記住我
ORIGINAL ARTICLE
Year : 2023 | Volume
: 22
| Issue : 4 | Page : 456-464
Assessment of factors affecting response of direct-acting antivirals in chronic hepatitis C patients
Nipun Jain1, Ravinder Garg1, Gagan Preet Singh2, Sarabjot Kaur1, Sumit Pal Singh Chawla1, Preeti Padda3
1 Department of Medicine, Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, India
2 Department of Community Medicine, Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, India
3 Department of Community Medicine, Government Medical College, Amritsar, Punjab, India
Correspondence Address:
Sarabjot Kaur
Department of Medicine, Guru Gobind Singh Medical College and Hospital, Sadiq Road, Faridkot - 151 203, Punjab
India
Source of Support: None, Conflict of Interest: None
CheckDOI: 10.4103/aam.aam_183_22
Abstract
Background: Hepatitis C virus (HCV) is a universally prevalent pathogen and a major cause of liver-related morbidity and mortality worldwide. The evolution of antiviral therapy for HCV has rapidly progressed from interferon (IFN)-based therapies to IFN-free combinations of direct-acting antivirals (DAAs). Aims: This study aims to assess the response of DAAs in chronic hepatitis C (CHC) patients and to study the various factors affecting the response of DAAs in CHC. Settings and Design: This longitudinal observational study spanning over a year was conducted in the Medicine department of a tertiary care teaching hospital. Materials and Methods: The study was conducted on 400 adult CHC patients, diagnosed by a positive anti-HCV antibody test and a detectable viral load (HCV RNA) by real time polymerase chain reaction (RT-PCR), registered for treatment with DAAs. The first 400 patients satisfying the eligibility criteria were enrolled by non-probability consecutive sampling. All the participants were treated as per the National Viral Hepatitis Control Programme (NVHCP) guidelines. Repeat HCV viral load was done at or after 12 weeks of completion of anti-viral therapy to ascertain sustained virological response (SVR). Various factors which might predict treatment response were analyzed. Statistical Analysis Used: The continuous variables were expressed as mean and standard deviation, while the categorical variables were summarized as frequencies and percentages. The Student's independent t-test was employed for the comparison of continuous variables. The Chi-square or Fisher's exact test, whichever is appropriate, was employed for the comparison of categorical variables. Multivariate Logistic Regression was used to identify the independent predictors of treatment nonresponse. A P < 0.05 was considered statistically significant. Results: The mean age of the subjects was 42.3 ± 15.23 years with a male-to-female ratio of 1.96:1. Most of the patients (80.5%) were non-cirrhotic; among 19.5% cirrhotic, 13% were compensated while 6.5% were decompensated cirrhotic. The overall SVR done at or after 12 weeks of completion of treatment was 88.75%. Age, gender distribution, occupation, socioeconomic status, educational status, body mass index, treatment regimen, duration of treatment, and baseline viral load did not alter the treatment response. Among comorbidities, only diabetes mellitus (DM) and human immunodeficiency virus (HIV) co-infection adversely affected the treatment response (P = 0.009 and P < 0.001, respectively). Intravenous (IV) drug abuse was significantly associated with treatment failure (P < 0.001). The presence of liver cirrhosis (P < 0.001), thrombocytopenia (P < 0.001), elevated transaminases (alanine transaminase: P = 0.021, aspartate transaminase: P < 0.001), and previous treatment experience (P = 0.038) were other significant predictors of treatment failure. Conclusions: DAAs are highly efficacious drugs in the treatment of CHC with a high rate of treatment response. Significant predictors of CHC treatment failure included comorbidities especially DM and HIV co-infection, IV drug abuse, presence of liver cirrhosis, thrombocytopenia, elevated transaminases, and previous treatment experience. However, independent predictors of treatment nonresponse observed in this study were thrombocytopenia, IV drug abuse, and liver cirrhosis.
