Your points are excellent and I fully agree with your criticisms of the Petty/Bone approach to clinical trials. I would just like to see a refactoring of themes, with each theme stated once, and designs of ideal trials as you have begun to do above.
What a hot topic!
Well, I will add a few points and then provide a foreign perspective on American critical care research.
First, the rings metaphor Erin suggested. Drawing rings on heterogeneous disease is precisely what is wrong with sepsis research. The current ring is too large, Erin admitted, and people are working in refined rings.
However, drawing any ring to triage patients in or out of a clinical trial for, say, E. coli UTI will cause researchers to miss all the âringed-outâ patients, leading to a massive bias. One should include all E. coli UTI patients to figure out why some patients fare worse, and only then formulate a testable intervention. Anyone grasps it.
I will rephrase it. Prognosis is not diagnosis. Sepsis-3 is a mistake because it uses a prognostic tool (qSOFA, a threshold ring) to define the presence of a disease. It is absurd from a medical perspective.
Medical researchers have forgotten that Medicine is a subsidiary of biology, and epidemiologists/data scientists happily waved Bradford-Hill off, saying plausibility is a matter for the field experts. Unfortunately, the field experts lost contact with real human biology and are trying to deduce biological associations from their desktop computers.
It leads to my second point, where I may disagree with Lawrence (a rare event). You can not deduce causal associations using causal models. There are two problems.
One is conceptual: itâs the hundred-year-old inductivist mistake addressed and clarified by Popper. This is why I wrote earlier in this thread that medical researchers are not scientists. Testing causality without having a previous biological model is inductivism.
The other is quite obvious. In biology, you will never compute all the variables. The inductivist will keep adding variables because inductivists never refute their conjecture. It is also unfeasible. Please think of the sepsis âdysregulated inflammationâ proposition. How many new molecules with inflammatory properties have been discovered in the past five years?
In summary, I am saying that:
- Any threshold definition (ring) is a mistake because it defines case severity, not the biologic nature of the disease.
- Top critical care researchers canât see it, and NIH grant reviewers also canât.
- Causal inference statistical exercises are useless and will never find a treatment for any disease.
Finally, my last point is that American influence locked all the world in the same trap. I am a Brazilian intensivist. I have spent the last 30 years in the reading side of American critical care research. I have witnessed the gradual âintellectual colonizationâ process. Brazilian researchers have recently hypothesized that aspirin reduces mortality in septic shock. This is the typical science-mimic they learned from Americans. The intellectual colonization process is complete, and soon no one in the world will be able to correct such mistakes.
Today, we are training new scientists in the mistakes made by American scientists decades ago. Young scientists look for established authorities as role models. We are not only wasting careers worldwide. We trained a generation that wonât understand why they will never find a breakthrough in critical care. Even worse, they might learn to play the grants game and keep kicking the can down the road.
This is no trivial topic. I apologize in advance if I sounded harsh. It comes from a perception that my field of medicine is forever stalled
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Thank you Dr. Harrell for the request for me to continue providing my view of the best approach. I am going to slightly disagree with Rafaelâs assessment of what I was saying but I will first apologize to Rafael for Americaâs intellectual colonization of his countryâs critical care scientists. I tried to intervene with one of the leading of Brazilian scientists in 2018 but was outgunned by proselytizing US scientists.
Rafael misunderstood my focus on DAGs. The goal is to map out the potential causal mechanisms prior to RCT to avoid pitfalls like REMAP CAP. I do not disagree with Rafael on this point generally but inductive analysis has come a long way and there is much to learn by this method⌠if it is not taken too far!
However, to emphasize my general agreement with Rafael, I loved the âBook of Whyâ but the causal bridge too far becomes briefly visible through the fog of brilliance, when the author tries to show that it is possible to prove that smoking induced tar causes lung cancer using DAGs. (Not withstanding this,much advances have been made in supportive processing since that book)
Relevant the best approach to the study of the hundreds of different advanced infection states, it is best to start from the beginning even with the terminology and with the goals. The first goal is to learn about the relational time dimensioned signals generated by the priors, the infection, the humanâs response to the infection and the interventions. We need a deep understanding of what the relational time series patterns of onset, worsening, recovery, and recovery failure are in relation to the priors and infections.
