A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen: The HEMATITE study.

AIM: Fatigue is the most commonly reported symptom of Hepatitis C Virus (HCV) infected patients and severely impacts their quality of life. The aim of this study was to measure the impact of 3D regimen treatment on the fatigue, daytime physical activity and sleep efficiency of HCV infected patients with fatigue.
METHODS: HEMATITE was an observational, prospective, open-label, single-arm, Swiss multi-centric study in mono-infected HCV genotype 1 patients. The 28 week observation period comprised of 4 weeks preparation, 12 weeks treatment and 12 weeks follow-up. Fatigue was assessed using the fatigue severity scale (FSS) questionnaire. Patients with FSS ≥ 4 (clinically significant fatigue) were included. The activity tracker, ActiGraph GT9X Link®, was used to measure daytime physical activity and sleep efficiency. Outcome analysis was performed on a scaled down intention to treat (sdITT) population, which excluded patients with insufficient tracker data at all study visits and a modified ITT (mITT) population, which consisted of patients with complete tracker data at all study visits.
RESULTS: Forty of 41 patients in the ITT population had a sustained virologic response 12 weeks post-treatment (SVR12). Mean baseline FSS score was 6.0 for the sdITT population and 5.9 for the mITT population and decreased from baseline to 12 weeks post-treatment by 2.6 (95% confidence interval [CI]: 2.1, 3.1) for the sdITT (n = 37) population and 2.8 (95% CI: 2.2, 3.4) for the mITT (n = 24) population. Mean daytime physical activity or sleep efficiency did not change considerably over the course of the study.
CONCLUSION: Measurement by the activity tracker of mean day time physical activity did not show a considerable change from baseline to SVR12 upon treatment with 3D regimen. Nevertheless, a reduction of fatigue as assessed with the validated fatigue severity scale (FSS) was observed, suggesting a causative role of HCV in this extrahepatic manifestation. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03002818.

Introduction

Chronic hepatitis C virus (HCV) infection, characterized by persistent hepatic inflammation, affects around 71 million people worldwide and can lead to liver fibrosis, cirrhosis and hepatocellular carcinoma [1]. Extrahepatic systems, such as the central nervous system (CNS), are also associated with chronic HCV, and neuropsychiatric disorders are more prevalent among HCV infected patients than the general population [2-4]. The reason for this is manifold: HCV infected patients are more likely to have pre-existing mental illness or to abuse psychoactive substances [5]; cirrhosis may lead to minimal or overt encephalopathies with a broad range of neuropsychological symptoms [6]; pharmaceutical treatment for HCV can exacerbate certain neuropsychiatric symptoms [7]; and HCV itself may have a direct biologic effect on the CNS [8].

HCV infected patients may present with many neuropsychiatric disorders including fatigue, anxiety, depression and cognitive impairment, which can significantly reduce their quality of life [3, 9–12]. Of these, fatigue is the most commonly reported symptom [10, 13, 14] and may occur in conjunction with sleep disturbances [15]. Reduced quality of life in HCV infected patients is independent of liver damage [13]. HCV eradication following treatment with interferon alpha and ribavirin has been shown to improve neurocognitive symptoms in patients [16].

Treatment of HCV has progressed in recent years with the approval of 3D regimen, an interferon-free regimen indicated in HCV genotype 1 infected patients. It has been shown to result in high rates of sustained virologic response 12 weeks post-treatment (SVR12) [17, 18]. The treatment consists of paritaprevir, a protease inhibitor boosted by ritonavir; ombitasvir, a HCV nonstructural protein 5A (NS5A) replication complex inhibitor; and dasabuvir, a non-nucleoside RNA polymerase inhibitor. To date, there has been only few publications on the characterization of fatigue through treatment with newly developed direct-acting antivirals [19], and no research into the effect of 3D regimen on the quality of life of HCV patients suffering from fatigue. A recently published work of Durcan et al. found, that direct antivirals did not lead to depression, anxiety or fatigue and did not decrease liver-specific quality of life [20].

The aim of this observational study, HEMATITE, was to measure the impact of 3D regimen on the daytime physical activity, fatigue and sleep efficiency of HCV patients with fatigue. Patients with predisposing factors of fatigue, such as severe depression, cirrhosis and cancer were excluded from the study.

Materials and methods

Study design

HEMATITE was a single-arm, prospective, post-marketing, observational study in HCV patients receiving 3D regimen according to routine clinical practice. The 28 week observation period was comprised of 4 weeks preparation, 12 weeks treatment and 12 weeks follow-up. At Study Visit 1 (Day -28; before treatment start), patient screening took place; at Study Visit 2 (Day 1; treatment start), baseline data were obtained; Study Visit 3 (Day 28) was an interim visit; Study Visit 4 (Day 84) was at the end of treatment; and at Study Visit 5 (Day 168), SVR12 was assessed (S1 Fig). The study enrolled patients over a 12 month period and was conducted at five HCV competence centers in Switzerland from Mar 2017 to Apr 2018: Kantonsspital St. Gallen, St. Gallen; University Hospital Zürich, Zürich; Universitätsspital Bern, Bern; Fondazione Epatocentro Ticino, Lugano and Hôpital Neuchâtelois Pourtalès, Neuchâtel. The protocol was approved by the Ethikkommission Ostschweiz, St. Gallen and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants included in the study.

Patients

Patients diagnosed with HCV, whom the physician had already decided to treat with 3D regimen, were offered the opportunity to participate in the study. Patients were eligible if they were aged 18 years or older, were mono-infected with chronic HCV genotype 1, had fatigue (fatigue severity scale [FSS] ≥ 4 [21]), were treatment naïve and did not have liver cirrhosis. Patients were excluded if they had fatigue from sources other than HCV (e.g. severe depression, cancer and hormonal disorders), had conditions that did not allow them to adhere to the protocol or were wheelchair dependent.

Treatment

Commercially available 3D regimen, i.e. ombitasvir, paritaprevir and ritonavir tablets (Viekirax®, AbbVie Deutschland GmbH & Co. KG, Germany) and dasabuvir tablets (Exviera®, AbbVie Deutschland GmbH & Co. KG, Germany), was used as per routine clinical practice, local label and guidelines. The treatment regimen was at the discretion of the physician and was decided upon prior to offering the patient the opportunity to participate in the study.

Fatigue severity scale

Fatigue was assessed at baseline (Day 1), Day 28, Day 84 and 12 weeks post-treatment (Day 168) using the FSS questionnaire which has been validated for use in chronic HCV and is an adequate measure of fatigue outcomes in HCV clinical trials [21]. Patients answered nine questions using a scale from one (strongly disagree = 1 point) to seven (strongly agree = 7 points) (S2 Fig). A fatigue score was obtained by calculating the mean score of the nine items. The higher the score (with a maximum of 7), the greater the fatigue. Clinically significant fatigue was defined as a score equal or above four [21, 22].

