2Veterans Affairs Long Beach Healthcare System, Long Beach, California, United States of America
3Departments of Medicine and Surgery, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California, United States of America
4World Wide Health Economics and Outcomes Research, Bristol-Myers Princeton New Jersey, United States of America
Methods: A cohort of patients was selected from the Veterans Health Affairs (VHA) Hepatitis C Clinical Case Registry (Hep C CCR) of confirmed HCV patients. The primary outcomes were time to death and time to a composite for first liver-related clinical events. Cox proportional hazards models were estimated using time to a patient's FIB-4 exceeding 1.45 or 3.25 as the risk factor of interest. Cox models were performed to examine the risk for patients using age, gender, genotype, race, ethnicity, BMI, prior hospitalizations and HIV and hepatitis B co-infection.
Results: 187,860 patients met study requirements. Patients whose FIB-4 level exceeded 3.25 were at significantly higher risk of death [Hazard Ratio (HR) = 3.56 (3.47-3.65)] and an adverse liverrelated clinical event [HR = 4.01 (3.92-4.10)]. Exceeding FIB-4 > 1.45 was also associated with a significant but smaller increased risk of death [HR = 2.27 (2.21-2.33)] and the composite event [HR = 2.23 ([2.18-2.28)].
Conclusion: FIB-4 is a significant predictor of risk, even at the lower threshold (1.45).
Keywords: Hepatitis C; Morbidity and Mortality; Cox proportional hazards models; FIB-4
Standard therapy prior to 2011 [Pegylated interferon plus Ribavirin] was commonly met with patient reluctance to initiate therapy. For example, our previous analysis of VA patients with a detectable viral load at baseline found that less than 25% of patients were ever treated and only 4% of patients with a detectable viral load ever achieved an undetectable viral load [14]. As new therapies began to emerge, patients and their physicians faced a decision whether or not to initiate interferonbased therapy or wait for better treatment options [15]. These treatment options are now available but at very high cost per course of therapy. This welcomed change in clinical options has created a demand for information on how best to focus limited resources on HCV patients at highest risk for adverse clinical events, especially in managed care plans and government programs in the U.S. and Europe.
The purpose of this study is to evaluate the feasibility of using a single clinical marker to monitor the progression of HCV as measured by the risk of future adverse events and death. Specifically, we investigate the use of the FIB-4 score, a noninvasive biomarker of fibrosis, as a predictor of future risk of liver related events and death. The FIB-4 score is used to estimate liver fibrosis stages, with a FIB-4 index > 3.25 having been found to have a positive predictive value of 82.1% to confirm the existence of significant fibrosis in a HCV infected cohort, while a FIB-4 index < 1.45 found to have a negative predictive value of 94.7% [16]. Recent research has further explored the use of FIB-4 as well as gender as predictors of HCV disease progression [17,18].
To better accomplish this goal, we expanded our earlier sample of VA/HCV patients with a detectable viral load at baseline to include patients with or without a baseline viral load but with sufficient data to calculate at least one FIB-4 score over time. Using this larger VA cohort, we tested the hypothesis that FIB-4 index values of greater than 1.45 and 3.25 are associated with increased risk of liver-related complications and death.
Multivariate statistical models will be estimated to better clarify the utility of FIB-4 as clinical marker for HCV progression. These models will generate estimates of the effects of numerous other risk factors. Previous research has documented the impact of age, male gender, alcohol consumption, HIV co-infection and a fatty liver on the likelihood of disease progression [19]. BMI and Hispanic ethnicity have been found to be associated with disease progression [20], while African Americans may have a lower rate of disease progression relative to white patients [20-22]. Results from our earlier analysis of 128,769 VA patients with detectable viral loads at baseline found that black patients were at lower risk for the composite late-stage liver event [HR = 0.72 (0.71- 0.72)] and death [HR = 0.65 (0.62-0.67)] than white patients. But more importantly, this study documented that achieving viral suppression reduced risk of the composite clinical endpoint by 27% [HR = 0.73 (0.66-0.82)] and the risk of death by 45% [HR = 0.55 (0.47-0.64)] [14].
