Clinical and cost-effectiveness analysis of community-based screening strategies for non-alcoholic fatty liver disease in patients with type-2 diabetes mellitus

doi:10.21203/rs.3.rs-2135338/v1

Background & Aims:

We investigated the prevalence of non-alcoholic fatty liver disease(NAFLD) in patients with type 2 diabetes mellitus(T2DM) in primary care and developed a risk-stratification pathway. We also assessed the cost-utility of different screening strategies for NAFLD in the diabetic community.

Methods

Consecutive T2DM patients underwent screening for liver diseases, including liver stiffness measurement(LSM). Binary logistic was used to predict factors associated with significant fibrosis. We used independent predictors of significant and advanced fibrosis to generate a predictive score for this population (BIMAST),and validated it internally and externally. Five screening strategies were compared against standard of care (SOC): BIMAST score, ultrasound plus abnormal liver function tests, FIB-4, NAFLD fibrosis score, and fibroscan. A Markov model was built upon four health states based on fibrosis status. We generated the cost per quality-adjusted life year(QALY) gained and calculated the incremental cost-effectiveness ratio (ICER) in the base-case analysis conducted over a lifetime horizon.

Results

Among 300 patients enrolled (287 included), 64% (186) had NAFLD and 10% (28) other causes of liver disease. Patients with significant fibrosis, advanced fibrosis, and cirrhosis due to NAFLD accounted for 17% (50/287), 11% (31/287), and 3% (8/287), respectively. BIMAST score validation showed an excellent diagnostic performance in primary care improving false negatives from 38–10% compared to FIB-4. In the cost-utility analysis, ICER was £2,337.92/QALY for BIMAST and £2,480/QALY for fibroscan. When transition probabilities, utilities, screening effect, and cost inputs were modified, we found a > 99% probability of NAFLD screening tests being cost-effective compared to SOC in all evaluated scenarios.

Conclusion

Screening for NAFLD in diabetic patients in primary care is cost-effective and should become part of the holistic assessment in the community.

Endocrinology & Metabolism

Non-alcoholic fatty liver disease

screening

primary care

liver fibrosis

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of abnormal liver function tests worldwide, with a global estimated prevalence of 29.8%[1]. NAFLD is also expected to become the leading cause of end-stage liver disease in the coming decades [2]. Histologically, NAFLD encompasses a spectrum of disorders from steatosis with or without hepatocellular injury and/or inflammation (Non-Alcoholic Steato-Hepatitis, NASH) and a variable degree of fibrosis through to cirrhosis [3]. Fibrosis stage represents the strongest predictor of clinical outcomes – liver and non liver-related – in these patients [4, 5]. From a clinical perspective, the presence of T2DM is an independent predictor of advanced fibrosis in NAFLD patients, with a greater prevalence of advanced disease in diabetic compared to non-diabetic individuals, especially in younger ages [6, 7].

Given the high prevalence - estimated at 55.5% [8] - and severity of NAFLD in the diabetic population, there is a major interest in early detection of the disease, especially in primary care [9, 10]. From a primary care perspective, diagnosing NAFLD is perceived as a clinical challenge, with specific concerns on performing risk-stratification among patients [11]. Notably, current diabetes management guidelines do not advise for NAFLD screening. Nevertheless, the European Association for the Study of the Liver (EASL) guidelines recommend screening for NAFLD in high risk-groups (i.e. patients with metabolic syndrome) following a 2-tier system. Specifically, patients should be stratified using non-invasive markers of fibrosis such as FIB-4 and/or NAFLD fibrosis score in primary care, followed by ELF and/or transient elastography in a specialist setting [9]. Such strategy relies heavily on scores, which were derived from a tertiary care setting, and whose diagnostic accuracy in primary care is still under debate [12]. Furthermore, there is still a debate on whether screening might be cost-effective in this population [10].

In this study, we aimed to establish the prevalence of NAFLD in the diabetic community. Moreover, we tested the performance of non-invasive markers in primary care and further developed a risk-stratification pathway. We also built a Markov model simulating NAFLD screening and assessed the cost-utility of five potential screening strategies for NAFLD in the diabetic community.

2.1 Study population

This single-centre, cross-sectional study prospectively recruited consecutive patients with T2DM in primary care and community clinics from the North-West London general practitioner (GP) network. Patients were invited to take part in the study by their routine care team, such as GPs and diabetes nurses. Inclusion criteria were ability to give informed consent, age >18 years and presence of T2DM, as defined by medical history or recent 2 h post-challenge plasma glucose ≥11.1 mmol/L. Patients were excluded if they had known liver disease.

2.2 Screening for liver disease and NAFLD

All patients were screened for liver disease and the presence of NAFLD with imaging ultrasound (US) and blood tests: liver function tests (LFTs), lipid and metabolic profile, fasting insulin, HbA1c, ferritin, anti-hepatitis C virus antibodies, hepatitis B virus serologic panel, auto-antibodies, ferritin, caeruloplasmin, alpha-1 antitrypsin and autoimmune profile. Non-invasive markers of fibrosis (such as NAFLD fibrosis score and FIB-4) were calculated as per published formulas [13, 14]. Transient elastography (TE, Fibroscan) was also performed in all patients. Medical history, social history, alcohol consumption, dietary intake assessment and anthropometric parameters (Body Mass Index (BMI), waist and hip circumference) were recorded for each patient. The patients’ ethnic backgrounds were clustered into six groups: White Caucasian, White Hispanic, South Asian, East Asian, Black African or Afro-Caribbean, and Arab. As an estimation of socio-economic status, patients’ postcodes were used to assign a deprivation rank according to the English Index of Multiple Deprivation (IMD). More details on IMD are provided in Supplementary material.

