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C-reactive protein for simple prediction of mortality in patients with acute non-variceal upper gastrointestinal bleeding: A retrospective analysis.

ABSTRACT:

Abstract

In upper gastrointestinal bleeding (UGIB), scoring systems using multiple variables were developed to predict patient outcomes. We evaluated serum C-reactive protein (CRP) for simple prediction of patient mortality after acute non-variceal UGIB.The associated factors for 30-day mortality was investigated by regression analysis in patients with acute non-variceal UGIB (N = 1232). The area under the receiver operating characteristics (AUROC) curve was analyzed with serum CRP in these patients and a prospective cohort (N = 435). The discriminant validity of serum CRP was compared to other prognostic scoring systems by means of AUROC curve analysis.Serum CRP was significantly higher in the expired than survived patients (median, 4.53 vs 0.49; P < .001). The odds ratio of serum CRP was 4.18 (2.10-9.27) in multivariate analysis. The odds ratio of high serum CRP was higher than Rockall score (4.15 vs 1.29), AIMS65 (3.55 vs 1.71) and Glasgow-Blatchford score (4.32 vs 1.08) in multivariate analyses. The AUROC of serum CRP at bleeding was 0.78 for 30-day mortality (P < .001). In the validation set, serum CRP was also significantly higher in the expired than survived patients, of which AUROC was 0.73 (P < .001). In predicting 30-day mortality, the AUROC with serum CRP was not inferior to that of other scoring systems.Serum CRP at bleeding can be simply used to identify the patients with high mortality after acute non-variceal UGIB.

SUBMITTER: Park SH

PROVIDER: S-EPMC7748191 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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C-reactive protein for simple prediction of mortality in patients with acute non-variceal upper gastrointestinal bleeding: A retrospective analysis.

Park Se Hwan SH Mun Yoon Gwon YG Lim Chul-Hyun CH Cho Yu Kyung YK Park Jae Myung JM

Medicine 20201201 51

<h4>Abstract</h4>In upper gastrointestinal bleeding (UGIB), scoring systems using multiple variables were developed to predict patient outcomes. We evaluated serum C-reactive protein (CRP) for simple prediction of patient mortality after acute non-variceal UGIB.The associated factors for 30-day mortality was investigated by regression analysis in patients with acute non-variceal UGIB (N = 1232). The area under the receiver operating characteristics (AUROC) curve was analyzed with serum CRP in th ...[more]

PMID: 33371112

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Project description:BackgroundUpper gastrointestinal bleeding is a common presentation in emergency departments and carries significant morbidity worldwide. It is paramount that treating physicians have access to tools that can effectively evaluate the patient risk, allowing quick and effective treatments to ultimately improve their prognosis. This study aims to establish a mortality risk assessment model for patients with acute upper gastrointestinal bleeding at an emergency department.MethodsA total of 991 patients presenting with acute upper gastrointestinal bleeding between July 2016 and June 2019 were enrolled in this retrospective single-center cohort study. Patient demographics, parameters assessed at admission, laboratory test, and clinical interventions were extracted. We used the least absolute shrinkage and selection operator regression to identify predictors for establishing a nomogram for death in the emergency department or within 24 h after leaving the emergency department and a corresponding nomogram. The area under the curve of the model was calculated. A bootstrap resampling method was used to internal validation, and decision curve analysis was applied for evaluate the clinical utility of the model. We also compared our predictive model with other prognostic models, such as AIMS65, Glasgow-Blatchford bleeding score, modified Glasgow-Blatchford bleeding score, and Pre-Endoscopic Rockall Score.ResultsAmong 991 patients, 41 (4.14%) died in the emergency department or within 24 h after leaving the emergency department. Five non-zero coefficient variables (transfusion of plasma, D-dimer, albumin, potassium, age) were filtered by the least absolute shrinkage and selection operator regression analysis and used to establish a predictive model. The area under the curve for the model was 0.847 (95% confidence interval [0.794-0.900]), which is higher than that of previous models for mortality of patients with acute upper gastrointestinal bleeding. The decision curve analysis indicated the clinical usefulness of the model.ConclusionsThe nomogram based on transfusion of plasma, D-dimer, albumin, potassium, and age effectively assessed the prognosis of patients with acute upper gastrointestinal bleeding presenting at the emergency department.

Project description:BackgroundIt is unclear whether an upper gastrointestinal bleed is an isolated gastrointestinal event or an indicator of a deterioration in a patient's overall health status. Therefore, we investigated the excess causes of death in individuals after a non-variceal bleed compared with deaths in a matched sample of the general population.Methods and findingsLinked longitudinal data from the English Hospital Episodes Statistics (HES) data, General Practice Research Database (GPRD), and Office of National Statistics death register were used to define a cohort of non-variceal bleeds between 1997 and 2010. Controls were matched at the start of the study by age, sex, practice, and year. The excess risk of each cause of death in the 5 years subsequent to a bleed was then calculated whilst adjusting for competing risks using cumulative incidence functions. 16,355 patients with a non-variceal upper gastrointestinal bleed were matched to 81,523 controls. The total 5-year risk of death due to gastrointestinal causes (malignant or non-malignant) ranged from 3.6% (≤ 50 years, 95% CI 3.0%-4.3%) to 15.2% (≥ 80 years, 14.2%-16.3%), representing an excess over controls of between 3.6% (3.0%-4.2%) and 13.4% (12.4%-14.5%), respectively. In contrast the total 5-year risk of death due to non-gastrointestinal causes ranged from 4.1% (≤ 50 years, 3.4%-4.8%) to 46.6% (≥ 80 years, 45.2%-48.1%), representing an excess over controls of between 3.8% (3.1%-4.5%) and 19.0% (17.5%-20.6%), respectively. The main limitation of this study was potential misclassification of the exposure and outcome; however, we sought to minimise this by using information derived across multiple linked datasets.ConclusionsDeaths from all causes were increased following an upper gastrointestinal bleed compared to matched controls, and over half the excess risk of death was due to seemingly unrelated co-morbidity. A non-variceal bleed may therefore warrant a careful assessment of co-morbid illness seemingly unrelated to the bleed.

Project description:IntroductionThis 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.MethodsWe 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 11 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.ResultsIn 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 11 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.ConclusionsPatient 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.

Dataset Information

C-reactive protein for simple prediction of mortality in patients with acute non-variceal upper gastrointestinal bleeding: A retrospective analysis.

Abstract

Publications

C-reactive protein for simple prediction of mortality in patients with acute non-variceal upper gastrointestinal bleeding: A retrospective analysis.

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