Mortality In Sepsis Patients Research Articles

Prognostic Value of Shock Index, Modified Shock Index, and Age Shock Index in Patients with Sepsis in the Intensive Care Unit

Objective: We aimed to evaluate the usability of the Shock Index (SI), Modified Shock Index (MSI), and Age Shock Index (ASI) in predicting clinical outcomes and mortality in sepsis patients followed in the intensive care unit. Methods: Records of patients admitted to the intensive care unit of Muş State Hospital between January 2022, and April 2024, who developed sepsis were retrospectively recorded from the hospital data system. Results: A total of 127 patients were included in the study. The average age of these patients was found to be 50.83±12.01 years. The average age of discharged patients was 46.14±10.12 years, while the average age of deceased patients was 58.54±10.89 years. In the gender distribution, 63.78% of all patients were male and 36.22% were female (p=0.028). SI was determined as 0.88±0.33 in discharged patients and 1.11±0.32 in deceased patients (p=0.0002). MSI was found to be 1.45±0.33 in discharged patients and 1.68±0.32 in deceased patients (p=0.00001). ASI was determined as 40.73±18.41 in discharged patients and 64.94±22.85 in deceased patients (p=0.0001). The cut-off value for SI was found to be 0.76, with an AUC value of 0.69. The cut-off value for the MSI was determined to be 1.35, with an AUC value of 0.72. The cut-off value for the ASI was found to be 38.76, with an AUC value of 0.85. Conclusion: All three shock indices were statistically significant in predicting mortality in sepsis patients. We believe they are valuable indices that can be used bedside and non-invasively in intensive care units.

Machine learning based clinical prediction model for 1-year mortality in Sepsis patients with atrial fibrillation

Machine learning based clinical prediction model for 1-year mortality in Sepsis patients with atrial fibrillation

The supplementation of l-carnitine in critically ill patients with sepsis: a systematic review and meta-analysis of randomized controlled trials

ObjectivesThe evidence suggests that l-carnitine may reduce mortality in critically ill patients with sepsis. However, the conclusions of different studies are inconsistent. A meta-analysis was conducted to evaluate the effect of l-carnitine compliance on mortality in patients with sepsis.MethodsA search of the PubMed, Embase, and Cochrane Library databases was conducted on 1 June 2024. The risk ratio (RR) was pooled with a 95% confidence interval (CI) for dichotomous data. The publications were subjected to a review in accordance with the guidelines set forth in the Cochrane Handbook and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). This study has been registered with INPLASY (number INPLASY202460086).ResultsA total of 356 patients were included in four randomized controlled trials. The results indicated that L-carnitine supplementation was not associated with 28-day mortality in sepsis patients (RR: 0.65; 95% CI 0.33–1.28; I2 = 70%; P = 0.21). And there was no significant effect on 12-month mortality (RR: 0.72; 95% CI 0.47–1.11; I2 = 0%; P = 0.14) compared to placebo.ConclusionsThe use of l-carnitine was not found to be significantly correlated with 28-day or 12-month mortality in patients with sepsis.

Predictive value of IL-8 for mortality risk in elderly sepsis patients of emergency department