TE and US were performed by a single operator, after fasting for 4 hours. Liver stiffness measurement (LSM) and controlled attenuation parameters (CAP) scores were recorded. Only LSM meeting published criteria were included in the analysis: ≥ 10 valid measurements, ≥ 60% success rate, and interquartile range/median ratio ≤0.3 [15]. The presence of hepatic steatosis on US was defined as per previously published criteria [16]. NAFLD was defined as presence of steatosis on US and absence of other causes of liver disease, such as alcohol consumption > 14 IU/week (regardless of gender) and use of steatogenic medications. A diagnosis of Both alcoholic and non-alcoholic fatty liver disease (BAFLD) was made when patients had evidence of NAFLD and chronic alcohol consumption > 14 IU per week [17]. Based on the results of TE performed on the day of the enrolment, a cut-off of LSM ≥ 8.1 kPa was used to stratify patients. The subgroup of patients with elevated LSM were referred to specialist care following guidelines’ recommendations and invited for further investigations (such as a liver biopsy) [9]. A diagnosis of cirrhosis was based on a combination of histological (where available) or imaging, biochemical and elastographic features (LSM≥12 kPa) ^[¹⁸^].

2.3 Statistical analysis

The distribution of variables was explored using the Shapiro‐Wilk test. Continuous variables were reported as medians and interquartile range (IQR), while categorical variables were expressed as relative frequencies and percentages. Binary logistic regression was used to generate a formula for predicting significant fibrosis secondary to NAFLD. Calibration was estimated as the Brier score, while goodness-of-fit as the Hosmer‐Lemeshow test. Further details regarding descriptive statistics and diagnostic performance are provided in Supplementary material.

2.4 External cohorts

Two centres provided a retrospective cohort of patients with T2DM diagnosed with NAFLD as external validation cohorts for our derived formula: the Royal Free Hospital (London, United Kingdom) and the Palermo University Hospital (Palermo, Italy). In both cohorts, NAFLD was diagnosed based on either US or histology, and other causes of liver disease were excluded, including use of steatogenic drugs and chronic alcohol consumption > 14 UI per week [17]. Anthropometric parameters, blood tests and TE measurements were recorded for all the patients.

The Royal Free Hospital cohort, included consecutive patients with T2DM who were firstly referred from primary care through the Camden and Islington pathway [19]. As such, this group included patients selected from primary care based on FIB-4>1.3 and/or ELF>9.5 (selected primary care population). The Palermo Hospital cohort included consecutive patients with T2DM who were followed-up in the specialist NAFLD clinic (tertiary care population).

2.5 Cost-effectiveness analysis

2.5.1 Screening strategies and identification rates

In this study, five screening strategies were compared against standard of care (SOC): 1) US plus LFTs, 2) FIB-4, 3) NAFLD fibrosis score, 4) derived formula (BIMAST score), 5) fibroscan. SOC was derived from previously published economic evaluations of NAFLD screening where this entailed LFTs or no screening [20, 21] (Supplementary Table 1). As part of SOC, abnormal LFTs were assumed to prompt referral to hospital, with a 65% specificity and 35% sensitivity [22].

In the first tier of each strategy, patients were divided into two groups: no NAFLD/NAFLD without significant fibrosis vs NAFLD with significant and advanced fibrosis. No NAFLD and NAFLD without significant fibrosis were considered the same group for this analysis as the management would be similar and would not trigger referral to secondary care compared to NAFLD with significant and advanced fibrosis that triggers referral to specialist care [9]. In strategy 1 (US plus LFTs), NAFLD with significant fibrosis was defined as evidence of steatosis and features of chronic liver disease on ultrasound, plus elevated LFTs. In strategy 2 (FIB-4) and 3 (NAFLD fibrosis score), NAFLD with significant fibrosis was defined as FIB-4 >1.3 and NAFLD fibrosis score >-1.45 respectively. In strategy 4 (BIMAST score) and 5 (fibroscan), significant fibrosis was defined as BIMAST >0.063 and LSM ≥8.1 kPa respectively.

2.5.2 Decision-analytic model

We developed a decision tree to characterise the risk stratification and diagnostic performance of each of the primary care screening strategies evaluated. We adapted previously published Markov models [20, 22] to characterise the subsequent health states and disease pathways of patients based on their initial primary care screening risk stratification (Figure 1).

The model was built upon four health states:

Mild liver disease (MLD) if NAFLD with LSM≤8 kPa or no NAFLD;
Significant and advanced liver disease (SLD) if LSM≥8.1 kPa;
Compensated cirrhosis (CC) (histological (where available) or biochemical/ radiological evidence of cirrhosis without evidence of decompensation);
False negatives if LSM ≥8.1 kPa but were false negatives at screening with other non-invasive markers of fibrosis.

