Characteristics of Mid-Term Post-Intensive Care Syndrome in Patients Attending a Follow-Up Clinic: A Prospective Comparison Between COVID-19 and Non-COVID-19 Survivors
Bibliographic record
Abstract
IMPORTANCE: At present, it is not clear if critically ill COVID-19 survivors have different needs in terms of follow-up compared with other critically ill survivors, and thus if duplicated post-ICU trajectories are mandatory. OBJECTIVES: To compare the post-intensive care syndrome (PICS) of COVID-19 acute respiratory distress syndrome and non-COVID-19 (NC) survivors referred to a follow-up clinic at 3 months (M3) after ICU discharge. DESIGN, SETTING, AND PARTICIPANTS: Adults who survived an ICU stay greater than or equal to 7 days and attended the M3 consultation were included in this observational study performed in a post-ICU follow-up clinic of a single tertiary hospital. MAIN OUTCOMES AND MEASURES: Patients underwent a standardized assessment, addressing health-related quality of life (3-level version of EQ-5D), sleep disorders (Pittsburgh Sleep Quality Index [PSQI]), physical status (Barthel index, handgrip and quadriceps strengths), mental health disorders (Hospital Anxiety and Depression Scale and Impact of Event Scale-Revised [IES-R]), and cognitive impairment (Montreal Cognitive Assessment [MoCA]). RESULTS: A total of 143 survivors (86 COVID and 57 NC) attended the M3 consultation. Their median age and severity scores were similar. NC patients had a shorter ICU stay (10 d [8–17.2 d]) compared with COVID group (18 d [10.8–30 d]) ( p = 0.001). M3 outcomes were similar in the two groups, except for a higher PSQI ( p = 0.038) in the COVID group (6 [3–9.5]) versus NC group (4 [2–7]), and a slightly lower Barthel index in the NC group (100 [100–100]) than in the COVID group (100 [85–100]) ( p = 0.026). However, the proportion of patients with abnormal values at each score was similar in the two groups. Health-related quality of life was similar in the two groups. The three MoCA (≥ 26), IES-R (<33), and Barthel (=100) were normal in 58 of 143 patients (40.6%). In contrast, 68.5% (98/143) had not returned to their baseline level of daily activities. CONCLUSIONS AND RELEVANCE: In our follow-up clinic at 3 months after discharge, the proportion of patients presenting alterations in the main PICS domains was similar whether they survived a COVID-19 or another critical illness, despite longer ICU stay in COVID group. Cognition and sleep were the two most affected PICS domains.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".