Effect of P2Y12 Inhibitors, SGLT2 Inhibitors and P-Selectin Inhibitors on One-year Quality-of-Life Outcomes in Critically Ill Patients Hospitalized for COVID-19: A Pre-specified Secondary Analysis of the ACTIV4a Randomized Clinical Trial
Notice bibliographique
Résumé
Abstract Introduction Post-Acute Sequelae of COVID-19 (PASC) is a significant complication of SARS-CoV-2 infection, leading to persistent symptoms and diminished functional status. Long-term QoL data, particularly in relation to therapeutic interventions like P2Y12 inhibitors, SGLT2 inhibitors, and crizanlizumab, remain limited. This study aimed to evaluate the effect of these treatments on one-year QoL outcomes in critically ill COVID-19 patients. Methods: This analysis is part of the Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV-4a) randomized controlled platform trial, conducted at 34 hospitals in the United States and Spain from September 2020 to June 2021. Participants were randomized to receive standard care or one of the following interventions: P2Y12 inhibitors, SGLT2 inhibitors, or crizanlizumab. QoL outcomes were assessed one year after hospitalization using the Patient-Reported Outcomes Measurement Information System (PROMIS), which measured domains such as physical and mental health, sleep disturbance, and neurological QoL. Univariate and multivariable analyses were conducted to evaluate treatment effects and identify independent predictors of QoL. Results: In total, 750 participants completed PROMIS questionnaires one year post-trial (n=258 SGLT2 inhibitor, n=191 crizanlizumab, n=19 P2Y12 inhibitor). Subject characteristics including age, sex, race, and baseline comorbidities were not significantly different between treatment and control groups. There were no significant differences in the PROMIS t-scores for physical and mental health, sleep disturbance, and neurological QoL (all p>0.05) based on receipt of P2Y12 inhibitors, SGLT2 inhibitor, or crizanlizumab. Average PROMIS t-scores for mental health were consistently lower than the reference score, and physical health across all groups approached 1 standard deviation below the U.S. general public. Pre-infection respiratory disease was associated with lower physical health (difference = -4.99, p < 0.0001), lower mental health (difference = -4.09, p = 0.0001), and higher sleep disturbance t-scores (difference = 4.38, p < 0.0001). Severe disease status was associated with higher physical health t-scores (difference = 4.06, p = 0.0302). Female sex was associated with lower neurological QoL score (difference = -3.02, p = 0.0175), while Hispanic ethnicity (difference = 6.15, p = 0.0005) and pre-infection immunosuppressive disease were associated with higher neurological QoL scores (difference = 3.88, p = 0.0077). Conclusion: In this secondary analysis of a randomized controlled platform trial, P2Y12 inhibitors, SGLT-2 inhibitors, and crizanlizumab were not associated with significant improvement in one-year QoL outcomes among hospitalized COVID-19 patients. Reduced physical and mental health scores were observed across multiple groups, with predictors of diminished QoL including pre-infection respiratory disease, female sex, and ethnicity.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».