Activity tracker

The activity tracker, ActiGraph GT9X Link® (ActiGraph LLC, Pensacola, Florida, USA), was used to measure daytime physical activity and sleep efficiency. ActiGraph GT9X Link® is a Class I medical device within the European Union [23] and ActiGraph® devices have been used in several research studies and clinical trials to measure physical activity, energy expenditure and sleep/wake behavior [24-26]. Patients wore the activity trackers on their non-dominant arm for 4 weeks prior to each study visit (baseline [Day 1], Day 28, Day 84 and 12 weeks post-treatment [Day 168]). The patients were instructed on the use of the device by study personnel and were reminded when to put it on. The daytime physical activity was recorded by the activity tracker in counts. Counts were the sum of accelerometer values (raw data at 30 Hz) that had passed through a proprietary filtering process to eliminate non-human movement. Sleep efficiency was also recorded by the activity tracker and was defined as the percentage of time scored as sleep during the sleep period. At 12 weeks post-treatment (Day 168), tracker data collected using device specific software (ActiSync) were processed by the biostatistician. The data were not visible to the Investigator or patients during the study.

Patient characteristics, vital signs and laboratory parameters

Patient data (demographics, disease characteristics, comorbidities, concomitant medication and treatment details) were obtained from the patient’s medical records.

Vital signs (blood pressure, pulse, weight and height, body mass index [BMI] and temperature) and laboratory parameters (HCV RNA level, alanine aminotransferase, aspartate aminotransferase, total bilirubin, hemoglobin, creatinine, ferritin, thyroid stimulating hormone, fasting glucose and human chorionic gonadotropin) were measured at each study visit.

Variables

The primary outcome variable was a change in mean daytime physical activity from baseline (Day 1) to 12 weeks post-treatment (Day 168). The secondary variables were a change in FSS score from baseline (Day 1) to 12 weeks post-treatment (Day 168); a correlation between mean daytime physical activity and FSS score from baseline (Day 1) to 12 weeks post-treatment (Day 168); sleep efficiency at baseline (Day 1), during and after 12 weeks post-treatment (Day 168); and the proportion of patients who achieved SVR12.

Statistical methods

A sample size of 100 was planned, which would have had 80% power to detect a change from baseline of the effect size 0.29 using a two-sided one-sample t-test with a significance level of 5%. However, the anticipated sample size was not met. The main reasons for this were the launches of new pangenotypic treatment options and the abolition of a limitatio laid down by the Swiss Federal Office of Public Health.

Safety analysis was performed on the intention to treat (ITT) population which comprised all patients who received the study treatment at least once. Outcome analysis was performed on the following two populations: the scaled down ITT (sdITT) population, defined as the ITT population minus patients who discontinued or were excluded because of missing or insufficient tracker data at all study visits, and the modified ITT (mITT) population, defined as patients in the sdITT population who had complete tracker data at all five study visits.

Activity tracker data for 10 working days before each scheduled study visit (baseline [Day 1], Day 28, Day 84 and 12 weeks post-treatment [Day 168]) were used to calculate the mean daytime physical activity and sleep efficiency for each tracker phase of the study. Days missing ≥ 2 hours activity data during the daytime (as determined by a built-in wear time sensor) were excluded. Data for these days were replaced by daytime values in a pre-defined order, starting with the most recent day of the preceding week.

Data for each study visit were analyzed using descriptive statistical analysis. For daytime physical activity, FSS and sleep efficiency, the mean change and the 95% confidence interval (CI) between baseline (Day 1) and Day 28, Day 84 and 12 weeks post-treatment (Day 168) was calculated. The difference between mean physical activity at baseline (Day 1) and at 12 weeks post-treatment (Day 168) was analyzed by a two-sided one-sample t-test with a significance level of α = 0.05. The correlation between mean daytime physical activity and FSS score was analyzed by Spearman’s rank correlation coefficient. The proportion of patients who achieved SVR12 and the 95% CI were also calculated as standard Wald intervals using the estimated standard error.

Subgroup analysis was performed to assess the effect of the following factors on daytime physical activity, FSS and sleep efficiency: concomitant ribavirin, gender, fibrosis stage, age and genotype 1 subtype. Differences were determined by t-test or Mann-Whitney-U test with a significance level of α = 0.05. All statistical analysis was performed using SPSS® for Windows, version 22.0.

Safety analysis

Safety assessments were performed in a standardized method at each study visit and included the evaluation of adverse events (AEs): serious AEs (SAEs), severe AEs, treatment-related AEs, procedure-related AEs (i.e. AEs associated with wearing the activity tracker) and AEs leading to discontinuation. An increase in the FSS score by ≥ 1 was also reported as an AE.

Results

The number of patients who participated in the study are shown in . Forty-five patients were screened, of whom 41 were eligible and received 3D regimen treatment at least once (ITT population). The outcome analysis sdITT population comprised 37 patients and excluded patients who discontinued (n = 1) or had missing tracker data for all study visits (n = 3). The outcome analysis mITT population comprised 24 patients and excluded patients with partial tracker data sets (n = 13).

Flow chart of study participants.

ITT = intention to treat population; sdITT = scaled down ITT population; mITT = modified ITT population.

The patient characteristics at screening are shown in . Patients had been diagnosed with HCV infection an average of 14.0 (± 9.3) years before screening and were HCV treatment naïve. The most reported (≥ 8.0%) conditions at screening were drug abuse (21.9%), asthenia (9.8%), depression (9.8%), hepatic steatosis (9.8%) and hypertension (9.8%). Forty patients completed the 3D regimen treatment.

Primary and secondary variables

Mean daytime physical activity (measured in day counts) decreased slightly but did not change significantly over the course of the study (). The mean baseline (Day 1) day counts was 1,469,569 for the sdITT population and 1,513,166 for the mITT population. The change from baseline (Day 1) to 12 weeks post-treatment (Day 168) was -108,266 day counts (95% CI: -235,037, 18,504) for the sdITT population (p = 0.091) and -98,373 day counts (95% CI: -235,667, 38,921) for the mITT population (p = 0.152) (). There was no correlation between daytime physical activity and FSS score over the course of the study as assessed by Spearman’s rank correlation coefficient.

Change in mean daytime physical activity from baseline (Day 1) to Day 28, Day 84 and 12 weeks post-treatment (Day 168) is shown for (a) the sdITT population and (b) the mITT population. ° = outlier (value maximum 1.5 to 3 fold box length); * = extreme value (> 3 fold box length); mITT = modified intention to treat; sdITT = scaled down intention to treat; V = study visit.

The distribution of FSS score from screening (Day -28) to 12 weeks post-treatment (Day 168) is shown in for both analysis populations. From screening (Day -28) to baseline (Day 1), no change in FSS was observed (). The mean baseline (Day 1) FSS score was 6.0 for the sdITT population and 5.9 for the mITT population. A reduction in fatigue (i.e. a decrease in mean FSS score) was observed during the treatment period and was sustained until 12 weeks post-treatment (SVR12) (. The FSS score decreased from baseline (Day 1) to 12 weeks post-treatment (Day 168) by 2.6 (95% CI: 2.1, 3.1) for the sdITT population and 2.8 (95% CI: 2.2, 3.4) for the mITT population (.

