Associations between smoking and clinical outcomes after total hip and knee arthroplasty: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Smoking increases risk of several complications after total hip or knee arthroplasty (THA/TKA), so we systematically reviewed and meta-analyzed the literature to take into account all relevant evidence, particularly studies published since 2010. Methods The PubMed, Ovid Embase, Web of Science, and EBSCOHost databases were searched and studies were selected and analyzed according to MOOSE recommendations. Methodological quality of included studies was assessed using the Newcastle-Ottawa Scale. Data were qualitatively synthesized or meta-analyzed using a random-effects model. Results A total of 40 studies involving 3,037,683 cases were included. Qualitative analysis suggested that smoking is associated with worse patient-reported outcomes within one year after surgery, and meta-analysis showed that smoking significantly increased risk of the following outcomes: total complications ( OR 1.41, 95% CI 1.01–1.98), wound complications ( OR 1.77, 95% CI 1.50–2.10), prosthetic joint infection ( OR 1.84, 95% CI 1.52–2.24), aseptic loosening ( OR 1.62, 95% CI 1.12–2.34), revision ( OR 2.12, 95% CI 1.46–3.08), cardiac arrest ( OR 4.90, 95% CI 2.26–10.60), cerebrovascular accident ( OR 2.22, 95% CI 1.01–4.85), pneumonia ( OR 2.35, 95% CI 1.17–4.74), acute renal insufficiency ( OR 2.01, 95% CI 1.48–2.73), sepsis ( OR 4.35, 95% CI 1.35–14.00), inpatient mortality ( OR 12.37, 95% CI 4.46–34.28), and persistent opioid consumption (OR 1.64, 95% CI 1.39–1.92). Conclusion Smoking patients undergoing THA and TKA are at increased risk of numerous complications, inpatient mortality, persistent opioid consumption, and worse 1-year patient-reported outcomes. Pre-surgical protocols for these outcomes should give special consideration to smoking patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".