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• Authors response to comments by Tom Boyles
  Sydney Rosen, Matthew Fox and Francois Venter
  Published on: 12 July 2017
• Clinical algorithm and sample size
  Tom Boyles
  Published on: 26 June 2017

• Published on: 12 July 2017

  Authors response to comments by Tom Boyles

  • Sydney Rosen, Professor Boston University School of Public Health
  • Other Contributors:
    • Matthew Fox, Professor
    • Francois Venter, Professor

  Dear Tom,

  Thank you for your comments.

  Your first point addresses the poor negative predictive value of a TB symptom screening test. We agree that the symptom screen that is currently called for in South Africa’s national guidelines, as well as WHO’s guidelines, is inadequate in terms of both negative and positive predictive value. We do not have, and you do not propose, however, an easily-available alternative. We consulted a number of TB experts in designing the algorithm and concluded that at the time of study initiation, there was no readily available clinical prediction rule or point-of-care instrument that we could expect to be adopted at a national scale if the trial’s results are compelling. We also designed the study to adhere as closely as possible to existing national guidelines, with the main difference being the timing of the initiation process rather than its content. Our goal is an algorithm that can be incorporated into routine practice as simply as possible, in the hope of benefiting patients at a large scale.

  We also observe that initiating ART in the presence of undiagnosed TB poses the risk of delayed TB treatment (which would have been delayed anyhow, due to the patient not having symptoms) or IRIS (which, while clinically occasionally challenging, is rarely fatal, especially in this group of ambulatory and largely healthy patients). We hypothesize that the well-characterized loss to follow-up and subsequent morbidity and mortal...

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  Dear Tom,

  Thank you for your comments.

  Your first point addresses the poor negative predictive value of a TB symptom screening test. We agree that the symptom screen that is currently called for in South Africa’s national guidelines, as well as WHO’s guidelines, is inadequate in terms of both negative and positive predictive value. We do not have, and you do not propose, however, an easily-available alternative. We consulted a number of TB experts in designing the algorithm and concluded that at the time of study initiation, there was no readily available clinical prediction rule or point-of-care instrument that we could expect to be adopted at a national scale if the trial’s results are compelling. We also designed the study to adhere as closely as possible to existing national guidelines, with the main difference being the timing of the initiation process rather than its content. Our goal is an algorithm that can be incorporated into routine practice as simply as possible, in the hope of benefiting patients at a large scale.

  We also observe that initiating ART in the presence of undiagnosed TB poses the risk of delayed TB treatment (which would have been delayed anyhow, due to the patient not having symptoms) or IRIS (which, while clinically occasionally challenging, is rarely fatal, especially in this group of ambulatory and largely healthy patients). We hypothesize that the well-characterized loss to follow-up and subsequent morbidity and mortality due to delays in ART initiation will outweigh any benefit from an intensive investigation for sub-clinical TB (which you do not define, but is likely to be complex and not inexpensive, if utilising technology with high sensitivity such as Xpert MTB/RIF). We think the algorithm can easily be adapted to accommodate future objective tests that may supplement or replace the screening tests. However, even in the absence of these tests, we believe that a basic symptom screen that can be performed by nurses and requires no additional technology has high utility.

  Regarding the sample size, you are correct that a high proportion of those who screen out of immediate ART initiation using SLATE will receive standard of care treatment and therefore are not likely to have improved outcomes compared to our control group. We anticipate the numbers who screen out will be lower than you suggest, and we calculated our original sample size using the best available data at that time. Still, you are correct that if the proportion screening out is higher than anticipated, we will have less power than anticipated. We recently increased our sample size in South Africa to 660 to allow for detection of smaller differences should they occur and to improve our ability to analyze those who screen out as a population in themselves.

  Next, you suggest that we could shift randomization to after the SLATE screen. This would defeat the purpose of the algorithm entirely, as the point of the study is to test whether we can effectively screen out those who should not start on ART immediately, screen in provide efficient initiation to those who can, and improve overall outcomes for the population of patients who are eligible for ART (i.e. nearly all HIV-positive individuals not already on ART). If we took your suggestion and randomized after screening, we would be asking a very narrow question that lacks policy relevance—namely, does immediate ART initiation among a selected population that is already at low risk for poor outcomes have better outcomes if offered same day treatment compared to standard of care. We don’t believe this is the right question to ask.

  Finally, with regard to your conclusion, we are responding to a pressing need for guidance on same day initiation, and we used WHO and other symptom screen algorithms and national guidelines to construct that algorithm. Algorithms are often constructed from imperfect science, and as you note, the perfect algorithm is not available. We would argue we have thoughtfully weighed the potential for harm against the need for public health clinics to move on a pressing health problem, and the only harm we can see in our approach (other than perhaps screening too many patients out with the screening tools, which does not disadvantage these patients, merely returns them to standard care) is a slightly increased risk of IRIS, which may in fact not differ from that in standard care.

