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The NAFLD Decompensation Risk Score: External Validation and Comparison to Existing Models to Predict Hepatic Events in a Retrospective Cohort Study

Published:November 12, 2022DOI:https://doi.org/10.1016/j.jceh.2022.11.005

      Abstract

      Background

      The NAFLD Decompensation Risk Score (the Iowa Model) was recently developed to identify patients with nonalcoholic fatty liver disease (NAFLD) at highest risk of developing hepatic events using three variables—age, platelet count, and diabetes.

      Aims

      We performed an external validation of the Iowa Model and compared it to existing non-invasive models.

      Methods

      We included 249 patients with NAFLD at Boston Medical Center, Boston, MA in the external validation cohort and 949 patients in the combined internal/external validation cohort. The primary outcome was the development of hepatic events (ascites, hepatic encephalopathy, esophageal or gastric varices, or hepatocellular carcinoma). We used Cox proportional hazards to analyze the ability of the Iowa Model to predict hepatic events in the external validation. We compared the performance of the Iowa Model to the AST-to-platelet count Ratio Index (APRI), NAFLD Fibrosis Score (NFS), and the FIB-4 Index in the combined cohort.

      Results

      The Iowa Model significantly predicted the development of hepatic events with hazard ratio of 2.5 (95% confidence interval [CI] 1.7-3.9, p<0.001), and area under the receiver operating curve (AUROC) of 0.87 (CI 0.83—0.91). The AUROC of the Iowa Model (0.88, CI: 0.85-0.92) was comparable to the FIB-4 Index (0.87, CI: 0.83-0.91) and higher than NFS (0.66, CI: 0.63-0.69) and APRI (0.76, CI: 0.73-0.79).

      Conclusions

      In an urban, racially and ethnically diverse population, the Iowa Model performed well to identify NAFLD patients at higher risk for liver-related complications. The model provides the individual probability of developing hepatic events identifies patients in need of early intervention.

      Graphical abstract

      Keywords

      List of Abbreviations:

      NAFLD
      nonalcoholic fatty liver disease;
      NASH
      nonalcoholic steatohepatitis;
      HCV
      hepatitis C infection;
      ALD
      alcoholic liver disease;
      VCTE
      vibration-controlled transient elastography;
      APRI
      AST-to-Platelet Ratio Index;
      AASLD
      the American Association for the Study of Liver Disease;
      CT
      computed tomography;
      A1AT
      alpha-1-antitrypsin;
      BMI
      body mass index;
      ALT
      alanine aminotransferase;
      AST
      aspartate aminotransferase;
      HE
      hepatic encephalopathy;
      AUROC
      area under the receiver operating characteristic;
      SAS
      Statistical Analysis Software

      Introduction:

      Nonalcoholic fatty liver disease (NAFLD) is rapidly becoming one of the most common liver disorders worldwide, with prevalence in the US and Europe ranging from 21-24%, and up to 31-32% in South America and the Middle East (
      • Younossi Z.
      • Quentin M.A.
      • Marietti M.
      • et al.
      Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention.
      ,
      • Browning J.D.
      • Szczepaniak L.S.
      • Dobbins R.
      • et al.
      Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity.
      ). While some patients with NAFLD have a good prognosis and do not progress to advanced liver disease (
      • Vernon G.
      • Baranova A.
      • Younossi Z.M.
      Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults.
      ,
      • Wang X.J.
      • Malhi H.
      Nonalcoholic Fatty Liver Disease.
      ) up to 20% of patients with nonalcoholic steatohepatitis (NASH) experience disease progression and develop cirrhosis with liver-related complications (
      • Bhala N.
      • Angulo P.
      • van der Poorten D.
      • et al.
      The natural history of nonalcoholic fatty liver disease with advanced fibrosis or cirrhosis: an international collaborative study.
      ). NAFLD is now becoming one of the leading indications for liver transplantation in the US and is projected to overtake chronic hepatitis C infection (HCV) and alcoholic liver disease (ALD) (
      • Wong R.J.
      • Aguilar M.
      • Cheung R.
      • et al.
      Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting transplantation in the United States.
      ). Recent data trends forecast a further exacerbation of the donor shortage for liver transplantation, and the 1-year probability of receiving a liver transplant is lower in patients with NAFLD than those with HCV or ALD, raising the need to identify patients at highest risk of developing hepatic decompensation and preventing disease progression (
      • Wong R.J.
      • Aguilar M.
      • Cheung R.
      • et al.
      Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting transplantation in the United States.
      ,
      • Hussain A.
      • Patel P.J.
      • Rhodes F.
      • et al.
      Decompensated cirrhosis is the commonest presentation for NAFLD patients undergoing liver transplant assessment.
      ).
      The degree of hepatic fibrosis is one of the most significant predictors of hepatic decompensation, but the current gold-standard of liver biopsy is invasive, associated with sampling error, and not routinely performed (
      • Angulo P.
      • Kleiner D.E.
      • Dam-Larsen S.
      • et al.
      Liver Fibrosis, but No Other Histologic Features, Is Associated With Long-term Outcomes of Patients With Nonalcoholic Fatty Liver Disease.
      ,
      • Al Knawy B.
      • Shiffman M.
      Percutaneous liver biopsy in clinical practice.
      ,
      • Ratziu V.
      • Charlotte F.
      • Heurtier A.
      • et al.
      Sampling variability of liver biopsy in nonalcoholic fatty liver disease.
      ). Non-invasive measurement of hepatic steatosis and fibrosis through vibration-controlled transient elastography (VCTE) is not universally accessible in primary-care settings (
      • Xiao G.
      • Zhu S.
      • Xiao X.
      • et al.
      Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: A meta-analysis.
      ). Multiple models have been developed to assess for degree of fibrosis, including the FIB-4 index, NAFLD Fibrosis Score, and AST-to-Platelet Ratio Index (APRI), but these models were not developed nor validated to predict hepatic decompensation events or hepatic event-free survival in patients with NAFLD (
      • Angulo P.
      • Hui J.M.
      • Marchesini G.
      • et al.
      The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD.
      ,
      • Harrison S.A.
      • Oliver D.
      • Arnold H.L.
      • et al.
      Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease.
      ,
      • Alkhouri N.
      • Morris-Stiff G.
      • Campbell C.
      • et al.
      Neutrophil to lymphocyte ratio: a new marker for predicting steatohepatitis and fibrosis in patients with nonalcoholic fatty liver disease.
      ,
      • Loaeza-del-Castillo A.
      • Paz-Pineda F.
      • Oviedo-Cardenas E.
      • et al.
      AST to platelet ratio index (APRI) for the noninvasive evaluation of liver fibrosis.
      ,
      • Xun Y.H.
      • Guo J.C.
      • Lou G.Q.
      • et al.
      Non-alcoholic fatty liver disease (NAFLD) fibrosis score predicts 6.6-year overall mortality of Chinese patients with NAFLD.
      ,
      • McPherson S.
      • Stewart S.F.
      • Henderson E.
      • et al.
      Simple non-invasive fibrosis scoring systems can reliably exclude advanced fibrosis in patients with non-alcoholic fatty liver disease.
      ,
      • D'Amico G.
      • Pasta L.
      • Morabito A.
      • et al.
      Competing risks and prognostic stages of cirrhosis: a 25-year inception cohort study of 494 patients.
      ).
      The Iowa NAFLD Decompensation Risk Score (hereafter referred to as the Iowa Model) was recently developed and internally validated in a cohort of 700 patients to identify patients with NAFLD without clinically evident cirrhosis who are at higher risk of developing hepatic decompensation and provides an individual patient’s probability of development of hepatic events (
      • Ahmed H.S.
      • Pedersen N.
      • Jayanna M.B.
      • et al.
      Predictive Factors and Time to Development of Hepatic Decompensation in Patients with Non-alcoholic Fatty Liver Disease.
      ). Our primary objective was to perform external validation of the Iowa Model in an urban, racially and ethnically diverse cohort of patients with NAFLD and without clinically evident cirrhosis. We also aimed to compare the performance of the Iowa Model with currently existing non-invasive scoring systems in the combined internal and external validation cohort.

