Dataset Information

Treatment for hepatorenal syndrome in people with decompensated liver cirrhosis: a network meta-analysis.

ABSTRACT:

SUBMITTER: Best LM

PROVIDER: S-EPMC6740336 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Treatment for hepatorenal syndrome in people with decompensated liver cirrhosis: a network meta-analysis.

Best Lawrence Mj LM Freeman Suzanne C SC Sutton Alex J AJ Cooper Nicola J NJ Tng Eng-Loon EL Csenar Mario M Hawkins Neil N Pavlov Chavdar S CS Davidson Brian R BR Thorburn Douglas D Cowlin Maxine M Milne Elisabeth Jane EJ Tsochatzis Emmanuel E Gurusamy Kurinchi Selvan KS

The Cochrane database of systematic reviews 20190912

<h4>Background</h4>Hepatorenal syndrome is defined as renal failure in people with cirrhosis in the absence of other causes. In addition to supportive treatment such as albumin to restore fluid balance, the other potential treatments include systemic vasoconstrictor drugs (such as vasopressin analogues or noradrenaline), renal vasodilator drugs (such as dopamine), transjugular intrahepatic portosystemic shunt (TIPS), and liver support with molecular adsorbent recirculating system (MARS). There i ...[more]

PMID: 31513287

Similar Datasets

Project description:Background and aimsHepatorenal syndrome - Acute Kidney Injury (HRS-AKI) is a severe complication of decompensated cirrhosis that is challenging to predict. Sentiment analysis, a computational process of identifying and categorizing opinions and judgment expressed in text, may enhance traditional prediction methodologies based on structured variables. Large language models (LLMs), such as generative pretrained transformers (GPTs), have demonstrated abilities to perform sentiment analyses on non-clinical texts. We sought to determine if GPT-performed sentiment analysis could improve upon predictions using clinical covariates alone in the prediction of HRS-AKI.MethodsAdult patients admitted to a single academic medical center with decompensated cirrhosis and AKI. We used a protected health information (PHI) compliant version of Microsoft Azure OpenAI GPT-4o to derive a sentiment score ranging from 0 to 1 for HRS-AKI, and conduct natural language processing (NLP) extraction of clinical terms associated with HRS-AKI in clinical notes. The area under the receiver operator curve (AUROC) was compared in logistic regression models incorporating structured variables (socio-demographics, MELD 3.0, hemodynamic parameters) with compared to without sentiment scores and NLP-extracted clinical terms.ResultsIn our cohort of 314 participants, higher sentiment score was associated with the diagnosis of HRS-AKI (OR 1.33 per 0.1, 95% CI 1.02-1.79) in multivariate models. AUROC of the baseline model using structured clinical covariates alone was 0.639. With the addition of the GPT-4o derived sentiment score and clinical terms to structured covariates, the final model yielded an improved AUROC of 0.758 (p=0.03).ConclusionsClinical texts contain large amounts of data that are currently difficult to extract using standard methodologies. Sentiment analysis and NLP-based variable derivation with GPT-4o in clinical application is feasible and can improve the prediction of HRS-AKI over traditional modeling methodologies alone.

Dataset Information

Treatment for hepatorenal syndrome in people with decompensated liver cirrhosis: a network meta-analysis.

Publications

Treatment for hepatorenal syndrome in people with decompensated liver cirrhosis: a network meta-analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets