Hypoxic hepatitis in cardiac intensive care unit: a study of cardiovascular risk factors, clinical course, and outcomes

Chávez-Tapia, Norberto C.; Balderas-Garces, Brenda; Meza-Meneses, Patricia; Herrera-Gomar, Magali; García-López, Sandra; González-Chon, Octavio; Uribe, Misael

doi:10.2147/tcrm.s59312

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…MV and high lactate levels are also manifestations of hypoxia’s impact on hemodynamics in HH patients. A higher proportion of patients in the non-survivor group required MV on the first day of admission, and the use of MV increased mortality risk, a finding that was also reported by Chavez Tapia et al ( 46 ). Typically, lactate elevation results from improved anaerobic metabolic processes triggered by tissue hypoxia, which can be observed in many severe diseases and is strongly associated with prognosis.…”

Section: Discussionsupporting

confidence: 75%

Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model

Sun

Wang²,

Jiang³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundThis study aimed to explore the impact of hypoxic hepatitis (HH) on survival in heart failure (HF) patients and to develop an effective machine learning model to predict 30-day mortality risk in HF patients with HH.MethodsIn the Medical Information Mart for Intensive Care (MIMIC)-III and IV databases, clinical data and survival situations of HF patients admitted to the intensive care unit (ICU) were retrospectively collected. Propensity Score Matching (PSM) analysis was used to balance baseline differences between HF patients with and without HH. Kaplan Meier analysis and multivariate Cox analysis were used to determining the effect of HH on the survival of CF patients. For developing a model that can predict 30-day mortality in CF patients with HH, the feature recurrence elimination (RFE) method was applied to feature selection, and seven machine learning algorithms were employed to model construction. After training and hyper-parameter optimization (HPO) of the model through cross-validation in the training set, a performance comparison was performed through internal and external validation. To interpret the optimal model, Shapley Additive Explanations (SHAP) were used along with the Local Interpretable Model-agnostic Explanations (LIME) and the Partial Dependence Plot (PDP) techniques.ResultsThe incidence of HH was 6.5% in HF patients in the MIMIC cohort. HF patients with HH had a 30-day mortality rate of 33% and a 1-year mortality rate of 51%, and HH was an independent risk factor for increased short-term and long-term mortality risk in HF patients. After RFE, 21 key features (21/56) were selected to build the model. Internal validation and external validation suggested that Categorical Boosting (Catboost) had a higher discriminatory capability than the other models (internal validation: AUC, 0.832; 95% CI, 0.819–0.845; external validation: AUC, 0.757 95% CI, 0.739–0.776), and the simplified Catboost model (S-Catboost) also had good performance in both internal validation and external validation (internal validation: AUC, 0.801; 95% CI, 0.787–0.813; external validation: AUC, 0.729, 95% CI, 0.711–0.745).ConclusionHH was associated with increased mortality in HF patients. Machine learning methods had good performance in identifying the 30-day mortality risk of HF with HH. With interpretability techniques, the transparency of machine learning models has been enhanced to facilitate user understanding of the prediction results.

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Section: Discussionsupporting

confidence: 75%

Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model

Sun

Wang²,

Jiang³

et al. 2022

Front. Cardiovasc. Med.

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“…In some cases, patients will require mechanical cardiac support via intraaortic balloon pump or a ventricular assist device. (42) Additional research is needed to define the optimal pressor, timing of diuresis and the cohort that would benefit most from mechanical cardiac support.…”

Section: Discussionmentioning

confidence: 99%

The Incidence and Outcomes of Ischemic Hepatitis: A Systematic Review with Meta-analysis

Tapper

Sengupta

Bonder

2015

The American Journal of Medicine

130

120

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“…Shock liver can influence the overall mortality rate in ICU patients (9). The mortality rate of patients with hypoxic hepatitis in cardiac intensive care unit is high and associated with ventilator disturbances ( 7 , 11 , 23 , 36 , 37 ). Acute and chronic heart failure are other risk factors for hypoxic hepatitis ( 4 ).…”

Section: Resultsmentioning

confidence: 99%

“…Critically low levels of oxygen for metabolic processes cause hepatocellular dysfunction ( 4 ) and is frequently observed in critically ill patients ( 6 , 9 ) and is associated with high mortality and morbidity ( 6 , 19 ). Hypoxic hepatitis is detected frequently in intensive care units ( 6 , 7 ). Most cases of hypoxic hepatitis occur following cardiac failure, respiratory failure and septic shock ( 8 , 20 , 21 , 24 , 25 ).…”

Section: Contextmentioning

confidence: 99%

Hepatic Shock Differential Diagnosis and Risk Factors: A Review Article

et al. 2015

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Context:Liver as an important organ has a vital role in physiological processes in the body. Different causes can disrupt normal function of liver. Factors such as hypo-perfusion, hypoxemia, infections and some others can cause hepatic injury and hepatic shock.Evidence Acquisition:Published research resources from 2002 to May 2015 in some databases (PubMed, Scopus, Index Copernicus, DOAJ, EBSCO-CINAHL, Science direct, Cochrane library and Google scholar and Iranian search database like SID and Iranmedex) were investigated for the present study.Results:Different causes can lead to hepatic shock. Most of these causes can be prevented by early resuscitation and treatment of underlying factors.Conclusions:Hepatic shock is detected in ill patients, especially those with hemodynamic disorders. It can be prevented by early treatment of underlying disease. There is no definite treatment for hepatic shock and should be managed conservatively. Hepatic shock in patients can increase the mortality rate.

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Hypoxic hepatitis in cardiac intensive care unit: a study of cardiovascular risk factors, clinical course, and outcomes

Cited by 13 publications

References 15 publications

Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model

Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model

The Incidence and Outcomes of Ischemic Hepatitis: A Systematic Review with Meta-analysis

Hepatic Shock Differential Diagnosis and Risk Factors: A Review Article

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