Abstract
Complications affecting multiple organs, including the liver, contribute to early mortality in patients with sickle cell disease (SCD). Earlier diagnosis and monitoring of "sickle hepatopathy", preferably using non-invasive methods, would enable earlier intervention and therefore, potentially a better outcome for patients.
In many non-SCD chronic liver diseases, fibrosis stage is the most important predictor of morbidity and mortality. Two non-invasive methods of assessing liver fibrosis are used: Transient Elastography (TE, Echosens) and Enhanced Liver Fibrosis Score™ (ELF, iQUR). TE detects fibrosis using ultrasound and low frequency elastic waves with a propagation velocity directly related to the stiffness of the liver. The ELF score examines the balance between matrix deposition and degradation using levels of three proteins analysed using a patented formula.
We report a prospective study correlating liver function tests (LFTs) with the level of liver fibrosis (as ascertained by TE and ELF) in a cohort of patients with SCD. Ethical approval for this study was obtained from the NHS ethics committee (11/LO/0005) and patients were consecutively recruited in the steady-state sickle cell clinic at Kings' College Hospital, London, during 2012. Patients were excluded if they had viral hepatitis or were pregnant. Clinical (transfusion and hydroxyurea therapy), imaging (liver iron concentration, LIC) and laboratory data were collected during steady-state.
TE and ELF were performed on 194 patients. The patients ranged from 17-72 years (mean age 35 years); 78 (40%) were male, 134 (68%) had HbSS or HbSb0 (SCA), 48 (26%) Hb SC, and the remainder, Hb Sβ+ (excluded due to low numbers). Statistical analysis was undertaken in IBM SPSS version 20.
There was significant correlation between both TE and ELF with age, when corrected for sickle genotype (TE β = 0.19, p = 0.006 and ELF β = 0.2, p = 0.005) (Figure). Patients with SCA had significantly higher TE results and mean ELF scores than those with HbSC (TE, 6.8 vs 5.3, p<0.0001 and ELF, 9.2 vs 8.6 p <0.0001) (Table).
In SCA patients, TE correlated significantly with all serum LFTs (Albumin R = -0.35 p<0.0001, AST R = 0.44 p<0.0001, ALP R = 0.29 p<0.0001, GGT R = 0.40 p<0.0001, conjugated bilirubin R = 0.26 p = 0.004). Positive correlation was found with LDH (R = 0.24 p = 0.004) and negative correlation with Hb (R= -0.25 p = 0.002). In the Hb SC group, TE correlations were weaker for AST (R = 0.39 p = 0.004), ALP (R = 0.30 p = 0.03), WBC (R = 0.39 p = 0.02) and reticulocyte count (R = 0.35 p = 0.01). All markers of iron loading correlated with TE values, when corrected for sickle genotype (serum ferritin β = 0.25, p <0.0001, total top-up units β = 0.22, p = 0.001, total units transfused β = 0.25, p <0.0001 and LIC β = 0.32, p = 0.046).
In SCA patients, ELF score correlated with serum LFTs (Albumin R = -0.30 p<0.0001, AST R = 0.39 p<0.0001, ALP R = 0.25 p = 0.003, GGT R = 0.28 p = 0.001, conjugated bilirubin R = 0.36 p<0.0001). Positive correlation was seen with LDH (R = 0.26 p = 0.002) and negative correlation with Hb (R = -0.25 p = 0.004). Negative correlation was also seen with HbF levels (R = -0.24 p = 0.01). In the HbSC group, there were no significant correlations between ELF and serum LFTs. However associations were seen between ELF score and LDH (R = 0.40 p = 0.004) and Hb level (R = -0.31 p = 0.01). ELF score correlated with serum ferritin (β = 0.25 p <0.0001), and total blood transfusion units (β = 0.24 p = 0.001).
These data show significant levels of liver dysfunction in our SCD population using both TE and ELF score which correlated significantly with abnormal LFTs and markers of hemolysis and iron overload. The role of TE and ELF in monitoring liver dysfunction in SCD needs to be further validated, preferably with longitudinal, and if possible histological data.
. | Whole cohort n = 194 (%) . | SCA n = 134 (%) . | Hb SC n = 48 (%) . | p value . |
---|---|---|---|---|
FibroScan results (kPa) | ||||
Mean (range) | 6.3 (2.0 - 21.3) | 6.8 (2.0 - 21.3) | 5.3 (2.0 - 16.0) | < 0.0001 |
None/mild (0-7.65) | 153 (79) | 98 (73) | 43 (90) | |
Moderate (7.66-13.00) | 33 (17) | 29 (22) | 4 (8) | |
Severe (≥13.01) | 8 (4) | 7 (5) | 1 (2) | |
ELF score | ||||
Mean (range) | 9.1 (7.1 - 11.6) | 9.2 (7.1 - 11.6) | 8.6 (7.5 - 10.3) | <0.0001 |
None/mild (≤9.7) | 164 (84) | 107 (79) | 46 (96) | |
Moderate (9.8 - 11.2) | 27 (14) | 24 (19) | 2 (4) | |
Severe (≥11.3) | 3 (2) | 3 (2) | 0 (0) |
. | Whole cohort n = 194 (%) . | SCA n = 134 (%) . | Hb SC n = 48 (%) . | p value . |
---|---|---|---|---|
FibroScan results (kPa) | ||||
Mean (range) | 6.3 (2.0 - 21.3) | 6.8 (2.0 - 21.3) | 5.3 (2.0 - 16.0) | < 0.0001 |
None/mild (0-7.65) | 153 (79) | 98 (73) | 43 (90) | |
Moderate (7.66-13.00) | 33 (17) | 29 (22) | 4 (8) | |
Severe (≥13.01) | 8 (4) | 7 (5) | 1 (2) | |
ELF score | ||||
Mean (range) | 9.1 (7.1 - 11.6) | 9.2 (7.1 - 11.6) | 8.6 (7.5 - 10.3) | <0.0001 |
None/mild (≤9.7) | 164 (84) | 107 (79) | 46 (96) | |
Moderate (9.8 - 11.2) | 27 (14) | 24 (19) | 2 (4) | |
Severe (≥11.3) | 3 (2) | 3 (2) | 0 (0) |
No relevant conflicts of interest to declare.
Author notes
Asterisk with author names denotes non-ASH members.