Post Intensive Care Syndrome (PICS) in COVID-19 ARDS Survivors: A 6-Month Longitudinal Study from South India
Bibliographic record
Abstract
ABSTRACT Background Post-Intensive Care Syndrome (PICS) includes cognitive, psychological, and physical impairments following critical illness. The long-term impact of COVID-19-related ARDS on PICS domains remains under-explored, particularly in resource-limited settings. Objective To assess the prevalence and trajectory of cognitive, mental, and physical impairments among COVID-19 ARDS survivors at ICU discharge and at 6 months, and to explore associated risk factors. Methods This was an observational cohort study of 30 mechanically ventilated COVID-19 patients admitted to a tertiary ICU in South India during the second wave (Delta variant). Patients were assessed at ICU discharge (or first follow-up) and at 6 months using the Montreal Cognitive Assessment (MoCA), SF-36 health survey, modified MRC dyspnea scale, 6-minute walk test (6MWT), and physical examination. Risk factors were analyzed using multivariable linear regression. Results At discharge, mild cognitive impairment was prevalent (MoCA: 25.17±3.63), with significant improvement at 6 months (27.07±2.72, p<0.001). SF-36 domains showed persistent deficits in emotional well-being (36.31→50.83), fatigue (33.28→45.91), and pain (51.38→71.47) (all p<0.01). Functional capacity improved on 6MWT, with >350m walked increasing from 23% to 53%. Risk factors included steroid duration, SOFA score, antifungal exposure, and fasting hypoglycemia. Other parameters like Muscle wasting, dyspnea, and gastrointestinal symptoms also showed partial recovery. Conclusion COVID-19 ARDS survivors experience significant but partially reversible PICS across multiple domains. Structured post-ICU rehabilitation and early identification of modifiable risk factors may improve recovery trajectories. Findings highlight the need for integrated post-ICU care pathways in similar settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".