BackgroundSepsis significantly impacts morbidity and mortality, particularly among older adults. Despite extensive research, early recognition and prognosis prediction of sepsis remain challenging. IL-8, a chemokine produced by inflammatory cells like monocytes and endothelial cells, has shown potential in predicting mortality in sepsis patients, though its role in elderly sepsis remains unexplored. ObjectivesThe present study aimed to explore the predictive ability of interleukin-8 (IL-8) for mortality risk in elderly septic patients. Methods220 elderly sepsis patients were included in the present study. Serum samples were obtained within 1 h of admission to assess serum IL-8, white blood cell (WBC), procalcitonin (PCT), C-reactive protein (CRP), and lactic acid (LAC) levels. The Sequential Organ Failure Score (SOFA) and Acute Physiological and Chronic Health Assessment II (APACHE II) were recorded. Logistic regression analysis was employed to identify independent predictors of mortality within 28 days for elderly patients diagnosed with sepsis. Further, the capacity of these factors to predict 28-day mortality within this patient cohort was evaluated. ResultsSOFA score, APACHE II score, LAC, and IL-8 were all significant independent predictors for 28-day mortality in elderly sepsis patients (P < 0.05). The AUC of the ROC curve for IL-8 was calculated to be 0.701, indicating a moderately predictive performance. In comparison, the AUC for LAC was marginally higher at 0.708. Nevertheless, the results of the statistical analysis revealed no significant difference in the predictive value between IL-8 and LAC. Moreover, the present findings indicate that the combined assessment of IL-8 and SOFA score demonstrated superior predictive value for mortality compared to using IL-8 alone. ConclusionsIL-8 LAC, APACHE II, and SOFA can be considered independent predictors factors for mortality of elderly sepsis patients. Utilizing the combination of IL-8 and SOFA demonstrates a heightened predictive capability compared to using any single index alone.

The value of C-reactive protein velocity (CRPv) on mortality in sepsis patients who are emergently hospitalized in the ICU: A retrospective single-center study

PurposeThe C-reactive protein (CRP) velocity (CRPv) is an indicator of the change in CRP over time. In individuals with sepsis, the second values of CRP and CRPv have been shown to have more importance than the first CRP value measured at admission. This study examined the importance of CRPv for mortality among individuals who were hospitalized in the intensive care unit (ICU). MethodsThe study was conducted between January 2021 and December 2022. CRPv was calculated according to the change in the second CRP value compared to the first. ResultsThe median age of the patients was 79 years (interquartile range (IQR), 69–85 years), and 53.2 % were male. The in-hospital mortality rate was 45.5 %. The presence of diabetes increased the odds of mortality by 2.17 times (confidence interval (CI): 1.06–4.4, p=0.032). Each increase in CRPv by 1 mg/dl/hour increased the odds of mortality by 1.07 times (CI: 1.01–1.14, p=0.015), while each one-point increase in the Sequential Organ Failure Assessment (SOFA) score increased the odds of mortality by 1.21 times (CI: 1.07–1.35, p=0.002). The SOFA score had the highest area under the curve (AUC) value for in-hospital mortality (AUC = 0.699 p=<0.001). When the SOFA was >7, its sensitivity in predicting mortality was 46.7 %, and its specificity was 85.1 %. The AUC value of CRPv in predicting mortality was 0.629 (p=0.006). When CRPv was >0.75, its sensitivity in predicting mortality was 68.2 %, and its specificity was 57 %. ConclusionCRPv performed well in predicting mortality and had satisfactory discriminative ability. Additionally, diabetes, SOFA score, and CRPv elevation were significant risk factors for mortality.

Elevated serum albumin-to-creatinine ratio as a protective factor on clinical outcomes among critically ill patients with sepsis: a retrospective study.

