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Enregistrement W1817044009 · doi:10.1111/j.1537-2995.2010.02879.x

Hitting the “tipping point” of TRICC?

2010· letter· en· W1817044009 sur OpenAlexaff
Christine Cserti‐Gazdewich

Notice bibliographique

RevueTransfusion · 2010
Typeletter
Langueen
DomaineMedicine
ThématiqueArtificial Intelligence in Healthcare and Education
Établissements canadiensToronto General HospitalUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésTipping point (physics)MedicineEngineering

Résumé

récupéré en direct d'OpenAlex

“This is what is meant by translation. What Mavens and Connectors and Salesmen do to an idea in order to make it contagious is to alter it in such a way that extraneous details are dropped and others are exaggerated so that the message itself comes to acquire a deeper meaning. If anyone wants to start an epidemic, then . . . he or she has to find some person or some means to translate the message . . . into something the rest of us can understand.”1 (Chapter 6: Case Study: Rumors, Sneakers, and the Power of Translation, p. 203) In Malcolm Gladwell's bestseller on the potency of influential people, the art of rendering messages memorable, and environmental context, the “tipping point” for momentous social change is methodically deconstructed. Tipping new medical knowledge, when it is of an objective significance great enough to warrant practice change(s), is a major challenge in health care. We wish that translation could be as effortlessly “viral” as a fad and as sustained as tradition, but the dissemination of findings, and the widespread adoption of appropriate responses, is known to take highly variable lengths of time, while suffering sometimes vast heterogeneities in uptake. “Knowledge translation,” or the closure of the “evidence-to-practice gap,”2 has necessarily become a field unto itself in the era of burgeoning health information. Although it is easy to blame some resistance to change on limitations in capital, as with the stalled expansion of pathogen reduction measures, money is not always the barrier. Sometimes, resistance is seen despite invocations to do what is easiest. Such is the interesting case for the application of restrictive transfusion practices to patients whose objective needs for banked blood remain uncertain, despite the chance to spend less and conserve more. One can only speculate on the roots of such staunch transfusion liberalism. Are we culturally disposed to using blood the way we use money (although we are now better appreciating the consequences of “customary and stimulative profligacy” in the global financial crisis3) or are we good-heartedly holding onto a precautionary bias against undertransfusion, all the while assuming that we can functionally recapitulate the benefits of a naturally higher red blood cell (RBC) mass with stored RBCs despite their derangements4 and risks?5 The best-known study to foster confidence in transfusing less is the now 11-year-old Transfusion Requirements in Critical Care (TRICC) trial,6 judged in part by how often it leads lists of the most important and influential studies in transfusion medicine.7-9 Hébert and colleagues found that a restrictive strategy of RBC transfusion (trigger < 7 g/dL, target 7-9 g/dL) is at least as effective as and possibly superior to a liberal transfusion strategy (trigger < 10 g/dL, target 10-12 g/dL) in critically ill patients. Certain conservatively transfused subgroups (such as those with lower APACHE II scores and age less than 55 years) experienced a significant 30-day survival advantage, and conversely, the liberally transfused manifested with significantly higher numbers of cardiac events, myocardial infarction, congestive heart failure, and acute respiratory distress syndrome. The role of transfusion in this latter point is also inferred by the demonstration of fewer acute lung injuries in mechanically ventilated critically ill patients by transfusion avoidance.10,11 Gladwell describes “laggards” as those who impede the full momentum of the tipping point. Laggards may be so because of stubborn passivity or thoughtful skepticism toward the wisdom of taking on something new and different. The full (and especially the extrapolated) assimilation of TRICC's restrictive strategy within and beyond critical care medicine is sometimes reciprocally denied on the principle of the TRICC trial's deliberate and unplanned exclusions. Namely, even with acute coronary syndromes and hemodynamic instability specifically excluded, final enrollment was no greater than 52% of the revised lower interim sample size requirement. The 59% of the screened who refused consent were also enriched for higher-severity heart disease compared with the study subjects, challenging the real-world generalizability of TRICC to intensive care unit (ICUs) where roughly 30% of patients have ischemic heart disease.12 The champions and dissenters on TRICC nevertheless agree that anemic critically ill patients13 are at a strained organ-perfusion disadvantage compared with their less anemic but otherwise similarly sick counterparts, with the question being whether or not (and if so, when) homologous RBCs improve, glance past, or worsen this inadequate organ function. Where bleeding, hypoxia, and cardiac output are adequately addressed, adjustments in hemoglobin (Hb) concentration may nevertheless fail to improve microcirculatory oxygen delivery