Abstract 18298: 10-Year Trends of In-Hospital Outcomes of Patients That Underwent Percutaneous Coronary Intervention for Cardiogenic Shock Complicating Acute Coronary Syndrome
Bibliographic record
Abstract
Background: Cardiogenic shock complicating acute coronary syndrome (CS-ACS) is a life-threatening condition and percutaneous coronary intervention (PCI) remains a fundamental approach in its management. In recent years, notable advancements have emerged, including the utilization of mechanical circulatory support (MCS) devices and an enhanced understanding of patient selection for PCI. This study aims to assess the temporal trends in patient background and outcomes of patients who underwent PCI for CS-ACS. Methods: The Keio interhospital Cardiovascular Studies (KiCS)-PCI registry prospectively collects approximately 200 variables, which were defined in accordance with the National Cardiovascular Data Registry (NCDR), from the major teaching hospitals in Tokyo metropolitan area in Japan. Between 2009 and 2019, 24,671 consecutive cases were registered, 10,053 patients (40.7%) undergoing PCI for ACS, of which 870 patients (8.5%) had CS-ACS. We stratified CS-ACS patients into 4-time frame groups (T1: 2009-2011, T2: 2012-2013, T3: 2014-2016, and T4: 2017-2019), and examined the temporal trends in patient risk profiles using the NCDR score (ranging 0-100, reflecting predicted mortality rates). Additionally, we assessed the incidence and types of employed MCS as well as the in-hospital mortality rate. Results: During the study period, the proportion of CS-ACS remained stable, accounting for 8.0% in T1 to 8.2% in T4 of ACS-related PCI cases. The calculated NCDR risk score demonstrated minimal variation, ranging from 70.6 in T1 to 70.1 in T4. There was a notable decrease in the utilization of MCS (66.5% in T1 to 56.7% in T4), mainly driven by a reduction in the use of intra-aortic balloon pump (70.3% to 55.0%, p for trend<0.001), with a increase in the concomitant use of veno-arterial-extracorporeal membrane oxygenation (27.6% to 36.2%, p for trend<0.001). However, the in-hospital mortality rate for CS-ACS patients remained persistently high at 30%. Conclusion: Over the past decade, there have been notable changes in the peri-procedural management of CS-ACS, albeit no significant improvements in patient outcomes was observed. The result calls for additional efforts to enhance their management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".