The aim of this study was to examine the prognostic significance of serum albumin-to-creatinine ratio (ACR) in critically ill patients with sepsis. This retrospective study analyzed sepsis cases admitted to the Affiliated Hospital of Jiangsu University between January 2015 and November 2023. The patients were divided into four groups based on their ACR upon admission to the intensive care unit (ICU). Laboratory data were collected at the time of ICU admission, and the primary outcome measure was in-hospital all-cause mortality. Kaplan-Meier survival curves were generated to illustrate the differences in 30-/60-day mortality among the various groups. Multivariate Cox regression models and restricted cubic splines (RCS) were utilized to explore the association between ACR and all-cause mortality in sepsis patients. Subgroup analyses were conducted to examine the impact of other covariates on the relationship between ACR and all-cause mortality. A total of 1,123 eligible patients were included in the study, with a median ACR of 0.169. The in-hospital mortality rate was 33.7%, the ICU mortality rate was 31.9%, and the 30-day mortality rate was 28.1%. Kaplan-Meier survival analysis demonstrated that patients with higher ACR had a significantly lower risk of 30-/60-day mortality (log-rank p < 0.001). Multivariable Cox proportional hazards analyses revealed that ACR was an independent predictor of in-hospital death (HR: 0.454, 95% CI 0.271-0.761, p = 0.003), ICU death (HR: 0.498, 95% CI 0.293-0.847, p = 0.010), and 30-day death (HR: 0.399, 95% CI 0.218-0.730, p = 0.003). For each 1-unit increase in ACR, there was a 1.203-fold decrease in the risk of death during the hospital stay. The RCS curve illustrated a non-linear negative correlation between ACR and in-hospital mortality (p for non-linear =0.018), ICU mortality (p for non-linear =0.005), and 30-day mortality (p for non-linear =0.006). Sensitivity analysis indicated consistent effect sizes and directions in different subgroups, confirming the stability of the results. Low ACR levels were identified as independent risk factors associated with increased in-hospital, ICU, and 30-day mortality in sepsis patients. ACR can serve as a significant predictor of the clinical outcome of sepsis.

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3

BackgroundSepsis 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.MethodsA 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.ResultsUsing 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.ConclusionsIn 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.

Can We Improve Mortality Prediction in Patients with Sepsis in the Emergency Department?

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED.

Comparing the Predictive Value of SOFA and SIRS for Mortality in the Early Hours of Hospitalization of Sepsis Patients: A Systematic Review and Meta-analysis.

Sepsis, a deadly infection causing organ failure and Systemic Inflammatory Response Syndrome (SIRS), is detected early in hospitalization using the SIRS criteria, while sequential organ failure (SOFA) assesses organ failure severity. A systematic review and meta-analysis was evaluated to investigate the predictive value of the SIRS criteria and the SOFA system for mortality in early hospitalization of sepsis patients. Inclusion criteria were full reports in peer-reviewed journals with data on sepsis assessment using SOFA and SIRS, and their relationship with outcomes. For quality assessment, we considered study population, sepsis diagnosis criteria, and outcomes. The area under the curve (AUC) of these criteria was extracted for separate meta-analysis and forest plots. Twelve studies met the inclusion criteria. The studies included an average of 56.1% males and a mean age of 61.9 (±6.1) among 32,979 patients. The pooled AUC was 0.67 (95% CI: 0.60-0.73) for SIRS and 0.79 (95% CI: 0.73-0.84) for SOFA. Significant heterogeneity between studies was indicated by an I2 above 50%, leading to a meta-regression analysis. This analysis, with age and patient number as moderators, revealed age as the major cause of heterogeneity in comparing the predictive value of the SOFA score with SIRS regarding the in-hospital mortality of sepsis patients (P<0.05). The SOFA score outperformed the SIRS criteria in predicting mortality, emphasizing the need for a holistic approach that combines clinical judgment and other diagnostic tools for better patient management and outcomes.

ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database.

Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients. This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics. From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70-0.83 for GPT-4, 0.51-0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51-0.59 for GPT-4, 0.47-0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5. GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Initial Sofa Score and Mortality of Sepsis Patients in the Intensive Care Unit of Haji Adam Malik Hospital Medan: Does It Correlate?