The impact of viral genotype on the risk of future liverrelated events and death is much less clear. Preliminary data suggested patients with genotype 1 may be at higher risk of disease progression [23]. However, follow up studies did not confirm these observations [5, 24]. More recent studies have found that, genotype 3 carries an increased risk of worse clinical outcome [25-27]. Our previous study using VA patients found that patients with genotype 2 were at significantly lower risk, and patients with genotype 3 were at higher risk for all study outcomes relative to genotype 1 (p < 0.01 for all estimates)[14].
Other studies have looked at the impact of laboratory tests on disease progression and death including albumin, AST/ALT ratio, and platelets [28-30]. The results of our recently published analysis of the VA data identified 5 laboratory tests associated with increased risk [31]. The estimated hazard ratios for the composite of liver-related complications/death were 1.35/1.84 for the AST/ALT ratio > 1; 2.35/5.01 for albumin < 3 g/dl; 1.58/1.15 for GGT > 195 IU/L; 3.85/1.55 for platelet count < 100 k/mm2 and 4.48/2.39 for alpha fetoprotein > 144 ng/mL. But more importantly, this analysis determined that patients who delayed starting drug therapy until after any one of the above lab tests became abnormal significantly reduced the effectiveness of drug therapy in reducing the risk of adverse clinical events and death.
HCV patients included in the CCR were initially identified using routine computer-based scans of the Electronic Medical Record (EMR) data for the presence of an HCV-related ICD-9 diagnosis code [see Appendix 1] or a positive HCV exposure assessment using the Hepatitis C Antibody Test, the Hepatitis C Recombinant Immunoblot Assay [RIBA] or the Qualitative Hepatitis C RNA Test. A local CCR coordinator was provided a list of all newly identified HCV patients. The local CCR coordinator removed a patient from the CCR manually if they determine that the patient had been included in the HCV/CCR erroneously. Upon this confirmation, all historical data from the patient's EMR were pulled and added to the CCR. The VA EMR system was fully implemented in 1999 and the data period for this study covers the entire time period over which EMR data were available from all VA regions from 1999 to 2010 [32].
An intermediate patient-level analytic database was created consisting of summary variables for each month before and after the patient's HCV confirmation date [index date], defined as earliest date of detectable HCV viral load or genotype. The following summary data were created:
2. The patient's diagnostic profile was created consisting of monthly dichotomous variables reflecting the diagnoses recorded each month.
3. Monthly dichotomous variables were created for hospital admissions for any diagnosis and liver related diagnoses.
4.Prescription drug data were used to create monthly dichotomous variables indicating when patients received HCV-related treatment [peg-interferon alfa [2a or 2b] , interferon alfa [2a or 2b] and interferon alfacon-1]. The use of ribavirin as monotherapy was not considered to be a drug therapy for HCV.
5. Monthly values for most common laboratory tests, including viral load [VL] and viral genotype were created. These laboratory data were used to calculate an FIB- 4 score in those months in which sufficient data were available. Missing values were assigned when no tests were recorded during the month. These FIB-4 values were then used to calculate the patient's time to exceeding the FIB-4 levels under study [1.45, 3.25] as the patient's FIB-4 level changed over time. The specification of the patient's FIB-4 level as a time dependent variable allows us to test the temporal relationship between changes in FIB-4 and patient outcomes. For example, in the analysis using the critical FIB-4 value of 3.25, the estimated effect of the FIB-4 variable measures the impact on patient risk of those patient having exceeded an FIB-4 > 3.25. but only if this level is exceeded before the event.
6.The objective of treatment is to suppress the patients HCV viral load to undetectable levels. Another important factor of interest of this research was to document the impact of viral load suppression, while taking into account the temporal relationship between achieving an undetectable viral load and event dates. To achieve this, we specified undetectable viral load as a time dependent variable. This specification represents a practical improvement in the real world data analysis relative to Sustained Viral Response [SVR], the gold standard for measuring treatment response in clinical trials. Whether or not the patient has achieved an undetectable viral load will be updated in the Cox model whenever a more recent measurement is available regardless of the interval between tests. This time-dependent specification can help better capture the long-term sustainability of viral suppression beyond 6 months.
