(204) National Trends in the Treatment of Fournier’s Gangrene: 2016-2019
Bibliographic record
Abstract
Abstract Introduction Fournier’s gangrene (FG) is a surgical emergency with significant inpatient mortality and long-term morbidity. Population-based databases such as the National Inpatient Sample (NIS) are valuable tools in investigating rare conditions, yet no recent studies have looked at trends and outcomes of FG at a national level. An increasing burden of this disease may be placed on large academic centers due to regional demographics and hospital transfer to these centers for specialized expertise. Objective To evaluate national trends in the management of FG to identify predictive factors of in-hospital mortality or inter-hospital transfer. Methods All patients hospitalized with FG between 2016-2019 were extracted from the NIS using the International Classification of Diseases (10th Revision) Diagnosis Code N49.3. Stratified cluster sampling was used to create weighted national estimates. The primary combined outcome was the proportion of patients who died during hospitalization or required inter-hospital transfer. Multivariable logistic regression was performed to identify predictors of this outcome. Results Between 2016 and 2019, there were an estimated 21,715 inpatients treated for FG in the US. There was an 8.5% increase in the patients treated yearly over the study period. The average age was 45.8 years, the average length of stay was 10.8 days, and the average total charge of hospitalization was $127,140. Overall, 4.3% of patients died during hospitalization and 7.2% were transferred to another short-term hospital facility. The region with the highest prevalence was the South (44.8%) and most admissions were to urban teaching hospitals (72.6%). Significant predictors of the combined outcome of in-hospital mortality or inter-hospital transfer included older age, non-private insurance, and greater Elixhauser Comorbidity Index (p<0.05). Protective factors included medium/high volume hospital, urban setting (vs. rural), Midwest/Southern region (vs. Northeast), and 2nd or 3rd cost tertile (vs. 1st tertile) (p<0.05) [Table]. Conclusions We highlight an association of poor outcomes from FG with non-private insurance in older adults with more comorbidities. We also noted an increased burden of FG patients at large, urban teaching facilities due to regional demographics and inter-hospital transfer. Further work needs to be done to determine how inter-hospital transfer and potential delays in life-saving surgical debridement affect outcomes in FG. Disclosure No.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".