Résumé
Contexte: Le virus de l'hépatite C (VHC) est un agent pathogène universellement répandu et une cause majeure de morbidité et de mortalité liées au foie dans le monde. L'évolution de la thérapie antivirale pour le VHC a rapidement progressé des thérapies à base d'interféron (IFN) à des combinaisons sans IFN de médicaments à action directe antiviraux (AAD). Objectifs: Cette étude vise à évaluer la réponse des AAD chez les patients atteints d'hépatite C chronique (HCC) et à étudier les différents facteurs affectant la réponse des AAD dans les CHC. Cadres et conception : Cette étude observationnelle longitudinale s'étalant sur un an a été menée dans le département de médecine d'un hôpital universitaire de soins tertiaires. Matériels et méthodes: L'étude a été menée sur 400 patients adultes atteints d'HCC, diagnostiqués par un test d'anticorps anti-VHC positif et une charge virale détectable (ARN du VHC) par réaction en chaîne par polymérase en temps réel, inscrit pour le traitement par DAA. Les 400 premiers patients répondant aux critères d'éligibilité ont été enrôlés par échantillonnage consécutif non probabiliste. Tous les participants étaient traités conformément aux directives du programme national de contrôle de l'hépatite virale. La charge virale répétée du VHC a été effectuée à ou après 12 semaines d'achèvement traitement antiviral pour déterminer la réponse virologique soutenue (RVS). Divers facteurs susceptibles de prédire la réponse au traitement ont été analysés. Analyse statistique utilisée: les variables continues ont été exprimées sous forme de moyenne et d'écart-type, tandis que les variables catégorielles ont été résumés sous forme de fréquences et de pourcentages. Le test t indépendant de Student a été utilisé pour la comparaison des variables continues. Le chi carré ou Le test exact de Fisher, selon le cas, a été utilisé pour la comparaison des variables catégorielles. La régression logistique multivariée a été utilisée identifier les prédicteurs indépendants de la non-réponse au traitement. A P < 0.05 était considéré comme statistiquement significatif. Résultats: L'âge moyen des sujets était de 42.3 ± 15.23 ans avec un ratio hommes-femmes de 1.96:1. La plupart des patients (80.5%) étaient non cirrhotiques ; parmi 19.5% de cirrhose, 13% étaient compensés alors que 6.5% étaient cirrhotiques décompensés. La RVS globale effectuée à 12 semaines ou après la fin du traitement était 88.75%. Âge, répartition par sexe, profession, statut socio-économique, niveau d'instruction, indice de masse corporelle, schéma thérapeutique, durée du traitement, et la charge virale de base n'a pas modifié la réponse au traitement. Parmi les comorbidités, seuls le diabète sucré (DM) et l'immunodéficience humaine la co-infection par le virus (VIH) a affecté négativement la réponse au traitement (P = 0.009 et P < 0.001, respectivement). L'abus de drogues par voie intraveineuse (IV) a été significativement associée à l'échec du traitement (P < 0.001). La présence de cirrhose du foie (P < 0.001), thrombocytopénie (P < 0.001), élévation les transaminases (alanine transaminase: P = 0.021, aspartate aminotransférase: P < 0.001) et l'expérience de traitement antérieure (P = 0.038) étaient d'autres facteurs prédictifs significatifs d'échec thérapeutique. Conclusions: les AAD sont des médicaments très efficaces dans le traitement de l'HCC avec un taux de traitement élevé réponse. Les facteurs prédictifs significatifs d'échec du traitement des CHC comprenaient les comorbidités, en particulier la co-infection par le diabète et le VIH, l'abus de drogues par voie intraveineuse, la presence de cirrhose du foie, de thrombocytopénie, d'élévation des transaminases et d'antécédents de traitement. Cependant, des prédicteurs indépendants du traitement les non-réponses observées dans cette étude étaient la thrombocytopénie, l'abus de drogues intraveineuses et la cirrhose du foie.
