assess the prediction model, and the accuracy, sensitivity and specificity were calculated. Calibration were also used to assess the actual and predicted values of the predictive model.

A total of 1733 septic patients were included, among of whom 382 (22%) died during ICU stay. Ten variables, namely mechanical ventilation (MV) requirement, hemofiltration (HF) requirement, norepinephrine (NE) requirement, septicemia, multiple drug-resistance infection (MDR), thrombocytopenia, hematocrit, red-cell deviation width coefficient of variation (RDW-CV), C-reactive protein (CRP), and antithrombin (AT) III, showed the strongest association with sepsis-related mortality according to LASSO regression. When these variables were combined into a predictive model, the area under the curve (AUC) was found to be 0.801. The AUC of the validation group was 0.791. The specificity of the model was as high as 0.953. Within the probability range of 0.25 to 0.90, the predictive performance of the model surpassed that of individual predictors within the cohort.

Our findings suggest that a predictive model incorporating the variables of MV requirement, HF requirement, NE requirement, septicemia, MDR, thrombocytopenia, HCT, RDW-CV, CRP, and AT III exhibiting an 80% likelihood of predicting ICU mortality in sepsis and demonstrates high accuracy.

To establish and validate an individualized nomogram to predict mortality risk within 30 days in patients with sepsis from the emergency department.

Data of 1205 sepsis patients who were admitted to the emergency department in a tertiary hospital between Jun 2013 and Sep 2021 were collected and divided into a training group and a validation group at a ratio of 7:3. The independent risk factors related to 30-day mortality were identified by univariate and multivariate analysis in the training group and used to construct the nomogram. The model was evaluated by receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis. The model was validated in patients of the validation group and its performance was confirmed by comparing to other models based on SOFA score and machine learning methods.

The independent risk factors of 30-day mortality of sepsis patients included pro-brain natriuretic peptide, lactic acid, oxygenation index (PaO2/FiO2), mean arterial pressure, and hematocrit. The AUCs of the nomogram in the training and verification groups were 0.820 (95% CI: 0.780-0.860) and 0.849 (95% CI: 0.783-0.915), respectively, and the respective P-values of the calibration chart were 0.996 and 0.955. The DCA curves of both groups were above the two extreme curves, indicating high clinical efficacy. The AUC values were 0.847 for the model established by the random forest method and 0.835 for the model established by the stacking method. The AUCs of SOFA model in the model and validation groups were 0.761 and 0.753, respectively.

The sepsis nomogram can predict the risk of death within 30 days in sepsis patients with high accuracy, which will be helpful for clinical decision-making.

Acute respiratory failure (ARF) is a prevalent and serious condition in intensive care unit (ICU), often associated with high mortality rates. High-flow nasal oxygen (HFNO) therapy has gained popularity for treating ARF in recent years. However, there is a limited understanding of the factors that predict HFNO failure in ARF patients. This study aimed to explore early indicators of HFNO failure in ARF patients, utilizing machine learning (ML) algorithms to more accurately pinpoint individuals at elevated risk of HFNO failure. Utilizing ML algorithms, we developed seven predictive models. Their performance was evaluated using various metrics, including the area under the receiver operating characteristic curve, calibration curve, and precision recall curve. The study enrolled 700 patients, with 490 in the training group and 210 in the validation group. The overall HFNO failure rate was 14.1% among the 700 patients. The ML algorithms demonstrated robust performance in our study. This research underscores the potential of ML techniques in creating clinically relevant models for predicting HFNO outcomes in ARF patients. These models could play a pivotal role in enhancing the risk management of HFNO, leading to more patient-centered and personalized care approaches.

Ventilator-associated pneumonia (VAP) causes heavy losses in terms of finances, hospitalization, and death for elderly patients in the intensive care unit (ICU); however, the risk is difficult to evaluate due to a lack of reliable assessment tools. We aimed to create and validate a nomogram to estimate VAP risk to provide early intervention for high-risk patients.

Between January 2016 and March 2021, 293 patients from a tertiary hospital in China were retrospectively reviewed as a training set. Another 84 patients were enrolled for model validation from April 2021 to February 2022. Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analysis were employed to select predictors, and a nomogram model was constructed. The calibration, discrimination, and clinical utility of the nomogram were verified. Finally, a web-based online scoring system was created to make the model more practical.

