BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation.
METHODS: In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power.
RESULTS: The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980-0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM\'s gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve.
CONCLUSIONS: Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients.

BACKGROUND: To investigate the association between maternal sepsis during pregnancy and poor pregnancy outcome and to identify risk factors for poor birth outcomes and adverse perinatal events.
METHODS: We linked the Taiwan Birth Cohort Study (TBCS) database and the Taiwanese National Health Insurance Database (NHID) to conduct this population-based study. We analysed the data of pregnant women who met the criteria for sepsis-3 during pregnancy between 2005 and 2017 as the maternal sepsis cases and selected pregnant women without infection as the non-sepsis comparison cohort. Sepsis during pregnancy and fulfilled the sepsis-3 definition proposed in 2016. The primary outcome included low birth weight (LBW, < 2500 g) and preterm birth (< 34 weeks), and the secondary outcome was the occurrence of adverse perinatal events.
RESULTS: We enrolled 2,732 women who met the criteria for sepsis-3 during pregnancy and 196,333 non-sepsis controls. We found that the development of maternal sepsis was highly associated with unfavourable pregnancy outcomes, including LBW (adjOR 9.51, 95% CI 8.73-10.36), preterm birth < 34 weeks (adjOR 11.69, 95%CI 10.64-12.84), and the adverse perinatal events (adjOR 3.09, 95% CI 2.83-3.36). We also identified that socio-economically disadvantaged status was slightly associated with an increased risk for low birth weight and preterm birth.
CONCLUSIONS: We found that the development of maternal sepsis was highly associated with LBW, preterm birth and adverse perinatal events. Our findings highlight the prolonged impact of maternal sepsis on pregnancy outcomes and indicate the need for vigilance among pregnant women with sepsis.

BACKGROUND: There is no evidence to determine the association between the lactate dehydrogenase to albumin ratio (LAR) and the development of sepsis-associated acute kidney injury (SAKI). We aimed to investigate the predictive impact of LAR for SAKI in patients with sepsis.
METHODS: A total of 4,087 patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC IV) database were included. Logistic regression analysis was used to identify the association between LAR and the risk of developing SAKI, and the relationship was visualized using restricted cubic spline (RCS). The clinical predictive value of LAR was evaluated by ROC curve analysis. Subgroup analysis was used to search for interactive factors.
RESULTS: The LAR level was markedly increased in the SAKI group (p < 0.001). There was a positive linear association between LAR and the risk of developing SAKI (p for nonlinearity = 0.867). Logistic regression analysis showed an independent predictive value of LAR for developing SAKI. The LAR had moderate clinical value, with an AUC of 0.644. Chronic kidney disease (CKD) was identified as an independent interactive factor. The predictive value of LAR for the development of SAKI disappeared in those with a history of CKD but remained in those without CKD.
CONCLUSIONS: Elevated LAR 12 h before and after the diagnosis of sepsis is an independent risk factor for the development of SAKI in patients with sepsis. Chronic comorbidities, especially the history of CKD, should be taken into account when using LAR to predict the development of AKI in patients with sepsis.

BACKGROUND: The contribution of the central nervous system to sepsis pathobiology is incompletely understood. In previous studies, administration of endotoxin to mice decreased activity of the vagus anti-inflammatory reflex. Treatment with the centrally-acting M1 muscarinic acetylcholine (ACh) receptor (M1AChR) attenuated this endotoxin-mediated change. We hypothesize that decreased M1AChR-mediated activity contributes to inflammation following cecal ligation and puncture (CLP), a mouse model of sepsis.
METHODS: In male C57Bl/6 mice, we quantified basal forebrain cholinergic activity (immunostaining), hippocampal neuronal activity, serum cytokine/chemokine levels (ELISA) and splenic cell subtypes (flow cytometry) at baseline, following CLP and following CLP in mice also treated with the M1AChR agonist xanomeline.
RESULTS: At 48 h. post-CLP, activity in basal forebrain cells expressing choline acetyltransferase (ChAT) was half of that observed at baseline. Lower activity was also noted in the hippocampus, which contains projections from ChAT-expressing basal forebrain neurons. Serum levels of TNFα, IL-1β, MIP-1α, IL-6, KC and G-CSF were higher post-CLP than at baseline. Post-CLP numbers of splenic macrophages and inflammatory monocytes, TNFα+ and ILβ+ neutrophils and ILβ+ monocytes were higher than baseline while numbers of central Dendritic Cells (cDCs), CD4+ and CD8+ T cells were lower. When, following CLP, mice were treated with xanomeline activity in basal forebrain ChAT-expressing neurons and in the hippocampus was significantly higher than in untreated animals. Post-CLP serum concentrations of TNFα, IL-1β, and MIP-1α, but not of IL-6, KC and G-CSF, were significantly lower in xanomeline-treated mice than in untreated mice. Post-CLP numbers of splenic neutrophils, macrophages, inflammatory monocytes and TNFα+ neutrophils also were lower in xanomeline-treated mice than in untreated animals. Percentages of IL-1β+ neutrophils, IL-1β+ monocytes, cDCs, CD4+ T cells and CD8+ T cells were similar in xanomeline-treated and untreated post-CLP mice.
CONCLUSIONS: Our findings indicate that M1AChR-mediated responses modulate CLP-induced alterations in serum levels of some, but not all, cytokines/chemokines and affected splenic immune response phenotypes.