      Methods

      Settings and Participants

      For external validation, we included patients who received care in the primary care clinics, hepatology clinics, and inpatient clinical services at the Boston University Medical Center in Boston, Massachusetts between 2010 and 2019 with ICD-9 and ICD-10 codes of NAFLD, NASH, fatty liver disease NASH cirrhosis, cryptogenic cirrhosis, or cirrhosis of unknown cause (Appendix Table 1). After creating an initial patient pool from ICD codes, we reviewed individual charts to confirm the presence of hepatic steatosis based on the definition from the American Association for the Study of Liver Disease (AASLD): 1) evidence of hepatic steatosis on imaging (including abdominal ultrasound, computed tomography [CT], and magnetic resonance imaging [MRI]) or liver histology and 2) lack of secondary causes of liver fat accumulation, including heavy alcohol use, long-term medication use, or hereditary disorders (
      • Chalasani N.
      • Younossi Z.
      • Lavine J.E.
      • et al.
      The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases.
      ). Patients with controlled attenuation parameter (CAP) score >238 dB/m on Fibroscan were also confirmed to have hepatic steatosis (
      • Sasso M.
      • et al.
      Controlled attenuation parameter (CAP): a novel VCTE guided ultrasonic attenuation measurement for the evaluation of hepatic steatosis: preliminary study and validation in a cohort of patients with chronic liver disease from various causes.
      ).
      Table 1Baseline characteristics for external validation cohort.
      CharacteristicTotal Cohort

      n = 249
      Developed Hepatic Decompensation

      n = 15
      Mean (SD) or n (%)Mean (SD) or n (%)
      Age (years)51.9±12.255.4±12.5
      Female (%)151 (60.6)12 (80.0)
      Race/Ethnicity (%)Hispanic103 (41.3)6 (40.0)
      White73 (29.3)6 (40.0)
      Black56 (22.5)3 (20.0)
      Other17 (6.8)0 (0.0)
      Diabetes (%)96 (38.6)8 (53.3)
      BMI (kg/m2)35.8±16.934.8±8.2
      Platelet count (109/L)248±71188±62.9
      AST (U/L)37±2347±27.7
      ALT (U/L)52±4560.5±59.2
      Albumin (g/dL)4.2±0.43.9±0.7
      Triglyceride (mg/dL)192±128208±145.5
      BMI: body mass index; AST: aspartate aminotransferase; ALT: alanine aminotransferase; NAFLD: nonalcoholic fatty liver diseaseBaseline characteristics with demographic and clinical variables for the 249 patients included in the external validation cohort and the 15 patients who developed hepatic decompensation.
      For the comparison to existing models, we combined 249 patients from the external validation cohort with the 700 patients from the original construction and internal validations cohorts from The University of Iowa Hospitals and Clinics, Iowa City, IA as has previously been described (
      • Ahmed H.S.
      • Pedersen N.
      • Jayanna M.B.
      • et al.
      Predictive Factors and Time to Development of Hepatic Decompensation in Patients with Non-alcoholic Fatty Liver Disease.
      ). The combined cohort was used in order to provide adequate sample size for comparison of models.
      We excluded patients who had clinical evidence of cirrhosis (on imaging, or liver biopsy, or elastography) at time of diagnosis of NAFLD (including METAVIR stages F3 and F4), significant alcohol use (as defined as >7 drinks per week for women, >14 drinks per week for men), use of long-term medications that can cause hepatic steatosis (e.g. corticosteroids, methotrexate), other risk factors for chronic liver disease (including viral hepatitis, drug-induced liver disease, autoimmune disease, or genetic liver diseases such as Wilson’s disease or alpha-1-antitrypsin [A1AT] deficiency), and missing variables to calculate the Iowa Model. We also excluded patients who developed a hepatic event within 12 months of diagnosis of NAFLD as they likely had advanced fibrosis or underlying cirrhosis at time of diagnosis. The inclusion and exclusion criteria of the patient population in this Boston cohort were similar to the original cohort of 700 patients from which the Iowa model was developed and validated.
      All authors had access to the study data and reviewed and approved the final manuscript.