Patients with advanced stages of liver disease could progress to health states that reflect end-stage liver disease, including decompensated cirrhosis (DC), hepatocellular carcinoma (HCC), liver transplant (LT) and death [20]. With a diagnosis present (status of significant liver disease and/or compensated cirrhosis), there is a probability that the management of the patient is modified to reduce the risk of progression to CC, decompensation or death. In this case, progression rates of SLD and CC groups were assumed as slower, compared to those whose diagnosis was missed at screening (false negatives) [20].

2.5.3 Model input parameters

For this analysis, we derived health state transition probabilities, utility values, and direct medical costs based on previously published data (Supplementary Table 2). For all 5 screening strategies, sensitivity and specificity were derived from the study population, while these values were derived from the literature with regards to SOC [22].

We derived most of the transition probabilities from a published economic evaluation of

community-based diagnostic pathway for adults with T2DM identified as having a risk factor for developing NAFLD, which used a Markov model structure to our analysis ²¹. Probabilities from NMD to SLD and SLD to CC health states were adjusted by a risk ratio for undiagnosed compared to diagnosed individuals in the cohort (Supplementary table 4) ²¹. Transition probabilities to death state were derived from all-cause mortality rates based on another economic evaluation of NAFLD diagnostic pathways in general population ²². In the absence of NAFLD-specific data, utilities for NMD and SLD states were approximated from quality-of-life data for T2DM patients, whereas utilities for DC, HCC and LT health states were taken from a study of patients with hepatitis C, based on the same approach used in a published NAFLD economic evaluation ²¹.

Direct medical costs were modelled from the NHS perspective and converted to 2020 GBP, using UK-specific inflation rates [23]. Cost inputs included screening tests (derived from the study population), GP consultations, specialist and dietician appointments, medications and treatments associated with each liver disease stage, liver transplantation intervention and follow-up clinician visits[20]. Detailed cost components and unit costs are reported in the supplementary table 3.

2.5.4 Model outcomes

The cost-utility analysis in the base-case was conducted over a lifetime horizon and generated the cost per quality-adjusted life year (QALY) gained. A discount rate of 3.5% per year was applied to outcomes and costs, as recommended by NICE guidelines [24]. We calculated the average cost-effectiveness and the incremental cost-effectiveness ratio (ICER) compared to SOC [20, 22]. Life expectancy, lifetime costs, and number of correct diagnoses were also estimated. A cost-effectiveness threshold (CET) of £20,000/QALY gained was set for the base-case analysis as per previous studies [25]. Key input parameters with the highest level of uncertainty (i.e. transition probabilities, utility values, costs and screening ratios) were varied to determine the impact of their variability on cost-effectiveness results. Sensitivity analysis ranges and probabilistic distributions were derived from previous literature and are reported in detail in Supplementary table 4-8.

2.7 Ethical approval

All patients’ recruitment was conducted in line with Good Clinical Practice and sample handling according to Human Tissue Act regulations. The main cross-sectional study (derivation cohort) obtained full ethical approval from the Research Ethics Committee (REC approval 18/LO/1742, IRAS 251274), sponsorship from Imperial College London and adoption from Clinical Research Network (CRN) Portfolio.

3.1 Study population

Between April 2019 and January 2021, a total of 300 patients with T2DM were enrolled from the North-West London GP network. Overall, 287 patients underwent the whole screening procedure, while 13 did not complete the screening and were excluded (Supplementary figure 1). The study population was diverse in terms of ethnic background, with the most represented ethnicities being White-Caucasian (102/287=32%), Arab (74/287=28%) and South-Asian (47/287=17%). The study population was also diverse in terms of severity of T2DM and anti-diabetic treatments. Demographic and clinical characteristics of the study population are shown in Table 1 and Table 2.

The overall prevalence of NAFLD, based on ultrasound, was 64% (186/287), while the prevalence of other liver diseases was 9% (28/287: 27 BAFLD and 1 with chronic hepatitis B). There were no cases of secondary NAFLD, i.e. secondary to medications. The overall prevalence of significant liver disease, as defined by LSM ≥ 8.1 kPa, was 17% (50/287) in the whole diabetic population and 26% (50/186) in the NAFLD sub-group. The prevalence of advanced fibrosis, as defined by LSM ≥12.1 kPa, was 10% (31/287) in the whole population and 16% (31/186) in the NAFLD sub-group. The prevalence of newly diagnosed cirrhosis (either histologically proven or clinically diagnosed) secondary to NAFLD was 3% (8/287) in the whole diabetic population and 5% (8/184) in the NAFLD subgroup (Supplementary figure 1). The number needed to treat/screen (NNT) in this population was 4.56 (3.38-7). Due to the COVID-19 related restrictions, only 11 patients underwent a liver biopsy among those with elevated LSM (as per SOC): all the biopsied cases had liver fibrosis stage ≥2 as per CRN scoring system.

