Download Describing rare and serious harms of interventions

Describing rare and serious harms of interventions
BC
1
Reeves ,
A
2
Herxheimer ,
GA
1
Wells ,
G
3
Gyte
[[email protected]]
1. Non-Randomised Studies Methods Group; 2. Adverse Effects Methods Group; 3. Pregnancy and
Childbirth Collaborative Review Group
Introduction
Objectives
Systematic reviews need to consider all effects of an intervention,
i.e. harms as well as benefits. Failing to do so means that a
review presents a partial summary of the evidence about the
effects of an intervention (even if the evidence about benefit is not
biased), which may mislead health care professionals & users).
To describe (a) relevant information when reporting rare, serious
adverse effects (SAEs) of interventions and (b) factors that
influence requirements.
Evidence about rare and serious harms rarely comes from
randomised controlled trials (RCTs). Frequencies of serious harms
(SAEs) are usually estimated from databases, longitudinal case
series, case reports or custom-designed cohort studies.
Evidence about SAEs from RCTs may not be applicable because
RCTs often exclude people most at risk of serious harms from a
new intervention. In non-randomised studies (NRS), data quality
may be poor and ascertainment of SAEs uncertain. Intervention
effects estimated from NRS are susceptible to confounding.
Methods
We considered of examples of SAEs associated with specific
interventions (see Tables 1 & 2). Examples were chosen to
illustrate different combinations of factors hypothesised to
influence the information requirements of users when weighing
up beneficial and harmful effects of an intervention:
• Margin of benefit over next best treatment
• “Valuation” of estimated beneficial and harmful effects
• Availability of alternative intervention (with lower risk of SAE)
• Background risk of SAE (rare,>1% & <5% vs
very rare, ≤1%)
Table 1: Examples of interventions and implicated SAEs
Indication
Intervention Comparator
Population
1. Planning where to
give birth
Home birth
Pregnant women, <35 years, Less morbidity from
uncomplicated pregnancy &
obstetric intervention
no known risk factors for IPPM
Hospital birth
Intended benefit
Implicated SAE
Intra-partum related
perinatal mortality
(IPPM)
2.
Cerivastatin
Hypercholesteraemia
Alternative drug to Women or men with hyperreduce low density cholesteraemia & no contralipoprotein level
indications to statin therapy
Reduction in low density Rhabdomyolysis
lipoprotein level
3. Neovascular age- Ranibizumab
related macular
degeneration (nAMD)
Pegaptanib
Halt progression of
Arterial thrombochoroidal neovascembolic event
ularisation & visual loss
Elderly women or men
Table 2: Estimated SAE frequencies with best practice and with intervention
Indication
SAE frequency RCT or SAE frequency RCT or Rare/
with best
NRS? with
NRS? very
practice
intervention
rare?
Alternative Effectiveness Benefit
intervention of alternative highly
available? intervention
valued?
1. Planning where to
give birth
≈0.7-4.1
/1,000 births
NRS
Not known
NRS
Very rare Midwifery-led
unit
2.
Hypercholesteraemia
5
/100,000 pyrs
RCT
≈250
/100,000 pyrs
NRS
Very rare
3. Neovascular AMD
≈19-78
/1,000 pyrs
RCT &
NRS
≈23
/1,000 pyrs
RCT
Rare
Not known
Yes
Yes
Similar
No
Yes
Less effective
Yes
Economic factors (e.g. cost-effectiveness, cost impact) have not been considered. The SAE frequency with intervention for example 3 is very imprecise but expected to be higher
than for best practice because of evidence for a similar drug.
Observations
• If an SAE is very rare, the difference in SAE freq between intervention & “best practice” ≈ SAE freq in people with the intervention, and
the SAE freq in people with the intervention will be rare even if the intervention ‘obviously’ causes the SAE (e.g. relative effect >10)
• Highly valued benefits, when no alternative intervention is available, may override aversion to a very rare SAE even if the intervention
‘obviously’ causes the SAE (Example 1)
• If alternative interventions have similar benefits, an intervention that obviously causes an SAE is unacceptable (Example 2)
• If an SAE is relatively common (≥1% and <5%), phase 3 RCTs may fail to identify relative risks <2 (n required >1,000) and a causal link
is difficult to establish from non-randomised studies (Example 3)
• Describing the risk of an SAE among people with an intervention is a prognostic research question; factors influence the risk of an SAE
can be investigated to allow estimates of SAE frequency to be customised for individual patients.
Conclusions:
We propose that reviewers should distinguish evidence for a causal link between an intervention and an SAE, and the risk of the SAE
among people having the intervention.
For SAEs that are very rare with “best practice”, the risk of an SAE among people having an intervention is highly relevant and may be
sufficient for decision-making. This is a simple descriptive statistic, easily understood and avoids debate about susceptibility to bias of
data from NRS. Estimates of SAE freq can be qualified by describing the populations from which they were obtained, information about
the characteristics of individuals that influence the SAE freq, and study limitations that compromise the validity of the estimates.
Weighing up benefits and harms is particularly difficult when SAEs freq with best practice ‘rare’ (≥1% & <5%), cf. very rare (<1%).