Society of Critical Care Medicine’s International Consensus Conference on Prediction and Identification of Long-Term Impairments After Critical Illness
Bibliographic record
Abstract
BACKGROUND: After critical illness, new or worsening impairments in physical, cognitive, and/or mental health function are common among patients who have survived. Who should be screened for long-term impairments, what tools to use, and when remain unclear. OBJECTIVES: Provide pragmatic recommendations to clinicians caring for adult survivors of critical illness related to screening for postdischarge impairments. PARTICIPANTS: Thirty-one international experts in risk-stratification and assessment of survivors of critical illness, including practitioners involved in the Society of Critical Care Medicine's Thrive Post-ICU Collaboratives, survivors of critical illness, and clinical researchers. DESIGN: Society of Critical Care Medicine consensus conference on post-intensive care syndrome prediction and assessment, held in Dallas, in May 2019. A systematic search of PubMed and the Cochrane Library was conducted in 2018 and updated in 2019 to complete an original systematic review and to identify pre-existing systematic reviews. MEETING OUTCOMES: We concluded that existing tools are insufficient to reliably predict post-intensive care syndrome. We identified factors before (e.g., frailty, preexisting functional impairments), during (e.g., duration of delirium, sepsis, acute respiratory distress syndrome), and after (e.g., early symptoms of anxiety, depression, or post-traumatic stress disorder) critical illness that can be used to identify patients at high-risk for cognitive, mental health, and physical impairments after critical illness in whom screening is recommended. We recommend serial assessments, beginning within 2-4 weeks of hospital discharge, using the following screening tools: Montreal Cognitive Assessment test; Hospital Anxiety and Depression Scale; Impact of Event Scale-Revised (post-traumatic stress disorder); 6-minute walk; and/or the EuroQol-5D-5L, a health-related quality of life measure (physical function). CONCLUSIONS: Beginning with an assessment of a patient's pre-ICU functional abilities at ICU admission, clinicians have a care coordination strategy to identify and manage impairments across the continuum. As hospital discharge approaches, clinicians should use brief, standardized assessments and compare these results to patient's pre-ICU functional abilities ("functional reconciliation"). We recommend serial assessments for post-intensive care syndrome-related problems continue within 2-4 weeks of hospital discharge, be prioritized among high-risk patients, using the identified screening tools to prompt referrals for services and/or more detailed assessments.
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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 | 0.165 | 0.257 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.020 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.014 | 0.012 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".