Mots-clés: Cirrhose, antiviraux à action directe, virus de l'hépatite C, toxicomanie par voie intraveineuse, réponse virologique soutenue, thrombocytopénie
Keywords: Cirrhosis, direct-acting antivirals, hepatitis C virus, intravenous drug abuse, sustained virological response, thrombocytopenia
Introduction 
Hepatitis C virus (HCV) is a universally prevalent pathogen and a major cause of liver-related morbidity and mortality. Chronic hepatitis C (CHC) is a significant health problem in Punjab, India due to the higher prevalence of unsafe medical practices (unsafe injections and dental procedures) and intravenous (IV) drug abuse. The reported prevalence of HCV in Punjab was 5.2% in 2012, while a later study has shown the prevalence to be 3.2% in 2016.[1] Globally, an estimated 700,000 people die annually due to complications related to HCV infection.[2]
CHC patients are at high risk to develop life-threatening sequelae, including cirrhosis in 20% of cases and hepatocellular carcinoma with an incidence of 4%–5% per year in cirrhotic patients.[3],[4],[5] HCV is also associated with several extrahepatic manifestations including insulin resistance, type 2 diabetes mellitus (DM), glomerulopathies, and oral manifestations.[6],[7],[8],[9]
HCV is a small, enveloped, single-stranded positive-sense RNA virus belonging to the family Flaviviridae.[10] The N-terminal portion of the genome codes for the core and structural proteins while the nonstructural proteins (NS2-NS5) are coded by the remaining genome. The newer Direct-Acting Antiviral Drugs (DAAs) act on these HCV-encoded nonstructural proteins that are vital for its replication. DAAs have resulted in very high cure rates even with reduced treatment duration and excellent tolerability compared to pegylated-interferon and ribavirin (RBV)-based therapies.[11]
The new generation DAAs target different HCV proteins: Polymerase NS5B, NS3/4A serine protease, and the protein NS5A. The polymerase NS5B inhibitors are sofosbuvir and dasabuvir. Daclatasvir, elbasvir, ledipasvir, ombitasvir, pibrentasvir, and velpatasvir are NS5A protein inhibitors. Glecaprevir, grazoprevir, paritaprevir, simeprevir, and voxilaprevir are NS3/4A serine protease inhibitors.[11] In India, sofosbuvir has been made available at a compassionate price; thus, only those DAA-based management strategies, which contain sofosbuvir are adopted in India.
Successful treatment of CHC, also described as Sustained Virologic Response (SVR), is defined as an absence of detectable HCV RNA 12 weeks after the completion of treatment. The different combinations of DAAs make it possible to reach SVR rates that are higher than 90%, with few and mild associated adverse drug reactions.[12] CHC patients who achieve SVR have both lower rates of hepatic and extrahepatic complications and lower overall mortality.[13]
This study was undertaken to assess the response of DAAs in CHC patients and to study the various factors affecting the response of DAAs in CHC.
Materials and Methods This longitudinal study was carried out in the Medicine department of a tertiary care teaching hospital over 1 year (November 2020–November 2021) and enrolled 400 patients registered for CHC treatment at the Liver Clinic under National Viral Hepatitis Control Programme (NVHCP) by non-probability consecutive sampling. Under NVHCP, CHC was diagnosed by a positive anti-HCV antibody (screening) test and a detectable HCV viral load (HCV RNA) by real-time-polymerase chain reaction (RT-PCR). Patients with any detectable viral load were eligible for treatment with DAAs.
Inclusion criteria
CHC patients of either gender aged ≥18 years and eligible for treatment with DAAs.
Exclusion criteria
Pregnant and lactating females and patients on anti-tubercular drugs were excluded from the study.
Data collection procedure
The study conducted was in accordance with the Helsinki Declaration. Written informed consent was taken from each respondent and the study was carried out after approval from the institutional ethics committee. All the participants were informed of the purpose of the study and were ensured strict confidentiality.
Enrolled patients were treated as per the guidelines of NVHCP:
Regimen A, i.e., Sofosbuvir (400 mg) with Daclatasvir (60 mg) given to treatment-naive non-cirrhotic patients for 12 weeksRegimen B, i.e., Sofosbuvir (400 mg) with Velpatasvir (100 mg) given to treatment-naive patients having compensated cirrhosis for 12 weeksRegimen C, i.e., Sofosbuvir (400 mg) with Velpatasvir (100 mg) and weight-based RBV given to treatment-naive patients having decompensated cirrhosis for 12 weeks or treatment-experienced patients for 24 weeks.Eligible candidates were subjected to detailed history followed by a thorough clinical examination. All enrolled patients underwent investigations including complete blood count, liver function tests, renal function tests, prothrombin index and international normalized ratio (INR), random blood sugar, viral markers, and ultrasound whole abdomen. Repeat HCV viral load (HCV RNA) by RT-PCR was done for all enrolled subjects (irrespective of regimen) at or after 12 weeks of completion of antiviral therapy to check for SVR, in accordance with the NVHCP guidelines. All those subjects who achieved SVR (i.e., undetectable HCV RNA) were deemed “cured” while those who did not achieve SVR (i.e., detectable HCV RNA) were labeled “treatment failure.” Various factors which might predict treatment response (cured vs. treatment failure) were analyzed using appropriate statistical tools for their significance.
