The LACE+ Index as a Predictor of 30-Day Patient Outcomes in a Plastic Surgery Population: A Coarsened Exact Match Study
Bibliographic record
Abstract
BACKGROUND: This study used coarsened exact matching to investigate the effectiveness of the LACE+ index (i.e., length of stay, acuity of admission, Charlson Comorbidity Index, and emergency department visits in the past 6 months) predictive tool in patients undergoing plastic surgery. METHODS: Coarsened exact matching was used to assess the predictive ability of the LACE+ index among plastic surgery patients over a 2-year period (2016 to 2018) at one health system (n = 5744). Subjects were matched on factors not included in the LACE+ index such as duration of surgery, body mass index, and race, among others. Outcomes studied included emergency room visits, hospital readmission, and unplanned return to the operating room. RESULTS: Three hundred sixty-six patients were matched and compared for quarter 1 to quarter 4 (n = 732, a 28.2 percent match rate); 504 patients were matched for quarter 2 to quarter 4 (n = 1008, a 36.7 percent match rate); 615 patients were matched for quarter 3 to quarter 4 (n = 1230, a 44.8 percent match rate). Increased LACE+ score significantly predicted readmission within 30 days for quarter 1 versus quarter 4 (1.09 percent versus 4.37 percent; p = 0.019), quarter 2 versus quarter 4 (3.57 percent versus 7.34 percent; p = 0.008), and quarter 3 versus quarter 4 (5.04 percent versus 8.13 percent; p = 0.028). Higher LACE+ score also significantly predicted 30-day reoperation for quarter 3 versus quarter 4 (1.30 percent versus 3.90 percent; p = 0.003) and emergency room visits within 30 days for quarter 2 versus quarter 4 (3.17 percent versus 6.75 percent; p = 0.008). CONCLUSION: The results of this study demonstrate that the LACE+ index may be suitable as a prediction model for patient outcomes in a plastic surgery population. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".