      Data Collection

      We manually reviewed the electronical medical records of included patients to obtain baseline demographic, clinical, and biographical data at time of initial diagnosis of NAFLD. We obtained data on age, sex, race/ethnicity, presence of diabetes mellitus (defined as hemoglobinA1c ≥6.5%, fasting glucose ≥125mg/dL, or on anti-diabetic medications), body mass index (BMI) in kg/m2, serum platelet count, fasting glucose, serum alanine aminotransferase (ALT), serum aspartate aminotransferase (AST), and serum triglyceride at the time of diagnosis. We recorded the time of development of hepatic event (defined as ascites, esophageal or gastric varices, hepatic encephalopathy (HE), or hepatocellular carcinoma in clinical notes, imaging, or procedures). We also recorded the time of liver transplantation, or death. Last follow up date was defined as the most recent clinic hospital encounter, date of development of hepatic events, date of death, date of transplantation, whichever is earliest.
      Charts were reviewed independently by two researchers (HSA and NG). If there was ambiguity in the chart regarding the diagnosis, or time of development of hepatic event, both researchers then reviewed the same chart again to come to a consensus.

      Non-invasive Scoring Models

      We calculated the NAFLD Decompensation Risk Score for each patient with the following formula: NAFLD decompensation risk score = age×0.06335 + presence of diabetes (yes=1, no=0) ×0.92221 - platelet count×0.01522. This model is available as an online calculator at https://uihc.org/non-alcoholic-fatty-liver-disease-decompensation-risk-score-calculator. We calculated the FIB-4 index, the NAFLD Fibrosis Score, and the APRI for each patient with the following formulas: (age x AST)/(platelet count xALT1/2) (
      • Harrison S.A.
      • Oliver D.
      • Arnold H.L.
      • et al.
      Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease.
      ), NAFLD Fibrosis Score = -1.675 + (0.037 x age) + (0.094 x BMI) + (1.13 x impaired fasting glucose/diabetes [yes = 1, no = 0]) + (0.99 x AST/ALT) – (0.013 x platelet count) – (0.66 x albumin) (
      • Angulo P.
      • Hui J.M.
      • Marchesini G.
      • et al.
      The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD.
      ), and APRI = (AST/ALT)/platelet count x 100 (
      • Loaeza-del-Castillo A.
      • Paz-Pineda F.
      • Oviedo-Cardenas E.
      • et al.
      AST to platelet ratio index (APRI) for the noninvasive evaluation of liver fibrosis.
      ), respectively.

      Statistical Analysis

      We compared characteristics of patients who developed hepatic events to those who did not develop liver related-events with t-test for continuous variables and chi-squared test for categorical variables. We measured time from the date of diagnosis of NAFLD to the date of development of hepatic event, death, liver transplantation, or last follow-up. We used Kaplan-Meier analysis to calculate the hepatic event-free survival. We used Cox proportional hazards analysis to determine whether the Iowa Model was predictive of development of hepatic events in the external validation cohort. We used the Harrell ‘c’ statistic to calculate the time-dependent receiver operative characteristic (AUROC) curve.
      We then calculated and compared the AUROC of the Iowa Model, APRI, NAFLD fibrosis score, and FIB-4 index in the prediction of development of hepatic events in the combined cohort. The comparison was performed in the combined cohort as it provides better discrimination among the scores due to the higher sample size and event rate.
      Statistical analysis was performed using the Statistical Analysis Software (SAS), version 9.4, SAS Institute Inc., Cary, NC, USA, and the Stata Statistical Software: Release 13, College Station, TX: StataCorp LP.
      The Boston University and the University of Iowa Institutional Review Boards deemed this study exempt from review.

