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Record W4401461070 · doi:10.1161/jaha.123.032671

Impact of Chronic Kidney Disease on the Processes of Care and Long‐Term Mortality of Non–ST‐Segment–Elevation Myocardial Infarction: A Nationwide Cohort Study and Long‐Term Follow‐Up

2024· article· en· W4401461070 on OpenAlexaff
Nicholas Weight, Saadiq Moledina, Mohsin Ullah, Harindra C. Wijeysundera, Simon Davies, Nicholas Chew, Claire Lawson, Safi U. Khan, Chris P Gale, Muhammad Rashid, Mamas A. Mamas

Bibliographic record

VenueJournal of the American Heart Association · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersBirmingham Biomedical Research CentreDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsMedicineInternal medicineMyocardial infarctionKidney diseaseHazard ratioPercutaneous coronary interventionCardiologyPopulationProportional hazards modelConfidence interval

Abstract

fetched live from OpenAlex

Background A growing population of patients with chronic kidney disease (CKD) presents with non–ST‐segment–elevation myocardial infarction, although little is known about their longer‐term mortality. Methods and Results Using the MINAP (Myocardial Ischaemia National Audit Project) registry, linked to Office for National Statistics mortality data, we analyzed 363 559 UK patients with non–ST‐segment–elevation myocardial infarction, with or without CKD. Cox regression models were fitted, adjusting for baseline demographics. Compared with patients without CKD, patients with CKD were less frequently prescribed P2Y12 inhibitors (89% versus 86%, P <0.001) less likely to undergo invasive angiography (67% versus 41%, P <0.001) or percutaneous coronary intervention (41% versus 25%, P <0.001), and were less often referred to cardiac rehabilitation (80% versus 66%, P <0.001). Following non–ST‐segment–elevation myocardial infarction, patients with CKD had higher risk of 30‐day (adjusted hazard ratio [HR], 1.24 [95% CI, 1.20–1.29], 1‐year 1.47 [95% CI, 1.44–1.51]) and 5‐year mortality 1.55 (95% CI, 1.53–1.58) than patients without CKD (all P <0.001). Risk of mortality over the entire study period was highest in CKD Stage 5 (HR, 2.98 [95% CI, 2.87–3.10]), even after excluding mortality ≤30 days (HR, 3.03 [95% CI, 2.90–3.17]) ( P <0.001). There was no significant difference in proportion of deaths attributable to cardiovascular disease at 30 days (CKD; 76% versus no CKD; 76%), or 1 ‐year (CKD; 62% versus no CKD; 62%). Conclusions Patients with CKD were significantly less likely to receive invasive investigation or undergo percutaneous coronary intervention and had significantly higher risk of short‐ and longer‐term mortality. Risk of mortality increased with reducing CKD stage. Cardiovascular disease was the main cause of mortality in patients with CKD, but at comparable rates to the general population with non–ST‐segment–elevation myocardial infarction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.305
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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