When compared to those with NAFLD and normal LSM (n=136), patients with NAFLD and significant fibrosis (LSM ≥8.1 kPa) (n=50) presented higher body mass index (BMI) (36.8 vs 30.3 kg/m2, p=0.0001), larger hip (123 vs 110 cm, p=0.0001) and waist circumferences (120 vs 105 cm, p=0.0001). Those with significant liver disease also had higher alanine aminotransferase (ALT, 46 vs 30 IU/L, p=0.0001), aspartate aminotransferase (AST, 37 vs 26 IU/L, p=0.0001) and gamma-GGT values (GGT, 62 vs 27 IU/L, p=0.0001). Of note, 42% of the patients with 8.1 kPa ≤LSM≤ 12.1 kPa and 38% patients with LSM ≥12.1 kPa had normal LFTs at screening. In terms of metabolic control, patients with NAFLD and significant fibrosis showed higher median HbA1c (71 vs 59 mmol/mol, p=0.0001), fasting glucose (9.4 vs 6.7 mmol/l, p=0.001), insulin level (21 vs 12.4 µU/ml, p=0.001) and HOMA index (8.1 vs 3.3, p=0.001). There was no difference in terms of duration of diabetes, anti-diabetic medications or presence of diabetic complications (Table 2). In terms of socio-economic status, those with NAFLD and significant fibrosis lived in more deprived neighbourhoods according to their median education rank (18789 vs 23148, p=0.03) (Supplementary Table 9).

Overall, waist circumference (crude OR 1.086, 95%CI 1.021-1.154, p=0.008), BMI (crude OR 1.17, 95%CI 1.008-1.358, p=0.04), AST (crude OR 1.071, 95%CI 1.07-1.01, p=0.022) and education rank (crude OR 0.857, 95%CI 0.744-0.987) were independent predictors of significant liver disease in the whole diabetic population (Table 3).

3.2 Derivation of the BIMAST score

The whole study population was split into a derivation (n=194) and a validation (n=93) cohort, following a 2:1 random allocation. The derivation and the validation cohorts were similar in terms of clinical features (Supplementary table 10). Based on the clinical predictors of significant fibrosis on multivariate analysis, the BIMAST score was computed as:

0.17*(BMI, kg/m²) + 0.054*(AST, IU/L) – 8.771

The BIMAST score can be calculated online using the platform: https://callbuddy.eu/BIMAST/index.html.

Waist circumference and education rank were omitted a priori to increase the potential usability of the score. The Hosmer‐Lemeshow test and Brier score for the BIMAST score were 0.9 and 0.12, confirming that the derived model fitted well the derivation cohort and had good calibration. In the derivation cohort, the BIMAST score was able to predict the presence of significant fibrosis (LSM≥8.1 kPa, n=33) accurately, with an AUROC of 0.81 (95%CI: 0.72-0.9, p<0.0001) (Figure 2A). A cut-off of 0.063 gave 94% sensitivity and 44% specificity, with PPV 22% and NPV 97% for significant fibrosis. Moreover, the BIMAST score was able to predict the presence of advanced fibrosis (LSM≥12.1 kPa, n=17) accurately, with an AUROC of 0.84 (95%CI: 0.72-0.95, p<0.0001) (Figure 2B). A cut-off of 0.102 carried sensitivity 94%, specificity 50%, positive predictive value (PPV) 20% and negative predictive value (NPV) 99% for advanced fibrosis. The BIMAST performed similarly in the internal validation cohort (Supplementary material).

In the whole population, when compared to other screening strategies, the BIMAST score performed better. The AUROC curves for diagnosing significant fibrosis were 0.74 (95%CI: 0.66 - 0.83, p<0.0001) for US plus LFTs, 0.72 (95%CI: 0.65- 0.8, p=0<0.0001) for NAFLD fibrosis score and 0.62 (95%CI: 0.53 – 0.7, p=0.008) for FIB-4. The pairwise comparison of AUROC curves (DeLong method) demonstrated that the BIMAST score was better than US plus LFTs (p=0.01), NAFLD fibrosis score (p=0.009) and FIB-4 (p<0.0001) in diagnosing the presence of significant fibrosis in the community (Figure 3A). Similarly, the BIMAST score outperformed US plus abnormal LFTs (p=0.01), NAFLD fibrosis score (p<0.0001) and FIB-4 (p<0.0001) for diagnosing the presence of advanced fibrosis (DeLong method) (Figure 3B).

3.3 External validation of the BIMAST score

Both the Royal Free cohort (n=218) and the Sicilian cohort (n=168) presented higher LFTs and fibroscan values compared to the primary care cohort (Supplementary table 11 and supplementary figure 3).

In the Royal Free cohort, the Hosmer‐Lemeshow test and the Brier score for the BIMAST score were 0.67 and 0.31, suggesting that the BIMAST score had a moderate goodness-of-fit and calibration. Specifically, the BIMAST score predicted significant fibrosis (LSM≥ 8.1 kPa, n=105) with an AUROC of 0.7 (95%CI: 0.63-0.77, p<0.0001), while a cut-off of the BIMAST of 0.063 gave sensitivity 34%, specificity 91%, PPV 76% and NPV 40% (Supplementary Figure 4A). Furthermore, the BIMAST predicted advanced fibrosis (LSM≥ 12.1 kPa, n=66) with an AUROC of 0.72 (95%CI: 0.65-0.8, p<0.0001), while a cut-off of 0.102 gave sensitivity 43%, specificity 89%, PPV 62% and NPV 23%. vs 0.68 (95%CI: 0.6-0.76, p<0.0001) of FIB-4 (Supplementary Figure 4B). The pairwise comparison between AUROC curves (De Long method) confirmed that the BIMAST and the FIB-4 performed similarly in this cohort.