UNASSIGNED: Sepsis is a life-threatening condition with a very narrow golden period in which confirmatory diagnosis may change the outcome dramatically. No confirmatory biomarker is available till date for early diagnosis of sepsis. This study aimed to evaluate the combined and independent role of quick sequential organ failure assessment (qSOFA) score, lactate, and neutrophil-lymphocyte ratio (NLR) in diagnosis and mortality prediction in early sepsis.
UNASSIGNED: This was a hospital-based, single-center, prospective cohort study conducted in a tertiary care institute, Karnataka, India. Three hundred adult sepsis patients were recruited during 10-month period, and demographic data, qSOFA score, lactate, NLR, and culture samples were collected in ED within 1 h of admission. Outcome groups (survivor and nonsurvivor) were statistically analyzed with relative frequencies (%), median, mean ± standard deviation with 95% confidence interval (CI), univariate, bivariate, and multivariate logistic regression analysis, and Receiver -operating characteristic curve (ROC) curve to test the predictive ability of initial levels of three biomarkers.
UNASSIGNED: Sepsis was more prevalent among middle-aged male patients. Male gender (odds ratio [OR], 6.9; 95% CI: 1.61-30.1), qSOFA (OR, 154; 95% CI: 15-1565), and lactate (OR, 1.36; 95% CI: 22-833) show 97% (area under the curve) predictive accuracy of the model for sepsis on bivariate and multivariate logistic regression analysis. A significant rise in NLR was a poor outcome indicator on univariate analysis (P = 0.773).
UNASSIGNED: All three biomarkers are good outcome predictors whereas qSOFA and lactate have diagnostic significance in early sepsis. These markers can be used for patient triaging, minimizing culture report dependence for treatment and ultimately the outcome.

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BACKGROUND: Successful sepsis treatment depends on early diagnosis. We aimed to develop and validate a system to predict sepsis and septic shock in real time using deep learning.
METHODS: Clinical data were retrospectively collected from electronic medical records (EMRs). Data from 2010 to 2019 were used as development data, and data from 2020 to 2021 were used as validation data. The collected EMRs consisted of eight vital signs, 13 laboratory data points, and three demographic information items. We validated the deep-learning-based sepsis and septic shock early prediction system (DeepSEPS) using the validation datasets and compared our system with other traditional early warning scoring systems, such as the national early warning score, sequential organ failure assessment (SOFA), and quick sequential organ failure assessment.
RESULTS: DeepSEPS achieved even higher area under receiver operating characteristic curve (AUROC) values (0.7888 and 0.8494 for sepsis and septic shock, respectively) than SOFA. The prediction performance of traditional scoring systems was enhanced because the early prediction time point was close to the onset time of sepsis; however, the DeepSEPS scoring system consistently outperformed all conventional scoring systems at all time points. Furthermore, at the time of onset of sepsis and septic shock, DeepSEPS showed the highest AUROC (0.9346).
CONCLUSIONS: The sepsis and septic shock early warning system developed in this study exhibited a performance that is worth considering when predicting sepsis and septic shock compared to other traditional early warning scoring systems. DeepSEPS showed better performance than existing sepsis prediction programs. This novel real-time system that simultaneously predicts sepsis and septic shock requires further validation.

