American journal of respiratory and critical care medicine
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Am. J. Respir. Crit. Care Med. · Jul 2021
Randomized Controlled TrialPassive Prophylactic Administration with a Single Dose of Anti-Fel d 1 Monoclonal Antibodies REGN1908-1909 in Cat Allergen-Induced Allergic Rhinitis: A Randomized, Double-blind, Placebo Controlled Trial.
Rationale: Sensitization to Fel d 1 (Felis domesticus allergen 1) contributes to persistent allergic rhinitis and asthma. Existing treatment options for cat allergy, including allergen immunotherapy, are only moderately effective, and allergen immunotherapy has limited use because of safety concerns. Objectives: To explore the relationship among the pharmacokinetic, clinical, and immunological effects of anti-Fel d 1 monoclonal antibodies (REGN1908-1909) in patients after treatment. ⋯ Ex vivo assays demonstrated that REGN1908 and REGN1909 combined were more potent than each alone for inhibiting FcεRI- and FcεRII (CD23)-mediated allergic responses and subsequent T-cell activation. Conclusions: A single, passive-dose administration of Fel d 1-neutralizing IgG antibodies improved nasal symptoms in cat-allergic patients and was underscored by suppression of FcεRI-, FcεRII-, and T-helper cell type 2-mediated allergic responses. Clinical trial registered with www.clinicaltrials.gov (NCT02127801).
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Am. J. Respir. Crit. Care Med. · Jul 2021
Detecting Deteriorating Patients in Hospital: Development and Validation of a Novel Scoring System.
Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. ⋯ HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.