Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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%).