So we do not convene a 1990s style task force to determine the triage threshold set for âsepsisâ and then initiate a plurality of RCTs to determine treatment effect of drugs X,Y, Z respectively on triaged participants. Instead we start by learning about the infections we seek to cure.
Instead we accept that we are still close to the beginning. So we start with the extreme basics seeing the human from a secular perspective as a time series matrix of signals and an infection and the response to the infection as a distortion of that matrix. Each infection will induce a signature distortion comprised of a plurality of perturbations of signals; lab, vitals, molecules, etc and then recoveries from those perturbations. Those are the data we have in EMR archives in the hundreds of thousands so we start by learning from them.
So in the instant example, we obtain all the EMR available for a given infection (E Coli UTI) and study the distortions and its relational time series components of its baseline and priors, its onset, worsening, complete recovery, incomplete recovery, and recovery failure in timed relation to the TS of treatments.
An unfortunate but useful set of archived E Coli cases received very late or the wrong antibiotics so these provide an opportunity to see the entire E. coli induced distortion progression to death (onset, worsening, recovery failure) just as it would have occurred 2000 years ago. I emphasize this because we have to build on solid evidence. As Rafael points out we cannot start with a âringâ which might be different 30 years from now. A ring derived from evidence may be necessary at some point but thatâs not a place to start.
Only after acquiring a solid understanding of the distortion and its features (eg distortion recovery) induced by E Coli UTI we are ready for RCT and even then there must be a solid pathological/pharmacological basis for the tested treatment rather than a broad theory of potential efficacy. Here we need the best minds working toward the common goal of determining the best means to statistically process these data, with mathematical deference to the priors. That is where the physicians need the most help from the statisticians.
For example one of the problems with critical care research is the use of mortality as the primary endpoint. Yet in this discussion you see that deaths attributable to sepsis in a trial are a complicated matter and relate to the country of the trail. )high income vs low income.)
(https://x.com/pulmcrit/status/1187695603888852993?s=
However if the distortion and its recovery are not understood then this is the only hard endpoint one has other than broad one size all guessed threshold sets like SOFA which is not disease specific.
However if instead the task force of experts, physiologists, and statisticians acquires all available retrospective EMR data from all infection types and learn the relational time patterns of recovery to develop disease (infection and source) specific primary outcomes. The outcomes can then relate to the time to recovery TS pattern or the completeness of the TS recovery pattern.
Here you see a moonshot approach. I have used terms like distortions of the âhuman time series matrixâ (HTM) to emphasize the global approach and the requisite baseline state (defining the priors). More familiar but less inclusively economical terms are applicable such as relational time series patterns of both the baseline state and of a target disease and its recovery.
This infection and source specific approach embraces the biological feature of coevolution of each pathogen with humans. This sets the homogeneous thread. After we learn how different infections generate the same targetable pathways we can lump them for RCT testing treatment of that pathway.
I hope this will engender criticisms, discussion, new ideas, better ideas. Another approach is to start at the bench with time series measurements of molecules to derive phenotypic pathways. This still requires the first step of TS matrix analysis because the trajectory of any âdiagnosticâ of âtargetâ molecule must be considered as a relational part of the distortion and recovery of the TS matrix itself as all such molecules are.
In the last paragraphs of this paper (which paragraphs relate to Time-series patterns, the âintegers of physiologyâ), I talk about the HTM in my pre-pandemic warning about the need for medical education to move away from thresholds and embrace greater relational pattern complexity (which physicians have proven they are fully capable of learning).
âI may be wrong and you may be right, and by an effort, we may get nearer to the truth.â
Karl Popper.
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