Socioeconomic status was assessed according to the modified Bollam Gnana Prasad (BG Prasad) Scale [Table 1].[14]
Statistical methods
The recorded data were compiled and entered in a spreadsheet (Microsoft Excel) and then exported to the data editor of SPSS Version 20.0 (SPSS Inc., Chicago, Illinois, USA). The continuous variables were expressed as mean and standard deviation, while the categorical variables were summarized as frequencies and percentages. The Student's independent t-test was employed for the comparison of continuous variables. The Chi-square or Fisher's exact test, whichever is appropriate, was employed for the comparison of categorical variables. Multivariate Logistic Regression was applied to identify the independent predictors of treatment nonresponse. A P < 0.05 was considered statistically significant.
Results The age of the patients ranged from 18 to 85 years with a mean age of 42.3 ± 15.23 years. The maximum number of patients belonged to the age group of 21–30 years (n = 108, 27%), followed by 31–40 years (n = 86, 21.5%), while the extremes of age, i.e., <20 years and > 60 years constituted 2.5% (n = 10) and 12.3% (n = 49) of the study population, respectively. The study included 265 (66.3%) males and 135 (33.8%) females with a male-to-female ratio of 1.96:1.
The majority of patients were farmers (n = 130, 32.5%) and homemakers (n = 122, 30.5%). Other subjects were laborers (n = 48, 12%), self-employed (n = 41, 10.3%), in-service (n = 25, 6.3%), students (n = 22, 5.5%), or drivers (n = 12, 3.0%). According to the socioeconomic status, the maximum number of patients (n = 280, 70%) were from the lower middle class followed by the lower (n = 51, 12.8%) and middle class (n = 50, 12.5%) while the least number of patients belonged to the upper-middle (n = 14, 3.5%) and upper class (n = 5, 1.3%). The educational status of most subjects was low; 55.8% (n = 223) were below matric and 20.3% (n = 81) were illiterate. Seven percent (n = 28) of patients had cleared matric, 11% (n = 44) were educated up to higher secondary while only 6% (n = 24) were graduates.
The mean body mass index (BMI) of study subjects was 23.2 ± 1.23 kg/m2 (range: 18.2–28.4). 0.5% (n = 2) of patients had BMI <18.5 kg/m2, 93.25% (n = 373) had BMI between 18.5 and 24.9 kg/m2, and 6.25% (n = 25) had BMI between 25 and 29.9 kg/m2. Out of 400 patients studied, comorbidities were observed in 85 (21.3%) patients. The most common comorbidities were hypertension and DM, seen in 10.8% (n = 43) and 6.3% (n = 25) of the study population, respectively. Other comorbidities were coronary artery disease (n = 8, 2%), tuberculosis (n = 6, 1.5%), thalassemia (n = 2, 0.5%), and bronchial asthma (n = 1, 0.3%).
Substance abuse was found in 138 (34.5%) patients; of these, 70 (17.5%) were IV heroin addicts. Other addictions observed among subjects were alcoholism (n = 29, 7.3%), smoking (n = 23, 5.8%), and opioid abuse (oral and/or nasal, n = 16, 4%).
Various risk factors for transmission of HCV were observed in this study; the most common being surgery and dental treatment, which were found in 25.3% (n = 101) and 23.3% (n = 93) of patients, respectively. Other important risk factors were unsafe injection use (n = 79, 19.8%), IV drug abuse (n = 70, 17.5%), blood transfusion (n = 39, 9.8%), tattooing (n = 10, 2.5%), and unprotected sexual intercourse (n = 8, 2%). Most participants were non-cirrhotic; 322 (80.5%) were non-cirrhotic and 78 (19.5%) were cirrhotic. Among cirrhotic patients, 52 (13%) had compensated while 26 (6.5%) had decompensated liver disease.
All the participants underwent routine laboratory investigations [Table 2]. The mean values of the hemogram and biochemical tests were within the normal range, except for liver enzymes (alanine transaminase [ALT] and aspartate transaminase [AST]), which were above the upper normal limit.