      Results

      Baseline Characteristics

      We reviewed the medical records of 1597 patients to include 249 patients who met our inclusion/exclusion criteria (Figure 1). Baseline characteristics for the entire cohort and in the patients who developed hepatic decompensation are summarized in Table 1. The mean age of the cohort was 52±12 years, 151 (60.6%) of the cohort were women. The cohort was racially- and ethnically-diverse with 103 (41.4%) patients of Hispanic ethnicity, 73 (29.1%) non-Hispanic white, and 56 (22.4%) Black. Mean BMI was 35.8±16.9 kg/m2. Ninety-six patients (39%) had diabetes mellitus. The majority of patients were diagnosed with hepatic steatosis on imaging, with 171 patients diagnosed on ultrasound, 56 patients diagnosed with CT, two patients diagnosed with MRI, six patients with VCTE, and 12 patients with liver biopsy. The median follow-up for the entire cohort was 78 (43-139) months. Fifteen (6.0%) patients developed hepatic events, three patients with ascites, six patients with varices, one patient with HE, three patients with HCC; one patient with ascites, varices, and HE; and one patient with varices and HE. Ten (4.0%) patients died during follow-up, one with liver-related mortality, one with cardiovascular-related mortality, and 8 with unknown cause of death.
      Figure 1
      Figure 1Inclusion and exclusion criteria for eligible patients who received care at Boston Medical Center between 2010 and 2019.

      External Validation of the Iowa Model

      Univariate analysis confirmed that the variables of the Iowa Model were all significant predictors of hepatic events in our cohort, with age (p=0.009), platelet count (p=0.001), and presence of diabetes (0.005). The Iowa Model was significant in predicting the development of hepatic events with a hazard ratio (HR) of 2.5 (95% confidence interval [CI] 1.7—3.9, p<0.001). The AUROC was 0.87 (95% confidence interval (CI) 0.83-0.91) (Figure 2).
      Figure 2
      Figure 2Observed Kaplan-Meier “time-to-hepatic event” curve versus the predicted cumulative hazard curve. The blue line is the “time-to-hepatic event” curve; solid red line is the predicted cumulative hazard curve; dotted red line is interval bounds.

      Comparison of Non-invasive Scoring Models

      In the combined Iowa and Boston cohort of 949 patients, 63 (6.6%) patients developed hepatic decompensation at a median follow up of 72 (33-115) months. All four models—APRI, the NAFLD fibrosis score, the FIB-4 index, and the Iowa Model—were significant in predicting development of hepatic events (Table 2). The AUROC for APRI was 0.76 (95% CI 0.73-0.79), for NAFLD fibrosis score was 0.66 (95% CI 0.63—0.69), for FIB-4 index was 0.87 (95% CI 0.83—0.91), and for the Iowa model was 0.88 (95% CI 0.85—0.92) (Figure 3). The time dependent AUROC to predict the development of hepatic events at 5-, 10-, and 12-years was higher for Iowa model as compared to other models at each of the individual time-points (Table 2).
      Table 2Comparison of the Iowa Model with three other non-invasive scoring systems in the combined cohort.
      ‘c’ statistic (95% CI)AUROC at 5 yearsAUROC at 10 yearsAUROC at 12 years
      The Iowa Model0.88 (0.85—0.92)0.850.830.84
      APRI0.76 (0.73—0.79)0.700.690.65
      NAFLD Fibrosis Score0.66 (0.63—0.69)0.610.680.71
      FIB-4 Index0.87 (0.83—0.91)0.820.770.79
      AUROC: area under the receiver operator characteristic curve (AUROC); APRI: AST-to-platelet index; NAFLD: nonalcoholic fatty liver disease. The c-statistic and AUROC of the combined cohort of 949 patients for The Iowa Model, APRI, NAFLD Fibrosis Score, and FIB-4 Index.
      Figure 3
      Figure 3AUROC of the various non-invasive models to predict hepatic decompensation in patients with NAFLD at 5 years. 3.a. The Iowa Model. 3.b. AST-to-platelet ratio index (APRI). 3.c. NAFLD Fibrosis score. 3.d. FIB-4 Index.

      Percentage of At-Risk Patients Based on the Iowa Model

      As determined by the Iowa model, the percentage of patients with a >5% predicted probability of developing hepatic events at 5 years was 23% in the Boston cohort and 20% in the combined cohort. Similarly, the percentage of patients with a >10% predicted probability of hepatic events at 5 years was 12% in Boston cohort and 10% in the combined cohort.

