Application Prospect of the SOFA Score and Related Modification Research Progress in Sepsis

SOFA in sepsis: with or without GCS

PURPOSE

Sepsis is a global public health burden. The sequential organ failure assessment (SOFA) is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of the Glasgow Coma Score (GCS) is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis.

METHODS

First, 3048 patients with sepsis admitted to Peking Union Medical College Hospital (PUMCH) were enrolled in this survey. The data were collected from June 8, 2013 to October 12, 2022. Second, 18,108 patients with sepsis in the eICU database were enrolled. Third, 2397 septic patients with respiratory system ≥ 3 points in SOFA in the eICU database were included. We investigated the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis in various ICUs of PUMCH, and then we validated the results in the eICU database.

MAIN RESULTS

In data of ICUs in PUMCH, the predictive efficiency of SOFA without GCS (AUROC [95% CI], 24 h, 0.724 [0.688, 0.760], 48 h, 0.734 [0.699, 0.769], 72 h, 0.748 [0.713, 0.783], 168 h, 0.781 [0.747, 0.815]) was higher than that of SOFA with GCS (AUROC [95% CI], 24 h, 0.708 [0.672, 0.744], 48 h, 0.721 [0.685, 0.757], 72 h, 0.735 [0.700, 0.757], 168 h, 0.770 [0.736, 0.804]) on ICU mortality in patients with sepsis, and the difference was statistically significant (P value, 24 h, 0.001, 48 h, 0.003, 72 h, 0.004, 168 h, 0.005). In septic patients with respiratory system ≥ 3 points in SOFA in the eICU database, although the difference was not statistically significant (P value, 24 h, 0.148, 48 h, 0.178, 72 h, 0.132, 168 h, 0.790), SOFA without GCS (AUROC [95% CI], 24 h, 0.601 [0.576, 0.626], 48 h, 0.625 [0.601, 0.649], 72 h, 0.639 [0.615, 0.663], 168 h, 0.653 [0.629, 0.677]) had a higher predictive efficiency on ICU mortality than SOFA with GCS (AUROC [95% CI], 24 h, 0.591 [0.566, 0.616], 48 h, 0.616 [0.592, 0.640], 72 h, 0.628 [0.604, 0.652], 168 h, 0.651 [0.627, 0.675]).

CONCLUSIONS

In severe sepsis, it is realistic and feasible to discontinue the routine GCS for SOFA in patients with a respiratory system ≥ 3 points, and even better predict ICU mortality.

Analysis of the diagnostic and prognostic value of serum PAD2 in patients with sepsis in the intensive care unit

BACKGROUND

Sepsis is a common disease in the intensive care unit (ICU). In recent years, the incidence rate and mortality rate remain high. Early diagnosis of sepsis is crucial for treatment and can effectively reduce mortality. So far, the ability of serum peptidylarginine deaminase 2 (PAD2) in the diagnosis and prognosis of sepsis patients is still unclear. We conducted this study to reveal the clinical value of PAD2 as a biomarker for sepsis patients.

METHODS

A prospective study method was used to select 207 patients in the ICU of Renmin Hospital of Wuhan University from May 2022 to May 2023. They were divided into the sepsis group (n = 135) and control group (n = 72), and data were collected within 24 h of hospitalization. Sepsis patients were divided into a survival group (n = 80) and a non-survival group (n = 55) based on their 28-day survival status. Using statistical methods to evaluate the diagnostic and prognostic value of PAD2 in sepsis.

RESULTS

The serum PAD2 concentrations in the sepsis group were significantly higher than in the control group (median 16.70 vs 35.32 ng/ml, P < 0.001). Multivariate logistic regression analysis showed that the Quick Sequential Organ Failure Assessment (qSOFA), C-reactive protein (CRP), procalcitonin (PCT), and PAD2 were independent risk factors for sepsis. The Receiver operating characteristic (ROC) curve showed that the combined diagnostic value of qSOFA, CRP, PCT, and PAD2 was the highest. The serum PAD2 concentrations in the non-survival group of patients with sepsis were significantly higher than those in the survival group (median 29.26 vs 50.08 ng/ml, P < 0.05). The COX regression analysis showed that PAD2, sequential organ failure score Assessment (SOFA) score, and Acute Physiology and Chronic Health Evaluation (APACHE II) score were independent factors affecting the prognosis of sepsis patients. The ROC analysis showed that the combined prognostic value of PAD2, SOFA, and APACHE II scores was significantly higher than any single indicator. The Kaplan-Meier survival analysis showed that patients with PAD2 ≤ 48.62 ng/ml had a better prognosis.

