- Email: [email protected]
Viral-load kinetics and CMV disease
CORRESPONDENCE
patients with the same initial viral load and sampling interval will have the same rate, even if they have totally different patterns of kinetics. Based on the plot, therefore, each patient is predicted to have the same risk of developing CMV disease, which is not always true. An average of 57% of patients in Emery and colleagues’ study had the initial viral load which was identical to the peak value. That is, their rates of increase in viral load after the day of the first CMV-PCR-positive result were zero or less, which are completely different from and cannot be represented by the calculated positive rates. To resolve this difficulty, one more viral load must be measured as quickly as possible after the first positive result. Thus, the rate of increase in viral load between the first and second positive samples should depict the kinetics and is available at an early stage. A new contour plot could be constructed if there is an association. Second, the contour plot is constructed by combining data from three different groups of patients (renal, liver, and bone-marrow recipients). Emery and colleagues show a significant difference in the peak CMV load among groups, as well as the initial viral load. These findings infer that each group had unique kinetics of viral replication. Consequently, this generalised plot is not appropriate to predict the risk, and a contour plot should have been done for each group. Finally, some values of the rate of change in CMV load in the contour plot exceed the range of the source data. For example, when a patient presenting with an initial viral load of 5 log10 genomes/mL blood and a rate of change of 0·1 log10 genomes/mL blood daily, the time between two consecutive samples is 27 days. This interval is longer than the maximum length of time (14 days) used in the study. Conversely, this rate is lower than the possible minimum rate (0·19 log10 genomes/mL blood per day). Without advising readers, however, this example was used in the text and a 33% risk was extrapolated. Therefore, to prevent the risk from extrapolating, a contour plot should have been presented within the range of data. *Yen-Hong Kuo, Yen-Liang Kuo *Office of Academic Affairs, Jersey Shore Medical Center, Meridian Health System, Neptune, NJ 07753, USA and Department of Surgery, Tri-Service General Hospital, Taipei, Taiwan, Republic of China (e-mail: [email protected])
THE LANCET • Vol 356 • October 14, 2000
1
Emery VC, Sabin CA, Cope AV, et al. Application of viral-load kinetics to identify patients who develop cytomegalovirus disease after transplantation. Lancet 2000; 355: 2032–36.
Authors’ reply Sir—Our overall aim was to present an approach that was viable in the real world of transplants and diagnostic virology rather than producing a detailed description and estimation of CMV replication dynamics, which are available elsewhere.1–3 Nevertheless, many of the points raised are worthy of consideration. For ease of presentation we used the rate of increase in viral load for our analysis. We know that the kinetics of replication in the early phases of active infection is given by V(t)=V(0)ekt, where V is the viral load and k the rate constant for exponential growth. Calculation of the doubling time of virus from these values of k does not change the overall results presented. All calculations are estimated but, although two consecutive viral loads would potentially allow a more accurate representation of the replication kinetics, three points must be borne in mind. First, many centres only do weekly surveillance for CMV; second, the growth rate of virus slows as viral load approaches its peak; third, there is a finite time delay before a clinically useful result is obtained. We are auditing such an approach and will report these results in due course. We calculated the rate of increase from the dates of last negative PCR and first positive PCR, not from the initial positive to the peak viral load. Therefore, the rate of increase in patients whose initial and peak virus loads were the same was not zero, as Yen-Hong Kuo and Yen-Liang Kuo suggest, but was a positive value. The initial viral loads and rates did differ by risk group, but disease rates also differed, and the group with the lowest initial viral loads and rates had lower rates of disease as a consequence. Therefore, as long as the relations between the initial viral load or rate and the disease outcome are the same in the three groups, a single contour plot is acceptable for all patients. We incorporated interaction terms between the viral load, rate of increase, and group of patients into the model to investigate whether the relations between the parameters and disease outcomes differed, but the interactions were not significant. Thus, separate plots for each group were not needed. However, with larger numbers of patients, which might show more subtle differences, a refined version for
bone-marrow recipients could be produced. Kuo and Kuo correctly observe that one of the examples used in our paper exceeded the range of the source data. The purpose of these examples was, however, simply to illustrate how the plot can be used. Clearly, care should always be taken when extrapolating from plots to values outside the range. *V C Emery , C A Sabin, A F Hassan-Walker, P D Griffiths Departments of *Virology and Primary Care and Population Sciences, Royal Free and University College Medical School, London NW3 2PF, UK 1
2
3
Emery VC, Sabin CA, Cope AV, Gor D, Hassan-Walker AF, Griffiths PD. Application of viral-load kinetics to identify patients who develop cytomegalovirus disease after transplantation. Lancet 2000; 355: 2032–36. Emery VC, Cope AV, Bowen EF, Gor D, Griffiths PD. The dynamics of human cytomegalovirus replication in vivo. J Exp Med 1999; 190: 177–82. Emery VC, Griffiths PD. Prediction of cytomegalovirus load and resistance patterns after antiviral chemotherapy. Proc Natl Acad Sci USA 2000; 97: 8039–44.