Distribution of mean FSS score at screening (Day -28), baseline (Day 1), Day 28, Day 84 and 12 weeks post-treatment (Day 168) is shown for (a) the sdITT population, (b) the mITT population and (c) all patients that had a completed FSS questionnaire for all study visits (n = 39). The decrease in mean FSS score from baseline (Day 1) to Day 28, Day 84 and 12 weeks post-treatment (Day 168) is shown for (d) the sdITT population and (e) the mITT population. ° = outlier (value maximum 1.5 to 3 fold box length); FSS = fatigue severity scale, mITT = modified intention to treat; sdITT = scaled down intention to treat; V = study visit.

Similar to daytime physical activity, sleep efficiency decreased slightly but did not change considerably over the course of the study. The mean change in sleep efficiency from baseline (Day 1) to treatment week 4 (day 28) was -0.87% (95% CI: -1.7, -0.03) for the sdITT population and -0.44% (95% CI: -1.5, 0.6) for the mITT population. The mean change in sleep efficiency from baseline (Day 1) to end of treatment (Day 84) was -0.06% (95% CI: -1.0, 0.9) for the sdITT population and 0.01% (95% CI: -1.2, 1.2) for the mITT population. The mean change in sleep efficiency from baseline (Day 1) to 12 weeks post-treatment (Day 168) was -0.6% (95% CI: -0.7, 1.9) for the sdITT population and 0.7% (95% CI: -0.6, 2.1) for the mITT population.

The percentage of patients that reached SVR12 was 97.3% (95% CI: 92.3, 100.0) in the sdITT population and 95.8% (95% CI: 88.3, 100.0) in the mITT population.

Subgroup analysis

Concomitant ribavirin, gender, fibrosis stage, age and genotype 1 subtype had no effect on daytime physical activity, FSS score or sleep efficiency in the sdITT or mITT populations.

An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. As predictors age (classified by median split: ≤ 50 years/>50 years), gender (male/female), HCV genotype (genotype 1a/genotype 1b), liver fibrosis (yes/no) and ribavirin use (yes/no) were investigated, results are shown for sdITT population (S1 Table) and for mITT population (S2 Table). Overall, for none of the analyzed possible predictive factors the univariate as well as the multivariate analysis showed significance. The corresponding effect sizes demonstrated no or very small effects for these factors. Therefore, the factors age class, sex, fibrosis, HCV genotype and ribavirin use had no influence of the three outcome variables mean daytime physical activity, sleep efficiency and FSS.

Safety

During the study, there were no SAEs, procedure-related AEs or AEs that led to discontinuation. Twenty-one (51.2%) of 41 patients experienced an AE during the study. Thirteen (31.7%) patients experienced at least one AE that was considered as treatment-related by the Investigator. The most commonly reported AEs and treatment-related AEs are shown in

Discussion

This observational study examined the impact of treatment with 3D regimen on HCV patients suffering from fatigue. Fatigue is experienced by over half of HCV patients [10, 13, 14] and is one of a number of extrahepatic manifestations responsible for their reduced quality of life [2, 27]. In our study, patient fatigue was independent of gender, age and genotype 1 subtype. In addition, patient fatigue seems to be independent of fibrosis stage, although our observation is based on F0 and F1 patients exclusively. Moreover, fatigue could not be attributed to cirrhosis, cancer, severe depression or prior-HCV treatment, as patients with these predisposing factors were excluded from the study. This would indicate that HCV infection itself is most likely responsible for fatigue; a finding that supports research by others [9, 28]. While it is not known exactly how HCV causes fatigue and other neuropsychiatric disorders, it is thought that the virus penetrates the CNS and causes neuroinflammation which results in alterations in cerebral metabolism, immune activation and neurotransmission [29].

In this study, treatment with 3D regimen reduced fatigue over time in non-cirrhotic, treatment naïve patients with HCV genotype 1 infection. A reduction in mean FSS score of 2.8 (95% CI: 2.21, 3.43) from baseline (Day 1) to 12 weeks post-treatment (Day 168) was observed in the mITT population. This change was significant with p < 0.001, Friedman’s ANOVA. This was a notable decrease considering that a mean FSS score difference of ≥ 0.7 is considered clinically important [22]. All but one patient treated with 3D regimen achieved viral elimination as determined by undetectable HCV RNA 12 weeks post-treatment (SVR12) resulting in an SVR12 rate of > 97%. This high SVR12 achieved in a real life setting confirms previous results from registered trials with 3D regimen [17, 30, 31]. In the patient who did not achieve SVR12, the FSS score decreased from baseline (Day 1) until end of treatment (Day 84) but increased again at 12 weeks post-treatment (Day 168). There was no virology data available for this patient at the end of treatment visit to determine if a corresponding decline in HCV RNA occurred at this time point.

The accuracy of the validated FSS questionnaire was supported by the fact that no change in FSS score was observed from screening (Day -28) to baseline (Day 1); FSS score only decreased over the treatment period and was sustained until 12 weeks post-treatment (SVR12).

Interestingly, fatigue reduction did not correlate with an increase in mean daytime physical activity during the working week. Indeed, mean daytime activity during the working week did not change during the course of treatment. This result may be partly explained by the fact that a high proportion of jobs are sedentary and most working conditions do not allow for large changes in physical activity. Similarly, sleep efficiency assessed by an activity tracker did not improve over the course of the study. This is in line with the findings of Heeren et al., where there was no correlation between the results of a fatigue questionnaire (fatigue impact scale) and sleep actigraphy data in HCV patients observed and the authors suggested that bad sleep quality may therefore not be associated with increased nocturnal motor activity [24].

This is the first study to use an activity tracker in HCV patients during a course of treatment. While activity trackers are useful tools in monitoring physical activity and sedentary behavior over time, these devices have some limitations. The accuracy of some activity trackers is variable and they may over- or underestimate total activity [32]. Differences in physical activity may be too small to be detected by the activity tracker and studies have shown that they may not accurately detect light physical activity such as washing dishes, cooking food and walking slowly [33]. For a device to accurately report on changes in daily activity, it must be worn consistently [34]. This study had a particularly long tracking period which may have led to study fatigue and inconsistent wearing of the tracking device resulting in missing data and therefore data that were not representative of actual physical activity. Furthermore, wearing a tracker may trigger physical activity in patients per se and thus may confound routine activity. The relatively small patient number and the inclusion of only F0 and F1 patients further add to the limitations of our study.

Differentiating between HCV-associated and non-associated fatigue, physical inactivity and comorbidities is difficult. In our study, 34.4% of patients were overweight (BMI ≥ 25), 64.1% consumed alcohol and 55.3% were smokers at screening. These factors may have contributed to fatigue and a lack of physical activity in patients during the study. In addition, there were concomitant medications that may have exacerbated some of the neuropsychiatric symptoms of HCV. In patients receiving concomitant sedative medication, there was no particular trend in FSS score over the course of the study (S3 Fig). Interestingly, despite its hemolytic characteristics and thus potential to cause anemia [35], co-administration of ribavirin did not impact fatigue or physical activity. This is in line with previous reports characterizing health related quality of life during co-administration of ribavirin and other direct-acting antivirals [36].

3D regimen was well-tolerated and the AE profile was in line with that previously reported [17, 30]. In conclusion, treatment with 3D regimen reduced fatigue in treatment naïve genotype 1 mono-infected HCV patients over time. Thus, successful treatment may reduce the physical burden of manifestations of HCV and improve the overall quality of life of patients.

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Study design and schedule of assessments.

