(474) Trends and Costs of Ambulatory vs Inpatient Penile Prosthesis Placement
Bibliographic record
Abstract
Abstract Introduction Current literature is limited in assessing nationwide trends of penile prosthesis (PP) placement in the United States largely due to a lack of data on outpatient PP placement. Particularly no nationwide comparison of ambulatory surgery center (ASC) versus inpatient hospital (IH) PP placement has been published. Objective Our objective is to determine the trends of PP placement in ASCs vs IHs utilizing representative nationwide data. Additionally, we examined patient demographics that are associated with the setting for PP placement. Methods Inpatient and outpatient encounters for PP placement between 2016 and 2019 were analyzed using data from the Nationwide Inpatient Sample (NIS) and the Nationwide Ambulatory Surgery Sample (NASS) from the Healthcare Cost and Utilization Project (HCUP). These are the largest publicly available inpatient and ambulatory surgery databases available in the United States. PP operations were identified by CPT and ICD-10-PCS codes. Subsequent analysis was performed using the established weighting provided by the HCUP, which utilizes stratified cluster sampling to create national estimates. Comparisons of demographics were performed using t-tests and chi-square tests with a p-value for significance <0.05. Results Over the study period, there were 67,722 PP placements in the United States. The percentage placed in ASCs was 93.1%. In 2016, 16,290 PP placements occurred, of which 92.3% were placed in ASCs. Comparatively in 2019, there was a 7.3% increase (17,473) in the number of PP placements, with 94.5% placed in ASCs (p<0.01). Over the study period, 33,008 PP placements were performed in the South (93.8% ASCs), 12,481 in the Midwest (94.6% ASCs), 12,192 in the Northeast (92.4% ASCs), and 10,040 in the West (90.1% ASCs, p<0.01). The average age of patients receiving a PP placement was 64.4 in ASCs compared with 62.5 in IHs (p<0.01). The average cost associated with PP placement in ASCs was $25,935, about half the cost of inpatient settings, which averaged $51,594 (p<0.01). Of all PP implants, the primary payer was medicare for 57.1% (38,660; 93.1% ASCs), medicaid for 4.4% (2980; 86.6% ASCs), private for 33.6% (22,778; 94.3% ASCs), and self-pay for 1.1% (768; 90.9% ASCs, p <0.01). Conclusions Our results suggest that the number of PP placements is consistently rising each year, with an increasing majority of PP placements performed in ASCs. The procedure costs roughly half on average when performed in ASCs compared to IHs. Further investigation is needed to compare complication rates and outcomes of PP placements in ASC versus IH. Disclosure No
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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 | 0.000 | 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".