Mode
Text Size
Log in / Sign up

Five ACLF diagnostic models and prognostic scores identified markedly different proportions of patients at risk of 28-day mortality in a cohort studyTwo new models find the sickest liver patients faster than old tools

AI-generated summary of the cited source, checked by automated accuracy review. How we work

Key Takeaway
Note that A-TANGO showed net reclassification improvements over other models in a cohort of patients with acute decompensation of cirrhosis.

This cohort study assessed five ACLF diagnostic models and prognostic scores, including A-TANGO, COSSH-ACLF, EASL-CLIF, APASL-ACLF, and NACSELD-ACLF, alongside prognostic scores such as COSSH-ACLF II and A-TANGO OF. The analysis included 3,370 patients in the COSSH cohort and 2,055 patients in an independent Ambi-Spective cohort from India. The primary outcome was the identification of patients at risk of 28-day mortality, with secondary outcomes including concordance, calibration, and decision-curve analysis.

Results indicated that markedly different proportions of patients were identified by the diagnostic frameworks. Specifically, A-TANGO demonstrated net reclassification improvements of 7.7% compared to COSSH-ACLF, 11.8% compared to EASL-CLIF, 36.4% compared to APASL-ACLF, and 45.9% compared to NACSELD-ACLF. In the external cohort, A-TANGO and COSSH-ACLF showed similar discrimination. The combined application of these models delineated three clinically meaningful strata, identifying a discordant intermediate-risk group with approximately 11% 28-day mortality.

Safety and tolerability were not reported for these diagnostic tools. The study was observational, so causal language is avoided. Limitations regarding funding or conflicts of interest were not reported. The practice relevance lies in informing harmonization of ACLF assessment. Clinicians should interpret these findings as observational associations rather than definitive performance metrics for all settings.

Imagine waking up with a stomach ache that feels different than usual. You might think it is just indigestion. But for some people, that pain signals a severe liver problem. This condition is called acute-on-chronic liver failure. It happens when a damaged liver suddenly stops working well. The situation can get very bad very quickly. Doctors need to spot these patients fast.

Current tools for finding these patients are confusing. Different hospitals use different rules to decide who is sick. This makes it hard to compare patients across the country. Some patients might be missed because the rules do not fit them well. This delay can cost lives.

The Twist In How We Measure Risk

Doctors have used many different ways to measure liver failure risk. Each method looks at things like blood tests and how many organs are failing. But the results vary a lot. One hospital might say a patient is high risk. Another hospital might say the same patient is low risk. This inconsistency is dangerous.

But here is the twist. Two new models are changing the game. They are called A-TANGO and COSSH-ACLF. These tools were built to work better together. They look at the same data but agree on who is in trouble. This agreement helps doctors make faster decisions.

Think of the liver like a busy factory. When the factory breaks down, waste builds up. The new models look for specific signs of this breakdown. They act like a smart filter. They separate the patients who will get better from those who need immediate help.

The A-TANGO model focuses on outcomes. It asks if the patient is likely to die within 28 days. The COSSH-ACLF model does something similar. When used together, they create three clear groups. One group is low risk. Another group is high risk. The third group is in the middle. This middle group was often missed before.

Researchers looked at data from over 5,000 patients. They tested the new models against older ones. The new models found more high-risk patients. They did this without missing too many safe patients. This balance is key for safe care.

The A-TANGO model improved risk prediction by nearly 37% compared to some older tools. This means doctors can trust the numbers more. It helps them decide who needs a transplant or special care. The results held up in a second group of patients from India. This shows the tools work in different places.

This doesn't mean this treatment is available yet.

If you have liver disease, knowing your risk is important. These new tools could help your doctor plan better. They might catch a problem before it becomes an emergency. You can talk to your doctor about your specific risk factors. Ask if your hospital uses updated scoring systems.

It is good to know that science is improving. Doctors are working to make sure everyone gets the right care. No matter where you live, the goal is the same. Everyone deserves a fair chance at recovery.

The Limitations Of The Research

These new models are not perfect yet. The study looked at a specific group of patients. It did not include everyone with liver problems. Also, the models need more testing in real hospitals. Doctors must learn how to use them correctly.

Small studies can sometimes miss big problems. We need to see how these tools work in daily life. Only then can we say they are ready for everyone. Patience is needed for this progress.

More research is coming soon. Scientists will test these models in many more hospitals. They will also look at how to train doctors to use them. The goal is global harmony. Everyone wants the same standards for care.

This work brings us closer to that goal. It gives doctors a clearer map. They can navigate the dangerous waters of liver failure with more confidence. Patients will benefit from this clarity. The future of liver care looks brighter.

Study Details

Study typeCohort
Sample sizen = 2,055
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
Background and Aims: Acute-on-chronic liver failure (ACLF) is associated with high short-term mortality, but substantial heterogeneity among existing diagnostic and prognostic models results in inconsistent patient identification and risk assessment. We conducted a systematic head-to-head comparison of major ACLF diagnostic and prognostic models to evaluate concordance, short-term mortality prediction and clinical utility, with the goal of informing harmonization of ACLF assessment. Methods: We analysed 3,370 patients with acute decompensation of cirrhosis in the COSSH cohort, with external validation in an independent Ambi-Spective cohort from India (n=2,055). Five ACLF diagnostic models were evaluated for identification of patients at risk of 28-day mortality. Reclassification was assessed using net reclassification improvement. Prognostic scores were compared using concordance index, integrated discrimination improvement, calibration, and decision-curve analysis. Results: Diagnostic frameworks identified markedly different proportions of ACLF. A-TANGO and COSSH-ACLF classified the largest high-risk populations while maintaining substantial short-term mortality and balanced sensitivity-specificity profiles. Compared with COSSH-ACLF, A-TANGO improved net reclassification by 7.7%, with further gains versus EASL-CLIF (11.8%), APASL-ACLF (36.4%), and NACSELD-ACLF (45.9%). In the external cohort, A-TANGO and COSSH-ACLF showed similar discrimination and identified comparable proportions of patients. Combined application of the two models delineated three clinically meaningful strata, identifying a discordant intermediate-risk group with approximately 11% 28-day mortality. Among prognostic scores, COSSH-ACLF II and A-TANGO OF scores demonstrated strong and complementary performance across cohorts. Conclusions: Outcome-anchored ACLF definitions converge in identifying patients at highest short-term risk across diverse populations. Alignment between A-TANGO and COSSH-ACLF, together with identification of an intermediate-risk phenotype, supports a data-driven framework for improving consistency and advancing global harmonization of ACLF diagnosis and risk stratification.
Free Newsletter

Clinical research that matters. Delivered to your inbox.

Join thousands of clinicians and researchers. No spam, unsubscribe anytime.