The 28 week observation period was comprised of 4 weeks preparation, 12 weeks treatment and 12 weeks follow-up. Screening took place at Day -28 (Study Visit 1). Fatigue was assessed by FSS questionnaire on Days 1, 28, 84 and 168 (Study Visits 2, 3, 4 and 5, respectively). Daytime physical activity and sleep efficiency were assessed by activity tracker during 4 x 4 week tracker phases. Baseline data were collected in tracker phase 1. A data set of 2 eligible weeks (10 working days) was used to assess daytime physical activity and sleep efficiency from tracker phases 1, 2, 3 and 4. SVR12 was determined on Day 168 (12 weeks post-treatment).

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Fatigue severity scale.

Taken from Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale. Application to patients with multiple sclerosis and systemic lupus erythematosus. Arch Neurol 1989;46:1121–1123.

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Distribution of mean FSS score at screening (Day -28), baseline (Day 1), Day 28, Day 84 and 12 weeks post-treatment (Day 168) is shown for all patients receiving sedative medication (n = 11).

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Possible predictors for the outcomes, sdITT (n = 37).

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Possible predictors for the outcomes, mITT (n = 24).

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5 May 2020

PONE-D-20-05447

A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen: the HEMATITE study.

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Additional Editor Comments (if provided):

The authors have studied the evolution of the quality of life (sleep efficiency, activity and fatigue) in a group of 41 patients with hepatitis C treated with the 3d combo (they expected to recruit 100). They have found that the treatment was followed by an improvement in the fatigue scale. This is an interesting issue, but there a number of issues that should be revised. Most of them have been mentioned by the reviewers.

- The conclusion of the abstract about the proportion of SVR should be eliminated. This is not an aim of the study.

- The references about the topic should be updated (not only the reference suggested by Reviewer 2).

- Quality of the figures should be improved.

- Fatigue severity score should be better explained in the methods section. It seems that 7 is the worst score. Is it true? Do the patients have a score of 6 at baseline? I think this is unlikely (all of them are F0/F1 patients). Could the authors give a reference value from general population?

- Could the authors give some information about day count in general population?

- Table 1 should be revised: AST, ALT, bilirubin levels are expressed in an uncommon way (it is more frequent to give them as median & IQR. HCV RNA level should be expressed as log. Hemoglobin, ferritin, TSH, and glucose values are missing from a high proportion of patients.

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Reviewer #1: No

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Reviewer #4: Yes

**********

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Reviewer #4: Yes

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5. Review Comments to the Author

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Reviewer #1: The authors conducted a multicenter, prospective observational study to measure the impact of 3D regimen on the fatigue, daytime physical activity and sleep efficiency of genotype 1 HCV mono-infected patients. Overall the manuscript was well-written and understandable.

I would like to highlight some points as follows:

Major Comments:

1) Data availability statement reflects data are fully available without restriction. However, I can´t see the study database. Is it fully available? If your data is only available upon request, explain it in the Data Availability Statement, as it will be published in the article.

2) One of the secondary variables analyzed is the sleep efficiency at baseline, during treatment and at day 168. In Lines 215-217 is presented the mean change from baseline to Day 168. Please, include results of sleep efficiency change from baseline to Day 28 and from baseline to Day 84, to see the treatment impact on sleep efficiency. Discuss the results in the Discussion Section.

3) Subgroup analysis: it is said that RBV use, gender, liver fibrosis and genotype 1 subtype had no effect on the different outcomes analyzed. The results of this analysis should be presented in a Table, or at least as Supplementary Material.

Minor Comments:

1) The resolution of Fig 2 and 3 is low and difficult to understand.

2) Characteristic of patients as mono-infected HCV is well described in the Methods Section. However, it should be clarified in other parts of the manuscript: Abstract (methods) and Discussion (line 288).

3) Exclusion criteria should be reported in the Abstract, if length allows it.

4) Clarify if informed consent was a written informed consent (line 78).

5) Table 1: BMI data only available in 32 patients could be reflected in the legend, or indicating n=32 as in the laboratory markers.

6) Line 194: Colud you indicate the Spearman´s rank correlation coefficient value? Is it 0?

7) Table 2 Legend: It is referred that data of "Any AE" is not available for all ITT population. Please, indicate in how many patients you have this information. Percentage is calculated taking into account all the 41 patients. Please, calculate this percentage for the "n" patients you have "Any AE" data.

8) Line 234: indicate in parentheses that fibrosis stage is (F0-F1).

9) Discussion section: it should be recognized as a limitation that no F2-F3 patients could be included in the study.

10) Other articles in the literature have found RBV does not impact on HRQoL. References could be included.

11) Corresponding author is not the same in the Title Page and the Submitted form.

Reviewer #2: Please provide a better images of figures. It is impossible to read numbers.

Authors can give the statistical data about the change in the fatigue parameters and comment on the significance of this reduction.

It is not true that fatigue was not investigated earlier in this field (line 59 “ To date, there has been no research into the effect of 3D regimen on the quality of life of HCV patients suffering from fatigue.”) because there is already a published paper about this.( Durcan E, Hatemi I, Sonsuz A, Canbakan B, Ozdemir S, Tuncer M. The effect of direct antiviral treatment on the depression, anxiety, fatigue and quality-of-life in chronic hepatitis C patients. Eur J Gastroenterol Hepatol. 2020 Feb; 32(2): 246-250 doi:10.1097/MEG.0000000000001501. PubMed PMID: 31441798.)

The fatigue is a common symptom in chronic liver disease even if it is not related to viral infection for example primary biliary cholangitis, because of that it is not sense to say “ Reduced quality of life in HCV infected patients is independent of liver damage, indicating that the virus itself is responsible.” The virus is not responsible, chronic liver disease is responsible of reduced quality of life.

Reviewer #3: An observational single-arm study aimed to measure the impact of 3D regimen treatment on fatigue in Hepatitis C Virus infected patients (n=41). Nearly all patients maintained a virologic response to treatment at 12 weeks, and compared to baseline the fatigue severity scale score decreased at 12 weeks. No changes were observed in daytime physical activity or sleep efficiency. The manuscript was clearly written.

Minor revisions:

1- Line 124: Modify the sentence for clarity. “The primary outcome variable was a change in mean daytime physical activity….”

2- Line 155: Indicate the statistical method used to calculate the 95% CI.

3- Paragraph beginning at line 156: Consider building models to predict daytime physical activity, FSS and sleep efficiency.

4- Line 161: Indicate if adverse events were collected according to a standardized method.

5- Line 192: Intraclass correlation coefficients may be superior to Spearman rank correlation coefficients due to repeated measures.

6- Line 221 states, "Concomitant ribavirin, gender, fibrosis stage, age and genotype 1 subtype had no effect on daytime physical activity, FSS score or sleep efficiency in the sdITT or mITT populations." The conclusion in this sentence cannot be supported by results from t-tests or Mann-Whitney-U tests. See comment 3.

Reviewer #4: Thank you very much for your paper. The topic is very important and is also a good investigation. I suggest it might need revision before being accepted. 1.The study design section is confusing: it is not clear when the study visit 1 took place. 2.The title can be revised as "...... observational study to measure the fatigue....", since the study did not measure the quality of life but measured only fatigue. 3.The small sample size can be mentioned as a limitation of the study.

