Survival with optimal medical management in a cohort of severe necrotizing bacterial lung infections
Bibliographic record
Abstract
Background: Necrotizing pneumonia and lung gangrene represent a continuum of severe lung infection. Traditionally, severe cases have been referred for surgical debridement. However, this has been linked to high mortality. Some groups have published encouraging results using a conservative medical approach. Unfortunately, lack of a standardized definition of necrotizing pneumonia has precluded meaningful comparison between medical and surgical approach in severe cases. Our objective was to describe the outcome of a cohort of severe necrotizing pneumonia treated with optimal medical management. Methods: We conducted an observational retrospective study by reviewing charts and radiology records of patients hospitalized between 2006-2019 in a tertiary center. We included all patients with severe necrotizing infection, defined as a necrotizing cavity involving at least 50% of a lobe, or smaller multilobar cavities. We made no distinction between necrotizing pneumonia and gangrene as there are no standardized criteria. Results: A total of 50 consecutive patients were included. On imaging, 42% had multilobar cavities and mean diameter of the largest cavity in each case was 5.9 cm. 50% required mechanical ventilation (median duration 12 days) and 44% needed vasopressors. Four patients (8%) had decortication surgery, while none underwent lung resection. Four patients (8%) died. The extent of infiltrates and number of cavities were not associated with mortality but the extent of infiltrates was associated with risk of intubation (P=0.004). Conclusions: We presented one of the largest series of medically-treated severe necrotizing lung infections in the pre-coronavirus disease-2019 (COVID-19) era. The overwhelming majority of patients recovered with optimal medical management alone. Our results strongly support avoiding pulmonary resection in patients with severe necrotizing bacterial lung infections.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".