Evaluating hemoptysis hospitalizations among patients with bronchiectasis in the United States: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: The burden of hospitalizations and mortality for hemoptysis due to bronchiectasis is not well characterized. The primary outcome of our study was to evaluate in-hospital mortality in patients admitted with hemoptysis and bronchiectasis, as well as the rates of bronchial artery embolization, length of stay, and hospitalization costs. METHODS: The authors queried the Nationwide Inpatient Sample (NIS) claims database for hospitalizations between 2016 and 2017 using the ICD-10-CM codes for hemoptysis and bronchiectasis in the United States. Multivariable regression was used to evaluate predictors of in-hospital mortality, embolization, length of stay, and hospital costs. RESULTS: There were 8240 hospitalizations (weighted) for hemoptysis in the United States from 2016 to 2017. The overall in-hospital mortality was 4.5%, but higher in males compared to females. Predictors of in-hospital mortality included undergoing three or more procedures, age, and congestive heart failure. Bronchial artery embolization (BAE) was utilized during 2.1% of hospitalizations and was more frequently used in those with nontuberculous mycobacteria and aspergillus infections, but not pseudomonal infections. The mean length of stay was 6 days and the median hospitalization cost per patient was USD $9,610. Having comorbidities and procedures was significantly associated with increased length of stay and costs. CONCLUSION: Hemoptysis is a frequent indication for hospitalization among the bronchiectasis population. In-hospital death occurred in approximately 4.5% of hospitalizations. The effectiveness of BAE in treating and preventing recurrent hemoptysis from bronchiectasis needs to be explored.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".