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Enregistrement W2086601302 · doi:10.1164/rccm.201105-0825ed

Predicting Mortality in Patients with Acute Lung Injury

2011· letter· en· W2086601302 sur OpenAlexaff
G. R. Scott Budinger, Keith R. Walley

Notice bibliographique

RevueAmerican Journal of Respiratory and Critical Care Medicine · 2011
Typeletter
Langueen
DomaineMedicine
ThématiqueRespiratory Support and Mechanisms
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesNational Institute of Environmental Health Sciences
Mots-clésMedicineARDSIntensive care medicinePneumoniaMechanical ventilationPopulationIncidence (geometry)Ventilator-associated pneumoniaMortality rateEmergency medicineIntensive care unitLungSurgeryInternal medicine

Résumé

récupéré en direct d'OpenAlex

Acute lung injury (ALI) and the acute respiratory distress syndrome (ARDS) are common disorders estimated to affect nearly 200,000 people per year in the United States alone (1). Despite improvements in supportive care, the overall mortality rate for these patients continues to hover near 40% (2). In King County, Washington, Rubenfeld and coworkers reported that the incidence of ARDS was twice as high in patients over 74 compared with patients between 60 and 64 years of age, and that older patients who developed the syndrome were significantly more likely to die (1). Therefore, the incidence of ALI/ARDS and its associated mortality are likely to rise as the population ages, particularly in the developed world. Novel therapies identified through bench and clinical research and translated to clinical practice have led to substantial improvements in ICU care in the past decade. Advances in imaging and laboratory diagnostics have dramatically improved our ability to identify and treat primary causes of critical illness, reducing diagnostic procedure–related morbidity and mortality. New antimicrobials and vasoactive agents, more effective therapies to prevent stress ulcer and venous thromboembolism, and a variety of new agents to treat other complications of critical illness effectively reduce the risk of ICU-related complications. In addition, we have learned to better use the resources available to us in the ICU. Positive pressure ventilation strategies designed to prevent ventilator-induced lung injury, interruptions in sedation, improved fluid management, limitation of empiric antibiotic usage, strategies to prevent ventilator-associated pneumonia, and the use of protocols and checklists to ensure consistent patient care delivery have all improved the supportive care of patients with ALI/ARDS (3–8). Given these and other research-driven improvements in supportive care, it is surprising that the crude mortality in most studies conducted in patients with ARDS remains relatively constant (2). One possible explanation is that the lack of change represents a true success as our ICUs fill with older patients with more co-morbidities. Even if this is true, it is clear that for a significant proportion of patients with ALI/ARDS, even the best supportive therapy is not enough. For these patients, novel therapies designed to interrupt ongoing lung injury or promote healing lung that investigators have identified as effective in preclinical or phase II trials may improve outcomes (9). Therefore, when testing potentially toxic new therapies investigators need biomarkers to identify subsets patients with ARDS at a higher risk of death, as the risk-to-benefit profile in these patients is more likely to be favorable. Investigators have frequently examined bronchoalveolar lavage fluid to identify predictive biomarkers, as the procedure can be done safely, is often performed as part of routine clinical care, and can reveal valuable insight into the severity of damage to the alveolar membrane (5, 10). The low-density lipoprotein receptor–related protein (LRP-1) is a large endocytic receptor that is expressed in many tissues, including the lung (11). LRP-1 recognizes more than 30 different ligands with varying affinity, facilitating their cellular uptake via endocytosis (11). In the low pH of the endosome, a conformational change in the structure in LRP-1 causes it to release the ligand, which can be recycled or degraded in the lysosome (12). In this issue of the Journal, Wygrecka and colleagues (pp. 438) found that the levels of soluble LRP-1 (sLRP-1) were increased in patients with ARDS compared with a control group of spontaneously breathing subjects or mechanically ventilated patients with cardiogenic pulmonary edema (13). The BAL levels of sLRP-1 were positively correlated with APACHE II scores and were significantly higher in patients who died from ARDS compared with those who survived. BAL fluid from patients with ARDS induced sLRP release from lung fibroblasts (but not epithelial cells or macrophages) through a mechanism that required TNF-α and membrane type-1 matrix metalloproteinase. The investigators went on to show that sLRP-1 inhibited the uptake of matrix metalloproteinases-2 and -9, which might enhance tissue injury. In support of this hypothesis they found that BAL fluid levels of MMP-2 and -9, and the basement membrane protein laminin, were positively correlated with the levels of sLRP-1 in the patients with ARDS. Wygrecka and coworkers have identified one important mechanism by which LRP-1 might contribute to the pathophysiology of ARDS. In addition, LRP-1 has been shown to regulate the activity of platelet-derived growth factor and transforming growth factor-β1, promote the clearance of Factor VIII, modulate the activity of the fibrinolytic system by binding with the urokinase-type plasminogen activator, contribute to the tPA-mediated increase in blood–brain barrier permeability after stroke, promote focal adhesion disassembly, and enhance cell migration, all of which may be important in the development and resolution of ALI (11). In addition, LRP-1 plays an important role in the phagocytosis of apoptotic cells and foreign particles. For example, Gardai and colleagues reported that in the lung LRP-1 interacts with surfactant proteins-A and -D to promote phagocytosis and induce inflammation in response to foreign particles or damaged cells (14). Further study will be required to determine the predominant mechanism(s) by which LRP-1 might contribute to the pathogenesis of ALI/ARDS. Before it can be used to direct treatment, a biomarker must be shown to have a high sensitivity, specificity, and positive and negative predictive value for the predicted outcome; be reproducible outside of the institution or laboratory in which it was developed; demonstrate biological plausibility; and be validated in a cohort of patients independent from the original cohort (15). Substantial further investigation is required before bronchoalveolar lavage fluid levels of sLRP-1 can be used as a biomarker to identify patients at risk of death from ARDS. However, the findings of Wygreka and coworkers add to those conducted by other investigators who collectively have identified an array of physiologic and laboratory parameters to improve our ability to identify patients at high risk of death earlier in their clinical course (16). Rigorous prospective examination of their utility as clinical prediction tools may open the door for the use of active pharmacologic or biological interventions to prevent ALI/ARDS-associated mortality.

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)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,018
Tête enseignante GPT0,305
Écart entre enseignants0,287 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations1
Publié2011
Routes d'admission1
Résumé présentoui

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