Comparison of sedation strategies for critically ill patients: a protocol for a systematic review incorporating network meta-analyses
Bibliographic record
Abstract
BACKGROUND: Sedatives and analgesics are administered to provide sedation and manage agitation and pain in most critically ill mechanically ventilated patients. Various sedation administration strategies including protocolized sedation and daily sedation interruption are used to mitigate drug pharmacokinetic limitations and minimize oversedation, thereby shortening the duration of mechanical ventilation. At present, it is unclear which strategy is most effective, as few have been directly compared. Our review will use network meta-analysis (NMA) to compare and rank sedation strategies to determine their efficacy and safety for mechanically ventilated patients. METHODS: We will search the following from 1980 to March 2016: Ovid MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science. We will also search the Cochrane Library, gray literature, and the International Clinical Trials Registry Platform. We will use a validated randomized control trial search filter to identify studies evaluating any strategy to optimize sedation in mechanically ventilated adult patients. Authors will independently extract data from eligible studies in duplicate and complete the Cochrane Risk of Bias tool. Our outcomes of interest include duration of mechanical ventilation, time to first extubation, ICU and hospital length of stay, re-intubation, tracheostomy, mortality, total sedative and opioid exposure, health-related quality of life, and adverse events. To inform our NMA, we will first conduct conventional pair-wise meta-analyses using random-effects models. Where appropriate, we will perform Bayesian NMA using WinBUGS software. DISCUSSION: There are multiple strategies to optimize sedation for mechanically ventilated patients. Current ICU guidelines recommend protocolized sedation or daily sedation interruption. Our systematic review incorporating NMA will provide a unified analysis of all sedation strategies to determine the relative efficacy and safety of interventions that may not have been compared directly. We will provide knowledge users, decision makers, and professional societies with ranking of multiple sedation strategies to inform future sedation guidelines. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016037480.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.165 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".