Introduction: Sepsis is a life-threatening organ dysfunction or failure that is the primary cause of death in infectious disease. the Sepsis-3 Task Force recommends The Sequential (Sepsis-Related) Organ Failure Assessment (SOFA) Score as a means of Sepsis identification. Objective: To determine the correlation between sepsis patients’ deaths in the intensive care unit (ICU) of Haji Adam Malik Hospital Medan and their initial SOFA score. Materials and Methods: This study uses a cross-sectional study design and an observational analytical investigation. The sample for this study was sepsis patients who were treated in the ICU of Haji Adam Malik Hospital Medan in 2021-2022, and they were selected using the purposive sampling method. After calculating the Slovin formula, 61 samples are required. The researchers obtained the data from patient medical records. The analyses used are univariate and bivariate, with the Independent-T test and Fisher’s exact. Results: From 71 patients, there were 36 patients (50.7%) in the age group of 46-65 years old; 39 patients (54.9%) were male; 50 patients (70.4%) had comorbidities; and 50 patients (70.4%) had non-surgical disease. The average initial SOFA score was 9.89 ± 3.95, with mortality for sepsis patients in the ICU of 74.6%. The findings of the statistical analysis indicated a substantial difference (p&lt;0.001) in the SOFA scores of those who survived and those who did not, as well as a significant correlation (p&lt;0.001) between the initial SOFA score and mortality. Conclusion: There is a correlation between initial SOFA score and the mortality of sepsis patients in the ICU of Haji Adam Malik Hospital.

Door-to-antibiotic time and mortality in patients with sepsis: Systematic review and meta-analysis

ObjectivesTo evaluate whether the timing of initial antibiotic administration in patients with sepsis in hospital affects mortality. MethodsThis systematic review and meta-analysis included studies from inception up to 19 May 2022. Interventional and observational studies including adult human patients with suspected or confirmed sepsis and reported time of antibiotic administration with mortality were included. Data were extracted by two independent reviewers. Summary estimates were calculated by using random-effects model. The primary outcome was mortality. ResultsWe included 42 studies comprising 190,896 patients with sepsis. Pooled data showed that the OR for patient mortality who received antibiotics ≤1 hr was 0.83 (95 %CI: 0.67 to 1.04) when compared with patients who received antibiotics >1hr. Significant reductions in the risk of death in patients with earlier antibiotic administration were observed in patients ≤3 hrs versus >3 hrs (OR: 0.80, 95 %CI: 0.68 to 0.94) and ≤6 hrs vs 6 hrs (OR: 0.57, 95 %CI: 0.39 to 0.82). ConclusionsOur findings show an improvement in mortality in sepsis patients with early administration of antibiotics at <3 and <6 hrs. Thus, these results suggest that antibiotics should be administered within 3 hrs of sepsis recognition or ED arrival regardless of the presence or absence of shock.

From Molecular Mechanisms to Clinical Therapy: Understanding Sepsis-Induced Multiple Organ Dysfunction.

Sepsis-induced multiple organ dysfunction arises from the highly complex pathophysiology encompassing the interplay of inflammation, oxidative stress, endothelial dysfunction, mitochondrial damage, cellular energy failure, and dysbiosis. Over the past decades, numerous studies have been dedicated to elucidating the underlying molecular mechanisms of sepsis in order to develop effective treatments. Current research underscores liver and cardiac dysfunction, along with acute lung and kidney injuries, as predominant causes of mortality in sepsis patients. This understanding of sepsis-induced organ failure unveils potential therapeutic targets for sepsis treatment. Various novel therapeutics, including melatonin, metformin, palmitoylethanolamide (PEA), certain herbal extracts, and gut microbiota modulators, have demonstrated efficacy in different sepsis models. In recent years, the research focus has shifted from anti-inflammatory and antioxidative agents to exploring the modulation of energy metabolism and gut microbiota in sepsis. These approaches have shown a significant impact in preventing multiple organ damage and mortality in various animal sepsis models but require further clinical investigation. The accumulation of this knowledge enriches our understanding of sepsis and is anticipated to facilitate the development of effective therapeutic strategies in the future.

Development and validation of an interpretable machine learning for mortality prediction in patients with sepsis.

