Costs variations for percutaneous nephrolithotomy in the U.S. from 2003–2015: A contemporary analysis of an all-payer discharge database
Bibliographic record
Abstract
Introduction: We sought to evaluate population-based cost variations and predictors of outlier costs for percutaneous nephrolithotomy (PCNL) in the U.S. Methods: Using the Premier Healthcare Database, we identified all patients diagnosed with kidney/ureter calculus who underwent PCNL from 2003–2015. We evaluated 90-day direct hospital costs, defining high- and low-cost surgery as those >90th and <10th percentile, respectively. We constructed a multilevel, hierarchical regression model and calculated the pseudo-R2 of each variable, which translates to the percentage variability contributed by that variable on 90-day direct hospital costs. Results: A total of 114 581 patients underwent PCNL during the 12-year study period. Mean cost in the low-cost group was $5787 (95% confidence interval [CI] 5716–5856) vs. $38 590 (95% CI 37 357–39 923) in the high-cost group. Cost variations were substantially impacted by patient (63.7%) and surgical (18.5%) characteristics and less so by hospital characteristics (3.9%). Significant predictors of high costs included more comorbidities (≥2 vs. 0: odds ratio [OR] 1.81; p=0.01) and hospital region (Northeast vs. Midwest: OR 2.04; p=0.03). Predictors of low cost were hospital bed size of 300–499 beds (OR 1.35; p<0.01) and urban hospitals (OR 2.77; p=0.01). Factors less likely to be associated with lowcost PCNL were more comorbidities (Charlson Comorbidity Index [CCI] ≥2: OR 0.69; p<0.0001), larger hospitals (OR 0.61; p=0.01), and teaching hospitals (OR 0.33; p<0.0001). Conclusions: Our contemporary analysis demonstrates that patient and surgical characteristics had a significant effect on costs associated with PCNL. Poor comorbidity status contributed to high costs, highlighting the importance of patient selection.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".