Associated Factors of Cognitive Impairment in Chronic Heart Failure: a Systematic Review
Bibliographic record
Abstract
Background Cognitive impairment (CI) is a common complication of chronic heart failure (CHF) , which may significantly increase the risk of poor prognosis, so early identification of associated factors of CI in CHF is of great significance. Although there have been many relevant studies recently, their conclusions are inconsistent. Objective To perform a systematic review of the influencing factors of CI in CHF. Methods In August 2021, studies relevant to influencing factors of CI among patients with CHF were searched in databases including PubMed, Embase, The Cochrane Library, Web of Science, CINAHL, PsychINFO, CNKI, Wanfang Data, CQVIP, and SinoMed from inception to August 2021. Two researchers independently screened studies based on the inclusion and exclusion criteria, extracted data, and performed risk of bias assessment using the Newcastle-Ottawa Scale and The Agency for Healthcare Research and Quality methodology checklist, then conducted a descriptive analysis of the factors associated with CI in CHF. RevMan 5.3 was adopted for meta-analysis. Results Fourteen studies were included, involving 6 324 cases of CHF, and 1 753 of them also with CI. Descriptive analysis indicated that five factors decreased the risk of CI in CHF, and 22 factors increased the risk, but the influence of sex and systolic blood pressure on CI is still far from inclusive. Meta-analysis demonstrated that education level〔OR=0.45, 95%CI (0.30, 0.70) 〕, age〔OR=1.17, 95%CI (1.10, 1.24) 〕, diabetes〔OR=2.17, 95%CI (1.17, 4.01) 〕, anemia〔OR=3.03, 95%CI (1.80, 5.10) 〕and left ventricular ejection fraction〔OR=0.91, 95%CI (0.88, 0.94) 〕were associated with CI in CHF. Conclusion High education level lowered the risk of CI in CHF, while older age, diabetes, anemia and decreased left ventricular ejection fraction increased the risk. Due to limited number and quality of included studies, the above-mentioned conclusion still needs to be verified by more high-quality studies.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".