Age-to-Glasgow Coma Scale score ratio predicts gastrointestinal bleeding in patients with primary intracerebral hemorrhage

Qiu, Weizhi; Liu, Chubin; Ye, Jinfu; Wang, Gang; Ye, Fuxing; Pan, Zhigang; Hu, Weipeng; Gao, Hongzhi

doi:10.3389/fneur.2023.1034865

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…GCS score can be utilized for the identi cation not only of high-risk cases but also in discerning underlying neurological diseases. This underscores the dual utility of the GCS score in not just assessing risk but also aiding in the determination of the root cause [22].…”

Section: Discussionmentioning

confidence: 91%

Early Prognosis Prediction for Non-variceal Upper Gastrointestinal Bleeding in the Intensive Care Unit: Based on Interpretable Machine Learning

Zhao,

Wei,

Pan

et al. 2024

Preprint

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Introduction: This study aims to construct a mortality prediction model for patients with non-variceal upper gastrointestinal bleeding (NVUGIB) in the intensive care unit (ICU), employing advanced machine learning algorithms. The goal is to identify high-risk populations early, contributing to a deeper understanding of patients with NVUGIB in the ICU.Methods: We extracted NVUGIB data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v.2.2) database spanning from 2008 to 2019. Feature selection was conducted through LASSO regression, followed by training models using eleven machine learning methods. The best model was chosen based on the area under the curve (AUC). Subsequently, Shapley additive explanations (SHAP) was employed to elucidate how each factor influenced the model. Finally, a case was randomly selected, and the model was utilized to predict its mortality, demonstrating the practical application of the developed model.Results: In total, 2716 patients with NVUGIB were deemed eligible for participation. Following selection, 30 out of a total of 64 clinical parameters collected on day 1 after ICU admission remained associated with prognosis and were utilized for developing machine-learning models. Among the eleven constructed models, the Gradient Boosting Decision Tree (GBDT) model demonstrated the best performance, achieving an AUC of 0.853 and an accuracy of 0.839 in the validation cohort. Feature importance analysis highlighted that Shock, Glasgow Coma Scale (GCS), renal disease, age, albumin, and alanine aminotransferase (ALP) were the top six features of the GBDT model with the most significant impact. Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death.Conclusions: Patient data from the MIMIC database were leveraged to develop a robust prognostic model for patients with NVUGIB in the ICU. The analysis using SHAP also assisted clinicians in gaining a deeper understanding of the disease.

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Section: Discussionmentioning

confidence: 91%

Early Prognosis Prediction for Non-variceal Upper Gastrointestinal Bleeding in the Intensive Care Unit: Based on Interpretable Machine Learning

Zhao,

Wei,

Pan

et al. 2024

Preprint

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“…Among these, Age stood out for its nuanced impact in our older population, suggesting that its predictive value might be surpassed by other determinants in individuals over 65 years old. The analysis also underscored the importance of other variables like ICH [ 18 , 19 ], eGFR [ 20 ] and Peptic Ulcer Bleeding [ 21 ], with renal function indicating significant implications for UGIB outcomes and Peptic Ulcer Bleeding presenting more frequently than variceal bleeding [ 22 ]. The variability in risk associated with these conditions underscores the heterogeneity of UGIB patient outcomes, necessitating a tailored approach to risk stratification.…”

Section: Discussionmentioning

confidence: 99%

Prediction of 30-day in-hospital mortality in older UGIB patients using a simplified risk score and comparison with AIMS65 score

Xue,

Che,

Xie

et al. 2024

BMC Geriatr

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Background Upper gastrointestinal bleeding (UGIB) in older patients is associated with substantial in-hospital morbidity and mortality. This study aimed to develop and validate a simplified risk score for predicting 30-day in-hospital mortality in this population. Methods A retrospective analysis was conducted on data from 1899 UGIB patients aged ≥ 65 years admitted to a single medical center between January 2010 and December 2019. An additional cohort of 330 patients admitted from January 2020 to October 2021 was used for external validation. Variable selection was performed using five distinct methods, and models were generated using generalized linear models, random forest, support vector machine, and k-nearest neighbors approaches. The developed score, “ABCAP,” incorporated Albumin < 30 g/L, Blood Urea Nitrogen (BUN) > 7.5 mmol/L, Cancer presence, Altered mental status, and Pulse rate > 100/min, each assigned a score of 1. Internal and external validation procedures compared the ABCAP score with the AIMS65 score. Results In internal validation, the ABCAP score demonstrated robust predictive capability with an area under the curve (AUC) of 0.878 (95% CI: 0.824–0.932), which was significantly better than the AIMS65 score (AUC: 0.827, 95% CI: 0.751–0.904), as revealed by the DeLong test (p = 0.048). External validation of the ABCAP score resulted in an AUC of 0.799 (95% CI: 0.709–0.889), while the AIMS65 score yielded an AUC of 0.743 (95% CI: 0.647–0.838), with no significant difference between the two scores based on the DeLong test (p = 0.16). However, the ABCAP score at the 3–5 score level demonstrated superior performance in identifying high-risk patients compared to the AIMS65 score. This score exhibited consistent predictive accuracy across variceal and non-variceal UGIB subgroups. Conclusions The ABCAP score incorporates easily obtained clinical variables and demonstrates promising predictive ability for 30-day in-hospital mortality in older UGIB patients. It allows effective mortality risk stratification and showed slightly better performance than the AIMS65 score. Further cohort validation is required to confirm generalizability.

