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JPEN J Parenter Enteral Nutr ; 43(3): 326-334, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30260486

RESUMO

Current randomized trials and observational studies evaluating higher versus lower protein doses in critically ill patients yield inconclusive results. Because of few studies and methodologic limitations, clinical guidelines suggest a wide range of protein intake based on weak evidence. Clinical equipoise about protein dosing exists. The purpose of the current manuscript is to provide the rationale and protocol for a randomized controlled trial (RCT) of 4000 critically ill patients randomly allocated to receive a higher or lower protein dose. We propose a global, volunteer-driven, registry-based RCT involving >100 intensive care units (ICUs). We will enroll mechanically ventilated patients with high nutrition risk, identified by low (≤25) or high (≥35) body mass index, moderate to severe malnutrition, frailty, sarcopenia, or when >96-hour duration of mechanical ventilation is expected. Exclusion criteria include patients who are >96 hours since initiation of mechanical ventilation, moribund, or pregnant, and where the clinician lacks clinical equipoise regarding protein dose. The intervention consists of higher (≥2.2 g/kg/d) or lower (≤1.2 g/kg/d) protein dose, achieved by enteral nutrition, parenteral nutrition, or both. The primary outcome will be 60-day mortality. Key secondary outcomes include time-to-discharge alive from hospital, ICU and hospital survival, and length of stay. As this is research based on existing medical practice, we will apply for a waiver of informed consent, where possible. The large sample size is a reflection of the small signal we expect to see in this large, pragmatic trial.


Assuntos
Cuidados Críticos/métodos , Proteínas Alimentares/administração & dosagem , Estudos Multicêntricos como Assunto , Apoio Nutricional/métodos , Ensaios Clínicos Controlados Aleatórios como Assunto , Sistema de Registros/estatística & dados numéricos , Estado Terminal , Mortalidade Hospitalar , Humanos , Tempo de Internação/estatística & dados numéricos , Respiração Artificial
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