View PDF Version - RePub - Erasmus Universiteit Rotterdam

More documents

Recommendations

Info

Chapter 2.3 62 ABSTRACT Peginterferon (PEG-IFN) results in HBeAg loss combined with virologic response in only a minority of patients with HBeAg positive chronic hepatitis B. Baseline predictors of response to PEG-IFN include HBV-genotype, pre-treatment HBV DNA levels and ALT. The aims of this study were to develop a model, which improves the baseline prediction of response to PEG-IFN for individual patients by including early HBV DNA measurements during treatment and to establish an early indication for cessation of treatment. One hundred and thirty six patients treated with PEG-IFN were included in the study. Response was defi ned as loss of HBeAg and HBV DNA
INTRODUCTION Dynamic prediction of response to PEG-IFN 63 With the emergence of new antiviral agents, treatment options for chronic hepatitis B virus (HBV) infection have changed considerably. At present, the Food and Drug Administration (FDA) and the European Medicines Agency (EMEA) have approved six agents for the treatment of chronic hepatitis B: pegylated interferon (PEG-IFN), lamivudine, adefovir, telbuvidine, entecavir and tenovovir. Current guidelines do not recommend any particular agent as fi rst-line therapy and the preferred initial treatment of individual patients remains controversial. Response to treatment of chronic hepatitis B patients with positive hepatitis B e antigen (HBeAg positive), defi ned as HBeAg seroconversion to anti-HBe, suppression of serum HBV DNA and normalization of ALT, is associated with a favourable long-term prognosis, independent of the drug by which the response has been induced. 1-3 PEG-IFN treatment results in HBeAg seroconversion in 30% of treated patients after one year of therapy. 4-5 Response is sustained in over 70% of these patients6-7 and has shown to prolong survival and to reduce the incidence of hepatolocellar carcinoma. 1 Unfortunately, PEG-IFN therapy is associated with side-effects ranging from fl u-like syndromes to neuropsychiatric disorders such as depression in a considerable number of patients. 8 Therefore, patients should be selected for PEG-IFN therapy on the basis of their pre-treatment probability of response, in order to optimize the balance between potentially benefi cial effects and harmful side-effects. A prognostic model, that uses readily available baseline variables, has recently been developed for these patients. This model is based on the data from two large trials (n=721) investigating the effect of PEG-IFN in HBeAg positive chronic hepatitis B4-5 and has shown that response to PEG-IFN depends on HBV-genotype, sex, age, prior treatment with IFN, baseline HBV DNA and ALT levels. 9 The model may be used to select patients at baseline who have a relatively high probability of response. However, for an individual patient uncertainty remains as to whether he or she will actually benefi t from this treatment. On-treatment HBV DNA levels may help to refi ne the prediction of response, and an optimal stopping rule based upon HBV DNA viral load decline during therapy may help to establish an early indication for cessation of treatment. In the current study the prediction model described by Buster et al9 was extended by including on-treatment HBV DNA and ALT measurements. This enabled an individual update of the response probability based upon new data acquired after each follow-up visit. Such a model can be benefi cial in guiding and supporting the patient through therapy and may help to identify those patients for whom treatment cessation should be considered. Different models were fi tted and compared in order to establish a model that not
Page 1:
Statistical Models of Treatment Eff
Page 4 and 5:
ISBN: 978-90-8559-069-9 © Bettina
Page 6 and 7:
PROMOTIECOMMISSIE Promotor: Prof.dr
Page 9 and 10:
CONTENTS Chapter 1. Introduction 11
Page 13: Cha pter 1 Introduction
Page 16 and 17: Chapter 1 14 Epidemiology Worldwide
Page 18 and 19: Chapter 1 16 Prediction models with
Page 20 and 21: Chapter 1 18 standard method fails
Page 22 and 23: Chapter 1 20 The extended non-linea
Page 24 and 25: Chapter 1 22 References 1. Blumberg
Page 27: Cha pter 2 Prediction of response t