We also conducted sensitivity analyses of the validity of the FIB-4 as a risk factor for predicting adverse events and death in HCV patient by replacing the FIB-4 with the AST to Platelet Ratio Index (APRI), a serological marker that has satisfactory sensitivity and specificity together with a high predictive value of fibrosis. The correlation of APRI with significant fibrosis and cirrhosis has been evaluated in various studies and patient cohorts [33-36]. The advantages of both the FIB-4 and APRI are that they utilize readily available laboratory though the APRI has not been validated in terms of following patients.
A patient's FIB-4 is a significant predictor of the risk of death. Patients whose FIB-4 value exceeds 1.45 at some point in their
With FIB-4 data |
No FIB-4 data |
||||
---|---|---|---|---|---|
Demographic characteristics |
N |
% |
N |
% |
P - value |
Age [Mean ± SD] |
52.32 ± 7.84 |
53.88 ± 8.31 |
< 0.0001 |
||
Male (n, %) |
182014 |
96.89 |
44093 |
96.77 |
0.2003 |
Ethnicity |
N |
% |
N |
% |
|
Hispanic |
11241 |
5.98 |
2176 |
4.78 |
< 0.0001 |
Non-Hispanic |
150086 |
79.89 |
35564 |
78.05 |
|
Multi-ethnic |
1037 |
0.55 |
55 |
0.12 |
|
Unknown |
25496 |
13.57 |
7769 |
17.05 |
|
Race |
N |
% |
N |
% |
|
White |
95486 |
50.83 |
23794 |
52.22 |
< 0.0001 |
Black |
54424 |
28.97 |
11252 |
24.69 |
|
Mixed |
3519 |
1.87 |
445 |
0.98 |
|
Other |
2302 |
1.23 |
690 |
1.51 |
|
Unknown |
32129 |
17.1 |
9383 |
20.59 |
|
Diabetes prior |
31269 |
16.64 |
7840 |
17.21 |
0.004 |
Hospitalization prior |
36925 |
19.66 |
6335 |
13.90 |
< 0.0001 |
Viral load at baseline |
N |
% |
N |
% |
|
Missing [no baseline readings] |
38193 |
20.33 |
5240 |
11.50 |
< 0.0001 |
Detectable |
144108 |
76.71 |
38240 |
83.93 |
|
Undetectable |
5559 |
2.96 |
2084 |
4.57 |
|
Ever treated |
39651 |
21.11 |
6345 |
13.93 |
< 0.0001 |
Genotype |
N |
% |
N |
% |
< 0.0001 |
Missing |
66663 |
35.49 |
22374 |
49.10 |
< 0.0001 |
1 |
96365 |
51.30 |
18251 |
40.06 |
|
2 |
13966 |
7.43 |
2875 |
6.31 |
|
3 |
9307 |
4.95 |
1811 |
3.97 |
|
other |
1559 |
0.83 |
253 |
0.56 |
|
Baseline FIB-4* |
N |
% |
N |
% |
|
Missing |
27572 |
14.68 |
45564 |
100 |
|
< 1.45 |
74702 |
39.76 |
0 |
0 |
|
1.45-3.25 |
57274 |
30.49 |
0 |
0 |
|
>3.25 |
28312 |
15.07 |
0 |
0 |
FIB-4 =
|
FIB-4 > 1.45 |
FIB-4 > 3.25 |
---|---|---|
|
N = 187,860 |
|
Number of Events [%] |
29,316 [15.6%] |
|
FIB-4 > Critical value |
2.27*** [2.21-2.33] |
3.56*** [3.47-3.65] |
Achieved undetectable VL |
0.71*** [0.67-0.75] |
0.78*** [0.72-0.83] |
Gender [Male] |
1.63*** [1.49-1.79] |
1.65*** [1.50-1.81] |
Age [vs. < 45] |
|
|
45-65 |
1.49*** [1.43-1.55] |
1.54*** [1.47-1.60] |
> 65 |
2.53*** [2.40-2.67] |
2.64*** [2.50-2.79] |
Race [vs White] |
|
|
Black |
0.71*** [0.69-0.73] |
0.79*** [0.77-0.82] |
Mixed |
0.73*** [0.67-0.81] |
0.77*** [0.70-0.84] |
Other |
0.83** [0.74-0.93] |