The majority of patients (n = 300, 75%) received treatment regimen A, while regimens B and C were administered to 16.3% (n = 65) and 8.8% (n = 35) of study patients, respectively. Treatment duration was 12 weeks in most of the patients (n = 354, 88.5%) while 11.5% (n = 46) patients were treated for 24 weeks. Treatment outcome was assessed by SVR done at or after 12 weeks of completion of treatment. Overall, 88.75% (n = 355) of patients achieved SVR, i.e., were cured of CHC, while 11.25% (n = 45) patients did not achieve SVR, i.e., were treatment failure.
The mean age of patients who achieved SVR was 42.7 ± 14.91 years as compared to 39.4 ± 17.15 years in patients who did not achieve SVR. On comparing the age distribution between the patients who achieved SVR and those who did not, it was found that in both the groups, maximum patients belonged to the age group of 20–60 years (82% in the “cured” vs. 77.8% in the “treatment failure” group). Hence, both groups were comparable with respect to age composition with no statistically significant difference (P = 0.125).
The patients who achieved SVR comprised 65.9% (n = 234) males and 34.1% (n = 121) females, while among patients who did not achieve SVR, there were 68.9% (n = 31) males and 31.1% (n = 14) females. Thus, both groups were comparable in terms of gender distribution (P = 0.691).
There were no statistically significant differences between the two groups with respect to occupation and socioeconomic status [Figure 1] and [Figure 2].
Figure 1: Occupation in relation to treatment outcome. SVR = Sustained virological response
Figure 2: Socioeconomic status in relation to treatment outcome. SVR = Sustained virological response[Table 3] compares the prevalence of different comorbidities in the two groups. The difference in the prevalence of DM between the two groups was statistically significant (P = 0.009). Among comorbidities, only DM adversely affected the treatment response while other comorbidities did not alter the treatment response.
[Figure 3] shows the distribution of co-infections (human immunodeficiency virus [HIV] and Hepatitis B) in the two patient groups. Among patients who achieved SVR, 1.97% were HIV positive; while 22.23% of patients were HIV positive among those who did not achieve SVR, and this difference was statistically significant (P < 0.001). On comparing the presence of Hepatitis B co-infection between the two groups, a statistically insignificant difference was observed (P = 0.143).
Figure 3: Co-infections (HIV and Hepatitis-B) in relation to treatment outcome. SVR = Sustained virological response, HIV = Human immunodeficiency virusOn comparing substance abuse among the study patients, it was observed that among patients who achieved SVR, 14.6% were IV heroin addicts compared to 40% among those who did not achieve SVR [Table 4]. This difference was statistically significant (P < 0.001). However, an insignificant association was observed when patients in the two groups were compared for other substance abuse such as alcohol (P = 0.289), opioids (oral and/or nasal, P = 0.809), and smoking (P = 0.459).
[Table 5] depicts the various laboratory parameters in the two study groups. On comparing mean hemoglobin (P = 0.907), INR (P = 0.893), serum urea (P = 0.398), creatinine (P = 0.08), albumin (P = 0.632), and bilirubin (P = 0.321) between the two groups, no statistically significant difference was observed. However, the mean values of platelet count, ALT, and AST showed a significant difference between the two groups. The mean platelet count was significantly lower in patients who did not achieve SVR (P < 0.001), while the mean ALT and AST levels were significantly higher in patients who did not achieve SVR (ALT: P = 0.02, AST: P = 0.02).
Various risk factors were evaluated influencing treatment outcomes among the study patients [Table 6]. Risk factors such as blood transfusion (P = 0.63), dental treatment (P = 0.19), surgery (P = 0.112), unsafe injection use (P = 0.96), tattooing (P = 0.61), and unprotected sexual intercourse (P = 1.0) were present with comparable frequencies in the two groups. However, the percentage of IV drug users (IVDUs) was significantly higher in patients who did not achieve SVR as compared to those who did (40% vs. 24.44%, P < 0.001).
The mean value of BMI in patients who achieved SVR was 22.9 ± 1.13 kg/m2 as compared to 23.1 ± 1.86 kg/m2 in patients who did not achieve SVR. Hence, the two groups were comparable with respect to BMI, with no statistically significant difference (P = 0.845).
