Toward Standardized Performance Metrics in the Cardiovascular ICU: A Systematic Review of Quality Indicators
Bibliographic record
Abstract
The cardiovascular intensive care unit (CVICU) requires robust quality indicators (QIs) to standardize performance measurement and improve patient outcomes. However, heterogeneity in QI definitions, measurement tools, and implementation practices persists. This systematic review synthesizes evidence on CVICU QIs, evaluates their methodological rigor, and proposes a framework for standardization. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we searched PubMed, Embase, Scopus, Web of Science, and CINAHL for relevant studies. Eight studies met the inclusion criteria, encompassing retrospective cohorts, predictive models, and mixed-methods designs. Quality assessment employed the Newcastle-Ottawa Scale (NOS) for cohort studies and the Mixed Methods Appraisal Tool (MMAT) for non-randomized studies. Narrative synthesis categorized QIs by Donabedian domains (structure, process, outcome). Included studies (n=8) predominantly focused on outcome QIs (5/8 studies), particularly mortality prediction using machine learning. Risk of bias was moderate to high, with most studies lacking prospective validation or objective measurements. Structural QIs were especially underrepresented, and although Delphi methods were employed, they lacked external validation and reproducibility, limiting generalizability. Process QIs relied on subjective surveys, while structural QIs lacked robust measurement frameworks. Alignment with Donabedian and Institute of Medicine (IOM) frameworks was reported in 6/8 studies, yet consistency in application was limited. CVICU QIs prioritize outcome measurement through artificial intelligence (AI)-driven tools but lack standardization in the development, validation, and operationalization of process and structural indicators. Future work should (1) validate predictive models in multicenter, prospective settings, (2) develop objective and reproducible process metrics, and (3) expand structural QIs for global applicability, accounting for resource constraints, variability in infrastructure, and cultural differences in care delivery. Given the limited number of studies, findings should be interpreted cautiously and considered hypothesis-generating rather than definitive. This review informs efforts to harmonize CVICU performance measurement.
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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 | 0.129 | 0.341 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.033 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".