0.83** [0.74-0.94] |
Unknown |
0.88*** [0.85-0.92] |
0.91*** [0.87-0.95] |
Ethnicity [vs non-Hispanic] |
|
|
Hispanic |
0.97 [0.92-1.02] |
0.91* [0.87-0.96] |
Mixed |
0.65*** [0.53-0.78] |
0.61*** [0.50-0.74] |
Other/Unknown |
2.18*** [2.09-2.27] |
2.07*** [1.99-2.15] |
Prior Admission [6 months] |
1.63*** [1.59-1.68] |
1.63*** [1.59-1.68] |
HCV Genotype [vs 1] |
|
|
2 |
0.92** [0.87-0.92] |
0.95* [0.90-1.00] |
3 |
1.08** [1.02-1.14] |
1.02 [0.97-1.08] |
Missing |
1.51*** [1.47-1.55] |
1.50*** [1.46-1.54] |
other |
0.91 [0.78-1.05] |
0.90 [0.78-1.04] |
Body Mass Index |
|
|
< 25 |
1.27*** [1.24-1.31] |
1.28*** [1.24-1.31] |
> 30 |
1.03 [1.00-1.06] |
1.02 [1.00-1.05] |
Missing |
1.16*** [1.08-1.24] |
1.11*** [1.04-1.19] |
Diagnosis at baseline |
|
|
Diabetes |
1.68*** [1.64-1.73] |
1.66*** [1.61-1.70] |
HIV |
1.41*** [1.20-1.66] |
1.41*** [1.20-1.66] |
HBV |
1.07 [0.99-1.16] |
1.05 [0.97-1.13] |
|
FIB-4 > 1.45 |
FIB-4 > 3.25 |
---|---|---|
Patient Characteristics |
N = 180,789 |
|
Number of Events [%] |
52,863 [29.2%] |
|
FIB-4 > Critical Value |
2.23*** [2.18-2.28] |
4.01*** [3.92-4.10] |
Achieved undetectable VL |
0.68*** [0.64-0.72] |
0.71*** [0.67-0.76] |
Gender [Male] |
1.10** [1.04-1.18] |
1.13** [1.06-1.20] |
Age [vs. < 45] |
|
|
45-65 |
0.86*** [0.83-0.89] |
0.90*** [0.88-0.93] |
> 65 |
0.58*** [0.55-0.62] |
0.59*** [0.55-0.63] |
Race [vs White] |
|
|
Black |
0.77*** [0.75-0.79] |
0.83*** [0.81-0.85] |
Mixed |
1.11*** [1.04-1.19] |
1.16*** [1.08-1.24] |
Other |
0.84** [0.76-0.92] |
0.84** [0.76-0.92] |
Unknown |
0.61*** [0.58-0.64] |
0.63*** [0.60-0.66] |
Ethnicity [vs non-Hispanic] |
|
|
Hispanic |
1.26*** [1.21-1.31] |
1.20*** [1.16-1.26] |
Mixed |
1.42*** [1.26-1.58] |
1.38*** [1.24-1.55] |
Other/Unknown |
0.97 [0.92-1.01] |
0.94** [0.89-0.98] |
Prior Admission [6 mo.] |
1.56*** [1.52-1.60] |
1.53*** [1.50-1.57] |
HCV Genotype [vs 1] |
|
|
2 |
0.85*** [0.82-0.88] |
0.87*** [0.83-0.90] |
3 |
1.07** [1.03-1.12] |
1.02 [0.97-1.06] |
Missing |
0.62**** [0.60-0.63] |
0.61**** [0.59-0.62] |
other |
0.93 [0.84-1.04] |
0.92 [0.83-1.02] |
Body Mass Index |
|
|
< 25 |
0.93*** [0.91-0.95] |
0.92*** [0.90-0.94] |
> 30 |
1.05** [1.02-1.07] |
1.05** [1.02-1.07] |
Missing |
0.43*** [0.39-0.48] |
0.42*** [0.38-0.46] |
Diagnosis at baseline |
|
|
Diabetes |
1.18*** [1.15-1.21] |
1.16*** [1.13-1.19] |
HIV |
0.89 [0.76-1.04] |
0.89 [0.76-1.04] |
HBV |
0.96 [0.89-1.04] |
0.95 [0.88-1.02] |
These results are relevant to the rational use of the newest therapies emerging onto the market with very high cure rates
EVENT Number of Events |
FIB-4 > 1.45 |
FIB-4 > 3.25 |
APRI > 0.70 |
APRI > 1.00 |
---|---|---|---|---|
Death N = 29,316 [15.6%] |
2.27*** [2.21-2.33] |
3.56*** [3.47-3.65] |
0.87*** [0.83-0.90] |
2.62*** [2.56-2.68] |
Composite Event N = 52,863 [29.2%] |
2.23*** [2.18-2.28] |
4.01*** [3.92-4.10] |