It was noted that 15.6% of patients in the treatment failure group had taken DAAs previously (i.e., were treatment-experienced) while among the patients who achieved SVR, only 6.8% patients were treatment-experienced [Table 7]. This difference was statistically significant with more treatment failures in patients with previous treatment experience (P = 0.038).
[Figure 4] and [Figure 5] show the association of treatment regimen and duration with the treatment outcome. There was no statistically significant difference in the treatment regimen (P = 0.151) and duration (P = 0.365) between the two patient groups.
Figure 4: Treatment regimen in relation to treatment outcome. SVR = Sustained virological response
Figure 5: Treatment duration (in weeks) in relation to treatment outcome. SVR = Sustained virological responsePatients who attained SVR had a greater number of noncirrhotics (83.7%) in comparison to those who did not attain SVR (55.6%). The percentage of cirrhotics in the “treatment failure” group was higher (44.4%) as compared to the “cured” group (16.3%), which was statistically significant (P < 0.001) [Table 8]. Hence, the presence of liver cirrhosis adversely affected the treatment outcome. However, a statistically insignificant difference was observed in the prevalence of compensated and decompensated cirrhosis between the two patient groups (P = 0.854) [Table 9]. Hence, decompensation in liver cirrhosis did not alter the treatment outcome.
Table 9: Decompensation in liver cirrhosis in relation to treatment outcomeAmong patients who attained SVR, the mean baseline viral load was 1620568.5 ± 4027643.1 IU/mL, while among patients who did not attain SVR, the same was 1881594.7 ± 5963000.4 IU/mL. The difference in baseline viral load between the two groups was statistically insignificant (P = 0.701).
[Table 10] shows the multivariate logistic regression depicting independent predictors of treatment nonresponse. It was observed that only low platelet count (odds ratio [OR] = 3.71, 95% Confidence Interval [CI] = 2.65–5.84, P = 0.004), IVDU (OR = 2.64, 95% CI = 1.79–4.63, P = 0.031) and presence of liver cirrhosis (OR = 9.18, 95% CI = 6.91–14.72, P < 0.001) were independent predictors of treatment failure.
Table 10: Multivariate logistic regression depicting independent predictors of treatment nonresponse Discussion Treatment outcome
In this study, 88.75% of patients achieved SVR, i.e., were cured of CHC, while 11.25% of patients did not achieve SVR, i.e., were treatment failure. Gayam et al. reported that 90.7% achieved while 9.3% did not achieve SVR.[15] Another study conducted by Torres et al. showed the SVR rate was 90.5% in a total of 252 individuals.[16] Arias et al. in their study of 363 CHC patients, observed treatment failure in 4% of patients who completed their course of DAA therapy.[17] Cárdaba-García et al. observed the SVR rate to be 94.6%.[18] Comparatively lower SVR in this study could be explained by the presence of a higher number of IVDUs (17.3%) who are prone to reinfections. The presence of cirrhosis in 19.5% of participants also contributed to treatment nonresponse as discussed below.
Age and gender
The mean age of patients who achieved SVR was 42.7 ± 14.91 years, as compared to 39.4 ± 17.15 years of the patients who did not achieve SVR; however, the difference was statistically insignificant (P = 0.125). Therefore, age did not alter the treatment response. Gayam et al. also observed no significant difference in the mean age of treatment responders and nonresponders.[15] Salmon et al. reported coherent findings; the mean age of patients with successful treatment and treatment failure in their study was 52 and 53 years, respectively.[19]
In this study, both groups were comparable in terms of gender distribution (P = 0.691); therefore, gender did not alter the treatment response. Gayam et al.,[15] Salmon et al.,[19] and Cárdaba-García et al.[18] reported the same result in their respective studies.
Socioeconomic status, occupation, comorbidities, and co-infections
No significant difference was seen with respect to socioeconomic status and occupation between the two groups.
Among patients who achieved SVR, 5.07% were diabetics compared to 15.5% among those who did not achieve SVR (P = 0.009). An insignificant difference was observed when patients in the two groups were compared for the presence of other comorbidities like hypertension, coronary artery disease, tuberculosis, thalassemia, and bronchial asthma. Thus, among comorbidities, only DM adversely affected the treatment response. Cárdaba-García et al. also noted that diabetic patients were significantly more in the “treatment failure” group (35.7%) as compared to the “cured” group (12.4%).[18] However, Gayam et al.[15] and Ahmed et al.[20] observed that DM did not play a significant role in the treatment outcome of CHC patients.