and/or the tissue utilization of oxygen (“cellular dysoxia”). The critically ill are thus arguably the truest canaries in the coal mine of transfusion medicine research, because the stakes (or the potential to adjust outcomes) are so high amid substantial case fatality rates, the physiology of oxygen delivery and utilization is so multidimensionally pathological, and the utilization of blood so high. In the meantime, recently published guidelines14 draw from the evidence of TRICC,6 other landmark clinical trials,15-17 and a meta-analysis18 to endorse the restrictive strategy more assertively than ever before, given the apparent harms of doing otherwise. Clinicians nevertheless wait for further guidance from ongoing studies in matters of still considerable debate and equipoise. Enter the important study of Netzer and colleagues19 in this issue of TRANSFUSION, which focused exactly (and only) on the demographic examined by TRICC. (The resemblance was remarkable with only half of patients [3659 of 7128] meeting criteria for analysis, as was the case for TRICC [3206 of 6451].) This single-center, retrospective, observational study took the longest and most innovative look at transfusion practices before and after TRICC to examine for its impact, eclipsing the critical care data (periods lasting merely weeks or months after TRICC) from any published group to date.6,12,15-17,20-23 Although this study occurred in a “closed-model” ICU in a hospital without institutional transfusion guidelines or a process to prospectively audit utilization, it was for these very reasons that this study can be said to reflect a real-world setting.24 There were nine intervals examined, with the first one before TRICC's publication (October 1, 1997, to February 10, 1999), followed thereafter by seven 12-month periods (February 11, 1999, to February 10, 2006 [1999-2005]), and a final 22-month period (February 11, 2006-December 31, 2007). The key finding was not only a lower mean pretransfusion hemoglobin over time for the earliest compared with the latest comparison periods (7.9 ± 0.3 to 7.3 ± 0.3, p < 0.001), but the entire assembly of significantly reduced (as opposed to stable or higher) proportions of transfused patients (falling from 31% to 18%, p < 0.001), fewer mean numbers of units transfused (4.3 ± 4.7 to 3.0 ± 3.8, p < 0.001), increasingly “single-unit” (as opposed to multiunit) transfusion events (1.4% increase per interval from 40.2% to 53.1%), and a parallel decrease in the nadir Hb of the untransfused. That the length of stay was not shorter for the untransfused suggested that their state of blood deprivation was not a matter of sooner escape, but that they endured comparable in-hospital sentences wherein the tempting logistics of product availability and surveillance phlebotomy were perhaps similar and certainly no less. There was thus every indication that greater levels of anemia were accepted before transfusion over time and that blood use had to have decreased overall. In other words, decreases in one domain did not lead to compensatory increases in others, so that if lower triggers or a shift toward virgin single-unit transfusions happened to be observed, there was no hiding those patients who might have been getting transfused with a later, greater load of blood. Furthermore, not only was the stability of admission Hb values verified, but the observed changes in transfusion practice were found to be a greater function of physician decisions than of other changes in patient characteristics over time. This study also examined transfusion rates by Hb strata over the intervals, referring to patients with a Hb level of less than 7 g/dL (the ideal, conservative-trigger category for transfusion), patients with a Hb level of between 7 and 10 g/dL (the liberal-trigger category for transfusion, representing the range of interest wherein most intensivists had been transfusing their patients before and early after TRICC's publication), and patients with a Hb level of greater than 10 g/dL (the most readily disputable and ultraliberal-trigger category of transfusion for nonbleeding patients). The adjusted rate of change for the middle group proved to reveal the “most negative” adjusted time coefficient slope for 1997 to 2001 (−9.3%), which was also significantly different from the lesser later decline (−0.5%) seen in 2001 to 2007. These findings suggested that the very group that TRICC had specifically “picked on” had indeed not only contracted, but had done so rapidly, and years later had avoided reexpansion in the way that nonsustainable yo-yo dieters tend not to. The less than 7 g/dL group indeed absorbed and thus accounted for an increasing proportion of transfusees, by 3.2% per interval (p < 0.001). In contrast, the time coefficient slopes for conservative and ultraliberal groups in both of these periods were statistically similar, −0.7% to −1.9% and −0.4% to 0%, respectively. Had the phenomenon of decreased transfusion been a mere function of generalized fears related to the adverse effects of blood in all patients, or reductions in inventory, the decreases might have occurred more consistently (i.e., even in conservative and liberal groups) or proportionately (in the most wasteful ultraliberal and liberal groups). To use Gladwell's terms, it is humbly encouraging that “TRICC stuck,” especially in light of the fact that there did not appear to be any efforts to sustain conformity