The predictors were hypoproteinemia, long-term combined antibiotic use, intubation time, length of mechanical ventilation, and tracheotomy/intubation. The area under the curve (AUC) was 0.937 and 0.925 in the training and validation dataset, respectively, suggesting the model exhibited effective discrimination. The calibration curve demonstrated high consistency with the observed result and the estimated values. Decision curve analysis (DCA) demonstrated that the nomogram was clinically applicable.

We have created a novel nomogram model that can be utilized to anticipate VAP risk in elderly ICU patients, which is helpful for healthcare professionals to detect patients at high risk early and adopt protective interventions.

Classifying medical reports written in Hebrew is challenging due to the ambiguity and complexity of the language. This study proposes Text Test Time Augmentation (TTTA), a novel method to improve the classification accuracy of cancer severity levels from PET-CT diagnostic reports in Hebrew. Hebrew, being a morphologically rich language, often leads to each word having multiple ambiguous interpretations. TTTA leverages test-time augmentation to enhance text information retrieval and model robustness. During training and testing phases, this method generates and evaluates sets of augmentations to enhance the semantics extracted from each report. Experiments utilize a large institutional report repository from Ziv hospital, Israel, where physicians manually labeled the reports. The results demonstrate that the proposed TTTA approach achieves superior performance over baseline models without TTA, improving PR-AUC by 15.18% on classifying cancer severity levels. The study highlights the efficacy of TTTA in extracting essential medical concepts from free text reports and accurately classifying the severity of cancer. The approach addresses the limitations of prior methods and contributes towards improved automated analysis of Hebrew medical reports. TTTA has the potential to assist physicians in cancer diagnosis and treatment planning.

Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points.

PubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction.

Fifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools.

Machine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units.

Studies on prognostic factors for older patients with intra-abdominal sepsis are scarce, and the association between skeletal muscle mass and prognosis among such patients remains unclear.

To develop a nomogram to predict in-hospital mortality among older patients with intra-abdominal sepsis.

Older patients with intra-abdominal sepsis were prospectively recruited. Their demographics, clinical features, laboratory results, abdominal computed tomography-derived muscle mass, and in-hospital mortality were recorded. The predictors of mortality were selected via least absolute shrinkage and selection operator and multivariable logistic regression analyses, and a nomogram was developed. The nomogram was assessed and compared with Sequential Organ Failure Assessment score, Acute Physiology and Chronic Health Evaluation II score, and Simplified Acute Physiology Score II.

In total, 464 patients were included, of whom 104 (22.4%) died. Six independent risk factors (skeletal muscle index, cognitive impairment, frailty, heart rate, red blood cell distribution width, and blood urea nitrogen) were incorporated into the nomogram. The Hosmer-Lemeshow goodness-of-fit test and calibration plot revealed a good consistency between the predicted and observed probabilities. The area under the receiver operating characteristic curve was 0.875 (95% confidence interval = 0.838-0.912), which was significantly higher than those of commonly used scoring systems. The decision curve analysis indicated the nomogram had good predictive performance.

Our nomogram, which is predictive of in-hospital mortality among older patients with intra-abdominal sepsis, incorporates muscle mass, a factor that warrants consideration by clinicians. The model has a high prognostic ability and might be applied in clinical practice after external validation.

Few studies have reported the risk factors or developed a risk predictive model of hypoglycemia patients with sepsis.

To develop a predictive model to assess the hypoglycemia risk in critically ill patients with sepsis.

For this retrospective study, we collected the data from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV). All eligible patients from the MIMIC-III were randomly divided into the training set for development of predictive model and testing set for internal validation of the predictive model at a ratio of 8:2. Patients from the MIMIC-IV database were used as the external validation set. The primary endpoint was the occurrence of hypoglycemia. Univariate and multivariate logistic model was used to screen predictors. Adopted receiver operating characteristics (ROC) and calibration curves to estimate the performance of the nomogram.

