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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".