by institutional policies or campaigns. The fact that 27 intensivists staffed the unit, with six in particular who attended every year, may have played a role. Could this subgroup have been the influential models and bearers of “institutional memory”? Would ICUs of a larger size or fluidity, if so scrutinized, have boasted the same success? The authors cite some interesting literature in which the assimilation of other evidence-based critical care practice guidelines, specific to ventilation or interruption of sedation, was not so successful or was so gradual and delayed that too short a period of scrutiny may have led to premature conclusions of missed uptake. The post-TRICC effect in this study was unmistakable, begging how or why transfusion medicine changes fared better. Nevertheless, why some clinicians remain “transfusion liberals” is still unclear from this and other studies. Despite the milestone progress achieved here, the majority of patients were still transfused at a trigger slightly above 7 g/dL, while double—or greater—RBC orders still accounted for almost half of the transfusions. If the best of the change that can happen has already occurred, then this experience may not be reassuring enough for those with higher aspirations. Sometimes clinician resistance is based more on doubts and fears on the adequacy of tissue perfusion for those patients who are believed to not resemble the typical TRICC subject. This concern is especially true for cardiac, coronary, and/or cardiovascular intensive care patients. Our arguments on Hb concentrations alone, drawn from studies with many exclusions, may never be credible enough to inspire conversions. We can hold all the educational conferences our vocal cords can stand, but without more evidence on the safety or sufficiency of restrictive strategies in other kinds of severely ill patients, and with the renewal of equipoise on the storage lesion,25 liberalism is not going away any time soon. This resistance soundly argues for the deployment of more objective, affordable, and hopefully safer, less-invasive surveillance technologies for goal-directed therapy.26,27 Should these be better validated and available, the most physiologically labile and relevant markers of tissue perfusion may be far more helpful to us as clinicians in real time than past-published studies emphasizing (inevitably) multifactorial mortality as the endpoint. Death makes us change our behavior systematically, but something observed nearer to the origins of harm will be much more helpful for the everyday fine-tuning of our decisions on our individual patients. In retrospective studies such as the study by Netzer and colleagues, there are barriers to successfully gathering more comprehensive information, such as zenith lactate levels or changes in other ICU practices related to monitoring and therapy (e.g., blood conservation tactics, hematinics, and dobutamine infusions28). These too may have improved over time and played their own role in the acceptability of restrictive transfusion practices. The authors examined whether or not there were any changes in mortality over these increasingly transfusion-restrictive periods, but these could not be ruled in or out. The case fatality rates varied too much over time for the apparent slight reduction to be deemed significant. Mortality cannot fairly be commented upon, because any patients with ICD-9CM codes for acute coronary syndromes (ACS) were excluded from the analysis. These may have been patients who were originally excluded because of ACS identified well before any observations were made on the effects of transfusion or the lack thereof, or ACS occurring subsequently as a possible result of transfusion or lack thereof. In other words, patients who were transfused at lower (i.e., potentially demand ischemic) or higher (i.e., potentially prothrombotic) triggers could have exacerbated or outright caused a survivable or fatal ACS, which in either case would have been excluded. The ultimate merit of the study by Netzer and colleagues is to show that another study like TRICC can indeed associate with an observable practice change, despite the absence of efforts to actively sell its findings. (This is not to disparage the pains of selling policies at our medical advisory committees to have best practices endorsed institution-wide, because it may be that with appropriateness-auditing,29 the success might have been even better.) What we ought to really pay attention to here is the power that TRICC-like studies have in their own right, and the consequences of ostensibly increasing blood utilization in those domains where these studies are still lacking.30 None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,029
score de la tête « metaresearch » (Gemma)0,146
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,151

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0290,146
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,004
Études des sciences et des technologies0,0140,040
Communication savante0,0220,030
Science ouverte0,0040,014
Intégrité de la recherche0,0140,022
Charge utile insuffisante (le modèle a refusé de juger)0,0340,024

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,121
Tête enseignante GPT0,383
Écart entre enseignants0,262 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2010
Routes d'admission1
Résumé présentoui

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