Selective decontamination of the digestive tract in critically ill patients treated in intensive care units: a mixed-methods feasibility study (the SuDDICU study)
Bibliographic record
Abstract
BACKGROUND: Hospital-acquired infections (HAIs) are a major cause of morbidity and mortality. Critically ill patients in intensive care units (ICUs) are particularly susceptible to these infections. One intervention that has gained much attention in reducing HAIs is selective decontamination of the digestive tract (SDD). SDD involves the application of topical non-absorbable antibiotics to the oropharynx and stomach and a short course of intravenous (i.v.) antibiotics. SDD may reduce infections and improve mortality, but has not been widely adopted in the UK or internationally. Hence, there is a need to identify the reasons for low uptake and whether or not further clinical research is needed before wider implementation would be considered appropriate. OBJECTIVES: The project objectives were to (1) identify and describe the SDD intervention, (2) identify views about the evidence base, (3) identify acceptability of further research and (4) identify feasibility of further randomised controlled trials (RCTs). DESIGN: A four-stage approach involving (1) case studies of two ICUs in which SDD is delivered including observations, interviews and documentary analysis, (2) a three-round Delphi study for in-depth investigation of clinicians' views, including semi-structured interviews and two iterations of questionnaires with structured feedback, (3) a nationwide online survey of consultants in intensive care medicine and clinical microbiology and (4) semistructured interviews with international clinical triallists to identify the feasibility of further research. SETTING: Case studies were set in two UK ICUs. Other stages of this research were conducted by telephone and online with NHS staff working in ICUs. PARTICIPANTS: (1) Staff involved in SDD adoption or delivery in two UK ICUs, (2) ICU experts (intensive care consultants, clinical microbiologists, hospital pharmacists and ICU clinical leads), (3) all intensive care consultants and clinical microbiologists in the UK with responsibility for patients in ICUs were invited and (4) international triallists, selected from their research profiles in intensive care, clinical trials and/or implementation trials. INTERVENTIONS: SDD involves the application of topical non-absorbable antibiotics to the oropharynx and stomach and a short course of i.v. antibiotics. MAIN OUTCOME MEASURES: Levels of support for, or opposition to, SDD in UK ICUs; views about the SDD evidence base and about barriers to implementation; and feasibility of further SDD research (e.g. likely participation rates). RESULTS: (1) The two case studies identified complexity in the interplay of clinical and behavioural components of SDD, involving multiple staff. However, from the perspective of individual staff, delivery of SDD was regarded as simple and straightforward. (2) The Delphi study (n = 42) identified (a) specific barriers to SDD implementation, (b) uncertainty about the evidence base and (c) bimodal distributions for key variables, e.g. support for, or opposition to, SDD. (3) The national survey (n = 468) identified uncertainty about the effect of SDD on antimicrobial resistance, infection rates, mortality and cost-effectiveness. Most participants would participate in further SDD research. (4) The triallist interviews (n = 10) focused largely on the substantial challenges of conducting a large, multinational clinical effectiveness trial. CONCLUSIONS: There was considerable uncertainty about possible benefits and harms of SDD. Further large-scale clinical effectiveness trials of SDD in ICUs may be required to address these uncertainties, especially relating to antimicrobial resistance. There was a general willingness to participate in a future effectiveness RCT of SDD. However, support was not unanimous. Future research should address the barriers to acceptance and participation in any trial. There was some, but a low level of, interest in adoption of SDD, or studies to encourage implementation of SDD into practice. FUNDING: This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 18, No. 25. See the NIHR Journals Library website for further project information.
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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 | 0.059 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".