Sequential Organ Failure Assessment (SOFA) Score for predicting mortality
                        in patients with sepsis in Vietnamese intensive care units: a multicentre,
                        cross-sectional study

Ngoc, Son; Dao, Co Xuan; Nguyen, Tuan Anh; Nguyen, My Ha; Pham, Duy Khanh; Nguyen, Nga T. T.; Huynh, Dai Quang; Hoang, Quoc Trong Ai; Bui, Cuong Van; Vu, Thang Dinh; Bui, Ha Nhat; Nguyen, Hung T.; Hoang, Hai Bui; Le, Thuy Thi Phuong; Nguyen, Lien Thi Bao; Duong, Phuoc Thien; Nguyen, Tuan Dang; Le, Vuong Hung; Pham, Giang Thi Tra; Bui, Giang Thi Huong; Bui, Tam Van; Pham, Thao Thi Ngoc; Nguyen, Chi Van; Nguyen, Anh Dat; Phua, Jason; Li, Andrew; Luong, Chinh Quoc

doi:10.1136/bmjopen-2022-064870

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…However, recent studies have shown that the accuracy of SOFA score in predicting mortality may be limited. 42 43 44 …”

Section: Discussionmentioning

confidence: 99%

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

Park,

Yeo,

Kang

et al. 2024

J Korean Med Sci

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Background Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

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“…However, recent studies have shown that the accuracy of SOFA score in predicting mortality may be limited. 42 43 44 …”

Section: Discussionmentioning

confidence: 99%

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

Park,

Yeo,

Kang

et al. 2024

J Korean Med Sci

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“…The utilization of both the SOFA and APACHE II scores effectively aided the prediction of mortality among ICU patients with sepsis. Nevertheless, owing to its superior discriminatory capability in forecasting ICU mortality, the preference leaned towards the SOFA score over the APACHE II score for mortality prediction [15].…”

Section: Significance Of Patient Triage In Icu Admissionsmentioning

confidence: 99%

Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review

Charan,

Khurana

et al. 2023

Galician med. j.

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Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes. Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning. Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023. Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects. Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery.

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SOFA in sepsis: with or without GCS

Wang,

Ma,

Zhou

et al. 2024

Eur J Med Res

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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.

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Sequential Organ Failure Assessment (SOFA) Score for predicting mortality in patients with sepsis in Vietnamese intensive care units: a multicentre, cross-sectional study

Cited by 4 publications

References 46 publications

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review

SOFA in sepsis: with or without GCS

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