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“…It's noteworthy that Age, Liver Diseases, and HGB, which were present in both the GBS and RS scores, were also consistently identi ed across a substantial body of research, underscoring their signi cance in predicting outcomes.Age's in uence, a pivotal prognostic factor in various medical contexts, demonstrated a nuanced impact in our population, indicating its potential to be outweighed by other factors for individuals over 65 years old. The presence of UGIB often coincides with ICH, linked to heightened mortality risk and prolonged stays in the ICU [20,21].Some studies found that renal function, as exempli ed by eGFR, emerged as a critical marker and was indicative of poorer UGIB outcomes [22]. Furthermore, the prevalence of Peptic Ulcer Bleeding outweighed that of variceal bleeding.…”

Section: Discussionmentioning

confidence: 99%

Prediction of 30-Day In-Hospital Mortality in Elderly UGIB Patients Using a Simplified Risk Score and Comparison with AIMS65 score

Xue,

Che,

Xie

et al. 2023

Preprint

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Background: Upper gastrointestinal bleeding (UGIB) in elderly patients is associated with substantial in-hospital morbidity and mortality. This study aimed to develop and validate a simplified risk score for predicting 30-day in-hospital mortality in this population. Methods: A retrospective analysis was conducted on data from 1899 UGIB patients aged ≥65 years admitted to a single medical center between January 2010 and December 2019. An additional cohort of 330 patients admitted from January 2020 to October 2021 was used for external validation. Variable selection was performed using five distinct methods, and models were generated using generalized linear models, random forest, support vector machine, and k-nearest neighbors approaches. The developed score, "ABCAP," incorporated Albumin <30 g/L, Blood Urea Nitrogen (BUN) >7.5 mmol/L, Cancer presence, Altered mental status, and Pulse rate >100/min, each assigned a score of 1. Internal and external validation procedures compared the ABCAP score with the AIMS65 score. Results: In internal validation, the ABCAP score demonstrated robust predictive capability with an area under the curve (AUC) of 0.878 (95% CI: 0.824-0.932), comparable to the AIMS65 score (AUC: 0.827, 95% CI: 0.751-0.904). External validation yielded an AUC of 0.799 (95% CI: 0.709-0.889) for the ABCAP score, slightly outperforming the AIMS65 score (AUC: 0.743, 95% CI: 0.647-0.838). The ABCAP score effectively stratified mortality risk into low (0-2 points), moderate (3 points), and high (4-5 points) categories. This score exhibited consistent accuracy across variceal and non-variceal UGIB subgroups. Conclusions: The ABCAP score incorporates easily obtained clinical variables and demonstrates promising predictive ability for 30-day in-hospital mortality in elderly UGIB patients. It allows effective mortality risk stratification and showed slightly better performance than the AIMS65 score. Further cohort validation is required to confirm generalizability.

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Age-to-Glasgow Coma Scale score ratio predicts gastrointestinal bleeding in patients with primary intracerebral hemorrhage

Cited by 4 publications

References 31 publications

Early Prognosis Prediction for Non-variceal Upper Gastrointestinal Bleeding in the Intensive Care Unit: Based on Interpretable Machine Learning

Early Prognosis Prediction for Non-variceal Upper Gastrointestinal Bleeding in the Intensive Care Unit: Based on Interpretable Machine Learning

Prediction of 30-day in-hospital mortality in older UGIB patients using a simplified risk score and comparison with AIMS65 score

Prediction of 30-Day In-Hospital Mortality in Elderly UGIB Patients Using a Simplified Risk Score and Comparison with AIMS65 score

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