Overall, the study is interesting and I enjoyed reading it.

**********

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Reviewer #1: Yes: Regina Juanbeltz

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes: Tatevik Balayan

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30 Jul 2020

PONE-D-20-05447 – Response to Reviewers

Dear Reviewers,

We would like to thank you for the detailed review of our manuscript entitled “HEMATITE Study: A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen.” Your comments helped to improve the manuscript.

Here, we provide a detailed point-by-point reply letter to all reviewers’ comments. We implemented all comments of the reviewers in the revised manuscript. Please find our answers below. The line numbers mentioned correspond to the manuscript version with Track Changes.

We strongly believe that this study will be of significant interest for the readership of PLOS ONE. Thank you for considering this manuscript for publication in PLOS ONE.

Sincerely,

David Semela, MD PhD & Lisa Ruckstuhl, on behalf of all the authors

Point-by-point reply

Journal Requirements:

1) Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Answer: The PLOS ONE’s style requirements have been adjusted.

2) Please ensure you have included the registration number for the clinical trial referenced in the manuscript.

Answer: The clinical trial registration number (ClinicalTrials.gov Identifier: NCT03002818) is included in the manuscript (line 513).

3) We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements.

Answer: We agree with your comment – accordingly a new supplementary figure (supporting information Fig S3) showing the mentioned data was included (line 364, and Fig S3).

4) Thank you for stating the following in the Competing Interests section:

'Nasser Semmo has received research grants, consulting fees and/or speaker fees

from AbbVie and Gilead and consulting fees from MSD. Beat Müllhaupt has received

speaking and/or consulting fees from Merck/MSD, AbbVie, Intercept, Astra, Bayer,

BMS, Gilead and research support from Gilead. Lorenzo Magenta has received

research grants, consulting fees and/or speaker fees from AbbVie, Gilead, Janssen,

BMS and MSD. Olivier Clerc has received consulting fees from AbbVie. David Semela

has received research grants, consulting fees and/or speaker fees from AbbVie, Bayer,

BMS, Gilead, Intercept and MSD. Ralph Torgler and Lisa Ruckstuhl are employees of

AbbVie and own stock/options.'

Answer: This information is included under the “Competing Interest Section” (line 491).

We note that one or more of the authors are employed by a commercial company: AbbVie.

1) Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study.

Answer: Not applicable, see also below.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

Answer: Not applicable, see also below.

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

Answer: Ralph Torgler and Lisa Ruckstuhl did play a role in the study design, data analysis, decision to publish, and preparation of the manuscript. The Conflict of interest statement has been amended accordingly in the manuscript (lines 491-499). Further, the Funding statement has been added (lines 501-503). Please note, that the funding is also declared in the Acknowledgements.

2) Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Answer: Ralph Torgler and Lisa Ruckstuhl did play a role in the study design, data analysis, decision to publish, and preparation of the manuscript. The Conflict of interest statement has been amended accordingly in the manuscript (lines 491-499). Further, the Funding statement has been added (lines 501-503). Please note, that the funding is also declared in the Acknowledgements.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

Answer: We absolutely agree and as requested have added the statement "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (lines 498/499).

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Answer: An updated Funding Statement and Competing Interests Statement was included in our cover letter.

Additional Editor Comments (if provided):

The authors have studied the evolution of the quality of life (sleep efficiency, activity and fatigue) in a group of 41 patients with hepatitis C treated with the 3d combo (they expected to recruit 100). They have found that the treatment was followed by an improvement in the fatigue scale. This is an interesting issue, but there a number of issues that should be revised. Most of them have been mentioned by the reviewers.

- The conclusion of the abstract about the proportion of SVR should be eliminated. This is not an aim of the study.

Answer: We agree with this comment and have adjusted the abstract’s conclusion accordingly.

- The references about the topic should be updated (not only the reference suggested by Reviewer 2).

Answer: In addition to references suggested by the other reviewers we have added relevant studies in the field of HCV and fatigue: Golabi P, Sayiner M, Bush H, Gerber LH, Younossi ZM. Patient-Reported Outcomes and Fatigue in Patients with Chronic Hepatitis C Infection (Clin Liver Dis. 2017) and Durcan E, Hatemi I, Sonsuz A, Canbakan B, Ozdemir S, Tuncer M. The effect of direct antiviral treatment on the depression, anxiety, fatigue and quality-of-life in chronic hepatitis C patients (Eur J Gastroenterol Hepatol. 2020) (lines 66 - 71).

- Quality of the figures should be improved.

Answer: Figure quality and resolution have been improved.

- Fatigue severity score should be better explained in the methods section. It seems that 7 is the worst score. Is it true?

Answer: Yes, 7 is the worst score. We have clarified this in the Methods section (lines 111-115). In addition, supplementary figure 2 shows the detailed FSS.

Do the patients have a score of 6 at baseline? I think this is unlikely (all of them are F0/F1 patients). Could the authors give a reference value from general population?

Answer: It is correct that patients had a score of 6 at baseline (boxplots figures 3a and 3b). The HEMATITE study included only subjects with severe fatigue (defined by an FSS score of ≥4), as described in the ‘materials & methods and patients’ section. We agree, that many patients with F0/F1 will have no or minimal fatigue. Only patients with severe fatigue were selected for this study. Data on FSS from the general population show that healthy subjects reported an FSS of 3.00 ± 1.08, n=454) (Valko PO et al “Validation of the Fatigue Severity Scale in a Swiss Cohort”, Sleep 2008, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2579971/).

Could the authors give some information about day count in general population?

Answer: Reference 22 (now Ref 24), Heeren et al., also included healthy subjects, which did not show different day counts (24-h activity level) compared to HCV patients. Such kind of activity data depend strongly on the setting, patient population and the device used (in contrast to our study, Heeren et al. used two different activity monitors) and thus, a direct comparison with our data is difficult (selected fatigue patients, different device). In the HEMATITE study, we only compared baseline activity against SVR12 activity in the same patient; no healthy subjects were included.

Table 1 should be revised:

AST, ALT, bilirubin levels are expressed in an uncommon way (it is more frequent to give them as median & IQR).

Answer: Thank you for this input - we have changed LFT levels expression to mean ±SD.

HCV RNA level should be expressed as log.

Answer: We have adjusted HCV RNA levels to log scale.

Hemoglobin, ferritin, TSH, and glucose values are missing from a high proportion of patients.

Answer: We agree with this remark; as the study setting was observational and non-interventional, the collection of these lab values at each visit was recommended but ultimately at the investigator’s discretion of the different study sites.

Reviewer #1:

The authors conducted a multicenter, prospective observational study to measure the impact of 3D regimen on the fatigue, daytime physical activity and sleep efficiency of genotype 1 HCV mono-infected patients. Overall the manuscript was well-written and understandable.

I would like to highlight some points as follows:

Major Comments:

1) Data availability statement reflects data are fully available without restriction. However, I can´t see the study database. Is it fully available? If your data is only available upon request, explain it in the Data Availability Statement, as it will be published in the article.

Answer: A data availability statement added: Data is available upon request (line 506).

2) One of the secondary variables analyzed is the sleep efficiency at baseline, during treatment and at day 168. In Lines 215-217 is presented the mean change from baseline to Day 168. Please, include results of sleep efficiency change from baseline to Day 28 and from baseline to Day 84, to see the treatment impact on sleep efficiency. Discuss the results in the Discussion Section.

Answer: As requested we have added data on sleep efficiency in the result section (lines 258-263):

“The mean change in sleep efficiency from baseline (Day 1) to treatment week 4 (day 28) was -0.87% (95% CI: -1.7, -0.03) for the sdITT population and -0.44% (95% CI: -1.5, 0.6) for the mITT population. The mean change in sleep efficiency from baseline (Day 1) to end of treatment (Day 84) was -0.06% (95% CI: -1.0, 0.9) for the sdITT population and 0.01% (95% CI: -1.2, 1.2) for the mITT population.”

As requested we have adjusted the discussion accordingly added data on sleep efficiency in the result section (lines 337-340):

“This is in line with the findings of Heeren et al., where there was no correlation between the results of a fatigue questionnaire (fatigue impact scale) and sleep actigraphy data in HCV patients observed and the authors suggested that bad sleep quality may therefore not be associated with increased nocturnal motor activity.”

3) Subgroup analysis: it is said that RBV use, gender, liver fibrosis and genotype 1 subtype had no effect on the different outcomes analyzed. The results of this analysis should be presented in a Table, or at least as Supplementary Material.

Answer: An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. These additional results are now described and presented in supplementary tables (S1 and S2), (lines 270-282):

“An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. As predictors age (classified by median split: ≤ 50 years/>50 years), gender (male/female), HCV genotype (genotype 1a/genotype 1b), liver fibrosis (yes/no) and ribavirin use (yes/no) were investigated, results are shown for sdITT population (supporting information S1 Table) and for mITT population (supporting information S2 Table). Overall, for none of the analyzed possible predictive factors the univariate as well as the multivariate analysis showed significance. The corresponding effect sizes demonstrated no or very small effects for these factors. Therefore, the factors age class, sex, fibrosis, HCV genotype and ribavirin use had no influence of the three outcome variables mean daytime physical activity, sleep efficiency and FSS.”

Minor Comments:

1) The resolution of Fig 2 and 3 is low and difficult to understand.

Answer: Resolution of figures 2 & 3 has been improved.

2) Characteristic of patients as mono-infected HCV is well described in the Methods Section. However, it should be clarified in other parts of the manuscript: Abstract (methods) and Discussion (line 288).

Answer: Done (lines 22 & 370)

3) Exclusion criteria should be reported in the Abstract, if length allows it.

Answer: Listing the exclusion criteria in the abstract would exceed the max. allowed words (300). We have therefore decided to keep the exclusion criteria in the methods section.

4) Clarify if informed consent was a written informed consent (line 78).

Answer: Done (line 90).

5) Table 1: BMI data only available in 32 patients could be reflected in the legend or indicating n=32 as in the laboratory markers.

Answer: We agree and have added according asterisks/legend.

6) Line 194: Could you indicate the Spearman´s rank correlation coefficient value? Is it 0?

Answer: Please find in the following table the correlation coefficients of Fatigue Severity Scale and mean daytime physical activity.

The authors find the statement in the manuscript sufficient (“There was no correlation between daytime physical activity and FSS score over the course of the study as assessed by Spearman’s rank correlation coefficient.”).

Population Visit n rSpearman* Interpretation

Scale down IT Baseline (V2) 35 0.300 Fair agreement

Day 28 (V3) 33 0.131 Poor agreement

Day 84 (V4) 33 -0.022 Poor agreement

Day 168 (V5) 28 0.195 Poor agreement

Subgroup 1, scale down ITT Baseline (V2) 10 0,468 Moderate agreement

Day 28 (V3) 10 0,170 Poor agreement

Day 84 (V4) 9 -0,268 Fair agreement

Day 168 (V5) 6 -0,493 Moderate agreement

Subgroup 2, scale down ITT Baseline (V2) 13 0,220 Fair agreement

Day 28 (V3) 13 0,203 Fair agreement

Day 84 (V4) 12 0,473 Moderate agreement

Day 168 (V5) 9 0,200 Fair agreement

Subgroup 3, scale down ITT Baseline (V2) 12 0,025 Poor agreement

Day 28 (V3) 9 0,444 Moderate agreement

Day 84 (V4) 10 0,176 Fair agreement

Day 168 (V5) 11 0,555 Moderate agreement

* Spearman correlation coefficient

Alternatively according to Bland and Altmann (1995) a multiple regression with subjects as a factor was performed to analyze whether the change of mean daytime physical activity in one subject during the study is paralleled by the change of FSS during the study resulting in the following correlation coefficients: sdITT = 0.1477

mITT = 0.1961.

Reference: Bland JM, Altman DG (1995). Calculating correlation coefficients with repeated observations. BMJ. 310: 446

The ICC, addressed by the reviewer, seems to be more appropriate for single variables measured by more than one investigator (e.g. raters) or variables within the same class which is not applicable in the HEMATITE study.

7) Table 2 Legend: It is referred that data of "Any AE" is not available for all ITT population. Please, indicate in how many patients you have this information. Percentage is calculated taking into account all the 41 patients. Please, calculate this percentage for the "n" patients you have "Any AE" data.

Answer: We have added the requested information (see Table 2 legend). We have collected the AE data (occurrence of AE or not) in all 41 patients (ITT population).

8) Line 234: indicate in parentheses that fibrosis stage is (F0-F1).

Answer: We have indicated the fibrosis stage as requested (lines 299-301) and added a phrase in the discussion: “The relatively small patient number and the inclusion of only F0 and F1 patients further add to the limitations of our study.” (lines 356-358).

9) Discussion section: it should be recognized as a limitation that no F2-F3 patients could be included in the study.

Answer: We agree with this comment. This fact has been considered in the phrase mentioned in comment/answer 8 (see above, lines 356-358). Please note that the inclusion of only F0/F1 patients does not minor the value of the observation as we exclusively included patients suffering from severe fatigue.

10) Other articles in the literature have found RBV does not impact on HRQoL. References could be included.

Answer: We thank the reviewer for this remark and have added a new reference (Ref 36: Younossi ZM, Stepanova M, Zeuzem S, et al. Patient-reported outcomes assessment in chronic hepatitis C treated with sofosbuvir and ribavirin: the VALENCE study. J Hepatol. 2014;61(2):228‐234. doi:10.1016/j.jhep.2014.04.003) (line 366)

11) Corresponding author is not the same in the Title Page and the Submitted form.

Answer: David Semela is the corresponding author and has submitted the manuscript.

Reviewer #2:

1) Please provide a better images of figures. It is impossible to read numbers.

Answer: All figures have been improved and adjusted accordingly.

2) Authors can give the statistical data about the change in the fatigue parameters and comment on the significance of this reduction.

