it should be less.
The correct figures are that the lower-threshold group received a median of 1 unit of blood (interquartile range, 0 to 3) and the higher-threshold group received a median of 4 units (interquartile range, 2 to 7).
Cheers
Tom
]]>I can’t see that the results of the HALT-IT trial rule out potential benefit in patients for whom other treatments will be unavailable or significantly delayed.
The Covid-19 pandemic which has caused even greater delays in transport to a higher level of care, has greatly exacerbated this issue.
]]>Interesting comment regarding ‘is COT an acceptable way to manage a patient right up until the time of intubation’…. interesting that there was no difference between this and HFNO, as I agree, you would not think this would be the optimal therapy up until this point. Perhaps because resources were so stretched at the time of the study, this was trialled and it proved no worse than HFNO. The RENOVATE trial currently underway in Brazil is comparing HFNO vs NIV in patients with acute respiratory failure. The primary outcome is intubation or death too, so It will be interesting to see if the results of this one support the result of RECOVERY RS trial, although it is not just for COVID patients.
Thanks for your interest/comments
Celia Bradford
Interesting that despite the use of ECCOR, they couldn’t achieve their target Vt of 3ml/kg.
Seems unlikely to be broadly implemented without better identification of subgroups that might benefit
]]>The biggest question mark on this trial is the control group. Is “conventional oxygen therapy” right up to the point of intubation the standard of care that we currently uphold?
Will be interesting to see the peer reviewed published manuscript in due course
]]>I agree with this comment. Also, the survival (=0) of the control arm was much lower than has been reported and as was used in the power calculation (12%). Finally, there is an issue with the definition of “refractory” ventricular fibrillation as one who has achieved ROSC after the 4th defibrillation was not excluded. It would seem that a uniform definition of “refractory” should mean not achieving ROSC.
]]>In my opinion a major issue of this study is, that only the ecmo group underwent coronary angiography. There is a high probability that resuscitative PCI had an impact on the survival rates of the ecmo group.
Especially if you keep in mind that duration of external compression in both groups was quite long (about 50 minutes) and still a good neurological outcome was possible.
In Europe PCI during cardiac arrest is a common practice and even recommended in the ERC Guidelines, so to compare ecmo treatment vs. standard care the control group should also be treated with rescue PCI.
]]>Your statement, “I will implement targeted temperature management if temperature exceeds 37.7°C” interested me as I presume you mean you will do this in preference to targeted hypothermia, rather than in preference to other methods of targeted normothermia such as that described in the TTM trial. Clearly TTM2 has not compared the two differing strategies for targeted normothermia, and therefore does not invalidate the TTM strategy.
I worry that the application of targeted temperature management in comatose survivors of cardiac arrest remains challenging, particularly outside the context of well-resourced multi-centre RCTs. Given the apparent importance of diligent avoidance of fever, perhaps some of the health system context can be informative? For example, even in this well-resourced trial in first-world health systems 55% of patients were not randomised due mostly to >180 minutes post-arrest. In addition, nearly 50% of patients in the normothermia group needed a cooling device and despite this 5% had temperature >38deg.
In my health service context I will argue that TTM2 does not invalidate the application of the learnings of the TTM trial, in fact it validates what we already know. All such comatose survivors of cardiac arrest should have cooling devices placed prophylactically, and fever avoided through the use of targeted temperature management.
]]>Quote:
A P-value of 0.05 infers, assuming the postulated null hypothesis is correct, any difference seen (or an even bigger “more extreme” difference) in the observed results would occur 1 in 20 (or 5%) of the times a study was repeated.
A P-value of 0.01 infers, assuming the postulated null hypothesis is correct, any difference seen (or an even bigger “more extreme” difference) in the observed results would occur 1 in 100 (or 1%) of the times a study was repeated.
According to the literature these two statements again perpetuate common misuses of the p-value:
1. the tendency to equate the decimal values originally cited by Fisher with percentages.
2. that it makes comment on what might occur with repeated testing – this is the domain of the other pioneers of scientific method Neyman and Pearson who did not use p-values.
Fishers p-value (null hypothesis significance testing) makes no comment on either percentage or arriving at these same results if the test was repeated.
Two investigators could seek to answer the same question, acquire their data and their data will generate different p-values and all that can be said is if it is below the classic 0.05 then the greater the unlikelihood that this data showing a difference could occur if the null hypothesis were true. That is all.
Ref:
Tam et al (2018) How doctors conceptualise P values: A mixed methods study AGP 47(10)
Gao (2020) P-values – a chronic conundrum BMC Medical Research Methodology 167