A Screening Tool to Predict Post-intensive Care Syndrome (PICS) in the Critically Ill
Bibliographic record
Abstract
Abstract RATIONALE: Many ICU patients experience physical, psychological, and cognitive impairments following resolution of critical illness, collectively referred to as post-intensive care syndrome (PICS). PICS has become widely recognized within the critical care field, but how to screen for PICS does not currently have consensus. Development of a validated, concise PICS screening tool would allow for rapid identification of this vulnerable and understudied population. Therefore, this study's goal was to develop and evaluate a screening tool to identify patients at risk for PICS. METHODS: We performed a prospective cohort study in a quaternary academic hospital. We included adults ≥ 18 years old with ≥48 hours spent in an ICU. Exclusion criteria were admission for suicide attempt, severe cognitive impairment/dementia, life expectancy <3 months, and non-English speaking. We assessed mental, cognitive, and physical function following ICU discharge and at 3 months using Hospital Anxiety and Depression Scale, Impact of Event Scale-Revised, Montreal Cognitive Assessment-blind, and Barthel Index. Our team, including critical care physicians, psychiatrists, psychologists, physical and occupational therapists, and nurses, developed a brief 15-item PICS screening tool (5 questions/domain). This tool was administered after patients were discharged from ICU stay to general care to assess its effectiveness in predicting impairments in all 3 domains at discharge and again at three months after hospital discharge, in comparison to previously mentioned validated questionnaires. Youden index statistical methods were used to determine potential “cutoff” values (scores) for the tool to maximize sensitivity and specificity. RESULTS: A total of 194 patients from 6 ICUs were recruited and completed the initial questionnaire, and 109 (56%) patients completed 3-month follow-up. Median age was 65 years (IQR 57-72) and 49% (95/194) were female. Screening tool is depicted in Figure 1. Overall, the screening tool weakly-to-moderately correlated with continuous outcomes of the validated measures upon ICU discharge and 3-month follow-up. When evaluating sensitivity and specificity for individual domains, the tool had an area under the receiver operating curve of >0.7 for the following domains: cognitive, anxiety, and physical impairments, indicating moderate-to-good sensitivity and specificity. CONCLUSIONS: This PICS screening tool was correlated with validated measures of PICS upon ICU discharge and at 3-month follow-up for all three domains. The tool's strength lies in its concise nature, making it easier for those caring for this patient population to screen for long-term effects of an ICU stay. Our proposed next step is validation of the PICS screening tool in an external cohort.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".