Answer: We have added the requested data in the discussion “In this study, treatment with 3D regimen reduced fatigue over time in non-cirrhotic, treatment naïve patients with HCV genotype 1 infection. A reduction in mean FSS score of 2.8 (95% CI 2.21 – 3.43) from baseline (Day 1) to 12 weeks post-treatment (Day 168) was observed in the mITT population. …” followed by: “This change was significant with p < 0.001, Friedman’s ANOVA.” (lines 311-312)

3) It is not true that fatigue was not investigated earlier in this field (line 59 “ To date, there has been no research into the effect of 3D regimen on the quality of life of HCV patients suffering from fatigue.”) because there is already a published paper about this.( Durcan E, Hatemi I, Sonsuz A, Canbakan B, Ozdemir S, Tuncer M. The effect of direct antiviral treatment on the depression, anxiety, fatigue and quality-of-life in chronic hepatitis C patients. Eur J Gastroenterol Hepatol. 2020 Feb; 32(2): 246-250 doi:10.1097/MEG.0000000000001501. PubMed PMID: 31441798.)

Answer: We thank the reviewer for this comment and have added the publication from Durcan et al.(reference 20) and commented accordingly (lines 66-71): “To date, there has been only few publications on the characterization of fatigue through treatment with newly developed direct-acting antivirals19, and no research into the effect of 3D regimen on the quality of life of HCV patients suffering from fatigue. A recently published work of Durcan et al. found, that direct antivirals did not lead to depression, anxiety or fatigue and did not decrease liver-specific quality of life.”

4) The fatigue is a common symptom in chronic liver disease even if it is not related to viral infection for example primary biliary cholangitis, because of that it is not sense to say “Reduced quality of life in HCV infected patients is independent of liver damage, indicating that the virus itself is responsible.” The virus is not responsible, chronic liver disease is responsible of reduced quality of life.

Answer: We agree with the reviewer and have adjusted the sentence (lines 58-59): “Reduced quality of life in HCV infected patients is independent of liver damage.”

Reviewer #3:

An observational single-arm study aimed to measure the impact of 3D regimen treatment on fatigue in Hepatitis C Virus infected patients (n=41). Nearly all patients maintained a virologic response to treatment at 12 weeks, and compared to baseline the fatigue severity scale score decreased at 12 weeks. No changes were observed in daytime physical activity or sleep efficiency. The manuscript was clearly written.

Minor revisions:

1) Line 124: Modify the sentence for clarity. “The primary outcome variable was a change in mean daytime physical activity….”

Answer: We have adjusted the sentence accordingly (line 145).

2) Line 155: Indicate the statistical method used to calculate the 95% CI.

Answer: We have clarified this aspect (line 179)

Calculation of 95% CI

The given 95% CIs were calculated as standard Wald intervals using the estimated standard error. In case of sample size n ≤ 40 Brown et al. (2011) recommended the Wilson interval using the null standard error instead of estimated standard error.

• 93.5 % (CI95%: 89.2 – 97.8) in the ITT (n = 41)

• 92.9 % (CI95%: 88.1 – 97.6) in the scale down ITT (n = 37)

• 89.5 % (CI95%: 82.5 – 96.5) in the mITT (n = 24).

References:

Brown LD, Cai T and DaGupta A (2011). Interval estimation for binominal proportion. Statistical Science. 16 (2): 101–133

Wilson, EB (1927). Probable inference the law of succession and statistical inference.

J Amer Stat Assoc 22 (158): 209-212

3) Paragraph beginning at line 156: Consider building models to predict daytime physical activity, FSS and sleep efficiency.

Answer: An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. These new analyses have been added in the supplement (tables S1 and S2, line 270-282):

“An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. As predictors age (classified by median split: ≤ 50 years/>50 years), gender (male/female), HCV genotype (genotype 1a/genotype 1b), liver fibrosis (yes/no) and ribavirin use (yes/no) were investigated, results are shown for sdITT population (supporting information S1 Table) and for mITT population (supporting information S2 Table). Overall, for none of the analyzed possible predictive factors the univariate as well as the multivariate analysis showed significance. The corresponding effect sizes demonstrated no or very small effects for these factors. Therefore, the factors age class, sex, fibrosis, HCV genotype and ribavirin use had no influence of the three outcome variables mean daytime physical activity, sleep efficiency and FSS.”

4) Line 161: Indicate if adverse events were collected according to a standardized method.

Answer: We have clarified this point (line 186): “Safety assessments were performed in a standardized method at each study visit and included the evaluation of adverse events (AEs):…”

5) Line 192: Intraclass correlation coefficients may be superior to Spearman rank correlation coefficients due to repeated measures.

Answer: Please find in the following table the correlation coefficients of Fatigue Severity Scale and mean daytime physical activity.

The authors find the statement in the manuscript sufficient (“There was no correlation between daytime physical activity and FSS score over the course of the study as assessed by Spearman’s rank correlation coefficient.”).

Population Visit n rSpearman* Interpretation

Scale down IT Baseline (V2) 35 0.300 Fair agreement

Day 28 (V3) 33 0.131 Poor agreement

Day 84 (V4) 33 -0.022 Poor agreement

Day 168 (V5) 28 0.195 Poor agreement

Subgroup 1, scale down ITT Baseline (V2) 10 0,468 Moderate agreement

Day 28 (V3) 10 0,170 Poor agreement

Day 84 (V4) 9 -0,268 Fair agreement

Day 168 (V5) 6 -0,493 Moderate agreement

Subgroup 2, scale down ITT Baseline (V2) 13 0,220 Fair agreement

Day 28 (V3) 13 0,203 Fair agreement

Day 84 (V4) 12 0,473 Moderate agreement

Day 168 (V5) 9 0,200 Fair agreement

Subgroup 3, scale down ITT Baseline (V2) 12 0,025 Poor agreement

Day 28 (V3) 9 0,444 Moderate agreement

Day 84 (V4) 10 0,176 Fair agreement

Day 168 (V5) 11 0,555 Moderate agreement

* Spearman correlation coefficient

Alternatively according to Bland and Altmann (1995) a multiple regression with subjects as a factor was performed to analyze whether the change of mean daytime physical activity in one subject during the study is paralleled by the change of FSS during the study resulting in the following correlation coefficients: sdITT = 0.1477

mITT = 0.1961.

Reference: Bland JM, Altman DG (1995). Calculating correlation coefficients with repeated observations. BMJ. 310: 446

The ICC, addressed by the reviewer, seems to be more appropriate for single variables measured by more than one investigator (e.g. raters) or variables within the same class which is not applicable in the HEMATITE study.

6) Line 221 states, "Concomitant ribavirin, gender, fibrosis stage, age and genotype 1 subtype had no effect on daytime physical activity, FSS score or sleep efficiency in the sdITT or mITT populations." The conclusion in this sentence cannot be supported by results from t-tests or Mann-Whitney-U tests.

Answer: We agree with this comment. An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. These additional results are now described and presented in supplementary tables (S1 and S2), (lines 270-282):

“An analysis of several predictors to the three outcome variables daytime physical activity, sleep efficiency and FSS were performed via generalized linear models with repeated measurement. As predictors age (classified by median split: ≤ 50 years/>50 years), gender (male/female), HCV genotype (genotype 1a/genotype 1b), liver fibrosis (yes/no) and ribavirin use (yes/no) were investigated, results are shown for sdITT population (supporting information S1 Table) and for mITT population (supporting information S2 Table). Overall, for none of the analyzed possible predictive factors the univariate as well as the multivariate analysis showed significance. The corresponding effect sizes demonstrated no or very small effects for these factors. Therefore, the factors age class, sex, fibrosis, HCV genotype and ribavirin use had no influence of the three outcome variables mean daytime physical activity, sleep efficiency and FSS.”