In the Sicilian cohort, the Hosmer‐Lemeshow test and the Brier score for the BIMAST score were 0.6 and 0.38, suggesting that the BIMAST score had a moderate goodness-of-fit and calibration. Specifically, the BIMAST score predicted LSM≥ 8.1 kPa (n=114) with an AUROC of 0.608 (95%CI: 0.5-0.71, p=0.037), while a cut-off of 0.063 gave sensitivity 27%, specificity 86%, PPV 83% and NPV 30% (Supplementary Figure 5A). Moreover, the BIMAST score predicted LSM≥ 12.1 kPa (n=65) with an AUROC of 0.602 (95%CI: 0.51-0.69, p=0.0001), while a cut-off of 0.102 gave sensitivity 20%, specificity 85%, PPV 48% and NPV 40%.vs 0.69 (95%CI: 0.609-0.77, p=0.0001) of FIB-4 (Supplementary Figure 5B). The pairwise comparison between AUROC curves (De Long method) revealed that FIB-4 performed better than the BIMAST score in this cohort.

3.4 Cost-effectiveness analysis

The cost-effectiveness analysis was based on the identification rates of the screening strategies from the study population (Supplementary figures 6-10). Of note, 19 (19/50=38%) patients with LSM≥8.1 kPa were missed by FIB-4 (false negatives), while only 5 (5/50=10%) were missed by BIMAST. Specifically, those who were misclassified by FIB-4 as low-risk were significantly younger (57 vs 62 years, p=0.03) and had lower AST (35 vs 41 IU/L, p=0.034) compared to those correctly classified as low-risk group.

Overall, screening for NAFLD by any of the strategies analysed improved the rate of diagnosis by 8-15%. All screening strategies were associated with QALY gains, ranging from 121-149 years, with fibroscan (148.73 years) resulting in the most substantial gains, followed by BIMAST score (141.01 years), FIB-4 (134.07 years) and NAFLD fibrosis score (121.25 years). The ICER of BIMAST score and fibroscan compared to SOC were £2,337.92 and £2,480 per QALY gained, respectively (Table 4).

The ICER was most sensitive to variations in progression rates (effect of early diagnosis on disease progression), screening test sensitivity and specificity, and model time horizon. Nevertheless, when transition probabilities, utilities, screening treatment effect, and cost inputs were modified, we found a >99% probability of NAFLD screening tests being cost-effective compared to SOC in all evaluated scenarios (Figure 4, Supplementary tables 6-10). When sensitivity and specificity of each screening test were varied in a range between 20% and 100%, the ICER remained cost-effective below £3,260 in all scenarios (Supplementary tables 4-6). Whereas all screening strategies were found to be cost-effective compared to SOC in the base-case, when the time horizon was decreased from 40 years (lifetime) to 5 years, only BIMAST and FIB-4 remained cost-effective within the NICE CET criteria.

Non-alcoholic fatty liver disease has now become the leading cause of chronic liver disease in Western countries and the fastest-growing indication for liver transplantation in the United States [26]. Defining and implementing models of care has been identified as an area of priority for tackling NAFLD worldwide [27]. Specifically, there is need for clearly defined, pragmatical referral management pathways, which are based on clinical context and shared with local primary care providers. Being a high-risk group for advanced liver disease [6, 7], patients with T2DM represent an ideal target for NAFLD screening in primary care.

In this study, we present a cohort of patients with diabetes who were screened for NAFLD and other liver disease in primary care, without any a priori selection. This cohort includes patients with a wide range of antidiabetic treatments, comorbidities, ranges of glycaemic control and length of disease. Furthermore, conducting this study in North-West London, this population is very diverse in terms of ethnic and social background. Overall, the prevalence of NAFLD based on US was 64%, while the prevalence of significant liver disease and cirrhosis secondary to NAFLD were 17% and 3% respectively. When compared to the results from a recent metanalysis [28], the prevalence of NAFLD in diabetic primary care is similar (64% vs 68%), while the prevalence of significant liver disease appears to be higher (17% vs 4.8%). In this cohort, visceral obesity (measured by waist circumference and BMI), education attainment (measured by IDM) and AST were the main clinical predictor for the presence of significant liver disease in primary care.

According to the latest published EASL guidelines, patients with T2DM should be screened for NAFLD using a two-tier system, i.e. FIB-4 and/or ELF in primary care, followed by TE in a specialist setting [9]. Nevertheless, standard of care for diagnosing NAFLD among GPs still relies on ultrasound and liver function tests, possibly due to limited awareness on the disease and/or screening policies [29, 30]. According to the results of this study, applying FIB-4 with a cut-off of 1.3 in this population would miss up to 38% of the patients with significant liver disease and these would mainly be younger patients with normal LFTs. These results are in line with recently published data and highlight the limitation of the use of FIB-4 in primary care [12].

