Evaluating Novel Scoring Systems to Predict Outcomes in Acute-on-Chronic Liver Failure
Evaluation of Novel Scores for Prediction of Outcomes in Patients With Acute-on-Chronic Liver Failure
Acute-on-chronic liver failure (ACLF) is a serious condition where a patient with long-term liver disease suddenly experiences severe liver worsening and failure of other organ systems, such as the kidneys or lungs. ACLF is associated with a high risk of short-term death, making early and accurate prediction of patient outcomes essential for guiding medical decisions.
Conventional scoring systems, such as the Child-Pugh and Model for End-Stage Liver Disease Sodium (MELD-Na) scores, are widely used to assess liver disease severity. However, these traditional models may not fully capture the intense systemic inflammation and multiorgan failure characteristic of ACLF. Recently, novel prognostic scoring models, specifically the CLIF-C ACLF-D score and the MELD-complication score, have been developed to better predict patient outcomes by integrating markers of inflammation, organ dysfunction, and clinical complications.
This study aims to evaluate and validate the clinical usefulness of these novel scoring models compared to conventional scores in predicting in-hospital and 28-day mortality among patients admitted with ACLF.
Participants enrolled in this prospective observational study will be recruited from the Intensive Care Unit (ICU) of the Tropical Medicine and Gastroenterology Department at Al-Rajhi University Hospital. Upon admission, participants will undergo routine clinical examinations, laboratory testing, and imaging. The various risk scores will be calculated at baseline. Participants will be monitored throughout their hospital stay, and their survival status will be followed for 28 days after admission to compare the accuracy of each scoring system.
Studienübersicht
Status
Detaillierte Beschreibung
Acute decompensation in liver cirrhosis is the primary cause of hospitalization among cirrhotic patients. Acute-on-chronic liver failure (ACLF) represents a distinct, severe clinical entity characterized by systemic inflammation, single- or multiple-organ failures, and high short-term mortality. While traditional scoring systems like the Child-Pugh and Model for End-Stage Liver Disease Sodium (MELD-Na) scores are routinely utilized, they may fail to capture the complex inflammatory response and multiorgan involvement inherent to ACLF.
To improve prognostic estimation, novel predictive tools, including the CLIF-C ACLF-D score and the MELD-complication score, have been introduced, incorporating inflammatory parameters and complication markers. Validation of these prognostic models across distinct patient populations is necessary prior to broad implementation. This study evaluates the prognostic performance of these novel models in patients with ACLF admitted to the Intensive Care Unit (ICU).
All enrolled participants will undergo systematic evaluation upon admission:
- Clinical and Demographic Assessment: Full patient history (demographics, etiology of underlying chronic liver disease, previous episodes of decompensation, drug history) and comprehensive physical examination (vital signs, grade of hepatic encephalopathy, jaundice, asterixis, edema, and clinical signs of infection or ascites/pleural effusion).
- Laboratory Diagnostics: Complete blood count with differential, liver function tests (total and direct bilirubin, serum albumin), renal function parameters (serum creatinine, blood urea nitrogen), coagulation profile (prothrombin time, INR), serum electrolytes (sodium, potassium), C-reactive protein (CRP), arterial blood gas analysis, viral hepatitis markers (HBsAg, anti-HCV), ascitic fluid analysis (if applicable), and biological cultures from suspected infectious foci.
- Imaging: Abdominal ultrasonography (evaluating hepatic architecture, portal vein parameters, splenic dimensions, collateral circulation, biliary structure, renal status, ascites) and chest radiography.
- Scoring Calculation: Baseline risk scores-including Child-Pugh, MELD-Na, CLIF-C OF, CLIF-C ACLF, CLIF-C ACLF-D, and MELD-complication scores-will be calculated at admission using standard mathematical formulas and validated online platforms.
Participants will be followed throughout their hospital stay and up to 28 days following admission. Survival outcomes will be confirmed in-hospital or post-discharge via telephone follow-up or clinical visits.
Studientyp
Einschreibung (Geschätzt)
Teilnahmekriterien
Zulassungskriterien
Studienberechtigtes Alter
- Erwachsene
- Älterer Erwachsener
Akzeptiert gesunde Freiwillige
Probenahmeverfahren
Studienpopulation
Beschreibung
Inclusion Criteria:
- Patients aged 18 years or older.
- Diagnosed with Acute-on-Chronic Liver Failure (ACLF) fulfilling the diagnostic criteria according to EASL-CLIF.
- Admitted to the Intensive Care Unit (ICU) of the Tropical Medicine and Gastroenterology Department, Al-Rajhi University Hospital.
Exclusion Criteria:
- Patients with hepatocellular carcinoma.
- Patients with advanced extrahepatic malignancy.
- Pregnant females.
- Patients younger than 18 years old.
- Patients with incomplete clinical or laboratory data.
- Patients or first-degree relatives refusing participation.
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Kohorten und Interventionen
Gruppe / Kohorte |
|---|
|
ACLF Patients
Patients aged 18 years or older diagnosed with Acute-on-Chronic Liver Failure (ACLF) according to EASL-CLIF criteria and admitted to the Intensive Care Unit of the Tropical Medicine and Gastroenterology Department at Al-Rajhi University Hospital.
All patients will undergo full clinical, laboratory, and radiological evaluations at baseline, followed by calculation of novel (CLIF-C ACLF-D, MELD-complication) and conventional (Child-Pugh, MELD-Na, CLIF-C ACLF) prognostic scores to predict short-term outcomes.
|
Was misst die Studie?
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
28-Day All-Cause Mortality Rate
Zeitfenster: 28 days post-hospital admission
|
Percentage of participants with Acute-on-Chronic Liver Failure (ACLF) who die from any cause within 28 days following hospital admission.
Survival status will be evaluated during the hospital stay and confirmed post-discharge via clinic visits or telephone follow-up.
|
28 days post-hospital admission
|
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
|---|---|---|
|
In-Hospital All-Cause Mortality Rate
Zeitfenster: From date of hospital admission up to hospital discharge, assessed up to 28 days
|
Percentage of participants with Acute-on-Chronic Liver Failure (ACLF) who die from any cause during their hospital stay.
|
From date of hospital admission up to hospital discharge, assessed up to 28 days
|
|
Prognostic Performance of the CLIF-C ACLF-D Score for 28-Day Mortality
Zeitfenster: Baseline (day 1 of admission) up to 28 days
|
Predictive accuracy of the novel CLIF-C ACLF-D score measured by the Area Under the Receiver Operating Characteristic Curve (AUROC) to predict 28-day all-cause mortality.
|
Baseline (day 1 of admission) up to 28 days
|
Mitarbeiter und Ermittler
Sponsor
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Geschätzt)
Primärer Abschluss (Geschätzt)
Studienabschluss (Geschätzt)
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Zusätzliche relevante MeSH-Bedingungen
Andere Studien-ID-Nummern
- Predicting Outcomes in ACLF
Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .