Cognition and coronary events: A narrative overview of neurocognitive impairment in ACS patients
Bibliographic record
Abstract
Neurocognitive dysfunction is a common but often overlooked complication in patients with Acute Coronary Syndrome (ACS). This narrative review aims to provide a comprehensive synthesis of current evidence on the prevalence, mechanisms, clinical assessment, and management strategies of cognitive impairment in ACS patients. We highlight how neurocognitive deficits including memory loss, reduced attention, and executive dysfunction arise from cerebral hypoperfusion, systemic inflammation, microvascular injury, and post-infarct metabolic stress. These deficits arise from mechanisms including cerebral hypoperfusion, systemic inflammation, microvascular injury, and post-infarct metabolic stress. Such impairments are associated with poorer clinical outcomes, decreased treatment adherence, and increased mortality. Routine cognitive assessment remains absent from standard ACS management, despite the availability of effective tools such as the Montreal Cognitive Assessment (MoCA), which can detect subtle cognitive deficits early in hospitalization. Integrating cognitive screening into clinical protocols enables timely interventions and better patient stratification. Management strategies should combine pharmacological treatment of cardiovascular risk factors with non-pharmacological interventions such as cognitive rehabilitation, mental health support, and lifestyle modification. Multidisciplinary collaboration between cardiology, neurology, psychology, and rehabilitation specialists is essential to address both cardiac and cognitive recovery. By integrating findings from clinical and epidemiological studies, this review underscores the need for routine cognitive screening, multidisciplinary care, and innovative interventions such as telemedicine to improve patient outcomes. Recognizing cognitive health as an integral part of ACS management offers a more holistic, patient-centered approach to recovery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".