Integration of the Intrinsic Surgical Risk With Patient Comorbidities and Severity of Congenital Cardiac Disease Does Not Improve Risk Stratification in Children Undergoing Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: The objective of this study is to estimate the surgical risk of noncardiac procedures on the incidence of 30-day mortality in children with congenital heart disease. METHODS: Children with congenital heart disease undergoing noncardiac surgery from 2012 to 2016 and included in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Pediatric database were included in the derivation cohort, while the 2017 database was used as a validation cohort. Intrinsic surgical risk quartiles were built utilizing 30-day mortality rates for each Current Procedural Terminology code and relative value units to create 2 groups defined as low surgical risk (quartiles 1-3) and high surgical risk procedures (quartile 4). We used multivariable logistic regression to determine the predictors for 30-day mortality including patient comorbidities and intrinsic surgical risk. A partially external validation of the model was performed using the 2017 version of the database. RESULTS: We included 37,658 children with congenital heart disease undergoing noncardiac surgery with an incidence of overall 30-day mortality of 1.7% in the derivation cohort and 1.5% in the validation cohort (n = 13,129). Intrinsic surgical risk of procedures represented by Current Procedural Terminology procedural codes and relative value units risk quartiles was significantly associated with 30-day mortality (unadjusted P < .001). Predicted probability of 30-day mortality ranges from 0.2% (95% confidence interval [CI], 0.2-0.2) with no comorbidities to 39.6% (95% CI, 23.2-56.0) when all comorbidities were present among high surgical risk procedures and from 0.3% (95% CI, 0.3-0.3) to 54.8% (95% CI, 39.4-70.1) among low surgical risk procedures. An excellent discrimination was reported for the multivariable model with area under the curve (AUC) of 0.86 (95% CI, 0.85-0.88). High surgical risk was not associated with increased odds of 30-day mortality after adjustment for all other predictors (adjusted odds ratio [OR]: 0.75, 95% CI, 0.62-0.91). We also estimated the discriminative ability of a model that does not include the surgical risk (0.86 [95% CI, 0.84-0.88], with P value for the direct comparison of the AUC of the 2 models = 0.831). The multivariable model obtained from an external validation cohort reported an optimism corrected AUC of 0.88 (95% CI, 0.85-0.91). CONCLUSIONS: Our study demonstrates that integration of intrinsic surgical risk to comorbidities and severity of cardiac disease does not improve prediction of 30-day mortality in children undergoing noncardiac surgery. In children with congenital heart disease, patient comorbidities, and severity of the cardiac lesion are the predominant predictors of 30-day mortality.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".