Predictors and Health Care Utilization of Sepsis Post-Ureteroscopy in a U.S.-Based Population: Results from the Endourological Society TOWER Collaborative
Bibliographic record
Abstract
Purpose: To investigate the incidence, predictive factors, and health care utilization of sepsis post-ureteroscopy (URS) in patients enrolled in commercial insurance plans. Materials and Methods: A retrospective claims analysis was conducted using the IBM ® MarketScan ® commercial database. Patients ≥18 years were included if they had URS between January 2015 and October 2019 and developed sepsis within 30 days of URS. Multivariate logistic regression was used to identify various clinical and demographic predictors of sepsis post-URS. All-cause health care utilization (i.e., inpatient admissions and intensive care unit [ICU] stays) and all-cause health care costs up to 1 month post-septic event were measured. Results: Among the 104,100 URS patients meeting the inclusion criteria, 5.5% developed sepsis. Patients with diabetes (odds ratio [OR] = 1.52; p < 0.0001), older age (age 55–64 vs 18–34; OR = 1.35; p < 0.0001), baseline sepsis (OR = 3.51; p < 0.0001), baseline inpatient visits (OR = 1.17; p = 0.0012), and higher Elixhauser comorbidity scores (OR = 1.09; p < 0.0001) had a significantly higher likelihood of developing sepsis post-URS. In septic patients, 94.8% required inpatient care and 35% were admitted to the ICU. Mean hospital stay for septic patients was 6.86 days. Average all-cause health care cost per patient at 1 month in the septic cohort was $49,625 vs $17,782 in the nonseptic cohort indicating an incremental all-cause cost of $31,843 ( p < 0.0001). Conclusions: A total of 5.5% of commercially insured patients undergoing URS developed sepsis post-URS. Diabetes, older age, baseline sepsis, baseline inpatient visit, and higher comorbidity score were all found to be independent predictors of post-URS sepsis. Patients with sepsis post-URS had higher health care utilization and costs indicating that sepsis is both a significant clinical and economic event.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".