Ideally, new screening tests should be derived from a population which mirrors the target population for the test, so that spectrum biases could be minimised [31]. We developed the BIMAST score based on the clinical predictors of significant fibrosis in this cohort (BMI and AST). The BIMAST score predicts the presence of significant and advanced fibrosis accurately and is easy to calculate in a primary care setting. Moreover, the BIMAST is characterised by an elevated NPV, therefore acting as an excellent screening test, and reduces the rate of false negatives from 38% to 10% compared to FIB-4. When validated internally, the BIMAST score showed an excellent diagnostic performance. Our score did not perform equally well in the two external cohorts, but this could be due to spectrum biases. Notably, the phenotype of the patients included in the Royal free and the Sicilian cohort was typical of those observed in tertiary care and therefore with higher prevalence of severe liver disease compared to the community. Collectively, our results suggest that the BIMAST score and the FIB-4 are both victims of spectrum effect but in an opposite way.

Overall, there is still little evidence on whether screening for NAFLD may be cost-effective in the community [10]. Nonetheless, it is of great importance to identify patients with high risk of progressive disease. As a result, there might be a subsequent reduction in progression rates to end-stage liver disease and associated healthcare burden. Furthermore, not only lifestyle intervention could delay or reverse fibrosis progression [32, 33] but also pharmacotherapies will soon be available. Collectedly, an early diagnosis could translate into better outcomes for this population.

In this study, we present a cost-effectiveness analysis for screening for NAFLD based on a real-life population of patients with T2DM in primary care. Five screening strategies were compared to standard of care: US plus LFTs, FIB-4, NAFLD fibrosis score, BIMAST and fibroscan. NAFLD screening in people with T2DM improved diagnostic outcomes and was cost-effective in all evaluated scenarios under a CET at £20,000. Fibroscan was the screening strategy associated with the greatest clinical gains (148.73 QALYs), while the BIMAST score was the most cost-effective (ICER 1967.11 £/QALY). Specifically, the BIMAST remained the most cost-effective strategy in both a life-time horizon (40 years) and in a shorter time-horizon (5 years). These results are in line with published work [34] and emphasize that screening for NAFLD is cost-effective compared to standard of care[20, 22, 35, 36]. Nevertheless, previous studies were based on either hypothetical cohorts or tertiary care populations, while this cost-effectiveness analysis relies on a real-life population from primary care. Based on the results of this study, either the BIMAST score or the fibroscan should be offered to all patients with T2DM in the community.

The cost-effectiveness analysis was subject to several limitations, including the need to derive transition probabilities and utility values from previous literature, due to the lack of data available especially for the T2DM population with NAFLD in primary care. To minimise the impact of such data uncertainties, all model input parameters were varied widely both in deterministic and in probabilistic sensitivity analyses.

To summarise, in this study, we found that liver disease due to NAFLD is highly prevalent among patients with diabetes in primary care. While FIB-4 may miss young patients with normal liver function tests, we developed the BIMAST score which predicts the presence of significant fibrosis accurately in the community. We also provide evidence that screening for NAFLD is cost-effective, with the BIMAST score and fibroscan being the most cost-effective approaches compared to standard of care. Screening for fibrosis due to NAFLD should become part of the holistic assessment of patients with type-2 diabetes in primary care.