Risks of Gaucher’s treatment Sir—Tim Cox (Aug 19, p 76)1 properly points to the differences in doses of OGT 918 between the Gaucher’s disease study and the HIV-1 study. Although these differences can be as high as 10-fold, some of the same side-effects of OGT 918 do occur at the lower dose used in the Gaucher’s disease study.2 Some patients withdrew because of the sideeffects. P K Mistry is correct to raise a caution about what the side-effects of OGT 918 might be after long-term use. Rather more important is the question of whether the risk is worth it. Some data seem to suggest no at least for type-1 cases. Small changes in clinical parameters that are measured by methods subject to unavoidably large variability put the results into question when few measurements are made. This is the case for organ sizes in the Gaucher’s disease study. The results are arguably significant, as are those for observed changes in haematological variables. These responses to OGT 918 are, small and therefore might not be reproducible. The results, have no real impact on the disease of type-1 patients. As a single agent, OGT 918 deserves no optimism as a treatment for Gaucher’s disease. Bantering about who is right about small
1353
For personal use only. Not to be reproduced without permission of The Lancet.
patients with the same initial viral load and sampling interval will have the same rate, even if they have totally different patterns of kinetics. Based on the plot, therefore, each patient is predicted to have the same risk of developing CMV disease, which is not always true. An average of 57% of patients in Emery and colleagues’ study had the initial viral load which was identical to the peak value. That is, their rates of increase in viral load after the day of the first CMV-PCR-positive result were zero or less, which are completely different from and cannot be represented by the calculated positive rates. To resolve this difficulty, one more viral load must be measured as quickly as possible after the first positive result. Thus, the rate of increase in viral load between the first and second positive samples should depict the kinetics and is available at an early stage. A new contour plot could be constructed if there is an association. Second, the contour plot is constructed by combining data from three different groups of patients (renal, liver, and bone-marrow recipients). Emery and colleagues show a significant difference in the peak CMV load among groups, as well as the initial viral load. These findings infer that each group had unique kinetics of viral replication. Consequently, this generalised plot is not appropriate to predict the risk, and a contour plot should have been done for each group. Finally, some values of the rate of change in CMV load in the contour plot exceed the range of the source data. For example, when a patient presenting with an initial viral load of 5 log10 genomes/mL blood and a rate of change of 0·1 log10 genomes/mL blood daily, the time between two consecutive samples is 27 days. This interval is longer than the maximum length of time (14 days) used in the study. Conversely, this rate is lower than the possible minimum rate (0·19 log10 genomes/mL blood per day). Without advising readers, however, this example was used in the text and a 33% risk was extrapolated. Therefore, to prevent the risk from extrapolating, a contour plot should have been presented within the range of data. *Yen-Hong Kuo, Yen-Liang Kuo *Office of Academic Affairs, Jersey Shore Medical Center, Meridian Health System, Neptune, NJ 07753, USA and Department of Surgery, Tri-Service General Hospital, Taipei, Taiwan, Republic of China (e-mail: [email protected])
THE LANCET • Vol 356 • October 14, 2000
1
Emery VC, Sabin CA, Cope AV, et al. Application of viral-load kinetics to identify patients who develop cytomegalovirus disease after transplantation. Lancet 2000; 355: 2032–36.