      Discussion

      In this study we have shown that the Iowa Model performs well in an external, racially and ethnically diverse population to identify NAFLD patients at highest risk of developing hepatic events. Additionally, we have shown that in our two-center combined cohort, the Iowa model performs as well as the FIB-4 Index and seems to be better than the APRI and the NAFLD Fibrosis Score to predict hepatic events.
      Several non-invasive models have been developed to predict the presence of steatosis or advanced fibrosis, but few models have been designed specifically to predict outcomes in patients with NAFLD. The Framingham Steatosis Index was developed in 2016 using a cohort of patients in the Framingham Heart Study, using ALT:AST ratio, age, sex, BMI, triglyceride level, hypertension, and diabetes to identify patients with NAFLD on CT imaging (
      • Angulo P.
      • Bugianesi E.
      • Bjornsson E.S.
      • et al.
      Simple noninvasive systems predict long-term outcomes of patients with nonalcoholic fatty liver disease.
      ), but has not been used in prediction for hepatic decompensation. Additionally, the FIB-4 index, APRI, and NAFLD Fibrosis Score have all been developed to predict the presence of advanced fibrosis, but were not specifically designed to predict outcomes in patient with NAFLD. Though studies later tried to evaluate the discriminatory ability of these models to predict the development of hepatic events (
      • Long M.T.
      • Pedley A.
      • Colantonio L.D.
      • et al.
      Development and validation of the Framingham Steatosis Index to identify persons with hepatic steatosis.
      ), the strength of the Iowa Model’s predictive capacity lies in the construction of the model based on patients with NAFLD. Unlike in these other models, the construction cohort and validation cohorts specifically excluded patients with advanced fibrosis, making the Iowa Model more likely to represent individuals with higher risk of NASH and progressive disease. The Iowa Model has excellent discrimination in predicting the risk of hepatic events in NAFLD patients, and the AUROC of our model was superior to both APRI and the NAFLD Fibrosis Score, and comparable to the FIB-4 index. Additionally, the Iowa model provides the probability that an individual patient with NAFLD will develop hepatic events in the future. This will not only allow patients to understand their future risk of liver-related events, but also helps clinicians determine when to refer a patient to a hepatologist for early and aggressive interventions.
      With the growing incidence and increased recognition of NAFLD, primary care providers will encounter an increasing number of patients with NAFLD, but not all may be feasibly referred to a hepatologist. This raises the important question of how to recognize which patients are at highest need for specialist care. Any patient with clinically obvious cirrhosis or decompensation should be referred to a hepatologist for further evaluation and potential pharmacologic therapy. Patients without clinical cirrhosis, however, should be distinguished into those at low-risk and at high-risk for the progression of liver disease and the development of hepatic events. We propose that patients with a risk of hepatic events greater than 10% at 5 years based on the Iowa Model to be considered as higher risk necessitating a referral to a hepatologist. We arrived at the 10% at 5 years cut-off as it may allow a high-risk NAFLD patient to be evaluated by a hepatologist within a reasonable time frame.
      The main strength of our study is the racial and ethnic diversity of the external validation cohort. Although the construction and internal validation of the Iowa Model was completed in a Midwestern, heavily white population, the model still performed well in our more diverse, urban population. Additionally, although ICD codes were used to identify an initial patient pool for review, individual chart review was performed to identify patients with steatosis either on imaging, VCTE, or biopsy, and exclude patients with other causes of chronic liver disease and those with clinical cirrhosis, per AASLD guidelines (
      • Chalasani N.
      • Younossi Z.
      • Lavine J.E.
      • et al.
      The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases.
      ). Interestingly, the mean platelet count of patients who developed hepatic events was lower than the mean for the total cohort (188 versus 248 109/L), although not within the range of thrombocytopenia associated with portal hypertension at the time of diagnosis. This highlights both the predictive value of platelet count within the Iowa Model and also strengthens the probability that individuals who developed hepatic decompensation did not have cirrhosis or portal hypertension at time of diagnosis of NAFLD.
      There are several limitations to the study. Liver biopsy was not performed in the majority of our patients, so we cannot histologically confirm the presence or absence of NASH nor estimate the degree of fibrosis at the time of study inclusion. However, as mentioned above, each patient chart was individually reviewed to rule out evidence of clinically-evident cirrhosis. Additionally, patients who developed hepatic events within one year of diagnosis of NAFLD were excluded as these patients likely had cirrhosis at the time of inclusion into the study. However, it is possible that some patients had at least moderate fibrosis at time of inclusion in the study. The decision to exclude patients with cirrhosis was made as we were specifically developing a risk stratification model for patients who do not have cirrhosis, as it is already understood that patients with a diagnosis of cirrhosis are at increased risk of hepatic decompensation. However, this exclusion could potentially have introduced a selection bias. The NAFLD Fibrosis Score, APRI, and FIB-4 Index were developed in cohorts that include individuals with advanced fibrosis and cirrhosis—this difference in degree of fibrosis may explain why our model performed better than NAFLD Fibrosis Score and APRI. Another limitation is the relatively low event rate in the validation cohort for a thorough multivariate analysis of all individual risk factors. Despite the small event rate, the hazard ratio of the NAFLD score was relatively narrow and achieved statistical significance.