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NAFLD

Non-alcoholic fatty liver disease

T2DM

type-2 diabetes mellitus

LSM

liver stiffness measurement

SOC

standard of care

QALY

quality adjusted life year

ICER

incremental cost-effectiveness ratio

US

ultrasound

LFTs

liver function tests

NASH

Non-alcoholic steatohepatitis

EASL

European association for the study of the liver

GP

general practitioner

TE

transient elastography

IMD

index of multiple deprivation

CAP score

controlled attenuation parameter

BAFLD

Both alcoholic and non-alcoholic fatty liver disease

IQR

interquartile range

MLD

mild liver disease

SLD

significant liver disease

CC

compensated cirrhosis

DC

decompensated cirrhosis

HCC

hepatocellular carcinoma

BMI

body mass index

ALT

alanine aminotransferase

AST

aspartate aminotransferase

GGT

gamma-GT

PPV

positive predictive value

NPV

negative predictive value

	Study population N=287	NAFLD N=186	Normal liver N=73
	Median (IQR)	Median (IQR)	Median (IQR)	P value*
Age, years	59 (59-66)	60 (54-66)	59 (53-65)	0.83
Waist circum, cm	107 (107-116)	108 (101-118)	98 (92-106)	0.0001
Hip circum, cm	110 (102-119)	112 (105-122)	103 (98-108)	0.0001
BMI, kg/m²	30.8 (26.9-34.4)	31.4 (28.4-35.8)	26.9 (24.8-30.3)	0.0001
PLT, x 10⁹/µL	250 (202-290)	245 (212-287)	249 (206-298)	0.88
ALT, IU/L	35 (22-45)	34 (23-49)	24 (18-28)	0.0001
AST, IU/L	31 (22-35)	28 (23-37)	24 (19-27)	0.0001
GGT, IU/L	47 (19-50)	32 (22-52)	19 (17-27)	0.0001
ALP, IU/L	88 (70-103)	84 (72-105)	85 (63-99)	0.7
Albumin, g/L	40 (39-42)	41 (39-42)	40 (39-42)	0.83
Bilirubin, µmol/L	10.6 (6-12)	9 (6-12)	8 (6-14)	0.55
Total Cholesterol, mmol/l	4.1 (3.5-4.7)	4.1 (3.4-4.7)	4 (3.6-4.5)	0.58
TRG, mmol/l	2.3 (1.02-2.08)	1.4 (1.07-2.1)	1.2 (0.98-1.5)	0.25
HDL, mmol/l	1.1 (0.9-1.3)	1.1 (0.9-1.2)	1.16 (1.06-1.39)	0.25
LDL, mmol/l	2.3 (1.6-2.7)	2.2 (1.6-2.8)	2.1 (1.7-2.6)	0.68
Ferritin, ng/ml	124 (43-155)	82 (39-140)	70 (28-178)	0.91
Diabetes characteristics
	Median (IQR)	Median (IQR)	Median (IQR)	P value*
Fasting glucose, mmol/l	7.9 (5.5)	7.4 (5.6-10.2)	6.2 (4.8-7.8)	0.001
HbA1c, mmol/mol	60 (49-70)	60 (50-74)	55 (48-61)	0.0001
Insulin, µU/ml	24 (8.1-26.5)	15.3 (9.8-28.2)	7.2 (5.8-12.2)	0.028
Homa index	8 (1.9-8.95)	4.6 (2.2-10.3)	2.1 (1.35-4.8)	0.0001
Duration DM, years	11 (4-16)	10 (3-16)	13 (7-16)	0.16
	N (%)	N (%)	N (%)	P value*
Diet controlled	39 (13)	25 (13)	13 (18)	0.11
On oral agents	227 (79)	170 (91)	55 (75)	0.16
On GLP-1RA	37 (13)	31 (16)	6 (8)	0.08
On insulin	74 (25)	51 (28)	23 (31)	0.18
Diabetic complications	45 (16)	26 (14)	15 (21)	0.82
Ethnic background and comorbidities
	N (%)	N (%)	N (%)	P value*
Male gender	160 (53)	104 (56)	34 (45)	0.07
White, Caucasian	102 (32)	64 (34)	15 (20)	0.02
White, Hispanic	6 (2)	3 (1)	2 (2)	0.43
Black African, Afro-Caribbean	33 (12)	22 (12)	10 (13)	0.41
Arab	74 (28)	52 (28)	20 (26)	0.52
South Asian	47 (17)	31 (17)	16 (21)	0.2
East Asian	24 (8)	14 (7)	10 (13)	0.09
Hypertension	191 (67)	120 (64)	50 (66)	0.32
Dyslipidaemia	148 (52)	98 (53)	39 (52)	0.51
Psychiatric disorder	41 (15)	27 (14)	11 (14)	0.53
Previous ACE	28 (10)	16 (8)	11 (14)	0.98
On statin	214 (75)	138 (74)	57 (76)	0.31

Table 1. Characteristics of the study population and differences between patients with and without NAFLD. The table shows the differences between patients with (n=186) and without (n=73) NAFLD in the whole study population (n=287). Variables are expressed as median and IQR or relative percentages. * p-value refers to differences between patients with NAFLD and normal liver.

Abbreviations: IQR: interquartile range, BMI: Body mass index, PLT: platelet, ALT: alanine aminotransferase, AST: aspartate aminotransferase, GGT: gamma-glutamyl transferase, ALP: alkaline phosphatase, TRG: triglycerides, HDL: high density lipoprotein, LDL: low density lipoprotein, HbA1c: glycated haemoglobin, GLP-1RA: glucagon like peptide-1 receptor agonist

	NAFLD, LSM≥8.1 kPa N=50	NAFLD, normal LSM N=136
	Median (IQR)	Median (IQR)	P value*
Age, years	60 (51-65)	61 (54-65)	0.49
Waist circum, cm	120 (112-127)	105 (99-113)	0.0001
Hip circum, cm	123 (123-132)	110 (103-119)	0.0001
BMI, kg/m²	36.8 (32-39.7)	30.3 (27.6-33.6)	0.0001
PLT, x 10⁹/uL	231 (198-266)	255 (215-300)	0.3
ALT, IU/L	46 (25-60)	30 (22-43)	0.0001
AST, IU/L	37 (28-48)	26 (22-32)	0.0001
GGT, IU/L	62 (35-96)	27 (19-39)	0.0001
ALP, IU/L	83 (70-110)	84 (72-101)	0.62
Albumin, g/L	40 (38-41)	41 (39-42)	0.06
Bilirubin, µmol/L	10 (7-16)	8 (6-11)	0.55
Total Cholesterol, mmol/l	3.9 (3.4-4.4)	4.1 (3.5-4.8)	0.14
TRG, mmol/l	1.3 (1.08-2.2)	1.5 (1.06-2.1)	0.92
HDL, mmol/l	1.1 (0.9-1.2)	1.08 (0.9-1.3)	0.51
LDL, mmol/l	1.9 (1.6-2.6)	2.2 (1.6-2.8)	0.42
Ferritin, ng/ml	108 (48-182)	81 (36-124)	0.31
Diabetes characteristics
	Median (IQR)	Median (IQR)	P value*
Fasting glucose, mmol/l	9.4 (6.2-13.4)	6.7 (5.2-9.2)	0.001
HbA1c, mmol/mol	71 (56-84)	59 (49-68)	0.0001
Insulin, µU/ml	21 (14-37.2)	12.4 (9-25)	0.001
Homa index	8.1 (4.5-14.1)	3.3 (2.1-8.4)	0.001
Duration DM, years	10 (4-16)	10 (3-16)	0.46
	N (%)	N (%)	P value*
Diet controlled	1 (2)	24 (17)	0.052
On oral agents	43 (86)	127 (93)	0.051
On GLP-1-RA	10 (20)	21 (15)	0.07
On insulin	15 (30)	36 (26)	0.25
Diabetic complications	10 (20)	16 (12)	0.82
Ethnic background and comorbidities
	N (%)	N (%)	P value*
Male gender	29 (58)	75 (55)	0.44
White, Caucasian	20 (40)	45 (33)	0.22
White, Hispanic	1 (2)	2 (1)	0.61
Black African, Afro-Caribbean	4 (8)	18 (13)	0.23
Arab	15 (30)	37 (27)	0.43
South Asian	8 (16)	22 (16)	0.47
East Asian	2 (4)	12 (9)	0.21
Hypertension	33 (66)	87 (63)	0.45
Dyslipidaemia	27 (54)	71 (52)	0.46
Psychiatric disorder	9 (18)	19 (13)	0.28
Previous ACE	3 (6)	13 (9)	0.29
On statin	39 (78)	99 (76)	0.32