Authors’ reply Sir—Our overall aim was to present an approach that was viable in the real world of transplants and diagnostic virology rather than producing a detailed description and estimation of CMV replication dynamics, which are available elsewhere.1–3 Nevertheless, many of the points raised are worthy of consideration. For ease of presentation we used the rate of increase in viral load for our analysis. We know that the kinetics of replication in the early phases of active infection is given by V(t)=V(0)ekt, where V is the viral load and k the rate constant for exponential growth. Calculation of the doubling time of virus from these values of k does not change the overall results presented. All calculations are estimated but, although two consecutive viral loads would potentially allow a more accurate representation of the replication kinetics, three points must be borne in mind. First, many centres only do weekly surveillance for CMV; second, the growth rate of virus slows as viral load approaches its peak; third, there is a finite time delay before a clinically useful result is obtained. We are auditing such an approach and will report these results in due course. We calculated the rate of increase from the dates of last negative PCR and first positive PCR, not from the initial positive to the peak viral load. Therefore, the rate of increase in patients whose initial and peak virus loads were the same was not zero, as Yen-Hong Kuo and Yen-Liang Kuo suggest, but was a positive value. The initial viral loads and rates did differ by risk group, but disease rates also differed, and the group with the lowest initial viral loads and rates had lower rates of disease as a consequence. Therefore, as long as the relations between the initial viral load or rate and the disease outcome are the same in the three groups, a single contour plot is acceptable for all patients. We incorporated interaction terms between the viral load, rate of increase, and group of patients into the model to investigate whether the relations between the parameters and disease outcomes differed, but the interactions were not significant. Thus, separate plots for each group were not needed. However, with larger numbers of patients, which might show more subtle differences, a refined version for
bone-marrow recipients could be produced. Kuo and Kuo correctly observe that one of the examples used in our paper exceeded the range of the source data. The purpose of these examples was, however, simply to illustrate how the plot can be used. Clearly, care should always be taken when extrapolating from plots to values outside the range. *V C Emery , C A Sabin, A F Hassan-Walker, P D Griffiths Departments of *Virology and Primary Care and Population Sciences, Royal Free and University College Medical School, London NW3 2PF, UK 1
2
3
Emery VC, Sabin CA, Cope AV, Gor D, Hassan-Walker AF, Griffiths PD. Application of viral-load kinetics to identify patients who develop cytomegalovirus disease after transplantation. Lancet 2000; 355: 2032–36. Emery VC, Cope AV, Bowen EF, Gor D, Griffiths PD. The dynamics of human cytomegalovirus replication in vivo. J Exp Med 1999; 190: 177–82. Emery VC, Griffiths PD. Prediction of cytomegalovirus load and resistance patterns after antiviral chemotherapy. Proc Natl Acad Sci USA 2000; 97: 8039–44.
Risks of Gaucher’s treatment Sir—Tim Cox (Aug 19, p 76)1 properly points to the differences in doses of OGT 918 between the Gaucher’s disease study and the HIV-1 study. Although these differences can be as high as 10-fold, some of the same side-effects of OGT 918 do occur at the lower dose used in the Gaucher’s disease study.2 Some patients withdrew because of the sideeffects. P K Mistry is correct to raise a caution about what the side-effects of OGT 918 might be after long-term use. Rather more important is the question of whether the risk is worth it. Some data seem to suggest no at least for type-1 cases. Small changes in clinical parameters that are measured by methods subject to unavoidably large variability put the results into question when few measurements are made. This is the case for organ sizes in the Gaucher’s disease study. The results are arguably significant, as are those for observed changes in haematological variables. These responses to OGT 918 are, small and therefore might not be reproducible. The results, have no real impact on the disease of type-1 patients. As a single agent, OGT 918 deserves no optimism as a treatment for Gaucher’s disease. Bantering about who is right about small
1353
For personal use only. Not to be reproduced without permission of The Lancet.