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Abstract 4345278: Association of Hospital Shock Center Level with In-Hospital Outcomes in Cardiogenic Shock: An Analysis of the Nationwide Readmissions Database

2025· article· en· W4415791501 on OpenAlexaff
Shubhadarshini Pawar, Kannu Bansal, J. Dawn Abbott, Jason N. Katz, David M. Dudzinski, Sean van Diepen, Michael A. Solomon, Van‐Khue Ton, Saraschandra Vallabhajosyula

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsImpellaCardiogenic shockPropensity score matchingMyocardial infarctionOdds ratioTrauma centerShock (circulatory)Multinomial logistic regression

Abstract

fetched live from OpenAlex

Background: Patients with cardiogenic shock (CS) have high in-hospital mortality. Regionalized systems of care, modeled after trauma and ST-segment-elevation myocardial infarction networks, have been proposed to improve outcomes. Expert consensus frameworks classify CS centers based on mechanical circulatory support (MCS) capabilities, but national outcome data remain limited. This study assessed the association between hospital shock center designation and clinical outcomes. Methods: Adults (≥18 y) hospitalized with a primary or secondary diagnosis of CS were identified from the Nationwide Readmissions Database (2016–2022). Hospitals were stratified annually into four CS center levels using procedural codes: Level 1 (≥1 durable left ventricular assist device or cardiac transplantation [LVAD/HTx] transplant case), Level 1A (extracorporeal membrane oxygenation, Impella 5.5 or TandemHeart capable without durable LVAD/HTx), Level 2 (percutaneous coronary intervention [PCI]-capable hospitals offering intra-aortic balloon pump [IABP] or Impella CP/RP), and Level 3 (non-PCI, non-MCS hospitals with ICU-level care only). Outcomes included in-hospital mortality, 30-day readmissions, MCS use, length of stay (LOS), and cost. Multinomial overlap propensity and hierarchical regression models were used to adjust and analyze outcomes. Results: Among 623,835 CS hospitalizations, 33.8% occurred at Level 1, 49.5% at Level 1A, 11.8% at Level 2, and 4.8% at Level 3 centers. After propensity weighting, baseline characteristics were well balanced across shock center levels. In-hospital mortality increased across levels: unadjusted 29.5% at Level 1, 38.4% at Level 1A, 41.1% at Level 2, and 45.2% at Level 3; adjusted analyses vs. Level 1: odds ratio 1.33 [95% CI, 1.29–1.38] for Level 1A; 1.44 [95% CI, 1.38–1.50] for Level 2; 1.63 [95% CI, 1.54–1.71] for Level 3; all p<0.001 (Figure 1). MCS use was highest at Level 1 centers (26.1%) and lowest at Level 2 centers (10.4%). Thirty-day readmission rates were lower at lower-level centers compared to Level 1. LOS and costs were highest at Level 1 centers. Pre-specified subgroup analyses showed consistent survival benefit favoring higher-level centers. Conclusion: In a large national study, higher tiers of CS centers were associated with improved short-term outcomes independent of patient comorbidity and acuity supporting the need for regionalization of CS care.

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)0.006
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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.247
Teacher spread0.235 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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