Establishment and Effectiveness Evaluation of a Scoring System-RAAS (RDW, AGE, APACHE II, SOFA) for Sepsis by a Retrospective Analysis

Huang, Yingying; Jiang, Shaowei; Li, Wenjie; Fan, Yiwen; Leng, Yuxin; Gao, Chengjin

doi:10.2147/jir.s348490

Cited by 17 publications

(11 citation statements)

References 37 publications

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“…[ 26 ] Huang et al reported AUC of APACHE II score for predicting 30-day survival in patients with sepsis was 0.680. [ 27 ] Our findings were consistent with their results. However, previous studies have confirmed that the APACHE II score has certain defects for some diseases with strong specialty characteristics or special populations, conditions with special organ damage or abnormal physiological indicators.…”

Section: Discussionsupporting

confidence: 93%

The value of NSE to predict ICU mortality in patients with septic shock: A prospective observational study

Zhang

Han

et al. 2022

Medicine

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To investigate the predictive value of neuron-specific enolase (NSE) on intensive care unit (ICU) mortality in patients with septic shock. Seventy-five patients with septic shock hospitalized in the emergency intensive care unit (EICU) of Hebei General Hospital from March 2020 to September 2021 were included, and the patients’ baseline characteristics and laboratory findings were collected. NSE levels on the first and fourth days after admission were retrieved. NSE% [(NSEday1 – NSEday4)/NSEday1 × 100%] and δNSE (NSEday1 – NSEday4) were calculated. The outcome indicator was ICU mortality. The patients were divided into the survivors group (n = 57) and the nonsurvivors group (n = 18). Multivariate analysis was performed to assess the relationship between NSE and ICU mortality. The predictive value of NSE was evaluated using receiver operating characteristic (ROC) curve. There were no significant differences in age, gender, systolic blood pressure (SBP), heart rate (HR), acute physiology and chronic health evaluation II score (APACHE II score), source of infection, and comorbidities between the 2 groups (all P > .05). Interleukin-6 (IL-6), NSE (day1), and NSE (day4) were significantly higher in patients in the nonsurvivors group (all P < .05), and there were no statistical differences in other laboratory tests between the 2 groups (all P > .05). APACHE II score, IL-6, lactate (Lac), total bilirubin (TBil), NSE (day1), and NSE (day4) showed a weak positive correlation with ICU mortality in patients with septic shock (all P < .05). Multivariate logistic regression analysis demonstrated that APACHE II score (odds ratio [OR] = 1.166, 95% confidence interval [95% confidence interval [CI]] 1.005–1.352, P = .042), IL-6 (OR = 1.001, 95% CI 1.000–1.001, P = .003) and NSE (day4) (OR = 1.099, 95% CI 1.027–1.176, P = .006) were independently associated with the ICU mortality of sepsis shock patients. The area under the curve (AUCs) of APACHE II score, IL-6, NSE (day1), and NSE (day4) for predicting prognosis were 0.650, 0.694, 0.758 and 0.770, respectively (all P < .05). NSE(day4) displayed good sensitivity and specificity (Sn = 61.11%, Sp = 91.23%) for predicting ICU mortality with a cutoff value of 25.94 ug/L. High-level NSE (day4) is an independent predictor of ICU mortality in sepsis shock patients, which may become a good alternate option for evaluating sepsis severity. More extensive studies are needed in the future to demonstrate the prognosis value of NSE.

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Section: Discussionsupporting

confidence: 93%

The value of NSE to predict ICU mortality in patients with septic shock: A prospective observational study

Zhang

Han

et al. 2022

Medicine

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“…Given the intricate nature of sepsis, there are inherent limitations to the diagnostic accuracy of various evaluation systems used in clinical practice. As a standard for assessing the severity of various diseases in ICUs, scoring systems have become critical tools to assist clinicians in making decisions (14).…”

Section: Discussionmentioning

confidence: 99%

“…Second, various scoring systems are used such as the Sequential Organ Failure Assessment (SOFA) score (8), quick SOFA score (9,10), Acute Physiology and Chronic Health Evaluation II (APACHE II) score (11,12), Systemic Inflammatory Response Syndrome (SIRS) score, National Early Warning Score (13), etc. In addition, novel scoring systems like the RAAS score (14) and Sepsis Alert Score (15) have been developed. Lastly, machine learning-based prediction models have garnered increasing attention in this field (16,17).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Nomogram for Predicting 28-Day in-Hospital Mortality in Sepsis Patients Based on an Optimized Acute Physiology and Chronic Health Evaluation Ii Score

Yuan,

Meng,

et al. 2024

Shock

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Purpose The objective of this study is to establish a nomogram that correlates optimized APACHE II score with sepsis-related indicators, aiming to provide a robust model for early prediction of sepsis prognosis in clinical practice and serve as a valuable reference for improved diagnosis and treatment strategies. Methods This retrospective study extracted sepsis patients meeting the inclusion criteria from the MIMIC-IV database to form the training group. An optimized Acute Physiology and Chronic Health Evaluation (APACHE) II score integrated with relevant indicators was developed using a nomogram for predicting the prognosis of sepsis patients. External validation was conducted using data from the Intensive Care Unit at Lanzhou University Second Hospital. Results The study enrolled 1805 patients in the training cohort and 203 patients in the validation cohort. A multifactor analysis was conducted to identify factors affecting patient mortality within 28 days, resulting in the development of an Optimized Score by simplifying evaluation indicators from APACHE II score. The results showed that the Optimized Score (AUC = 0.715) had a higher area under receiver operating characteristic curve than SOFA Score (AUC = 0.637) but slightly lower than APACHE II score (AUC = 0.720). Significant indicators identified through multifactor analysis included platelet count, total bilirubin level, albumin level, prothrombin time, activated partial thromboplastin time, mechanical ventilation (MV) use and renal replacement therapy (RRT) use. These seven indicators were combined with Optimized Score to construct a nomogram Based on these seven indicators. The nomogram demonstrated good clinical predictive value in both training cohort (AUC = 0.803) and validation cohort (AUC = 0.750). Calibration curves and decision curve analyses also confirmed its good predictive ability, surpassing the APACHE II score and SOFA score in identifying high-risk patients. Conclusions The Nomogram was established in this study using the MIMIC-IV database and validated with external data, demonstrating its robust discriminability, calibration, and clinical practicability for predicting 28-day mortality in sepsis patients. These findings aim to provide substantial support for clinicians' decision-making.

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“…Sepsis is among the leading causes of infection-related deaths, such as COVID-19, and scoring systems such as SOFA and APACHE II are used to assess the severity and prognosis of the disease in patients who develop sepsis. Especially in patients with the severe form of COVID-19, these scoring systems can play an important role in directing intensive care resources and determining the treatment plan [ 34 , 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

Aygun,

Yagin,

Yagin

et al. 2024

Diagnostics

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This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms—Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)—were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868–0.929) and area under the ROC curve (AUC) of 0.940 (0.898–0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil–lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.

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Establishment and Effectiveness Evaluation of a Scoring System-RAAS (RDW, AGE, APACHE II, SOFA) for Sepsis by a Retrospective Analysis

Cited by 17 publications

References 37 publications

The value of NSE to predict ICU mortality in patients with septic shock: A prospective observational study

The value of NSE to predict ICU mortality in patients with septic shock: A prospective observational study

Development and Validation of a Nomogram for Predicting 28-Day in-Hospital Mortality in Sepsis Patients Based on an Optimized Acute Physiology and Chronic Health Evaluation Ii Score

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

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