Table 2. Differences between NAFLD patients stratified per liver stiffness measurement greater than 8.1 kPa. The table shows the differences between patients with NAFLD with elevated (n=50) and normal (n=136) LSM. Variables are expressed as median and IQR or relative percentages. * p-value: differences between patients with NAFLD with elevated LSM and normal LSM.

Abbreviations: IQR: interquartile range, BMI: Body mass index, PLT: platelet, ALT: alanine aminotransferase, AST: aspartate aminotransferase, GGT: gamma-glutamyl transferase, ALP: alkaline phosphatase, TRG: triglycerides, HDL: high density lipoprotein, LDL: low density lipoprotein, HbA1c: glycated haemoglobin, GLP-1RA: glucagon like peptide-1 receptor agonist

Table 3. Predictive factors for the presence of significant liver disease in the whole diabetic population. The table shows predictive factors for LSM ≥8.1 kPa on multivariate analysis. Education rank is derived from the Index of multiple deprivation.

*Homa-index was calculated only in those not on insulin treatment.

Abbreviations: OR: odds ratio, 95%CI: 95% confidence interval, BMI: Body Mass Index, ALT: Alanine aminotransferase, AST: aspartate aminotransferase, HbA1c: glycated haemoglobin

*Screening strategy 1: US + LFTs*	Screening	Standard of care
Discounted life expectancy, entire cohort (years) 3,751.88 3,594.85
QALYs gained, entire cohort (years) Increase in correct diagnoses compared to baseline screening (%) Lifetime discounted per person cost (£) Incremental cost per person (£) Incremental cost-effectiveness ratio (£/QALY)	138.19 10.73 13,542.93 0.0008 2,337.92	- - 12,295.53 - -
*Screening strategy 2: FIB-4*
Discounted life expectancy, entire cohort (years) 3,747.62 3,594.85
QALYs gained, entire cohort (years) Increase in correct diagnoses compared to baseline screening (%) Lifetime discounted per person cost (£) Incremental cost per person (£) Incremental cost-effectiveness ratio (£/QALY)	134.07 8,29 13,361.87 0.0009 2,059.98	- - 12,295.53 - -
*Screening strategy 3: NAFLD fibrosis score*
Discounted life expectancy, entire cohort (years) QALYs gained, entire cohort (years) Increase in correct diagnoses compared to baseline screening (%) Lifetime discounted per person cost (£) Incremental cost per person (£) Incremental cost-effectiveness ratio (£/QALY)	3,734.50 121.25 -2.32 13,275.08 0.0010 2,092.47	3,594.85 - - 12,295.53 - -
*Screening strategy 4: BIMAST score*
Discounted life expectancy, entire cohort (years) QALYs gained, entire cohort (years) Increase in correct diagnoses compared to baseline screening (%) Lifetime discounted per person cost (£) Incremental cost per person (£) Incremental cost-effectiveness ratio (£/QALY	3,754.88 141.01 10.76 13,366.51 0.0009 1,967.11	3,594.85 - - 12,295.53 - -
*Screening strategy 5: Fibroscan (LSM)*
Discounted life expectancy, entire cohort (years) QALYs gained, entire cohort (years) Increase in correct diagnoses compared to baseline screening (%) Lifetime discounted per person cost (£) Incremental cost per person (£) Incremental cost-effectiveness ratio (£/QALY)	3,762.89 148.73 15.05 13,717.67 0.0007 2,476.57	3,594.85 - - 12,295.53 - -

Table 4. Base-case cost-effectiveness analysis of NAFLD screening strategies versus standard of care (baseline screening).

Statement of interests: none.

Financial support: RF is the recipient of the EASL PhD fellowship Juan Rodes 2018. BHM is the recipient of an NIHR Academic Clinical Lectureship (CL-2019-21-002). The Division of Digestive Diseases receives financial support from the National Institute of Health Research (NIHR) Biomedical Research Centre (BRC) based at Imperial College Healthcare NHS Trust and Imperial College London.

BIMASTandcosteffectivenesspaperSupplementarymaterial.docx

Clinical and cost-effectiveness analysis of community-based screening strategies for non-alcoholic fatty liver disease in patients with type-2 diabetes mellitus

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Methods

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3. Results

4. Discussion

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