Correction: Modified cardiovascular SOFA score in sepsis: development and internal and external validation

Lee, Hui Jai; Ko, Byuk Sung; Ryoo, Seung Mok; Han, Eunah; Suh, Gil Joon; Choi, Sae Byeol; Chung, Sung Phil; Lim, Tae Ho; Kim, Won Young; Kwon, Woon Yong; Hwang, Sung Yeon; Jo, You Hwan; Shin, Jonghwan; Shin, Tae Gun; Kim, Kyuseok

doi:10.1186/s12916-022-02694-6

Cited by 7 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Certainly, to adapt to different clinical settings, there have been numerous studies in the past that have made modifications to the SOFA score. For example, Lee et al (26) developed the Modified Cardiovascular SOFA score, replacing vasopressor medications in the cardiovascular component with norepinephrine. Vacheron et al (27) developed the sa-SOFA, which improves the predictive performance of SOFA by adjusting the cutoff points for individual component scores.…”

Section: Discussionmentioning

confidence: 99%

Development and Internal-External Validation of the Acci-Sofa Model for Predicting in-Hospital Mortality of Patients With Sepsis-3 in the Icu: A Multicenter Retrospective Cohort Study

Huang,

Gao,

Quan

et al. 2024

Shock

View full text Add to dashboard Cite

Objective To achieve a better prediction of in-hospital mortality, the Sequential Organ Failure Assessment (SOFA) score needs to be adjusted and combined with comorbidities. This study aims to enhance the prediction of SOFA score for in-hospital mortality in patients with Sepsis-3. Methods This study adjusted the maximum SOFA score within the first 3 days (Max Day3 SOFA) in relation to in-hospital mortality using logistic regression and incorporated the age-adjusted Charlson Comorbidity Index (aCCI) as a continuous variable to build the aCCI-SOFA model. The outcome was in-hospital mortality. We developed, internally validated, and externally validated the aCCI-SOFA model using cohorts of Sepsis-3 patients from the MIMIC-IV, MIMIC-III(CareVue), and the FAHWMU cohort. The predictive performance of the model was assessed through discrimination and calibration, which was assessed using the Area Under the Receiver Operating Characteristic (AUROC) and calibration curves, respectively. The overall predictive effect was evaluated using the Brier score. Measurements and main results Compared to the Max day3 SOFA, the aCCI-SOFA model showed significant improvement in AUROC with all cohorts: development cohort (0.81 vs 0.75, p < 0.001), internal validation cohort (0.81 vs 0.76, p < 0.001), MIMIC-III(CareVue) cohort (0.75 vs 0.68, p < 0.001), and FAHWMU cohort (0.72 vs 0.67, p = 0.001). In sensitivity analysis, it was suggested that the application of aCCI-SOFA in early non-septic shock patients had greater clinical value, with significant differences compared to the original SOFA scores in all cohorts (P < 0.05). Conclusions For septic patients in ICU, the aCCI-SOFA model exhibited superior predictive performance. The application of aCCI-SOFA in early non-septic shock patients had greater clinical value.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Internal-External Validation of the Acci-Sofa Model for Predicting in-Hospital Mortality of Patients With Sepsis-3 in the Icu: A Multicenter Retrospective Cohort Study

Huang,

Gao,

Quan

et al. 2024

Shock

View full text Add to dashboard Cite

show abstract

“…SOFA score) were used to predict in-hospital mortality for critically ill patients with sepsis [10][11][12][13] . Such systems, while effective, are often limited in the range of features they examine [14][15][16] . Other studies, such as retrospective analysis, are also popular methods for evaluating relationships between a specific feature and mortality.…”

Section: Introductionmentioning

confidence: 99%

“…SOFA score) were used to predict in-hospital mortality for critically ill patients with sepsis [12][13][14][15]. Such systems, while effective, are often limited in the range of features they examine [16][17][18]. For example, these scoring systems typically focus on a narrow set of clinical parameters, which might not capture the full complexity of sepsis.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sepsis Mortality in ICU Patients Using Machine Learning Methods

Gao,

Lu,

Ashrafi

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Sepsis, a life-threatening condition, is the cause of a large number of mortalities worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have explored machine learning for prognosis but have limitations in feature sets and model interpretability. Methods: This study analyzes intensive care patient outcomes using the MIMIC-IV database, focusing on adult sepsis cases. Employing the latest data extraction tools, such as Google Big Query, and following stringent selection criteria, we selected 38 features in this study. This selection is also informed by a comprehensive literature review and clinical expertise. We used statistical methods to handle the imbalances inherent in healthcare datasets. Our modeling focused on various classification techniques, with a train-test split preferred over cross-validation for its superior performance and computational efficiency. Results: The Random Forest model emerged as the most effective, achieving an AUROC of 0.94 with a confidence interval of 0.01, significantly outperforming other baseline models, and the best result in our literature review. This study not only yields a high-performing model but also provides granular insights into the factors affecting mortality, and demonstrates the value of advanced analytics in critical care. Conclusions: The study shows significant improvement in predicting sepsis outcomes, indicating the potential of machine learning in critical care. By enhancing model accuracy and stability, this research contributes to clinical decision-making, offering a pathway for data-driven approaches to reduce sepsis-induced fatalities.

show abstract

“…Despite the SOFA score being a significant inclusion criteria for detection of sepsis and septic shock, the limitations of the SOFA score in the cardiovascular domain are well recognized. Prior work has highlighted the use of modified shock index, lactic acid, and more granular vasopressor data to provide higher discrimination to the SOFA score (18)(19)(20). The impact of this on the clinical detection, management, and outcomes of right ventricular dysfunction remain to be studied.…”

Section: Introductionmentioning

confidence: 99%

Right Ventricular Dysfunction in Sepsis: An Updated Narrative Review

Bansal

Mehta

Balakrishna

et al. 2023

Shock

View full text Add to dashboard Cite

Sepsis is a multisystem disease process, which constitutes a significant public health challenge and is associated with high morbidity and mortality. Among other systems, sepsis is known to affect the cardiovascular system, which may manifest as myocardial injury, arrhythmias, refractory shock, and/or septic cardiomyopathy. Septic cardiomyopathy is defined as the reversible systolic and/or diastolic dysfunction of one or both ventricles. Left ventricle dysfunction has been extensively studied in the past, and its prognostic role in patients with sepsis is well documented. However, there is relatively scarce literature on right ventricle (RV) dysfunction and its role. Given the importance of timely detection of septic cardiomyopathy and its bearing on prognosis of patients, the role of RV dysfunction has come into renewed focus. Hence, through this review, we sought to describe the pathophysiology of RV dysfunction in sepsis and what have we learnt so far about its multifactorial nature. We also elucidate the roles of different biomarkers for its detection and prognosis, along with appropriate management of such patient population.

show abstract

Correction: Modified cardiovascular SOFA score in sepsis: development and internal and external validation

Abstract: The original article [1] mistakenly omitted a portion of affiliation #3 which has since been re-instated.

Cited by 7 publications

References 1 publication

Development and Internal-External Validation of the Acci-Sofa Model for Predicting in-Hospital Mortality of Patients With Sepsis-3 in the Icu: A Multicenter Retrospective Cohort Study

Development and Internal-External Validation of the Acci-Sofa Model for Predicting in-Hospital Mortality of Patients With Sepsis-3 in the Icu: A Multicenter Retrospective Cohort Study

Prediction of Sepsis Mortality in ICU Patients Using Machine Learning Methods

Right Ventricular Dysfunction in Sepsis: An Updated Narrative Review

Contact Info

Product

Resources

About