Reviewer #4:

Thank you very much for your paper. The topic is very important and is also a good investigation. I suggest it might need revision before being accepted.

1) The study design section is confusing: it is not clear when the study visit 1 took place.

Answer: Thank you for your comment, which we have clarified: V1 is on day -28. For better comprehension, we have described Visit 1 and Visit 2 as follows in the manuscript: Visit 1 (Day -28; before treatment start), Visit 2 (Day 1; treatment start). Together with the supporting information (Fig S1), we think the study design is now more comprehensive (lines 82-83).

2) The title can be revised as "...... observational study to measure the fatigue....", since the study did not measure the quality of life but measured only fatigue.

Answer: We prefer to keep the current title, since fatigue is a central element of quality of life. The term “quality of life” is commonly used in the literature assessing fatigue in patients and would in our opinion help to increase the visibility of this study.

3) The small sample size can be mentioned as a limitation of the study.

Answer: We have added this aspect in the discussion (“The relatively small patient number and the inclusion of only F0 and F1 patients further limit our observations.”).

Submitted filename: Response to Reviewers DS final.docx

Click here for additional data file.

27 Aug 2020

PONE-D-20-05447R1

A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen: the HEMATITE study.

PLOS ONE

Dear Dr. Semela,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please, include a comment about the baseline fatigue severity of the patients included in th study.

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We look forward to receiving your revised manuscript.

Kind regards,

Jose Ignacio Herrero

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

The authors have addressed all our previous comments. There is only a detail that should be revised. Please, add a comment in the abstract about the baseline fatigue score (severe in all the patients) included in the study.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

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Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

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Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

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Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

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Reviewer #3: (No Response)

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Reviewer #4: Yes: Tatevik Balayan

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7 Oct 2020

Dear Reviewers,

Thank you for reviewing our revised manuscript ‘A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen: the HEMATITE study.’

There has been raised one point and we did amend the abstract (in Methods and Results) accordingly, traceable in the track changed version of the manuscript:

Question by the editor: The authors have addressed all our previous comments. There is only a detail that should be revised. Please, add a comment in the abstract about the baseline fatigue score (severe in all the patients) included in the study.

Answer: We thank you for this valuable comment. Accordingly, we have added the information “Patients with FSS ≥ 4 (clinically significant fatigue) were included.” in lines 23-24 and “Mean baseline FSS score was 6.0 for the sdITT population and 5.9 for the mITT population and decreased from baseline to 12 weeks post-treatment by 2.6 (95% confidence interval [CI]: 2.1, 3.1) for the sdITT (n=37) population and 2.8 (95% CI: 2.2, 3.4) for the mITT (n=24) population.” in lines 30-33 of the abstract.

We hope that all your questions have been addressed adequately. We strongly believe that this study will be of significant interest for the readership of PLOS ONE. Thank you once again for considering this manuscript for publication in PLOS ONE.

Best regards,

David Semela, on behalf of the authors

Submitted filename: Response to Reviewers R2.docx

Click here for additional data file.

13 Oct 2020

A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen: the HEMATITE study.

PONE-D-20-05447R2

Dear Dr. Semela,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Jose Ignacio Herrero

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The authors have given adequate responses to all the previous comments

Reviewers' comments:

23 Oct 2020

PONE-D-20-05447R2

A prospective, multicenter, post-marketing observational study to measure the quality of life of HCV genotype 1 infected, treatment naïve patients suffering from fatigue and receiving 3D regimen: the HEMATITE study.

Dear Dr. Semela:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr Jose Ignacio Herrero

Academic Editor

PLOS ONE

Table 1

Patient characteristics at screening.

Characteristic	n/N (%)
Age [years], mean (± SD)	49.4	(± 12.7)
≤ 50 years	22/41	(53.7)
> 50 years	19/41	(46.3)
Gender
Male	14/41	(34.1)
Female	27/41	(65.9)
Race
Caucasian	41/41	(100.0)
BMI [kg/m2], mean (± SD)	23.7	(± 4.4)
BMI ≥ 25 (overweight)*	11/32	(34.4)
Fibrosis stage
No fibrosis	15/41	(36.6)
F1 stage	26/41	(63.4)
Source of HCV infection†
IV drug use	18/41	(43.9)
Tattoos or piercings	5/41	(12.2)
Sexual transmission	3/41	(7.3)
Transfusions	3/41	(7.3)
Other	5/41	(12.2)
Unknown	13/41	(31.7)
HCV genotype
Genotype 1a	24/41	(58.5)
Genotype 1b	17/41	(41.5)
Smoking and alcohol consumption
Smokers	21/38	(55.3)
Consume alcohol	25/39	(64.1)
Laboratory markers, mean (± SD)
HCV RNA level [log10 IU/mL], n = 38	6.6	(± 6.8)
AST [U/L], n = 38	40.7	(± 21.0)
ALT [U/L], n = 38	48.0	(± 28.2)
Total bilirubin [μmol/L], n = 37	11.7	(± 6.3)
Hemoglobin [g/L], n = 26 (normal range: 120–180 g/L)	143.6	(± 15.0)
Creatinine [μmol/L], n = 38 (normal range: 44–106 μmol/L)	67.3	(± 12.9)
Ferritin [μg/L], n = 22 (normal range: 30–200 μg/L)	172.4	(± 130.4)
TSH [mU/L], n = 24 (normal range: 0.3–3.5 mU/L)	1.7	(± 1.3)
Fasting glucose [mmol/L], n = 26 (normal range: 3.9–5.6 mmol/L)	5.0	(± 0.6)

ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; HCV = hepatitis C; IV = intravenous; RNA = ribonucleic acid; SD = standard deviation; TSH = thyroid stimulating hormone.

† Multiple answers were reported

*BMI data only available for 32 patients

Table 2

Adverse events and treatment-related adverse events occurring in ≥ 2.5% of patients (N = 41).

Adverse event †	n	(%)
Any AE ‡	21	(51.2)
Any SAE	0	(0)
AEs leading to discontinuation	0	(0)
AEs occurring in ≥ 2 patients
Nausea	6	(14.6)
Dizziness / vertigo	3	(7.3)
Anemia	2	(4.9)
Abdominal pain	2	(4.9)
Emesis	2	(4.9)
Epigastric pressure pain	2	(4.9)
Flu like symptoms	2	(4.9)
FSS increased ≥ 1	2	(4.9)
Total bilirubin increase	2	(4.9)
Any treatment-related AE	13	(31.7)
Treatment-related AEs occurring in ≥ 2 patients
Nausea	4	(9.8)
Total bilirubin increase	2	(4.9)
Anemia	2	(4.9)
FSS increased ≥ 1	2	(4.9)

AE = adverse event; FSS = fatigue severity scale: SAE = serious adverse event.

† Fatigue is not included as it was part of the inclusion criteria. An increase of ≥ 1 point in the FSS was documented as an AE.

‡ Any AE occurred in 21 out of 41 patients (51.2%).