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Record W4403633401 · doi:10.1056/evidoa2300323

Prognostic Factors Associated with Mortality in Cardiogenic Shock — A Systematic Review and Meta-Analysis

2024· review· en· W4403633401 on OpenAlexaff
Richard G. Jung, Cameron Stotts, Arnav Gupta, Graeme Prosperi‐Porta, Shan Dhaliwal, Pouya Motazedian, Omar Abdel‐Razek, Pietro Di Santo, Simon Parlow, Emilie P. Belley‐Côté, Alexandre Tran, Sean van Diepen, Lee Harel-Sterling, Vineet Goyal, Melissa Fay Lepage-Ratte, Rebecca Mathew, Jacob C. Jentzer, Susanna Price, Srihari S. Naidu, Mir B. Basir, Navin K. Kapur, Holger Thiele, F. Daniel Ramirez, George Wells, Bram Rochwerg, Shannon M. Fernando, Benjamin Hibbert

Bibliographic record

VenueNEJM Evidence · 2024
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of AlbertaMcMaster UniversityLakeridge HealthImpactOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsCardiogenic shockMeta-analysisMedicineSystematic reviewInternal medicineCardiologyIntensive care medicineMEDLINEMyocardial infarctionBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Cardiogenic shock remains highly associated with early mortality, with mortality often exceeding 50%. We sought to determine the association between prognostic factors and in-hospital and 30-day mortality in cardiogenic shock. METHODS: We performed a systematic review and meta-analysis of prognostic factors in cardiogenic shock, searching MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials for records up to June 5, 2023. English-language studies that investigated prognostic factors and in-hospital and/or 30-day mortality in cardiogenic shock were included. Studies were excluded if they evaluated the pediatric population, were postmortem studies, or included fewer than 100 patients. The primary aim was to identify modifiable and non-modifiable prognostic factors associated with in-hospital and 30-day mortality in cardiogenic shock. RESULTS: We identified 160 studies, including 2,459,703 patients with a median in-hospital mortality of 41.4% (interquartile range, 33.6% to 49.2%). The majority were retrospective cohort studies. Patient factors potentially associated with an increase in early mortality included an age greater than or equal to 75 years of age, peripheral arterial disease, chronic kidney disease, and female sex. Procedural and presentation factors potentially associated with increased mortality included out-of-hospital cardiac arrest, left main culprit artery, left ventricular ejection fraction less than 30%, dialysis, and need for mechanical circulatory support. Revascularization in the form of coronary artery bypass graft and percutaneous coronary intervention were potentially associated with reduced in-hospital mortality. CONCLUSIONS: This analysis quantifies the association between patient, presentation, and treatment-related factors and early mortality in cardiogenic shock. Increased certainty in the association of these prognostic factors with cardiogenic shock outcomes can aid in clinical risk assessment, development of risk tools, and analysis of clinical trials.

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)0.011
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.031
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.224
GPT teacher head0.358
Teacher spread0.135 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations44
Published2024
Admission routes1
Has abstractyes

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