Body Mass Index and the Risk of Postoperative Complications After Total Knee Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: Given the growing prevalence of obesity, it is crucial to understand the effect of obesity on complications after total knee arthroplasty (TKA). This study aims to assess the relationship between body mass index (BMI) and postoperative periprosthetic joint infection (PJI), medical complications, and surgical complications after TKA. METHODS: The Premier Healthcare Database was used to identify all primary elective TKAs between 2016 and 2021. The primary outcome was risk of PJI within 90 days of surgery. Using logistic regression, restricted cubic splines were generated to assess the relationship between BMI as a continuous variable and PJI risk. Bootstrap simulation was then done to identify a BMI inflection point on the final restricted cubic spline model past which the risk of PJI increased. The relationship between BMI and composite 90-day medical and surgical complications was also assessed. RESULTS: A direct relationship was observed between increasing BMI and increasing risk of PJI with a BMI changepoint of 31 kg/m 2 identified as being associated with increased risk. Above a BMI of 31 kg/m 2 , there was an average relative risk increase of PJI of 13.6% for every unit BMI. This relative risk per unit BMI increased from 5.8% for BMI 31 to 39 to 11.5% between BMI 40 and 49 kg/m 2 , and 21.3% for BMIs ≥50 kg/m 2 . Similarly, a direct relationship was also found between increasing BMI and both medical and surgical complications with BMI changepoints of 34 and 32 kg/m 2 identified, respectively. DISCUSSION: Obese patients with a BMI >31 kg/m 2 were at increased risk of PJI. Although the relative risk increase was small per unit BMI above 31 kg/m 2 , the cumulative increase in risk may be marked for patients with higher BMIs. CONCLUSION: These data should be used to inform discussions that involve shared decision making between patients and surgeons who weigh the risks and benefits of surgery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".