The median follow-up time was 5.13 (2.61-9.79) days. Diabetes, dyslipidemia, mean arterial pressure, anion gap, hematocrit, albumin, sequential organ failure assessment, vasopressors, mechanical ventilation and insulin were identified as the predictors for hypoglycemia risk in critically ill patients with sepsis. We constructed a nomogram for predicting hypoglycemia risk in critically ill patients with sepsis based on these predictors. An online individualized predictive tool: https://ghongyang.shinyapps.io/DynNomapp/. The established nomogram had a good predictive ability by ROC and calibration curves in the training set, testing set and external validation cohort.

A predictive model of hypoglycemia risk was constructed, with a good ability in predicting the risk of hypoglycemia in critically ill patients with sepsis.

No multivariable model incorporating erector spinae muscle (ESM) has been developed to predict clinical outcomes in older patients with severe community-acquired pneumonia (SCAP). This study aimed to construct a nomogram based on ESM to predict in-hospital mortality in patients with SCAP.

Patients aged ≥ 65 years with SCAP were enrolled in this prospective observational study. Least absolute selection and shrinkage operator and multivariable logistic regression analyses were used to identify risk factors for in-hospital mortality. A nomogram prediction model was constructed. The predictive performance was evaluated using the concordance index (C-index), calibration curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis.

A total of 490 patients were included, and the in-hospital mortality rate was 36.1%. The nomogram included the following independent risk factors: mean arterial pressure, peripheral capillary oxygen saturation, Glasgow Coma Scale score (GCS), lactate, lactate dehydrogenase, blood urea nitrogen levels, and ESM cross-sectional area. Incorporating ESM into the base model with other risk factors significantly improved the C-index from 0.803 (95% confidence interval [CI], 0.761-0.845) to 0.836 (95% CI, 0.798-0.873), and these improvements were confirmed by category-free NRI and IDI. The ESM-based nomogram demonstrated a high level of discrimination, good calibration, and overall net benefits for predicting in-hospital mortality compared with the combination of confusion, urea, respiratory rate, blood pressure, and age ≥ 65 years (CURB-65), Pneumonia Severity Index (PSI), Acute Physiology and Chronic Health Evaluation II (APACHEII), and Sequential Organ Failure Assessment (SOFA).

The proposed ESM-based nomogram for predicting in-hospital mortality among older patients with SCAP may help physicians to promptly identify patients prone to adverse outcomes.

This study was registered at www.chictr.org.cn (registration number Chi CTR-2300070377).

Emergency sepsis is a common and serious infectious disease, and its prognosis is influenced by a number of factors.

To analyse the factors influencing the prognosis of patients with emergency sepsis in order to provide a basis for individualised patient treatment and care. By retrospectively analysing the clinical data collected, we conducted a comprehensive analysis of factors such as age, gender, underlying disease, etiology and site of infection, inflammatory indicators, multi-organ failure, cardiovascular function, therapeutic measures, immune status and severity of infection.

Data collection: Clinical data were collected from patients diagnosed with acute sepsis, including basic information, laboratory findings, medical history and treatment options. Variable selection: Variables associated with prognosis were selected, including age, gender, underlying disease, etiology and site of infection, inflammatory indicators, multi-organ failure, cardiovascular function, treatment measures, immune status and severity of infection. Data analysis: The data collected are analysed using appropriate statistical methods such as multiple regression analysis and survival analysis. The impact of each factor on prognosis was assessed according to prognostic indicators, such as survival, length of stay and complication rates.

Descriptive statistics: Descriptive statistics were performed on the data collected from the patients, including their basic characteristics and clinical presentation.

Type 2 diabetes mellitus were independent factors affecting the prognosis of patients with sepsis.

Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021. The predictive performance was analyzed by comparing the area under the receiver operating characteristic curve (AU-ROC) of ML models to that of conventional severity-of-illness scoring systems. Three ML models were evaluated: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP). Among the 816 pneumonia patients included, 223 (27.3%) patients died. All ML models significantly outperformed the Simplified Acute Physiology Score II (AU-ROC: 0.650 [0.584-0.716] vs 0.820 [0.771-0.869] for logistic regression vs 0.827 [0.777-0.876] for LightGBM 0.838 [0.791-0.884] for MLP; P < 0.001). In the analysis for NRI, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all length of stay in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO₂/FiO₂ ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with mechanical ventilation, and use of inotropic agents. In conclusion, the ML models have excellent performance in predicting in-ICU mortality in patients with severe pneumonia. Moreover, this study highlights the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups.

