COPD Patient’s Outcomes following Total Knee Arthroplasty—An Analysis of the National Inpatients Sampling (2016–2020)
Bibliographic record
Abstract
Introduction: Total knee arthroplasty (TKA) is a common elective procedure aimed at improving patients’ quality of life. Patients undergoing this procedure can have a wide variety of comorbidities, including chronic obstructive pulmonary disease (COPD). Several studies demonstrated a higher risk of postoperative complications for this patient population. In this study, we examined the mortality risk of this group of patients, as well as the length of stay (LOS) and general costs. Methods: This study is a retrospective, case–control study. Using the National Inpatients Sampling (NIS) database, we defined a cohort of adults who received their inpatient primary TKA between 1 January 2016 and 31 December 2020. Preoperative variables include age, sex, race, primary payer, hospital location, and hospital type. Outcomes examined in this study include overall patient mortality as a primary outcome. Secondary outcomes include total LOS (in days) and inpatient costs in the United States (in USD). Results: A total of 2,835,499 patients who underwent TKA procedure in the United States were included. A total of 173,230 (6.1%) COPD patients were included in the COPD group. The mortality rate in the COPD group (0.1%) was more than three times higher than the control group (0.03%, p-value < 0.001). Patients in the COPD group had a longer in-hospital length of stay (2.76) compared to the control group (2.31, p-value < 0.001) and a higher treatment cost (average value of treatment per patient) (USD 69,386) compared to the control group (USD 64,446, p-value < 0.001). We also found higher mortality risk for patients older than 60 and patients of white ethnicity. Conclusion: COPD patients undergoing TKA have a higher mortality rate and this issue should be addressed in order to improve patient care and outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".