Among patients who achieved SVR, 1.97% were HIV positive compared to 22.3% among those who did not achieve SVR (P < 0.001). Therefore, HIV co-infection may predict poor treatment outcome in CHC. In previous studies also, patients infected with HIV reported higher numbers of treatment failures for CHC.[17],[21] On the contrary, Gayam et al.[15] and Cárdaba-García et al.[18] concluded that HIV has no role in predicting the treatment response in CHC patients. On comparing hepatitis B co-infection between the two groups, an insignificant association was observed with the treatment outcome (P = 0.143).
Body mass index
In this study, no difference was observed in the treatment outcome with respect to the BMI of the study subjects (P = 0.845). Similar results were observed by Gayam et al. (P = 0.353)[15] and Nabulsi et al. (P = 0.491).[21] However, Cárdaba-García et al. observed that BMI ≥30 kg/m2 was an independent predictor of treatment failure (P = 0.003) and this may be because a high BMI is associated with a chronic inflammatory state, liver inflammation, and a higher degree of hepatic fibrosis.[18] No association with BMI observed in this study might be because our study population did not have patients with BMI ≥30 kg/m2.
Substance abuse and risk factors
Among patients who achieved SVR, 14.6% were IV heroin addicts compared to 40% among those who did not achieve SVR; the difference being statistically significant (P < 0.001). However, an insignificant association was observed when patients in the two groups were compared for other substance abuse such as alcohol, opioids, and smoking. Nabulsi et al. also reported an insignificant association between alcohol intake and treatment outcome.[21] However, they also observed an insignificant association between IV drug abuse and treatment outcome, which contrasted with the present study.[21] In another study, ongoing IV drug abuse was significantly associated (P < 0.001) with treatment nonresponse.[22]
IV heroin abuse likely impacts treatment outcome through reduced adherence.[23],[24] Also, these patients are at a high risk of HCV transmission and reinfection.[25] Although SVR rates are comparatively lower among IVDUs, the treatment is still effective and should not be withheld. This finding stresses the need for integrated HCV care that includes de-addiction therapies, social support, and harm reduction services to improve treatment response in this priority population for HCV elimination.[26]
Lab parameters
On comparing the mean values of hemoglobin, INR, urea, creatinine, albumin, and bilirubin, no significant difference was observed between the two groups. This contrasted with the findings by Gayam et al. who observed a significant difference in the serum levels of albumin (P = 0.042) and bilirubin (P = 0.043) between the two groups, with bilirubin being higher and albumin lower in patients who did not achieve SVR.[15]
There was a statistically significant difference in the mean platelet count, ALT, and AST levels between the two groups. The mean platelet count was significantly lower in patients who did not achieve SVR (P < 0.001), while the mean ALT and AST levels were significantly higher in patients who did not achieve SVR. (ALT: P = 0.02, AST: P = 0.02). Gayam et al. also reported significantly lower platelet count in patients who did not achieve SVR and concluded that thrombocytopenia is a predictor of treatment failure (P = 0.020).[15] However, in their study, AST and ALT levels did not show any significant difference between the two groups. Cárdaba-García et al. observed a significant difference in AST, ALT, albumin, and platelet count between the two patient groups.[18] Ahmed et al.[20] and Torres et al.[16] also observed that the platelet count was significantly lower in patients who did not achieve SVR. Furthermore, it is known that thrombocytopenia is a surrogate marker of liver cirrhosis. It is strongly related to liver fibrosis owing to low thrombopoietin levels and splenic sequestration of blood cells as a result of portal hypertension. Thus, early treatment of CHC before the development of thrombocytopenia may improve the overall treatment outcome.[15] On the contrary, the elevation of AST and ALT constitutes an indirect indicator of liver inflammation; thus, higher baseline levels of these enzymes act as predictive factors of nonresponse to the antiviral treatment as observed in this study.[15]
Baseline viral load
Both the treatment groups were comparable with respect to baseline viral load with no statistically significant difference (P = 0.701). Therefore, it did not affect the treatment response. Similar results were shown by Gayam et al. (P = 0.330),[15] Salmon et al.(P = 0.886),[19] and Arias et al.(P = 0.400).[17]
Previous treatment experience, treatment regimen, and duration
It was observed that 15.6% of patients in the treatment failure group had experienced the antiviral treatment previously while among patients who achieved SVR, only 6.8% of patients were treatment-experienced. This difference was statistically significant with more treatment failures among patients with previous treatment history (P = 0.03). This result contrasted with the observations made by Ahmed et al. (P = 0.06),[20] Nabulsi et al. (P = 0.109),[21] and Cachay et al. (P = 0.71)[22] who did not find any significant association between previous treatment experience and treatment failure.