  We do agree that a better algorithm is needed. We are already discussing opportunities to improve on SLATE. The results of the SLATE study, when available, will guide us on the right way to go.

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  Conflict of Interest:
  None declared.

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• Published on: 26 June 2017

  Clinical algorithm and sample size

  • Tom Boyles, Infectious diseases specialist University of Cape Town

  Dear Sydney-
  I have a number of concerns regarding the design of the SLATE trial that I would like to share. There are two important questions regarding ART initiation in ambulatory patients that you are addressing. The first is how to appropriately screen patients for conditions that preclude immediate initiation of ART and the second is the best time to initiate ART in ambulatory patients.
  The SLATE protocol begins with addressing the first question with a step-wise algorithm to identify patients who require further investigation for a variety of clinical conditions but most notably tuberculosis and cryptococcal disease. The first stage is a standardised WHO TB symptom screen which has been shown to be have an inadequate negative predictive value in high burden settings, particularly in patients who are not on ART. Rangaka et al (Clinical Infectious Diseases 2012;55(12):1698–706) showed in a South African cohort that 8.9% of patients with a negative symptoms screen who were not on ART had culture confirmed TB. More recently Hanifa et al (CROI 2015 abstract number: 823) showed that around 5.6% of patients who were recently diagnosed with HIV had a negative symptom screen but culture confirmed TB. The SLATE algorithm provides for further assessment for TB in patients without symptoms but this relies on a symptom-guided physical exam, this is poorly defined and it is unclear how this will pick-up TB in asymptomatic patients. It is therefore likely that between 5...

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  Dear Sydney-
  I have a number of concerns regarding the design of the SLATE trial that I would like to share. There are two important questions regarding ART initiation in ambulatory patients that you are addressing. The first is how to appropriately screen patients for conditions that preclude immediate initiation of ART and the second is the best time to initiate ART in ambulatory patients.
  The SLATE protocol begins with addressing the first question with a step-wise algorithm to identify patients who require further investigation for a variety of clinical conditions but most notably tuberculosis and cryptococcal disease. The first stage is a standardised WHO TB symptom screen which has been shown to be have an inadequate negative predictive value in high burden settings, particularly in patients who are not on ART. Rangaka et al (Clinical Infectious Diseases 2012;55(12):1698–706) showed in a South African cohort that 8.9% of patients with a negative symptoms screen who were not on ART had culture confirmed TB. More recently Hanifa et al (CROI 2015 abstract number: 823) showed that around 5.6% of patients who were recently diagnosed with HIV had a negative symptom screen but culture confirmed TB. The SLATE algorithm provides for further assessment for TB in patients without symptoms but this relies on a symptom-guided physical exam, this is poorly defined and it is unclear how this will pick-up TB in asymptomatic patients. It is therefore likely that between 5 and 9% of patients will proceed to ART initiation despite culture positive TB. This in itself is unlikely to affect the trial results as a similar screening mechanism will be employed in the control arm. What is needed at this point is research to determine a clinical prediction rule with a sufficiently low negative likelihood ratio such that TB can be confidently ruled out in high burden settings, as has been suggested by the authors above.
  A second concern I have is with your sample size calculation which assumes that the intervention might improve the primary outcome from 65% to 80%. However, it is known that a positive WHO symptom screen is common in patients presenting for ART or IPT initiation. In Rangaka’s trial (above) 23.7% of pre-ART patients were symptomatic and in XPHACTOR (above) the figure was 38.6%. You should therefore predict that around 30% of patients will be delayed at the first stage. You could make assumptions about the number of patients who will be delayed in the next 3 stages and it would not be surprizing if 50% of patients were delayed. Delayed patients essentially are given care in the clinic that is similar to the control arm and you would therefore predict around 65% of these would achieve the primary outcome. It is hard to see how the intervention arm can improve the primary outcome to 80% when half of the patients are predicted to achieve the outcome at best 65% of the time. You would essentially need to achieve the primary outcome in 95% of patients who initiate immediate ART. Another approach might have been to shift the point of randomisation to after the evaluation. The point of randomisation in clinical trials is important, if chosen inappropriately it can lead to a loss of power as patients who cannot contribute to the effect are randomised. Patrick M M Bossuyt (Lancet 2000; 356: 1844–47) describes the issues of placement of randomisation very well.
  In summary, the SLATE protocol uses an algorithm for excluding patients in whom immediate ART is inappropriate without first testing that algorithm. The optimal algorithm is currently unknown but research is ongoing. Whilst this is unlikely to affect the trial outcome it might seriously compromise the generalisability if it is subsequently shown that the optimal algorithm is significantly different from that used in SLATE. Secondly, by randomising patients prior to clinical evaluation you might find that your study is significantly underpowered. The literature suggests that around 30% will be delayed at the first stage of symptom report and an unknown number in the subsequent three stages. As these patients essentially move to the same care as the control group they are unable to influence your primary outcome.

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  Conflict of Interest:
  None declared.

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