[Cognitive impairment after intensive care unit discharge: a Meta-analysis].
Bibliographic record
Abstract
OBJECTIVE: To investigate the cognitive impairment after intensive care unit (ICU) discharge and provide theoretical basis for prevention and intervention. METHODS: Studies about cognitive impairment after ICU discharge were retrieved in PubMed, Embase, Cochrane Library, Web of Science, Wanfang data, CNKI and SinoMed from their foundation to December 2019. The literature screening and data extraction were performed by two researchers independently, and the quality of different types of researches was evaluated using Cochrane Handbook 5.1.0, Newcastle-Ottawa scale (NOS) and agency for healthcare research and quality criteria (AHRQ). The Meta-analysis was performed by Stata 13.0 software. Sensitivity analysis was used to determine the reliability of the combined effect values. Funnel plot and Egger test were used to analyze publication bias. The non-parametric clipping was used to evaluate the impact of publication bias on the results. RESULTS: A total of 35 studies were enrolled, including 27 prospective cohort studies, 4 retrospective cohort studies, 2 randomized controlled trial (RCT) studies, 1 case-control study, and 1 cross-sectional study. Three literatures were published in Chinese and 32 were in English, which covered 13 countries, and a total of 102 504 ICU survivors were followed up successfully. Literature quality evaluation results showed that the NOS scores of 31 cohort studies were between 6 and 9, of which the case-control study scored 9. The quality grade of 2 RCT studies were both B. According to the AHRQ criteria, 1 cross-sectional study's design was scientifically rigorous and of high quality. Thirty-five studies reported that the overall incidence of cognitive impairment after ICU discharge ranged from 2.47% to 66.07%. For the multiple follow-ups studies, the first survey data was selected for Meta-analysis, and the results showed that the pooled incidence was 38.44% [95% confidence interval (95%CI) was 29.32-47.55]. Each study was removed for sensitivity analysis and the pooled results did not change much, which indicated that the results were reliable. The sub-group analysis was performed on different evaluation methods for cognitive impairment after ICU discharge, different types of ICU patients, and different follow-up time. The results showed that the pooled incidence of studies using neuropsychological test to evaluate cognitive impairment after ICU discharge was 31.42% (95%CI was 21.82-41.02), the pooled incidence of studies using questionnaires or scales was 38.75% (95%CI was 29.54-47.96), and the difference between the two groups was statistically significant (P < 0.01). The pooled incidence of cognitive impairment after ICU discharge in general ICU patients was 43.42% (95%CI was 30.88-55.95), acute respiratory distress syndrome (ARDS) patients' pooled incidence was 34.40% (95%CI was 23.02-45.79), and the pooled incidence of elderly ICU patients was 12.93% (95%CI was 8.48-17.37), the difference among the three groups was statistically significant (P < 0.01). The incidences of cognitive impairment < 1 year, 1 to 4 years, ≥ 5 years after ICU discharge were 43.30% (95%CI was 29.47-57.13), 34.21% (95%CI was 26.70-41.72), and 20.22% (95%CI was 4.89-35.55), respectively, and the differences among the three groups were statistically significant (P < 0.01). The funnel plot showed that the distribution of all studies was asymmetric, and the Egger test result also suggested that there might be publication bias (P < 0.05). The non-parametric clipping was used to estimate the impact of publication bias on the results, and the result showed that the difference in the incidence of cognitive impairment after ICU discharge before and after non-parametric clipping was large, suggesting that publication bias might influence the stability of the research results. CONCLUSIONS: The incidence of cognitive impairment after ICU discharge is relatively high and persistent for a long time, but diagnostic criteria of cognitive impairment and follow-up time are quite different. It is necessary to develop consistent evaluation criteria and rigorous designed research in the further.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".