Impact of Invasive Escherichia Coli Disease on Clinical Outcomes and Medical Resource Utilization Among Asian Patients in the United States
Bibliographic record
Abstract
INTRODUCTION: Invasive Escherichia coli disease (IED) can lead to sepsis and death and is associated with a substantial burden. Yet, there is scarce information on the burden of IED in Asian patients. METHODS: This retrospective study used US hospital data from the PINC AI™ Healthcare database (October 2015-March 2020) to identify IED cases among patients aged ≥ 60 years. IED was defined as a positive E. coli culture in blood or other normally sterile body site (group 1 IED) or positive culture of E. coli in urine with signs of sepsis (group 2 IED). Eligible patients with IED were classified into Asian and non-Asian cohorts based on their reported race. Entropy balancing was used to create cohorts with similar characteristics. Outcomes following IED were descriptively reported in the balanced cohorts. RESULTS: A total of 646 Asian and 19,127 non-Asian patients with IED were included (median age 79 years; 68% female after balancing). For both cohorts, most IED encounters had community-onset (> 95%) and required hospitalization (Asian 96%, mean duration 6.9 days; non-Asian 95%, mean duration 6.8 days), with frequent admission to intensive care (Asian 35%, mean duration 3.3 days; non-Asian 34%, mean duration 3.5 days), all standardized differences [SD] < 0.20. Compared to non-Asian patients, Asian patients were more likely to be discharged home (54% vs. 43%; SD = 0.22), and less likely to be discharged to a skilled nursing facility (24% vs. 31%; SD = 0.16). In-hospital fatality rates during the IED encounter were similar across cohorts (Asian 9%, non-Asian 10%; SD = 0.01). Most E. coli isolates showed resistance to ≥ 1 antibiotic (Asian 61%; non-Asian 64%) and 36% to ≥ 3 antibiotic classes (all SD < 0.20). CONCLUSION: IED is associated with a substantial burden, including need for intensive care and considerable mortality, in Asian patients in the USA that is consistent with that observed for non-Asian patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".