View PDF Version - RePub - Erasmus Universiteit Rotterdam

More documents

Recommendations

Info

Chapter 2.3 72 DISCUSSION Recently a model was presented that predicts sustained response (SR) of HBeAg positive chronic hepatitis B patients to PEG-IFN therapy, using baseline characteristics. 9 In the present study a simple statistical method was introduced for the assessment of dynamic prediction using single or repeated measurements applying contemporary methods and readily available programming procedures. 15 It enabled individual updates of prediction of SR by adding on-treatment data on HBV DNA decline. Patients without a 2 log10 HBV DNA decline within 24 weeks of treatment were identifi ed as having a lower probability of response, and discontinuation should be considered in these patients. The total number of patients who achieved SR, defi ned as HBeAg loss and HBV DNA below 10.000 copies/ml was only 18/125 = 14% in our study, thus, in order to minimize overfi tting, a simplifi ed model was reached using only duration of treatment and HBV DNA decline as additional variables. Ideally, the model fi t should be confi rmed using an independent and preferably larger dataset. Since such a dataset was not available, internal cross-validation and bootstrap methods were applied to verify the fi ndings. Absolute HBV DNA levels and HBV DNA decline were equally good predictors of (non-) response, but a model fi tted with HBV DNA decline provided better performance. Adding ALT levels or interaction terms did not signifi cantly improve the models. These fi ndings confi rm the importance of frequent HBV DNA monitoring during therapy as proposed in recent guidelines. 16 Different patterns of HBV DNA decline during PEG-IFN treatment have been described in relation to response in the literature, but no solid stopping rule has yet been identifi ed. In a previous study three response profi les during treatment were described; early HBV DNA decline within the fi rst four weeks, delayed decline between weeks 4 through 32, and late decline after week 32. 17 There was an association between HBV DNA levels and HBeAg and HBsAg loss at the end of follow-up, but a stopping rule was only suggested for patients with genotype A. Absence of a 1 log10 decline at week 32 was a good predictor of non-response in those patients. In another study HBV DNA kinetics during the fi rst 4 weeks of treatment were analyzed in detail, 18 but no association between the kinetic parameters and HBeAg loss was found. Using patients treated with nucleos(t)ide analogous or PEG-IFN Dahari et al19 described 5 HBV DNA decline profi les: a classic biphasic decline, a fl at-responder profi le, a rebounder profi le, a triphasic decline and a stepwise decline. These observations confi rm that the relationship between HBV DNA levels during PEG-IFN therapy and HBeAg loss is complex. The HBV DNA patterns as predictor of response are stronger in this study because the combined endpoint, HBeAg loss and HBV DNA below 10.000 copies/ml (2,000 IU/l), was chosen. As pointed out
Dynamic prediction of response to PEG-IFN 73 previously 9 the combined endpoint is more sustainable and associated with a better long term prognosis than HBeAg loss alone 6, 20 and therefore seems an optimal choice. In a recent study of HBeAg positive chronic hepatitis B patients the association between HBeAg seroconversion and quantitative HBeAg decline and HBV DNA decline during the fi rst 24 weeks of PEG-IFN were studied. 21 Fried et al reported that an HBeAg (PEIU/ ml) >100 at week 24 was the best predictor for non-response with a NPV of 96%, while an HBV DNA level (copies/ml) of >9 log10 at week 24 only provided a NPV of 86%. Quantitative HBeAg was not measured in this study since no standardized and approved test has yet been developed. Thus these measures are not available for clinical practice. When the cut-off level of >9 log10 HBV DNA copies/ml at week 24 was applied in our cohort and used to predict a combined response of HBeAg loss and HBV DNA 9 log10 at week 24, and none of these patients achieved SR. This is consistent with the fi ndings presented by Fried et al. Unlike the treatment of chronic hepatitis C where early stopping rules at week 4 or week 12 are used, the results of the present study and those of others21 suggest that in the treatment of HBeAg-positive chronic hepatitis B a minimum period of 24 weeks is necessary before cessation of PEG-IFN therapy is considered. In recently published guidelines of the European Association for the Study of the Liver16 absence of 1 log10 HBV DNA decline at week 12 is advised as a stopping rule, although it has never been justifi ed in clinical studies. When this rule is applied on the population in this manuscript, 59% of patients should stop treatment and a NPV of 88% is observed. Even more worrying is the low sensitivity of 50%; i.e. 50% of patients responding to therapy would have to discontinue it. Zoulim et al2 suggest this early stopping rule at week 12 for patients treated with nucleos(t)ide analogous, but as recently reported this rule is not applicable for patients treated with PEG-IFN. 22 This study has several limitations. The dose of PEG-IFN alfa-2b was reduced to 50 μg/wk after 32 weeks, which is not common practice. However, it is unlikely that this has infl uenced the results of the study. The response rates of the main study were comparable with those reported by Lau et al. investigating the effi cacy of PEG-IFN alfa-2a for the treatment of HBeAg-positive chronic hepatitis B. 5 Although PEG-IFN alfa-2a and PEG- IFN alfa-2b are not fully comparable, it has previously been shown that higher doses of PEG-IFN alfa-2a do not result in higher response rates. 23 Furthermore, the baseline prediction model used here was constructed on the combined data of the two trials of Janssen and Lau, to some extent adjusting for this difference. 9 Another limitation of this study is that the HBV DNA measurements were assessed with an in-house developed
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 and 64: Chap ter 2.3 Prediction of the resp
Page 65 and 66: INTRODUCTION Dynamic prediction of
Page 67 and 68: Statistical analysis Dynamic predic
Page 69 and 70: Dynamic prediction of response to P
Page 71 and 72: Dynamic prediction of response to P
Page 73: 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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?