Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis
Background Hydroxychloroquine or chloroquine, often in combination with a second-generation macrolide, are being widely used for treatment of COVID-19, despite no conclusive evidence of their benefit. Although generally safe when used for approved indications such as autoimmune disease or malaria, the safety and benefit of these treatment regimens are poorly evaluated in COVID-19.
Methods We did a multinational registry analysis of the use of hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19. The registry comprised data from 671 hospitals in six continents.
Findings 96 032 patients (mean age 53·8 years, 46·3% women) with COVID-19 were hospitalised during the study period and met the inclusion criteria. Of these, 14 888 patients were in the treatment groups (1868 received chloroquine, 3783 received chloroquine with a macrolide, 3016 received hydroxychloroquine, and 6221 received hydroxychloroquine with a macrolide) and 81 144 patients were in the control group. 10 698 (11·1%) patients died in hospital.
Interpretation We were unable to confirm a benefit of hydroxychloroquine or chloroquine, when used alone or with a macrolide, on in-hospital outcomes for COVID-19. Each of these drug regimens was associated with decreased in-hospital survival and an increased frequency of ventricular arrhythmias when used for treatment of COVID-19.PIIS0140673620311806 (1)
Where did you go my (CORONA) lovely?
This is a discussion in an email list-server between EBM experts.
“I was recently made aware that since April 16 or so, no new cases with COVID19 were reported in China [https://www.worldometers.
Another member chipped in saying that this article will shed some light- Seasonal and pandemic influenza: 100 years of progress, still much to learn https://www.nature.com/
I asked my friends in China and confirmed that there are some asymptomatic patients and few symptomatic patients in China everyday. If everyone wears a mask and keep social distance, we probably really do not need vaccine for COVID-19 or any influenza. However, it’s impossible and also I don’t like this life style. So, I still look forward to the effective vaccine.’
Why treatment comparisons must be fair
Untrustworthy treatment comparisons are those in which biases, or the play of chance, or both result in misleading estimates of the effects of treatments. Fair treatment comparisons avoid biases and reduce the effects of the play of chance.
Failure to test theories about treatments in practice is not the only preventable cause of treatment tragedies. Tragedies have also occurred because the tests used to assess the effects of treatments have been unreliable and misleading. The principles of fair tests have been evolving for at least a millennium (list records coded Principles of Testing) – and they continue to evolve today (Savovic et al. 2012; Jefferson et al. 2014).
For example, in the 1950s, theory and poorly controlled tests yielding unreliable evidence suggested that giving a synthetic sex hormone, diethylstilboestrol (DES), to pregnant women who had previously had miscarriages and stillbirths would increase the likelihood of a successful outcome of later pregnancies. Although fair tests had suggested that DES was useless, theory and the unreliable evidence, together with aggressive marketing, led to DES being prescribed to millions of pregnant women over the next few decades. The consequences were disastrous: some of the daughters of women who had been prescribed DES developed cancers of the vagina, and other children had other health problems, including malformations of their reproductive organs and infertility (Apfel and Fisher 1984).
Problems resulting from inadequate tests of treatments continue to occur. Again, because of unreliable evidence and aggressive marketing, millions of women were persuaded to use hormone replacement therapy (HRT). It was claimed that, not only could it reduce unpleasant menopausal symptoms, but also the chances of having heart attacks and strokes. When these claims were assessed in fair tests, the results showed that in women over 60, far from reducing the risks of heart attacks and strokes, HRT increases the risks of these life-threatening conditions, as well as having other undesirable effects. (McPherson 2004).
These examples of the need for fair tests of treatments are a few of many that illustrate how treatments can do more harm than good. Improved general knowledge about fair tests of treatments is needed so that – laced with a healthy dose of scepticism – we can all assess claims about the effects of treatments more critically. That way, we will all become more able to judge which treatments are likely to do more good than harm.
Fair tests entail taking steps to reduce the likelihood that we will be misled by the effects of biases of various sorts. Those addressed in the James Lind Library include design bias, allocation bias, co-intervention bias, observer bias, analysis bias, biases in assessing unanticipated effects, reporting bias, biases in systematic reviews, and researcher biases and fraud.
Essays on taking account of the play of chance address recording and interpreting numbers, quantifying uncertainty, and reducing the play of chance using meta-analysis.