Sepsis is a leading cause of death. However, there is a lack of useful model to predict outcome in sepsis. Herein, the aim of this study was to develop an explainable machine learning (ML) model for predicting 28-day mortality in patients with sepsis based on Sepsis 3.0 criteria. We obtained the data from the Medical Information Mart for Intensive Care (MIMIC)-III database (version 1.4). The overall data was randomly assigned to the training and testing sets at a ratio of 3:1. Following the application of LASSO regression analysis to identify the modeling variables, we proceeded to develop models using Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) techniques with 5-fold cross-validation. The optimal model was selected based on its area under the curve (AUC). Finally, the Shapley additive explanations (SHAP) method was used to interpret the optimal model. A total of 5,834 septic adults were enrolled, the median age was 66 years (IQR, 54-78 years) and 2,342 (40.1%) were women. After feature selection, 14 variables were included for developing model in the training set. The XGBoost model (AUC: 0.806) showed superior performance with AUC, compared with RF (AUC: 0.794), LR (AUC: 0.782) and SVM model (AUC: 0.687). SHAP summary analysis for XGBoost model showed that urine output on day 1, age, blood urea nitrogen and body mass index were the top four contributors. SHAP dependence analysis demonstrated insightful nonlinear interactive associations between factors and outcome. SHAP force analysis provided three samples for model prediction. In conclusion, our study successfully demonstrated the efficacy of ML models in predicting 28-day mortality in sepsis patients, while highlighting the potential of the SHAP method to enhance model transparency and aid in clinical decision-making.

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

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

The prognostic value of the combined neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-platelet ratio (NPR) in sepsis

Sepsis is a severe disease characterized by high mortality rates. Our aim was to develop an early prognostic indicator of adverse outcomes in sepsis, utilizing easily accessible routine blood tests. A retrospective analysis of sepsis patients from the MIMIC-IV database was conducted. We performed univariate and multivariate regression analyses to identify independent risk factors associated with in-hospital mortality within 28 days. Logistic regression was utilized to combine the neutrophil-to-lymphocyte ratio (NLR) and the neutrophil-to-platelet ratio (NPR) into a composite score, denoted as NLR_NPR. We used ROC curves to compare the prognostic performance of the models and Kaplan–Meier survival curves to assess the 28 day survival rate. Subgroup analysis was performed to evaluate the applicability of NLR_NPR in different subpopulations based on specific characteristics. This study included a total of 1263 sepsis patients, of whom 179 died within 28 days of hospitalization, while 1084 survived beyond 28 days. Multivariate regression analysis identified age, respiratory rate, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR), hypertension, and sequential organ failure assessment (SOFA) score as independent risk factors for 28 day mortality in septic patients (P < 0.05). Additionally, in the prediction model based on blood cell-related parameters, the combined NLR_NPR score exhibited the highest predictive value for 28 day mortality (AUC = 0.6666), followed by NLR (AUC = 0.6456) and NPR (AUC = 0.6284). Importantly, the performance of the NLR_NPR score was superior to that of the commonly used SOFA score (AUC = 0.5613). Subgroup analysis showed that NLR_NPR remained an independent risk factor for 28 day in-hospital mortality in the subgroups of age, respiratory rate, and SOFA, although not in the hypertension subgroup. The combined use of NLR and NPR from routine blood tests represents a readily available and reliable predictive marker for 28 day mortality in sepsis patients. These results imply that clinicians should prioritize patients with higher NLR_NPR scores for closer monitoring to reduce mortality rates.

Predictive value of the dynamics of absolute lymphocyte counts for 90-day mortality in ICU sepsis patients: a retrospective big data study

ObjectivesThe objective of the study was to assess the clinical predictive value of the dynamics of absolute lymphocyte count (ALC) for 90-day all-cause mortality in sepsis patients in intensive care...

Early versus delayed enteral nutrition in ICU patients with sepsis: a propensity score-matched analysis based on the MIMIC-IV database.

