Predictors of Mortality from Spontaneous Soft-Tissue Hematomas in a Large Multicenter Cohort Who Underwent Percutaneous Transarterial Embolization
Bibliographic record
Abstract
Purpose To evaluate the safety and efficacy of percutaneous transarterial embolization (PTAE) for the treatment of spontaneous soft-tissue hematomas (SSTHs) and identify variables predictive of short-term outcome. Materials and Methods Between 2011 and 2017, the outcome was retrospectively analyzed for 112 patients (mean age ± standard deviation, 72 years ± 14; range, 28–92 years), including 65 women (mean age, 73 years ± 12.7; range, 39–92 years) and 47 men (mean age, 70 years ± 14.9; range, 28–91 years), with SSTH treated with PTAE. Thirty-day mortality, technical and clinical success, simplified acute physiology score (SAPS) II, anticoagulation, embolic agent, hematoma volume and location, serum hemoglobin level, hemodynamic instability, and presence of active bleeding at CT and/or angiography were recorded. Clinical success was defined as cessation of bleeding as determined by hemodynamic stability and/or serum hemoglobin level stabilization after PTAE. Univariable and multivariable analyses were performed by using a Cox model to identify variables associated with time to death. Results Mortality rate was 26.8% (30 of 112 patients), angiographic success rate was 95.5% (107 of 112 patients), and clinical success rate was 83% (93 of 112 patients). For surviving patients, mean SAPS II was 19.6 ± 7.1 (range, 13–31) and mean hematoma volume was 862 cm3 ± 618 (range, 238–1887 cm3). For deceased patients, mean SAPS II was 42 ± 13.2 (range, 18–63) and mean hematoma volume was 1419 cm3 ± 788 (range, 251–3492 cm3). SAPS II (P < .001), hematoma volume (P = .01), and retroperitoneal location (P = .01) were independently associated with fatal outcome. Conclusion Percutaneous transarterial embolization is effective for the emergency treatment of spontaneous soft-tissue hematomas. Simplified acute physiology score II, hematoma volume, and retroperitoneal location are predictors of short-term outcome. © RSNA, 2019 Online supplemental material is available for this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".