Risk factors for rehospitalization within 90 days in patients with total joint replacement: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: The risk factors influencing the readmission within 90 days following total joint replacement (TJR) are complex and heterogeneous, and few systematic reviews to date have focused on this issue. METHODS: Web of Science, Embase, PubMed, and Chinese National Knowledge Infrastructure databases were searched from the inception dates to December 2022. Relevant, published studies were identified using the following keywords: risk factors, rehospitalization, total hip replacement, total knee replacement, total shoulder replacement, and total joint replacement. All relevant data were collected from the studies that meet the inclusion criteria. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: Of 68,336 patients who underwent TJR, 1,269,415 (5.4%) were readmitted within 90 days. High American Society of Anesthesiologists (ASA) class (OR, 1.502; 95%CI:1.405-1.605; P < .001), heart failure (OR,1.494; 95%CI: 1.235-1.754; P < .001), diabetes (OR, 1.246; 95%CI:1.128-1.377; P < .001), liver disease (OR, 1.339; 95%CI:1.237-1.450; P < .001), drinking (OR, 1.114; 95%CI:1.041-1.192; P = .002), depression (OR, 1.294; 95%CI:1.223-1.396; P < .001), urinary tract infection (OR, 5.879; 95%CI: 5.119-6.753; P < .001), and deep vein thrombosis (OR, 10.007; 95%CI: 8.787-11.396; P < .001) showed statistically positive correlation with increased 90-day readmissions after TJR, but high blood pressure, smoking, and pneumonia had no significant association with readmission risk. CONCLUSION: The findings of this review and meta-analysis will aid clinicians as they seek to understand the risk factors for 90-day readmission following TJR. Clinicians should consider the identified key risk factors associated with unplanned readmissions and develop strategies to risk-stratify patients and provide dedicated interventions to reduce the rates of readmission and enhance the recovery process.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.065 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".