Abstract 229: Burden And Predictors Of Sepsis-associated Cardiac Arrest: A National Inpatient Sample Analysis, 2018
Bibliographic record
Abstract
Background: Sepsis-induced myocardial dysfunction with the resultant cardiomyopathy carries a high risk of mortality. We aimed to study the risk factors of cardiac arrest (CA) in Sepsis-related hospitalizations (SRH). Methods: We identified SRHs using the National Inpatient Sample (2018) and ICD10 codes to categorized them into with vs without CA. We then compared baseline characteristics and performed multivariate analysis adjusting for confounders to identify predictors of sepsis-associated CA. Results: Of SRH (1,345,595) in 2018, 0.8% (11,365) had a CA (Table1) . SRH with CA often had elderly (median age 70 vs 66 years), males (55.3% vs 48.8%), blacks (19.6% vs 13.3%), Hispanics (12.3 vs 11.7%), Medicare enrollees (69.9 vs 59.1%), and had patients from lower-income households (LIH, 36.9% vs 30.7%) than non-CA cohort. Statistically significant predictors for CA in SRH were age (5% increased risk every 5 years), male sex (aOR 1.28, 95CI 1.16-1.4), black (aOR 1.49, 95CI 1.3-1.7) & Hispanic (aOR 1.26, 95CI 1.09-1.45) race, LIH (aOR 1.31, 95CI 1.13-1.52), CHF (aOR 2.4, 95CI 2.16-2.68), pulmonary circulation disorder (aOR 2.14, 95CI 1.72-2.66), prior cardiac arrest (aOR 1.95, 95CI 1.16-3.27), coagulopathy (aOR 1.69, 95CI 1.5-1.9), alcohol abuse (aOR 1.42 95CI 1.17-1.74), PVD (aOR 1.36, 95CI 1.17-1.58), CKD (aOR 1.26, 95CI 1.14-1.39), cancer without metastasis (aOR 1.49, 95CI 1.24-1.8) and with metastasis (aOR 1.24, 95CI 1.01-1.52). Urban non-teaching vs rural (aOR 1.32, 95CI 1.1-1.57) and Southern vs Northeast hospitals (aOR 1.26, 95CI 1.09-1.46) showed higher odds of CA. Conclusion: SRH associated CA had high mortality with prevalent demographic and regional disparities, evident from black and Hispanic, males, patients from LHI and Southern hospitals revealing a higher risk of sepsis-associated CA. Congestive heart failure, pulmonary disease, prior cardiac arrest, coagulopathy, alcohol abuse, PVD, CKD, and cancers were the strongest predictors of CA in SRH.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".