Dataset Information

Delirium in intensive care unit patients under noninvasive ventilation: a multinational survey.

ABSTRACT: To conduct a multinational survey of intensive care unit professionals to determine the practices on delirium assessment and management, in addition to their perceptions and attitudes toward the evaluation and impact of delirium in patients requiring noninvasive ventilation.An electronic questionnaire was created to evaluate the profiles of the respondents and their related intensive care units, the systematic delirium assessment and management and the respondents' perceptions and attitudes regarding delirium in patients requiring noninvasive ventilation. The questionnaire was distributed to the cooperative network for research of the Associação de Medicina Intensiva Brasileira (AMIB-Net) mailing list and to researchers in different centers in Latin America and Europe.Four hundred thirty-six questionnaires were available for analysis; the majority of the questionnaires were from Brazil (61.9%), followed by Turkey (8.7%) and Italy (4.8%). Approximately 61% of the respondents reported no delirium assessment in the intensive care unit, and 31% evaluated delirium in patients under noninvasive ventilation. The Confusion Assessment Method for the intensive care unit was the most reported validated diagnostic tool (66.9%). Concerning the indication of noninvasive ventilation in patients already presenting with delirium, 16.3% of respondents never allow the use of noninvasive ventilation in this clinical context.This survey provides data that strongly reemphasizes poor efforts toward delirium assessment and management in the intensive care unit setting, especially regarding patients requiring noninvasive ventilation.

SUBMITTER: Tanaka LM

PROVIDER: S-EPMC4738822 | biostudies-other | 2015 Oct-Dec

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. METHODS:This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. RESULTS:In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of -?2.73 (p?<?0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n?=?68) rated the E-PRE-DELIRIC model more feasible. CONCLUSIONS:While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. TRIAL REGISTRATION:ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Dataset Information

Delirium in intensive care unit patients under noninvasive ventilation: a multinational survey.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets