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1215 Multivariate models predict outcome to hepatitis C virus therapy at baseline and at weeks 4 and 8
HEPATOLOGY, Vol. 38, No. 4, Suppl.
i,
2003
AASLD ABSTRACTS
1215 MULTIVARIATE MODELS PREDICT OUTCOME TO HEPATITIS C VIRUS THERAPY AT BASELINE AND AT WEEKS 4 AND 8. Michele Ma~'not Peignoux, INS~ RM U481,
Clichy, France; Lorraine Comanor, Independent Research Consultant, Palo Alto, CA; James Minor, Independent Research Consultant, Los Altos, CA, CA; Marie Pierre Ripault, Natalie Boyer, IN~ RM U481, Clichy, France, France; Corinne Castelnau, INS~ RM U481, Clichy, France, France; Nathalie Guily, INS~ RM U481, Clichy, France, France; David Hendricks, Bayer DiagnosHcs, Berkeley, CA; Patrick Marcellin, INS~ RM U481, Clichy, France Background: Given the current b u r d e n of hepatitis C virus (HCV) therapy, physicians have been seeking a reliable early stopping rule for patients undergoing pegylated combination therapy who will be non-responders. We used data from 174 chronically infected HCV patients to develop multivariate models (MM) to predict non-response (NR), sustained response (SR) and relapse (RR) from information available in the first 8 weeks of therapy. We compared information from univariate models (UMs), based solely on quantitative or qualitative HCV RNA information, with that derived from MMs. Materials and Methods: 174 chronically infected HCV patients (94 treatment naive and 80 previously failed) were treated with pegyfated interferon c~ - 2b 1.5 mg/kg/week plus ribavirin 800-1200 mg/day. Naive genotype I or 4 patients and previously failed patients were treated for 48 weeks; patients with HCV genotype 2, 3, or 5, for 24 weeks. SR was defined as undetectable HCV RNA by the VERSANTR HCV RNA Qualitative Assay (TMA) (Bayer Diagnostics) at end-of-treatment (EOT) and follow-up at week 24 (FU 24). Serum HCV RNA was quantified by the VERSANTR HCV RNA 3.0 Assay (bDNA) (Bayer Diagnostics; at baseline, weeks 4, 8, EOT, and FU24. If the HCV RNA concentration was below 615 IU/mL, specimens were then tested by HCV Qual (TMA) (limit of detection -< 9.6 IU/mL.) Univariate models: Baseline UM utilized a prediction threshold of 6.14 log:0 copies/mL to predict outcome. Week 4 and 8 UM used a 2 log:0 drop rule ( -> or < 2 log drop in viral load from baseline; UM2 log) or clearance of virus or lack thereof by the HCV Qual (TMA) (UM TMA). Multivariate models: MM employ ordinal regression with similarity least squares technology to assign a given outcome to the highest probability of response. Different numbers of "critical patients" ( n - 33-57) were used to derive the models using design of experiment and functional techniques for each time point; the models were then tested on the remaining patients (n >110). Model variables included: HCV RNA concentrations at baseline and weeks 4 and 8, gender, age, baseline ALT, inflammatory and fibrosis scores, genotype and treatment status (naive or previous therapy). Results: The comparative sensitivity specificity, positive and negative predictive values (PPV and NPV) at for the UMs and MMs
745A
are shown in the table. Baseline and week 4 models combined NRs and RRs into non-responder group, whereas week 8 model correctly separated SRs, RRs, and NRs. Prediction of SR or NR At Baseline and Weeks 4 and 8 of Therapy percent of SR identified by model; 2 percent of non-responders identified by model; 3 percent of SR predicted by model who are true SR; 4 percent of NR predicted by model who are true NR; 5 -> or <6.14 log:0 c/mL; 6 -> or < 2 log:0 change in c/mL; 7 HCV RNA detected or not detected by TMA Conclusions: At each time point, the multivariate models demonstrates greater sensitivity, specificity, PPV and NPV than do the univariate models. Multivariate models can identify SR and non SR (NR and RR as one group) at baseline and week 4. By week 8 multivariate models can correctly identify SR, RR, and NR. The 100% prediction of non-response by the multivariate model at week 8 with the 97% NPV of < a 2 log drop suggests a week 8 stopping rule could be employed.
I~[t~e
UMs
37;97 ::: 38,'i ':", 67~i63 :=
66/77 ::: 85.7% 60160
>>:
37:48 ,:, 77,1~, 63~63
66~2~ .... Z,2~4% 6 0 '60 =
MM UM2
UM
MM UM. 2
O~
T~A
M~ .................................................... ?_JL?#_::_)~:
........... 2-":Y'L~:~_:L_Jg-'::._______X#L?A:;:__E~-5~:~_______}#L-::L';:__J.9~}*__.