0.97 [0.94-1.00] |
3.06*** [2.99-3.12] |
Cirrhosis N = 25,791 [14.3%] |
7.42*** [7.10-7.75] |
10.14*** [9.84-10.44] |
0.90*** [0.86-0.94] |
7.84*** [7.60-8.09] |
Decompensate Cirrhosis N = 12,313 [6.6%] |
23.74*** [21.48-26.25] |
18.54*** [17.69-19.43] |
0.75*** [0.70-0.80] |
12.89*** [12.27-13.55] |
HCC N = 6,837 [3.7%] |
9.02*** [8.23-9.88] |
8.85*** [8.38-9.34] |
0.86** [0.80-0.94] |
7.59*** [7.17-5.04] |
Liver-related Hospitalization N = 43,960 [23.4%] |
1.91*** [1.86-1.95] |
3.24*** [3.17-3.32] |
0.93*** [0.90-0.96] |
2.53*** [2.48-2.59] |
There are several important technical limitations in our study. First the VA study population differs significantly from the U.S population, consisting mostly of non-Asian men. Therefore, results for the risk associated with gender and the catch-all category of 'other race' should be viewed with caution. Nevertheless, most of the US patients with HCV are male [3-4], and the VA is the largest provider of care to chronically HCVinfected patients in the United States [45].
We do not measure Sustained Viral Response [SVR], which has been shown to reduce risk of mortality and disease progression [26,28,46]. SVR requires that an undetectable viral load be maintained for six months following the termination of treatment, a requirement that is difficult to document even in an EMR environment. Instead, we used time-dependent specification of undetectable VL variable, which is a more practical measure of viral suppression in this real world data analysis, and is a proxy for treatment in the majority of the cases.
This study does not estimate or control for the effects of treatment on clinical endpoints and death. This was done for two reasons. First, viral suppression without treatment is exceeding rare. Second, the parameters with which to determine if a patient completed an adequate course of therapy vary by genotype and other factors, such as allowable duration or breaks in the treatment. While developing counts of continuous days of therapy have been used by this research team in the past [47], we elected to use viral load suppression as our measure of treatment success. The effect of treatment and viral suppression before and after a patient has crossed these FIB-4 thresholds is also unknown and should be investigated further.
Finally, our study does not capture medical care outside the VA system, such as the Medicare program, which may cloud the relationship between viral load suppression and event risk. The potential for missing Medicare data lead us to enter age as a categorical variable and the "protective" effects of age > 65 for hospitalization likely reflects the availability of Medicare coverage for this age group and is consistent with our mixed results on the effect of age on the risk of events.
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