Tocilizumab for reduction of mortality in severe COVID-19
patients: how should we GRADE it?
Vladimir Trkulja
Vladimir Trkulja, MD, PhD
Department of Pharmacology
Zagreb University School of Medicine
Šalata 11
10000 Zagreb, Croatia
e-mail:
vladimir.trkulja@mef.hr
Number of words: 799
Number of figures/tables: 1
To the Editor,
A recent systematic review/meta-analysis 1 of
randomized trials (RCTs) of tocilizumab (plus standard of care [SoC]
vs. SoC w/wo placebo) in severe COVID-19 patients was a pleasure to read
owing to a clear presentation of a thorough approach to data (e.g.,
sensitivity analyses, accounting for corticosteroid use, need for
mechanical ventilation [MV] at baseline). Authors assigned high
quality (certainty) GRADE levels to the evidence of efficacy in
reduction of mortality overall (10 RCTs) and in patients without MV at
baseline (data from 9 RCTs), and reduction of incident MV (10 RCTs). The
grading was based on fixed-effect pooling, likely owing to low
inconsistency index (I2) and closely similar
fixed-effect and random-effects estimates1. It is this
point that deserves a few comments. Conceptually, fixed-effect
meta-analysis of RCTs in medicine is rarely justified, since the
underlying assumption is practically inevitably violated due to variety
of elements contributing to clinical heterogeneity2.
The authors1 presented a range of differences in trial
designs (e.g., one or repeated tocilizumab dose, more or less use of
concomitant corticosteroids, differences in proportion of subjects on
MV). When variance across trials is low, fixed and random-effects
estimates are numerically close/identical, but the conceptual
differences remain. Again, conceptually, the random-effects method is a
preferred approach2 (regardless of numerical closeness
of fixed/random estimates) and the choice (fixed/random) should not be
based on the heterogeneity estimates2. At this point,
the issue of the choice of the variance (τ2) estimator
should be mentioned. A number of estimators have been explored:
performance depends on the nature of the outcome, may vary across trial
sizes, depends on the differences in size of included trials, and is
problematic when the number of studies is lowe.g.,2-5. Variance reflects on the assigned trial
weights and measures of uncertainty about the pooled estimate. While no
τ2 estimator is ideal 2-5, it has
been suggested that the Paule-Mandel (PM) estimator performs better than
the common DerSimonian-Laird estimator for binary outcomes3.Another point to consider is the method to calculate
confidence intervals (CIs) around the pooled estimate. While not without
certain limitations 6, the Hartung-Knapp-Sidik-Jonkman
(HKSJ) method has been repeatedly shown (under variety of scenarios) to
result in more adequate coverage probability than the standard method4,7. Figure 1A re-creates meta-analysis (data
presented by the authors1) on mortality across the 10
RCTs (all subjects) – it is only that it uses PM variance estimator and
HKSJ correction: random-effects estimate suggests that the mean of the
distribution of the effects is 0.88 (as reported1),
but the CIs extend to 1.04, suggesting that it includes also effects
that are somewhat above unity. It also provides prediction intervals
(wider) - the best illustration of heterogeneity2,8.
When viewed from the present standpoint, data indicate a non-trivial
level of imprecision and heterogeneity. The authors themselves reported
apparent differences (mortality reduction vs. no reduction) between
estimates based on RCTs with a high proportion vs. low proportion of
patients concomitantly treated with corticosteroids 1(or those generated accounting only for corticosteroid-treated vs. not
treated patients, but such data were very scarce1):
so, there is apparent inconsistency of the estimates across clinical
settings. As re-created in Figure 1B-C, there was a tendency of reduced
mortality in trials with a high proportion of patients co-treated with
corticosteroids (corticosteroid treatment regimen likely variable), but
with quite some imprecision and heterogeneity; and no such tendency with
“low corticosteroid use”. Similarly, in patients not on MV at
baseline, there was a consistent reduction in mortality risk across
trials with a high proportion of steroid co-treated patients, but not in
trials with a low proportion of co-treated patients (Figure 1D-E). There
was also a consistent reduction of risk of incident MV in trials with a
high proportion of corticosteroid co-treated patients (Figure 1F),
whereas the estimate in trials with “low steroid use” is burdened with
heterogeneity and imprecision (Figure 1G).
Considering the above, if one were to assign a GRADE
level9 to evidence of benefit of tocilizumab in severe
COVID-19 patients based on the 10 RCTs addressed in the published
meta-analysis1, then the following seems reasonable:
a) considering (indiscriminately) all 10 RCTs (and all patients),
certainty about reduced mortality is closer to “low/moderate” then to
“high” due to imprecision (CIs 0.75-1.04) and
heterogeneity/inconsistency; b) data on the effect of
tocilizumab+corticosteroid combination that could be extracted from the
10 RCTs are scarce. Trials with high vs. low concomitant use of
corticosteroids could be perceived as a proxy, but this is indirect,
suggestive and not conclusive evidence. Therefore, while the effects of
tocilizumab on the risk of incident MV and mortality in patients not on
MV at baseline in trials with a high proportion of corticosteroid
co-treated patients were consistent and reasonably precisely estimated,
certainty about the benefit of tocilizumab (on top of corticosteroids;
regimen?) in this setting is at best moderate/low.
References
- Vela D, Vela-Gaxha Z, Rexhepi M, Olloni R, Hyseni V, nallbani R.
Efficacy and safety of tocilizumab versus standard of care/placebo in
patients with COVID-19; a systematic review and meta-analysis of
randomized controlled trials. Br J Clin Pharmacol . 2021; doi:
10.1111/bcp.15124.
- Higgins JPT, Thomson SG, Spiegelhalter DJ. A re-evaluation of
random-effects meta-analysis. J R Statist Soc A . 2009;
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between-study variance and its uncertainty in meta-analysis. Res
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for random-effects meta-analysis: a useful refinement but are there
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- IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman
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Figure 1 . Re-creation of the published
meta-analysis1 using data provided in the published
figures: the difference is in that the present estimates are generated
using the Paule-Mandel variance estimator (Q-profile method for variance
estimate confidence intervals) instead of the DerSimonian-Laired method
available in the RevMan software used by the authors1,
and Hartung Knapp Sidik Jonkman correction for random effects (see text
for explanation). Panel A corresponds to published1Figure 1, panels B and C correspond to published1supplemental Figure S4. Published meta-analysis1 does
not include figures that would correspond to panels D-G. Panels E and G
are reduced to summaries for brevity. Note that in all meta-analyses
point-estimates of I2 and τ2 were
low, but the upper limits of their confidence intervals were rather
high, particularly when only 4 RCTs were included (except in panel F
with highly consistent results across trials). “High%” or “low %”
steroid use refers to trials (as presented in the published
meta-analysis1) in which >50% or
<50% of the patients were co-treated with corticosteroids.
Meta-analyses were performed using packagemeta 10 in R.
MV – mechanical ventilation; RCT – randomized controlled trial; SoC –
standard of care