This study did not find any statistically significant difference in the treatment regimen (P = 0.151) and duration (P = 0.365) between the two patient groups. Therefore, the treatment regimen and duration did not alter the treatment outcome. Results of the studies conducted by Salmon et al.,[19] Ahmed et al.,[20] and Cachay et al.[22] are coherent with the present study.
Liver cirrhosis
Patients who did not achieve SVR had a higher percentage of cirrhotics (44.4%) as compared to those who achieved SVR (16.3%) (P < 0.001). Nabulsi et al. also reported cirrhosis as a predictor of treatment failure in CHC.[21] In their study, 50.4% of patients had cirrhosis in the “treatment failure” group and 40.3% of patients had cirrhosis in the “cured” group (P = 0.039).[21] Buti et al. stated that most of the approved oral DAA regimens provided high cure rates with a very low incidence of adverse events, especially in noncirrhotic patients.[27] Ahmed et al. also observed liver cirrhosis as a predictor of treatment nonresponse (P = 0.0002).[20] However, Salmon et al.[19] and Cachay et al.[22] observed no significant difference in the treatment response between cirrhotic and noncirrhotic patients. Poor response to DAAs in liver cirrhosis may be related to impaired perfusion of the drug associated with liver stiffness or altered drug metabolism.[18]
A statistically insignificant difference was, however, observed in the prevalence of compensated and decompensated cirrhosis between the two groups (P = 0.854). A similar observation was made by Salmon et al. (P = 0.537)[19] and Cachay et al. (P = 0.39).[22] This might be because our study population had very few numbers of decompensated cirrhotics (6.5% of the study population). However, this result was in contrast to the findings reported by Ahmed et al. (P < 0.0001)[20] and Arias et al. (P = 0.048),[17] who observed that advanced liver fibrosis and complications due to decompensated liver disease adversely affected the treatment response of DAAs in CHC.
Independent predictors of treatment nonresponse
On applying multivariate logistic regression analysis to the factors influencing treatment outcome, it was observed that only low platelet count, IVDU, and presence of liver cirrhosis were independent predictors of treatment nonresponse in CHC.
Conclusions DAAs are highly efficacious drugs in the treatment of CHC with a high rate of treatment response (i.e., SVR) and only a few factors adversely affect the treatment outcome. This study identified several variables that were significant predictors of treatment failure which included comorbidities especially DM, HIV co-infection, IV drug abuse, presence of liver cirrhosis, thrombocytopenia, elevated transaminases, and previous treatment experience. However, independent predictors of treatment nonresponse observed in this study were thrombocytopenia, IV drug abuse, and the presence of liver cirrhosis. Other host and viral baseline characteristics did not affect the treatment response.
Strength of the study
Data regarding factors affecting the response of DAAs in CHC patients are limited from the study region. In this study, multiple variables were identified as significant predictors of treatment response with DAAs in CHC which may be selectively targeted by appropriate interventions to improve the treatment outcomeMultiple logistic regression analysis was applied to the above variables to find out independent factors affecting the treatment response of DAAs in CHCBaseline viral load was comparable between patients who achieved SVR and those who failed on DAAs.Limitations of the study
It was a single-center study with a limited sample sizeThis study excluded patients who did not complete the planned course of therapy for any reasonSubjects were enrolled according to nonprobability convenient sampling. Therefore, a disproportionate number of cirrhotics and noncirrhotics were involvedHCV genotyping was not done for the study patients (as it is not a part of NVHCP).Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
References 
留言 (0)