Dataset Information

Hepatorenal Syndrome.

ABSTRACT: Hepatorenal syndrome (HRS) is the most serious hepatorenal disorder and one of the most difficult to treat. To date, the best treatment options are those that reverse the mechanisms underlying HRS: portal hypertension, splanchnic vasodilation, and/or renal vasoconstriction. Therefore, liver transplantation is the preferred definitive treatment option. The role of other therapies is predominantly to prolong survival sufficiently to allow patients to undergo transplantation. Terlipressin with the addition of adjunctive albumin volume expansion is the preferred pharmacologic therapy for the treatment of patients with HRS. Norepinephrine and vasopressin are acceptable alternatives in countries where terlipressin is not yet available. For patients with Type II HRS, midodrine plus octreotide appears to be an effective pharmacologic regimen that can be administered outside of an intensive care unit setting. Regardless of chosen vasoconstrictor therapy, careful monitoring is needed to ensure tissue ischemia and severe adverse effects do not occur. Artificial hepatic support devices, renal replacement therapy, and transjugular intrahepatic portosystemic shunt (TIPS) are non-pharmacologic options for patients with HRS. However, hepatic support devices and renal replacement therapies have not yet demonstrated improved outcomes and TIPS is difficult to be employed in patients with Type I HRS due to contraindications in the majority of patients. Despite advances in our understanding of hepatorenal syndrome, the disease is still associated with significant morbidity, mortality, and costs. More evidence is urgently needed to help improve patient outcomes in this difficult-to-treat population.

SUBMITTER: Kiser TH

PROVIDER: S-EPMC7153775 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Hepatorenal Syndrome.

Kiser Tyree H TH

International journal of clinical medicine 20140101 3

Hepatorenal syndrome (HRS) is the most serious hepatorenal disorder and one of the most difficult to treat. To date, the best treatment options are those that reverse the mechanisms underlying HRS: portal hypertension, splanchnic vasodilation, and/or renal vasoconstriction. Therefore, liver transplantation is the preferred definitive treatment option. The role of other therapies is predominantly to prolong survival sufficiently to allow patients to undergo transplantation. Terlipressin with the ...[more]

PMID: 32284909

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Project description:OBJECTIVE:Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS:A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS:The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION:This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.

Dataset Information

Hepatorenal Syndrome.

Publications

Hepatorenal Syndrome.

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