CONCLUSION

The significant increase in serum PAD2 concentrations in patients is an independent risk factor affecting the occurrence of sepsis and 28-day mortality. The combination of PAD2 and other indicators can further improve the diagnostic and prognostic value for ICU sepsis patients.

Clinical outcome prediction of acute neurological patients admitted to the emergency department: Sequential Organ Failure Assessment score and modified SOFA score

Background

The aim of this study was to determine the ability of the Sequential Organ Failure Assessment score (SOFA) and modified SOFA score (mSOFA) as predictive tools for 2-day and 28-day mortality and ICU admission in patients with acute neurological pathology treated in hospital emergency departments (EDs).

Methods

An observational, prospective cohort study in adults with acute neurological disease transferred by ambulance to an ED was conducted from 1 January 2019 to 31 August 2022 in five hospitals in Castilla-León (Spain). Score discrimination was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the score.

Results

A total of 640 adult patients with neurological disease were included. For the prediction of 2-day mortality (all-cause), mSOFA presented a higher AUC than SOFA (mSOFA = 0.925 vs. SOFA = 0.902). This was not the case for 28-day mortality, for which SOFA was higher than mSOFA (mSOFA = 0.852 vs. SOFA = 0.875). Finally, ICU admission showed that SOFA was higher than mSOFA (mSOFA = 0.834 vs. SOFA = 0.845).

Conclusion

Both mSOFA and SOFA presented similar predictive ability, with mSOFA being the best predictor for short-term mortality and SOFA being the best predictor for medium-term mortality, as well as for ICU admission. These results in a cohort of patients with acute neurological pathology pave the way for the use of both predictive tools in the ED. The inclusion of these tools could improve the clinical assessment and further treatment of neurological patients, who commonly present the worst outcomes.

Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome

BACKGROUND

Acute kidney injury (AKI) can make cases of acute respiratory distress syndrome (ARDS) more complex, and the combination of the two can significantly worsen the prognosis. Our objective is to utilize machine learning (ML) techniques to construct models that can promptly identify the risk of AKI in ARDS patients.

METHOD

We obtained data regarding ARDS patients from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV databases. Within the MIMIC-III dataset, we developed 11 ML prediction models. By evaluating various metrics, we visualized the importance of its features using Shapley additive explanations (SHAP). We then created a more concise model using fewer variables, and optimized it using hyperparameter optimization (HPO). The model was validated using the MIMIC-IV dataset.

RESULT

A total of 928 ARDS patients without AKI were included in the analysis from the MIMIC-III dataset, and among them, 179 (19.3%) developed AKI after admission to the intensive care unit (ICU). In the MIMIC-IV dataset, there were 653 ARDS patients included in the analysis, and among them, 237 (36.3%) developed AKI. A total of 43 features were used to build the model. Among all models, eXtreme gradient boosting (XGBoost) performed the best. We used the top 10 features to build a compact model with an area under the curve (AUC) of 0.850, which improved to an AUC of 0.865 after the HPO. In extra validation set, XGBoost_HPO achieved an AUC of 0.854. The accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and F1 score of the XGBoost_HPO model on the test set are 0.865, 0.813, 0.877, 0.578, 0.957 and 0.675, respectively. On extra validation set, they are 0.724, 0.789, 0.688, 0.590, 0.851, and 0.675, respectively.

CONCLUSION

ML algorithms, especially XGBoost, are reliable for predicting AKI in ARDS patients. The compact model maintains excellent predictive ability, and the web-based calculator improves clinical convenience. This provides valuable guidance in identifying AKI in ARDS, leading to improved patient outcomes.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.