Background: Pulmonary sepsis and abdominal sepsis have pathophysiologically distinct phenotypes. This study aimed to compare their clinical characteristics and predictors of mortality. Methods: In this multicenter retrospective trial, 1,359 adult patients who fulfilled the Sepsis-3 criteria were enrolled and classified into the pulmonary sepsis or abdominal sepsis groups. Plasma presepsin was measured, and the scores of Acute Physiology and Chronic Health Evaluation (APACHE) II, Mortality in Emergency Department Sepsis (MEDS), and Simplified Acute Physiology Score (SAPS) II were calculated at enrollment. Data on 28-day mortality were collected for all patients. Results: Compared with patients with abdominal sepsis (n = 464), patients with pulmonary sepsis (n = 895) had higher 28-day mortality rate, illness severity scores, incidence of shock and acute kidney injury, and hospitalization costs. Lactate level and APACHE II and MEDS scores were independently associated with 28-day mortality in both sepsis types. Independent predictors of 28-day mortality included Pa o2 /F io2 ratio (hazard ratio [HR], 0.998; P < 0.001) and acute kidney injury (HR, 1.312; P = 0.039) in pulmonary sepsis, and SAPS II (HR, 1.037; P = 0.017) in abdominal sepsis. A model that combined APACHE II score, lactate, and MEDS score or SAPS II score had the best area under the receiver operating characteristic curve in predicting mortality in patients with pulmonary sepsis or abdominal sepsis, respectively. Interaction term analysis confirmed the association between 28-day mortality and lactate, APACHE II score, MEDS score, SAPS II score, and shock according to the sepsis subgroups. The mortality of patients with pulmonary sepsis was higher than that of patients with abdominal sepsis among patients without shock (32.9% vs. 8.8%; P < 0.001) but not among patients with shock (63.7 vs. 48.4%; P = 0.118). Conclusions: Patients with pulmonary sepsis had higher 28-day mortality than patients with abdominal sepsis. The study identified sepsis subgroup-specific mortality predictors. Shock had a larger effect on mortality in patients with abdominal sepsis than in those with pulmonary sepsis.

Logistic regression models are widely used in clinical prediction, but their application in resource-poor settings or areas without internet access can be challenging. Nomograms can serve as a useful visualization tool to speed up the calculation procedure, but existing nomogram generators often require the input of raw data, inhibiting the transformation of established logistic regression models that only provide coefficients. Developing a tool that can generate nomograms directly from logistic regression coefficients would greatly increase usability and facilitate the translation of research findings into patient care.

We designed and developed simpleNomo, an open-source Python toolbox that enables the construction of nomograms for logistic regression models. Uniquely, simpleNomo allows for the creation of nomograms using only the coefficients of the model. Further, we also devoloped an online website for nomogram generation.

simpleNomo properly maintains the predictive ability of the original logistic regression model and easy to follow. simpleNomo is compatible with Python 3 and can be installed through Python Package Index (PyPI) or https://github.com/Hhy096/nomogram.

This paper presents simpleNomo, an open-source Python toolbox for generating nomograms for logistic regression models. It facilitates the process of transferring established logistic regression models to nomograms and can further convert more existing works into practical use.

The risk of death in sepsis patients with acute respiratory distress syndrome (ARDS) was as high as 20-50%. Few studies focused on the risk identification of ARDS among sepsis patients. This study aimed to develop and validate a nomogram to predict the ARDS risk in sepsis patients based on the Medical Information Mart for Intensive Care IV database.

A total of 16,523 sepsis patients were included and randomly divided into the training and testing sets with a ratio of 7:3 in this retrospective cohort study. The outcomes were defined as the occurrence of ARDS for ICU patients with sepsis. Univariate and multivariate logistic regression analyses were used in the training set to identify the factors that were associated with ARDS risk, which were adopted to establish the nomogram. The receiver operating characteristic and calibration curves were used to assess the predictive performance of nomogram.

