Comparison of conventional scoring systems versus MAGIC score to predict short-term mortality in patients hospitalized for alcoholic hepatitis

Abstract

Background

Multiple prognostic models are available to predict mortality in alcoholic hepatitis (AH) which are of modest benefit, but the best model remains unexplored.

Methods

This is a retrospective analysis (2012–2015) of AH patients. Conventional prognostic scoring systems viz. Maddrey’s Discriminant Function (mDF), Age Bilirubin International Normalized Ratio and Creatinine (ABIC), Glasgow Alcoholic Hepatitis Score (GAHS), and the Model for End-stage Liver Disease score (MELD), were compared with Model for AH to Grade the Severity in an Asian patient cohort (MAGIC) score, using area under the ROC curves for ascertaining 30/90-day mortality.

Results

Eighty-eight patients (100% male); mean (SD) age of 45.6 (7.6) years with a follow-up of 80.7 (45.1) days were included. The 30 and 90-day mortality were 21 (23.9%) and 42 (47.7%), respectively; the commonest cause being sepsis in 22 (48.9%) patients. Survival probabilities for mDF < 32 and mDF > 32 were 100% and 42.25% ± 4.46%, respectively (p = .001). The mean (SD) scores of mDF, MELD and GAHS were significantly higher in deceased patients 70.8 (26.5), 23.4 (5.2), 8.1 (1.01), respectively, as compared to those who survived 40.8 (23.1), 18.9 (5.1), 7.3 (0.9), respectively; p = .001. ABIC and MAGIC scores were higher among the deceased, but were not significant. mDF had the best predictive AUROC value 0.872, followed by MELD 0.772, and MAGIC 0.626, respectively. mDF was significantly superior in comparison to MAGIC score (p < .001).

Conclusion

This study showed that mDF had a better predictive performance than other scoring systems in patients with AH.

Keywords:

• Alcoholic hepatitis
• mortality
• scoring system
• discriminant function
• outcomes

Acknowledgements

We thank Mr. Jayakumar Parameswaran Pillai for assisting with the statistical analysis for this study. A sincere thanks and appreciation towards Dr. Nagesh Kamat for his manuscript writing assistance, technical and language editing, proofreading and his overall support throughout all aspects of this manuscript submission.

Author contributions

AI and SS: study concept and design. AI, AV, KD and SS were involved in the clinical management of all cases. AI and AV data acquisition. AI drafted the first version of the manuscript. SS and KD: critical revision and provided vital inputs. All the authors reviewed and approved the final version of the manuscript.

Disclosure statement

The authors declare that they have no conflict of interest.