UNASSIGNED: The contribution of the central nervous system to sepsis pathobiology is incompletely understood. In previous studies, administration of endotoxin to mice decreased activity of the vagus anti-inflammatory reflex. Treatment with the centrally-acting M1/M4 muscarinic acetylcholine (ACh) receptor (M1/M4AChR) attenuated this endotoxin-mediated change. We hypothesize that decreased M1/M4AChR-mediated activity contributes to inflammation following cecal ligation and puncture (CLP), a mouse model of sepsis.
UNASSIGNED: Basal forebrain cholinergic activity (immunostaining), serum cytokine/chemokine levels (ELISA) and splenocyte subtypes (flow cytometry) were examined at baseline and following CLP in male C57BL/6 male mice.
UNASSIGNED: At 48hrs. post-CLP, activity in basal forebrain cells expressing choline acetyltransferase (ChAT) was half of that observed at baseline. Lower activity was also noted in the hippocampus, which contains projections from ChAT-expressing basal forebrain neurons. Serum levels of TNFα, IL-1β, MIP-1α, IL-6, KC and G-CSF were higher post-CLP than at baseline. Post-CLP numbers of splenic macrophages and inflammatory monocytes, TNFa+ and ILb+ neutrophils and ILb+ monocytes were higher than baseline while numbers of central Dendritic Cells (cDCs), CD4+ and CD8+ T cells were lower. When, following CLP, mice were treated with xanomeline, a central-acting M1AChR agonist, activity in basal forebrain ChAT-expressing neurons and in the hippocampus was significantly higher than in untreated animals. Post-CLP serum concentrations of TNFα, IL-1β, and MIP-1α, but not of IL-6, KC and G-CSF, were significantly lower in xanomline-treated mice than in untreated mice. Post-CLP numbers of splenic neutrophils, macrophages, inflammatory monocytes and TNFα+ neutrophils also were lower in xanomeline-treated mice than in untreated animals. The effects of CLP on percentages of IL-1β+ neutrophils, IL-1β+ monocytes, cDCs, CD4+ T cells and CD8+ T cells were similar in xanomeline - treated and untreated post-CLP mice.
UNASSIGNED: Our findings indicate that M1/M4AChR-mediated responses modulate CLP-induced alterations in the distribution of some, but not all, leukocyte phenotypes and certain cytokines and chemokines.

Sepsis (and septic shock) is on of the most common causes of death worldwide. Bacteremia often, but not necessarily, occurs in septic patients, but the impact of true bacteremia on a patient\'s clinical characteristics and outcome remains unclear. The main aim of this study was to compare the characteristics and outcome of a well-defined cohort of 258 septic patients with and without bacteremia treated in the intensive care unit (ICU) of a tertiary center hospital in Prague, Czech Republic. As expected, more frequently, bacteremia was present in patients without previous antibiotic treatment. A higher proportion of bacteremia was observed in patients with infective endocarditis as well as catheter-related and soft tissue infections in contrast to respiratory sepsis. Multivariant analysis showed increased severity of clinical status and higher Charlson comorbidity index (CCI) as variables with significant influence on mortality. Bacteremia appears to be associated with higher mortality rates and length of ICU stay in comparison with nonbacteremic counterparts, but this difference did not reach statistical significance. The presence of bacteremia, apart from previous antibiotic treatment, may be related to the site of infection.

BACKGROUND: Sepsis surveillance using electronic health record (EHR)-based data may provide more accurate epidemiologic estimates than administrative data, but experience with this approach to estimate population-level sepsis burden is lacking.
METHODS: This was a retrospective cohort study including all adults admitted to publicly-funded hospitals in Hong Kong between 2009-2018. Sepsis was defined as clinical evidence of presumed infection (clinical cultures and treatment with antibiotics) and concurrent acute organ dysfunction (≥2 point increase in baseline SOFA score). Trends in incidence, mortality, and case fatality risk (CFR) were modelled by exponential regression. Performance of the EHR-based definition was compared with 4 administrative definitions using 500 medical record reviews.
RESULTS: Among 13,550,168 hospital episodes during the study period, 485,057 (3.6%) had sepsis by EHR-based criteria with 21.5% CFR. In 2018, age- and sex-adjusted standardized sepsis incidence was 759 per 100,000 (relative +2.9%/year [95%CI 2.0, 3.8%] between 2009-2018) and standardized sepsis mortality was 156 per 100,000 (relative +1.9%/year [95%CI 0.9,2.9%]). Despite decreasing CFR (relative -0.5%/year [95%CI -1.0, -0.1%]), sepsis accounted for an increasing proportion of all deaths (relative +3.9%/year [95%CI 2.9, 4.9%]). Medical record reviews demonstrated that the EHR-based definition more accurately identified sepsis than administrative definitions (AUC 0.91 vs 0.52-0.55, p < 0.001).
CONCLUSIONS: An objective EHR-based surveillance definition demonstrated an increase in population-level standardized sepsis incidence and mortality in Hong Kong between 2009-2018 and was much more accurate than administrative definitions. These findings demonstrate the feasibility and advantages of an EHR-based approach for widescale sepsis surveillance.