Totally 2422 (20.66%) sepsis patients occurred ARDS, with the median follow-up time of 8.47 (5.20, 16.20) days. The results found that body mass index, respiratory rate, urine output, partial pressure of carbon dioxide, blood urea nitrogen, vasopressin, continuous renal replacement therapy, ventilation status, chronic pulmonary disease, malignant cancer, liver disease, septic shock and pancreatitis might be predictors. The area under the curve of developed model were 0.811 (95% CI 0.802-0.820) in the training set and 0.812 (95% CI 0.798-0.826) in the testing set. The calibration curve showed a good concordance between the predicted and observed ARDS among sepsis patients.

We developed a model incorporating thirteen clinical features to predict the ARDS risk in patients with sepsis. The model showed a good predictive ability by internal validation.

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.

The present study aimed to evaluate the synergistic impact of acute heart failure (AHF) and acute kidney injury (AKI) on in-hospital mortality in critically ill patients with sepsis.

We undertook a retrospective, observational analysis using data acquired from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and eICU Collaborative Research Database (eICU-CRD). The effects of AKI and AHF on in-hospital mortality were examined using a Cox proportional hazards model. Additive interactions were analyzed using the relative extra risk attributable to interaction.

A total of 33,184 patients were eventually included, comprising 20,626 patients in the training cohort collected from the MIMIC-IV database and 12,558 patients in the validation cohort extracted from the eICU-CRD database. After multivariate Cox analysis, the independent variables for in-hospital mortality included: AHF only (HR:1.20, 95% CI:1.02-1.41, P = 0.005), AKI only (HR:2.10, 95% CI:1.91-2.31, P < 0.001), and both AHF and AKI (HR:3.80, 95%CI:13.40-4.24, P < 0.001). The relative excess risk owing to interaction was 1.49 (95% CI:1.14-1.87), the attributable percentage due to interaction was 0.39 (95%CI:0.31-0.46), and the synergy index was 2.15 (95%CI:1.75-2.63), demonstrated AHF and AKI had a strong synergic impact on in-hospital mortality. And the findings in the validation cohort indicated identical conclusions to the training cohort.

Our data demonstrated a synergistic relationship of AHF and AKI on in-hospital mortality in critically unwell patients with sepsis.

The average body weight is smaller in Asian patients compared with Western patients, but influence of body weight in antibiotic dosing is unknown. This study was to predict the optimal ceftazidime, cefepime, meropenem, piperacillin/tazobactam doses in Asian patients undergoing continuous venovenous hemofiltration (CVVH).

Monte Carlo simulations (MCS) were performed using published Asian demographics and pharmacokinetics parameters in 5000 virtual patients at three CVVH effluent rates (Qeff; 20, 30, 40 mL/kg/h). Various dosing regimens were assessed for the probability of target attainments using 60% fT > 1 × MIC or 4xMIC and neurotoxicity risk at 48-h using suggested neurotoxicity thresholds.

Ceftazidime 1 g q12h, meropenem 1 g q12h, and piperacillin/tazobactam 3.375 g q6h were optimal for all Qeff settings against fT > 1 × MIC. Cefepime 2 g q24h and 2 g q12h were optimal at 20 and 30-40 mL/kg/h respectively. For the aggressive PD target (4 × MIC), optimal ceftazidime regimens were 1.25 g q8h (20-30 mL/kg/h) and 1.5 g q8h (40 mL/kg/h). Cefepime 2 g q8h and meropenem 1 g q8h were optimal at all Qeff settings. No simulated piperacillin doses attained the aggressive PD target. Increased neurotoxicity risk was predicted with ceftazidime and cefepime doses attaining the efficacy.

MCS enabled the prediction of optimal β-lactam dosing regimens for Asian patients receiving CVVH at varying Qeff. Clinical validation is warranted.

Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transformed. The performance of the proposed method is compared with the original method under three models, including logistic regression, AdaBoost and XGBoost. The results show that the model XGBoost generally has the best performance measured by AUC, F1 and Sensitivity with values around 0.93, 0.91 and 0.90, at the prediction gaps 5, 10 and 20 separately. Under the model XGBoost, the method with transformed response variable has significantly better performance than that with the original response variable, with the performance metrics being around 1% to 4% higher, and the t values are all significant under the level 0.01. In order to explore the model performance under different baseline information, a subgroup analysis is conducted under sex, age, weight and height. The results demonstrate that sex and age have more significant influence on the model performance especially at the higher gaps than weight and height.