In the clinical arena, it is frequently important to discern whether steatohepatitic liver injury is related to ALD or NAFLD as this distinction may influence patient management and candidacy for liver transplantation6-9
. Despite a number of novel and sometimes expensive biomarkers that have been developed and evaluated, most of these have not been reproducibly demonstrated to perform more effectively than more traditional and inexpensive laboratory values21, 22
. Unfortunately, these laboratory values have evidenced limited sensitivity and specificity as well13, 22
. In the present studies, we generated a formula derived from a composite set of independent predictors after correction for disease severity, which comprise two commonly ordered and inexpensive laboratory values (AST/ALT ratio, MCV) with two objective clinical parameters (BMI, gender). This model, the ANI was then validated in complimentary patient cohorts including hospitalized, ambulatory and pre-transplant patients and found to compare favorably to several other traditional and proposed biomarkers. The ANI maintains important and unique methodologic aspects that enhance its utility as compared to prior studies designed to predict ALD. First, the ANI is adjusted for disease severity; prior studies have indicated that the validity of clinical predictors of ALD including AST/ALT ratio23, 24
, CDT, GGT25
, and histology26
are confounded by disease severity. ALD patients frequently present with more severe liver disease as compared to NAFLD patients, which often present as asymptomatic liver enzyme elevations10, 27
, and thus adjustment for disease severity in the derivation of the ANI is important. A second methodologic advantage of our study is the use of logistic regression which has facilitated appropriate weighting of the parameters that comprise the ANI. This probably accounts for the improved test characteristics of ANI as compared to prior analyses which analyzed individual parameters that comprise the ANI such as AST/ALT ratio23, 24
. Thirdly, the ANI has been derived using multiple complimentary and previously validated patient cohorts14-16, 18
including inpatients, outpatients, variations in disease severity and clinical status, collected at different points in time. Fourthly, the present study compares and validates the proposed ANI to other proposed biomarkers in a head-to-head manner. Thus, the ANI model fulfills many criteria for ideal models.
What caveats should we consider when utilizing the ANI in clinical practice? First, the gold standard of determining ALD from NAFLD remains a thorough interrogation of alcohol consumption history from the patient with corroboration from relatives and friends. However, the ANI may provide an auxiliary tool in the diagnosis, especially in the common scenarios of underreported and surreptitious alcohol consumption. Second, the ANI is a continuous variable. While we used an ANI of 0 as a cut-point to calculate sensitivity and specificity, the magnitude of the ANI must be considered in diagnosis of ALD in individual patients (see for examples). Third, it is well recognized that some patients that consume excess alcohol have features of metabolic syndrome as well, making it difficult to ascribe steatohepatitis to alcohol alone28
. A negative ANI makes ALD unlikely and suggests a diagnosis of NAFLD, however a positive ANI, while indicating the presence of ALD, does not exclude co-existing metabolic syndrome. Fourth, the ANI may be less reliable in patients with cirrhosis and MELD Score > 20 as noted in Validation Sample 3. This may be due to the elevation in MCV and AST/ALT ratio that are frequently observed in cirrhosis independent of an alcoholic etiology. Lastly, other liver diseases should be excluded before utilizing the ANI as such patients have not been included in the derivation analysis. In the future, it will be of interest to determine whether our results which focus on distinguishing an alcohol basis to a histologic pattern of steatohepatitis can also be extrapolated to radiographic cases of fatty liver, as clinical evaluations in practice settings often do not progress to biopsy after radiographic ascertainment of fatty liver.
Interestingly, short-term abstinence did not significantly affect the performance characteristics of the ANI, thereby providing utility to this model in clinical scenarios in which patients are abstinent but the etiology of liver damage is still relevant (ie; evaluation for liver transplantation). Whereas the ANI is highly accurate in distinguishing ALD from NAFLD regardless of recent alcohol consumption, it should be noted that the ANI is unlikely to be useful in detecting surreptious alcohol consumption in patients with known ALD. However, this conclusion must be tempered by the understanding that alcohol consumption in our study was not assessed by an objective score to ensure its accuracy. Another limitation of the retrospective nature of the present study related to the GGT. The GGT appeared to be a promising candidate as an independent predictor in our model as it had the highest c-statistic in univariate analysis in which 75 patients had GGT available. Unfortunately this variable had to be excluded from the multivariate analysis because most patients did not have an available GGT value. However, future prospective studies that might be conducted to validate the ANI or evaluate alternative models, may well find that the GGT is a predictive variable.
In conclusion, we have developed a novel scoring system that is highly accurate in distinguishing ALD from NAFLD. The ANI may be a useful tool for the frequent clinical scenarios in which it is useful to ascertain an alcohol basis for steatohepatitic liver injury.