Page 30 and 31: Chapter 2.1 28 ABSTRACT The aim of
Page 32 and 33: Chapter 2.1 30 been hepatitis B sur
Page 34 and 35: Chapter 2.1 32 HBV DNA decline The
Page 36 and 37: Chapter 2.1 34 Figure 2 continued.
Page 38 and 39: Chapter 2.1 36 Figure 4. Estimated
Page 40 and 41: Chapter 2.1 38 DNA was observed in
Page 42: Chapter 2.1 40 15. Kao JH. Role of
Page 46 and 47: Chapter 2.2 44 ABSTRACT Background:
Page 48 and 49: Chapter 2.2 46 low baseline HBV DNA
Page 50 and 51: Chapter 2.2 48 patient subgroups wa
Page 52 and 53: Chapter 2.2 50 p=0.002). Previous I
Page 54 and 55: Chapter 2.2 52 Application of the m
Page 56 and 57: Chapter 2.2 54 PEG-IFN, the proport
Page 58 and 59: Chapter 2.2 56 References 1. Lavanc
Page 60 and 61: Chapter 2.2 58 26. Bonino F, Lau GK
Page 63: Chap ter 2.3 Prediction of the resp
Page 67 and 68: Statistical analysis Dynamic predic
Page 69 and 70: Dynamic prediction of response to P
Page 77 and 78: References Dynamic prediction of re
Page 81 and 82: Cha pter 2.4 Dynamic prediction of
Page 83 and 84: INTRODUCTION Dynamic prediction of
Page 85 and 86: For the indirect approach we rewrit
Page 87 and 88: logit pi,j = logit Pr(Ri =1|visit =
Page 89 and 90: Dynamic prediction of response usin
Page 101 and 102: Table 1. Results of different stopp
Page 105: Dynamic prediction of response usin
Page 108 and 109: Chapter 2.5 106 ABSTRACT Peginterfe
Page 110 and 111: Chapter 2.5 108 and placebo daily.
Page 112 and 113: Chapter 2.5 110 for patients with a
Page 114 and 115:
Chapter 2.5 112 Mean HBV DNA declin
Page 116 and 117:
Chapter 2.5 114 peginterferon and n
Page 118 and 119:
Chapter 2.5 116 hepatocellular carc
Page 120 and 121:
Chapter 2.5 118 13. Werle-Lapostoll
Page 122:
Chapter 2.5 120 Greece - G Germanid
Page 127 and 128:
Chap ter 3.1 Long-term clinical out
Page 129 and 130:
INTRODUCTION Glycyrrhizin for IFN-n
Page 131 and 132:
Statistics Glycyrrhizin for IFN-non
Page 133 and 134:
Glycyrrhizin for IFN-non responders
Page 135 and 136:
Table 2. Time dependent Cox regress
Page 137 and 138:
Page 139 and 140:
Page 143 and 144:
Cha pter 3.2 Discriminant analysis
Page 145 and 146:
Introduction Discriminant analysis
Page 147 and 148:
Discriminant analysis using a MLMM
Page 149 and 150:
Estimation Discriminant analysis us
Page 151 and 152:
Page 153 and 154:
where wi,k(y i, θ) = (k =1,...,K).
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Appendix Discriminant analysis usin
Page 175:
Cha pter 4 Statistical models of ea
Page 178 and 179:
Chapter 4.1 176 ABSTRACT Nucleoside
Page 180 and 181:
Chapter 4.1 178 at day 1 at t =0 an
Page 182 and 183:
Chapter 4.1 180 Table 1. Baseline c
Page 184 and 185:
Chapter 4.1 182 Figure 1.
Page 186 and 187:
Chapter 4.1 184 Figure 1. Viral dec
Page 188 and 189:
Chapter 4.1 186 suppression in seru
Page 190 and 191:
Chapter 4.1 188 13. Seifer M, Hamat
Page 193 and 194:
Cha pter 4.2 Viral dynamics during
Page 195 and 196:
INTRODUCTION Viral dynamics during
Page 197 and 198:
Viral dynamics during tenofovir the
Page 199 and 200:
Page 201 and 202:
log HBV DNA (copies/mL) 10.0 9.0 8.
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 211 and 212:
Chap ter 4.3 Modelling of early vir
Page 213 and 214:
INTRODUCTION HBV viral kinetics dur
Page 215 and 216:
HBV viral kinetics during PEG-IFN 2
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229:
Page 233 and 234:
SUMMARY AND CONCLUSION Summary and
Page 235 and 236:
Summary and conclusion 233 The mode
Page 237 and 238:
Summary and conclusion 235 The infe
Page 239:
Summary and conclusion 237 Early st
Page 243 and 244:
SAMENVATTING EN CONCLUSIE Samenvatt
Page 245 and 246:
Samenvatting en conclusie 243 Er we
Page 247 and 248:
Samenvatting en conclusie 245 Data
Page 249 and 250:
Samenvatting en conclusie 247 Behan
Page 253:
Dan kwoord
Page 256 and 257:
Dankwoord 254 Lieve Marion en Margr
Page 259 and 260:
BIBLI OGRAPHY Bibliography 257 1. K
Page 261 and 262:
Bibliography 259 Interferon-ribavir
Page 263 and 264:
Bibliography 261 Long term clinical
Page 265 and 266:
Bibliography 263 Flares in chronic
Page 267 and 268:
Bibliography 265 Genetic characteri
Page 269:
Bibliography 267 106. Reijnders JG,
show all

View PDF Version - RePub - Erasmus Universiteit Rotterdam

Create successful ePaper yourself

Delete template?

Save as template?