Drivers of Inpatient Costs After Colorectal Surgery Within a Publicly Funded Healthcare System
Bibliographic record
Abstract
BACKGROUND: The morbidity and mortality associated with colorectal resections are responsible for significant healthcare use. Identification of efficiencies is vital for decreasing healthcare cost in a resource-limited system. OBJECTIVE: The purpose of this study was to characterize the short-term cost associated with all colon and rectal resections. DESIGN: This was a population-based, retrospective administrative analysis. SETTINGS: This analysis was composed of all colon and rectal resections with anastomosis in Canada (excluding Quebec) between 2008 and 2015. PATIENTS: A total of 108,304 patients ≥18 years of age who underwent colon and/or rectal resections with anastomosis were included. MAIN OUTCOME MEASURES: Total short-term inpatient cost for the index admission and the incremental cost of each comorbidity and complication (in 2014 Canadian dollars) were measured. Cost predictors were modeled using hierarchical linear regression and Monte Carlo Markov Chain estimation. RESULTS: Multivariable regression demonstrated that the adjusted average cost of a 50-year-old man undergoing open colon resection for benign disease with no comorbidities or complications was $9270 ((95% CI, $7146-$11,624; p = <0.001). With adjustment for complications, laparoscopic colon resections carried a cost savings of $1390 (95% CI, $1682-$1099; p = <0.001) compared with open resections. Surgical complications were the main driver for increased cost, because anastomotic leaks added $9129 (95% CI, $8583-$9670; p = <0.001). Medical complications such as renal failure requiring dialysis ($16,939 (95% CI, $15,548-$18,314); p = <0.001) carried significant cost. Complications requiring reoperation cost $16,313 (95% CI, $15,739-$16,886; p = <0.001). The costliest complication cumulatively was reoperation, which exceeded $95 million dollars over the course of the study. LIMITATIONS: Inherent biases associated with administrative databases limited this study. CONCLUSIONS: Medical and surgical complications (especially those requiring reoperation) are major drivers of increased resource use. Laparoscopic colorectal resection with or without adjustment for complications carries a clear cost advantage. There is opportunity for considerable cost savings by reducing specific complications or by preoperatively optimizing select patients susceptible to costly complication. See Video Abstract at http://links.lww.com/DCR/A839.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".