      Conclusion

      The Iowa Model performs well in an external racially- and ethnically diverse cohort of patients with NAFLD to predict hepatic events. The Iowa Model can especially be used at the primary care level to identify patients who would benefit from specialist referral and therapeutic interventions.

      Ethics Approval

      This study was performed in accordance with the ethical standards of the institutional committees and the 1964 Declaration of Helsinki and later amendments. The Boston University and the University of Iowa Institutional Review Boards deemed this study exempt from review.

      Funding

      Heidi S. Ahmed is supported in part by NIH 2 T32 DK 7201-42.

      Authors’ contributions

      HSA, ARM, and AS were involved in initial conception and design of the study. HSA and NG performed data curation. ARM performed statistical analysis. HSA drafted the manuscript. HSA, ARM, AS, and MTL contributed to major revisions of the manuscript. All authors read and approved the final manuscript.

      Availability of Data and Materials

      The datasets generated and analyzed in this study are not publicly available, but are available from corresponding author on reasonable request.

      Declaration of Competing Interest

      The authors declare they have no conflicting or competing interests.

      Appendix A. Supplementary data

      The following is/are the supplementary data to this article:

      Appendix

      Table 1International Classification of Disease (ICD) codes used to identify patients for inclusion in the study
      ICD VersionICD CodeDescription in EMR
      ICD-10K75.81Nonalcoholic steatohepatitis
      Nonalcoholic steatohepatitis (NASH)
      Steatohepatitis
      Steatohepatitis, nonalcoholic
      K76.0Fatty (change of) liver, not elsewhere classified
      Fatty infiltration of liver
      Fatty liver
      Fatty liver determined by biopsy
      Fatty liver disease, nonalcoholic
      Fatty metamorphosis of liver
      Hepatic steatosis
      Liver fatty degeneration
      NAFL (nonalcoholic fatty liver)
      NAFLD (nonalcoholic fatty liver disease)
      Nonalcoholic fatty liver disease
      Non-alcoholic fatty liver disease
      Nonalcoholic hepatosteatosis
      Steatosis of liver
      Steatosis, liver
      K74.60Other and unspecified cirrhosis of liver
      Advanced cirrhosis of liver
      Cirrhosis
      Cirrhosis of liver
      Cirrhosis of liver not due to alcohol
      Cirrhosis of liver without ascites
      Cirrhosis of liver without ascites, unspecified hepatic cirrhosis type
      Cirrhosis of liver without mention of alcohol
      Cirrhosis, nonalcoholic
      Cirrhosis, non-alcoholic
      Diffuse nodular cirrhosis of liver
      Hepatic steatosis
      Liver cirrhosis
      Unspecified cirrhosis of liver
      ICD-9571.8Other chronic nonalcoholic liver disease
      571.5Cirrhosis of liver nonspecific
      ICD: International Classification of Disease; EMR: electronic medical record.

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