Exploring long-term impacts on ICU survivors: A concept analysis of post-intensive care syndrome
Bibliographic record
Abstract
Post-intensive care syndrome (PICS) has emerged as a concern for intensive care unit (ICU) survivors, particularly in the context of the increasing survival rates of patients with severe illness. This syndrome encompasses a range of physical, cognitive, and psychological impairments that can continue long after ICU discharge. PICS impacts survivors’ quality of life, with common manifestations including muscle weakness, memory deficits, and depression. Despite growing awareness, PICS remains underexplored in clinical practice, with ongoing research focused on identifying its risk factors and effective management strategies. Risk factors such as prolonged mechanical ventilation, extended ICU stays, and delirium contribute to its development, particularly among older adults who face heightened vulnerability to long-term complications. This concept analysis, using the Walker and Avant (2011) method, aims to clarify the defining attributes, antecedents, and consequences of PICS, offering a framework to improve understanding, clinical management, and intervention. Through an extensive review of literature from 2012 to 2024, key elements of PICS are identified, and their implications for both healthcare providers and patients are discussed. The analysis highlights the need for standardized care pathways, including psychological support, physical rehabilitation, and follow-up care, and highlights the importance of early detection and intervention in the ICU and post-ICU settings. This paper contributes to the growing body of knowledge on PICS, offering a solid foundation for further research, clinical guidelines, and care protocols that will improve the long-term recovery, quality of life, and overall well-being of ICU survivors, ultimately improving patient outcomes and enhancing recovery strategies for all. Keywords: post-intensive care syndrome, ICU survivors, long-term recovery, critical care, concept analysis
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".