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Record W4399442750 · doi:10.1002/ehf2.14853

Decoding Cardiogenic Shock: Assessing Shock Index and Its Variants as Prognostic Indicators for In-Hospital Mortality

2024· article· en· W4399442750 on OpenAlexafffund
Luigi Colarusso, Darshan H. Brahmbhatt, Fernando Luís Scolari, Kristine A. Keon, Emily H. Shin, Ava Isabella De Pellegrin Overgaard, Mahrukh Nisar, Nicole Fung, Narmin Ibrahimova, Filio Billia, Christopher B. Overgaard, Adriana Luk

Bibliographic record

VenueESC Heart Failure · 2024
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsSouthlake Regional Health CenterUniversity of TorontoTed Rogers Centre for Heart ResearchUniversity Health Network
FundersUniversity of TorontoHeart and Stroke Foundation of Canada
KeywordsCardiogenic shockMedicineReceiver operating characteristicInternal medicineShock (circulatory)CardiologyIncidence (geometry)Retrospective cohort studyMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Cardiogenic shock (CS) is associated with high in-hospital mortality. Objective assessment of its severity and prognosis is paramount for timely therapeutic interventions. This study aimed to evaluate the efficacy of the shock index (SI) and its variants as prognostic indicators for in-hospital mortality. METHODS: A retrospective study involving 1282 CS patients were evaluated. Baseline patient characteristics, clinical trajectory, hospital outcomes, and shock indices were collected and analysed. Receiver operating characteristic (ROC) curves were employed to determine the predictive accuracy of shock indices in predicting in-hospital mortality. RESULTS: Of those evaluated, 866 (67.6%) survived until discharge. Non-survivors were older (66.0 ± 13.7 vs. 57.4 ± 16.2, P < 0.001), had a higher incidence of cardiac risk factors, and were more likely to present with acute coronary syndrome (33.4% vs. 16.1%, P < 0.001) and out-of-hospital cardiac arrest (11.3% vs. 5.3%, P < 0.001). All mean shock indices were significantly higher in non-survivors compared with survivors. ROC curves demonstrated that adjusted shock index (ASI), age-modified shock index (AMSI), and shock index-C (SIC) had the highest predictive accuracy for in-hospital mortality, with AUC values of 0.654, 0.667, and 0.659, respectively. Subgroup analysis revealed that SIC had good predictive ability in patients with STEMI (AUC: 0.714) and ACS (AUC: 0.696) while AMSI and ASI were notably predictive in the OHCA group (AUC: 0.707 and 0.701, respectively). CONCLUSIONS: Shock index and its variants, especially ASI, AMSI, and SIC, may be helpful in predicting in-hospital mortality in CS patients. Their application could guide clinicians in upfront risk stratification. SIC, ASI, and AMSI show potential in predicting in-hospital mortality in specific CS subsets (STEMI and OHCA). This is the first study to evaluate SI and its variants in CS patients.

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)0.005
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.324
Teacher spread0.303 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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