A new SOFA score calculation to improve the predictive performance for mortality in sepsis-associated disseminated intravascular coagulopathy patients

Enhancing Sepsis prognosis: Integrating social determinants and demographic variables into a comprehensive model for critically ill patients

BACKGROUND

The Sequential Organ Failure Assessment (SOFA) score monitors organ failure and defines sepsis but may not fully capture factors influencing sepsis mortality. Socioeconomic and demographic impacts on sepsis outcomes have been highlighted recently.

OBJECTIVE

To evaluate the prognostic value of SOFA scores against demographic and social health determinants for predicting sepsis mortality in critically ill patients, and to assess if a combined model increases predictive accuracy.

METHODS

The study utilized retrospective data from the MIMIC-IV database and prospective external validation from the Penn State Health cohort. A Random Forest model incorporating SOFA scores, demographic/social data, and the Charlson Comorbidity Index was trained and validated.

FINDINGS

In the MIMIC-IV dataset of 32,970 sepsis patients, 6,824 (20.7%) died within 30 days. A model including demographic, socioeconomic, and comorbidity data with SOFA scores improved predictive accuracy beyond SOFA scores alone. Day 2 SOFA, age, weight, and comorbidities were significant predictors. External validation showed consistent performance, highlighting the importance of delta SOFA between days 1 and 3.

CONCLUSION

Adding patient-specific demographic and socioeconomic information to clinical metrics significantly improves sepsis mortality prediction. This suggests a more comprehensive, multidimensional prognostic approach is needed for accurate sepsis outcome predictions.

Risk stratification and survival time of patients with gram-negative bacillary pneumonia in the intensive care unit

Introduction

Pneumonia is a common infection in the intensive care unit (ICU), and gram-negative bacilli are the most common bacterial cause. The purpose of the study was to investigate the risk factors for 30-day mortality in patients with gram-negative bacillary pneumonia in the ICU, construct a predictive model, and stratify patients based on risk to assess their short-term survival.

Methods

Patients admitted to the ICU with gram-negative bacillary pneumonia at Fujian Medical University Affiliated First Hospital between January 2018 and September 2020 were selected. Patients were divided into deceased and survivor groups based on whether death occurred within 30 days. Multifactorial logistic regression analysis was used to identify independent risk factors for 30-day mortality in these patients, and a predictive nomogram model was constructed based on these factors. Patients were categorized into low-, medium-, and high-risk groups according to the model's predicted probability, and Kaplan-Meier survival curves were plotted to assess short-term survival.

Results

The study included 305 patients. Lactic acid (odds ratio [OR], 1.524, 95% CI: 1.057-2.197), tracheal intubation (OR: 4.202, 95% CI: 1.092-16.169), and acute kidney injury (OR:4.776, 95% CI: 1.632-13.978) were identified as independent risk factors for 30-day mortality. A nomogram prediction model was established based on these three factors. Internal validation of the model showed a Hosmer-Lemeshow test result of X2=5.770, P=0.834, and an area under the ROC curve of 0.791 (95% CI: 0.688-0.893). Bootstrap resampling of the original data 1000 times yielded a C-index of 0.791, and a decision curve analysis indicated a high net benefit when the threshold probability was between 15%-90%. The survival time for low-, medium-, and high-risk patients was 30 (30, 30), 30 (16.5, 30), and 17 (11, 27) days, respectively, which were significantly different.

Conclusion

Lactic acid, tracheal intubation, and acute kidney injury were independent risk factors for 30-day mortality in patients in the ICU with gram-negative bacillary pneumonia. The predictive model constructed based on these factors showed good predictive performance and helped assess short-term survival, facilitating early intervention and treatment.

Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning

This study applied machine learning for the early prediction of 30-day mortality at sepsis diagnosis time in critically ill patients. Retrospective study using data collected from the Medical Information Mart for Intensive Care IV database. The data of the patient cohort was divided on the basis of the year of hospitalization, into training (2008-2013), validation (2014-2016), and testing (2017-2019) datasets. 24,377 patients with the sepsis diagnosis time < 24 h after intensive care unit (ICU) admission were included. A gradient boosting tree-based algorithm (XGBoost) was used for training the machine learning model to predict 30-day mortality at sepsis diagnosis time in critically ill patients. Model performance was measured in both discrimination and calibration aspects. The model was interpreted using the SHapley Additive exPlanations (SHAP) module. The 30-day mortality rate of the testing dataset was 17.9%, and 39 features were selected for the machine learning model. Model performance on the testing dataset achieved an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI 0.837-0.868) and an area under the precision-recall curves of 0.581 (95% CI 0.541-0.619). The calibration plot for the model revealed a slope of 1.03 (95% CI 0.94-1.12) and intercept of 0.14 (95% CI 0.04-0.25). The SHAP revealed the top three most significant features, namely age, increased red blood cell distribution width, and respiratory rate. Our study demonstrated the feasibility of using the interpretable machine learning model to predict mortality at sepsis diagnosis time.