Disclosures: Natalie Boyer - No relationships to disclose Corinne Castelnau - No relationships to disclose Lorraine Comanor - Bayer Diagnostics: Consultant/Advisor Nathalie Guily - No relationships to disclose David Hendricks - Bayer Diagnostics: Employee Patrick Marcellin - Bayer Diagnostics: Scientific Study/Trial Michele Martinot Peignoux - Bayer Diagnostics: Scientific Study/ Trial James Minor - No relationships to disclose Marie Pierre Ripault - No relationships to disclose
i,
2003
AASLD ABSTRACTS
1215 MULTIVARIATE MODELS PREDICT OUTCOME TO HEPATITIS C VIRUS THERAPY AT BASELINE AND AT WEEKS 4 AND 8. Michele Ma~'not Peignoux, INS~ RM U481,
Clichy, France; Lorraine Comanor, Independent Research Consultant, Palo Alto, CA; James Minor, Independent Research Consultant, Los Altos, CA, CA; Marie Pierre Ripault, Natalie Boyer, IN~ RM U481, Clichy, France, France; Corinne Castelnau, INS~ RM U481, Clichy, France, France; Nathalie Guily, INS~ RM U481, Clichy, France, France; David Hendricks, Bayer DiagnosHcs, Berkeley, CA; Patrick Marcellin, INS~ RM U481, Clichy, France Background: Given the current b u r d e n of hepatitis C virus (HCV) therapy, physicians have been seeking a reliable early stopping rule for patients undergoing pegylated combination therapy who will be non-responders. We used data from 174 chronically infected HCV patients to develop multivariate models (MM) to predict non-response (NR), sustained response (SR) and relapse (RR) from information available in the first 8 weeks of therapy. We compared information from univariate models (UMs), based solely on quantitative or qualitative HCV RNA information, with that derived from MMs. Materials and Methods: 174 chronically infected HCV patients (94 treatment naive and 80 previously failed) were treated with pegyfated interferon c~ - 2b 1.5 mg/kg/week plus ribavirin 800-1200 mg/day. Naive genotype I or 4 patients and previously failed patients were treated for 48 weeks; patients with HCV genotype 2, 3, or 5, for 24 weeks. SR was defined as undetectable HCV RNA by the VERSANTR HCV RNA Qualitative Assay (TMA) (Bayer Diagnostics) at end-of-treatment (EOT) and follow-up at week 24 (FU 24). Serum HCV RNA was quantified by the VERSANTR HCV RNA 3.0 Assay (bDNA) (Bayer Diagnostics; at baseline, weeks 4, 8, EOT, and FU24. If the HCV RNA concentration was below 615 IU/mL, specimens were then tested by HCV Qual (TMA) (limit of detection -< 9.6 IU/mL.) Univariate models: Baseline UM utilized a prediction threshold of 6.14 log:0 copies/mL to predict outcome. Week 4 and 8 UM used a 2 log:0 drop rule ( -> or < 2 log drop in viral load from baseline; UM2 log) or clearance of virus or lack thereof by the HCV Qual (TMA) (UM TMA). Multivariate models: MM employ ordinal regression with similarity least squares technology to assign a given outcome to the highest probability of response. Different numbers of "critical patients" ( n - 33-57) were used to derive the models using design of experiment and functional techniques for each time point; the models were then tested on the remaining patients (n >110). Model variables included: HCV RNA concentrations at baseline and weeks 4 and 8, gender, age, baseline ALT, inflammatory and fibrosis scores, genotype and treatment status (naive or previous therapy). Results: The comparative sensitivity specificity, positive and negative predictive values (PPV and NPV) at for the UMs and MMs
745A
are shown in the table. Baseline and week 4 models combined NRs and RRs into non-responder group, whereas week 8 model correctly separated SRs, RRs, and NRs. Prediction of SR or NR At Baseline and Weeks 4 and 8 of Therapy percent of SR identified by model; 2 percent of non-responders identified by model; 3 percent of SR predicted by model who are true SR; 4 percent of NR predicted by model who are true NR; 5 -> or <6.14 log:0 c/mL; 6 -> or < 2 log:0 change in c/mL; 7 HCV RNA detected or not detected by TMA Conclusions: At each time point, the multivariate models demonstrates greater sensitivity, specificity, PPV and NPV than do the univariate models. Multivariate models can identify SR and non SR (NR and RR as one group) at baseline and week 4. By week 8 multivariate models can correctly identify SR, RR, and NR. The 100% prediction of non-response by the multivariate model at week 8 with the 97% NPV of < a 2 log drop suggests a week 8 stopping rule could be employed.
I~[t~e
UMs
37;97 ::: 38,'i ':", 67~i63 :=
66/77 ::: 85.7% 60160
>>:
37:48 ,:, 77,1~, 63~63
66~2~ .... Z,2~4% 6 0 '60 =
MM UM2
UM
MM UM. 2
O~
T~A
M~ .................................................... ?_JL?#_::_)~:
........... 2-":Y'L~:~_:L_Jg-'::._______X#L?A:;:__E~-5~:~_______}#L-::L';:__J.9~}*__.
Disclosures: Natalie Boyer - No relationships to disclose Corinne Castelnau - No relationships to disclose Lorraine Comanor - Bayer Diagnostics: Consultant/Advisor Nathalie Guily - No relationships to disclose David Hendricks - Bayer Diagnostics: Employee Patrick Marcellin - Bayer Diagnostics: Scientific Study/Trial Michele Martinot Peignoux - Bayer Diagnostics: Scientific Study/ Trial James Minor - No relationships to disclose Marie Pierre Ripault - No relationships to disclose