A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices

Xu, Fengshuo; Zhang, Luming; Wang, Zichen; Han, Didi; Li, Chengzhuo; Zheng, Shuai; Yin, Haiyan; Lyu, Jun

doi:10.3389/fmed.2021.678646

Cited by 14 publications

(7 citation statements)

References 37 publications

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“…The maximum value of lambda when the cross-validation error was within one standard error of the minimum value was chosen. In accordance with previous studies, the probability threshold for entry was 0.05 and the threshold for removal was 0.10 in this step (Xu et al, 2021). The selected variables were then subjected to multivariate logistic regression analysis in order to identify independent prognostic factors, and the results were denoted as odds ratios (ORs) and 95% confidence intervals (CIs).…”

Section: Discussionmentioning

confidence: 95%

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

et al. 2022

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BackgroundIntracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.MethodsICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).ResultsA total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.ConclusionThis study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

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Section: Discussionmentioning

confidence: 95%

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

et al. 2022

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“…However, increased AG is an independent risk factor for in-hospital mortality. AG is influenced by more indicators, the most common one leading to increased AG is metabolic acidosis, which implies the overproduction of organic acids, such as the accumulation of lactic acid 25 . It is hypothesized that this is related to an excess production of lactic acid due to hypoxia and shock caused by infection and a decrease in lactic acid clearance due to impaired hepatic clearance of lactic acid 26 .…”

Section: Discussionmentioning

confidence: 99%

Development of a nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis

Lin,

Liao,

Lin

et al. 2024

Sci Rep

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In this study, we aimed to investigate the risk factors associated with in-hospital mortality in patients with cirrhosis and sepsis, establish and validate the nomogram. This retrospective study included patients diagnosed with liver cirrhosis and sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV). Models were compared by the area under the curve (AUC), integrated discriminant improvement (IDI), net reclassification index (NRI) and decision curve analysis (DCA). A total of 1,696 patients with cirrhosis and sepsis were included in the final cohort. Our final model included the following 9 variables: age, heartrate, total bilirubin (TBIL), glucose, sodium, anion gap (AG), fungal infections, mechanical ventilation, and vasopressin. The nomogram were constructed based on these variables. The AUC values of the nomograms were 0.805 (95% CI 0.776–0.833), which provided significantly higher discrimination compared to that of SOFA score [0.684 (95% CI 0.647–0.720)], MELD-Na [0.672 (95% CI 0.636–0.709)] and ABIC [0.674(95% CI 0.638–0.710)]. We established the first nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis based on these factors. This nomogram can performs well and facilitates clinicians to identify people at high risk of in-hospital mortality.

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“…Patients in the eICU database were used as validation cohort. The MIMIC-IV database is an updated version of the MIMIC-III database containing comprehensive and high-quality data on patients admitted to the ICU at Beth Israel Deaconess Medical Center (Boston, MA) between 2008 and 2019 (15,16). The eICU Collaborative Research Database is a multi-center ICU database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States (17).…”

Section: Study Populationmentioning

confidence: 99%

Identification of Subphenotypes of Sepsis-Associated Liver Dysfunction Using Cluster Analysis

He¹,

Cui²,

Guo³

et al. 2022

Shock

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We attempted to identify and validate the subphenotypes of sepsis-associated liver dysfunction (SALD) using routine clinical information. Design: This article is a retrospective observational cohort study. Setting: We used the Medical Information Mart for Intensive Care IV database and the eICU Collaborative Research Database. Patients: We included adult patients (age ≥18 years) who developed SALD within the first 48 hours of intensive care unit (ICU) admission. We excluded patients who died or were discharged from the ICU within the first 48 hours of admission. Patients with abnormal liver function before ICU admission were also excluded. Measurements and Main Results: Patients in the MIMIC-IV 1.0 database served as a derivation cohort. Patients in the eICU database were used as validation cohort. We identified four subphenotypes of SALD (subphenotype α, β, γ, δ) using K-means cluster analysis in 5234 patients in derivation cohort. The baseline characteristics and clinical outcomes were compared between the phenotypes using one-way analysis of variance/Kruskal-Wallis test and the χ 2 test. Moreover, we used line charts to illustrate the trend of liver function parameters over 14 days after ICU admission. Subphenotype α (n = 1,055) was the most severe cluster, characterized by shock with multiple organ dysfunction (MODS) group. Subphenotype β (n = 1,179) had the highest median bilirubin level and the highest proportion of patients with underlying liver disease and coexisting coagulopathy (increased bilirubin group). Subphenotype γ (n = 1,661) was the cluster with the highest mean age and had the highest proportion of patients with chronic kidney disease (aged group). Subphenotype δ (n = 1,683) had the lowest 28-day and in-hospital mortality (mild group). The characteristics of clusters in the validation cohort were similar to those in the derivation cohort. In addition, we were surprised to find that GGT levels in subphenotype δ were significantly higher than in other subphenotypes, showing a different pattern from bilirubin. Conclusions: We identified four subphenotypes of SALD that presented with different clinical features and outcomes. These results can provide a valuable reference for understanding the clinical characteristics and associated outcomes to improve the management of patients with SALD in the ICU.

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A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices

Cited by 14 publications

References 37 publications

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

Development of a nomogram for predicting in-hospital mortality in patients with liver cirrhosis and sepsis

Identification of Subphenotypes of Sepsis-Associated Liver Dysfunction Using Cluster Analysis

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