Early enteral nutrition (EN) is recommended for sepsis management, but its optimal timing and clinical benefits remain uncertain. This study evaluates whether early EN improves outcomes compared to delayed EN in patients with sepsis. We analyzed data of septic patients from the MIMIC-IV 2.2 database, focusing on those in the Medical Intensive Care Unit (MICU) and Surgical Intensive Care Unit (SICU). Patients who initiated EN within 3 days were classified into the early EN group, while those who started EN between 3 and 7 days were classified into the delayed EN group. Propensity score matching was used to compare outcomes between the groups. Among 1,111 patients, 786 (70.7%) were in the early EN group and 325 (29.3%) were in the delayed EN group. Before propensity score matching, the early EN group demonstrated lower mortality (crude OR = 0.694; 95% CI: 0.514-0.936; p = 0.018) and shorter ICU stays (8.3 [5.2, 12.3] vs. 10.0 [7.5, 14.2] days; p < 0.001). After matching, no significant difference in mortality was observed. However, the early EN group had shorter ICU stays (8.3 [5.2, 12.4] vs. 10.1 [7.5, 14.2] days; p < 0.001) and a lower incidence of AKI stage 3 (49.3% vs. 55.5%; p = 0.030). Subgroup analysis revealed that early EN significantly reduced the 28-day mortality rate in sepsis patients with lactate levels ≤4 mmol/L, with an adjusted odds ratio (aOR) of 0.579 (95% CI: 0.361, 0.930; p = 0.024). Early enteral nutrition may not significantly reduce overall mortality in sepsis patients but may shorten ICU stays and decrease the incidence of AKI stage 3. Further research is needed to identify specific patient characteristics that benefit most from early EN.

A novel clinical prediction model for in-hospital mortality in sepsis patients complicated by ARDS: A MIMIC IV database and external validation study

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

Mortality predictors in patients with suspected sepsis in the emergency department of a tertiary care hospital: a retrospective cohort study

BackgroundSepsis remains a worldwide major cause of hospitalization, mortality, and morbidity. To enhance the identification of patients with suspected sepsis at high risk of mortality and adverse outcomes in the emergency department (ED), the use of mortality predictors is relevant. This study aims to establish whether quick sofa (qSOFA) and the severity criteria applied in patients with suspicion of sepsis in a monitored ED are in fact predictors of mortality.MethodsWe performed a retrospective cohort study among adult patients with suspicion of sepsis at the ED of a tertiary care hospital in Brazil between January 1st, 2019 and December 31, 2020. All adult patients (ages 18 and over) with suspected sepsis that scored two or more points on qSOFA score or at least one point on the severity criteria score were included in the study.ResultsThe total of patients included in the study was 665 and the average age of the sample was 73 ± 19 years. The ratio of men to women was similar. Most patients exhibited qSOFA ≥ 2 (58.80%) and 356 patients (53.61%) scored one point in the severity criteria at admission. The overall mortality rate was 19.7% (131 patients) with 98 patients (14.74%) having positive blood cultures, mainly showing Escherichia coli as the most isolated bacteria. Neither scores of qSOFA nor the severity criteria were associated with mortality rates, but scoring any point on qSOFA was considered as an independent factor for intensive care unit (ICU) admission (qSOFA = 1 point, p = 0.02; qSOFA = 2 points, p = 0.03, and qSOFA = 3 points, p = 0.04). Positive blood cultures (RR, 1.63;95% CI, 1.10 to 2.41) and general administration of vasopressors at the ED (RR, 2.14;95% CI, 1.44 to 3.17) were associated with 30-day mortality. The administration of vasopressors at the ED (RR, 2.25; CI 95%, 1.58 to 3.21) was found to be a predictor of overall mortality.ConclusionsEven though an association was found between qSOFA and ICU admission, there was no association of qSOFA or the severity criteria with mortality. Therefore, patients with a tendency toward greater severity could be identified and treated more quickly and effectively in the emergency department. Further studies are necessary to assess novel scores or biomarkers to predict mortality in sepsis patients admitted to the ED’s initial care.