Integrating intraoperative physiology data into outcome analysis for the <scp>ACS</scp> Pediatric National Surgical Quality Improvement Program
Bibliographic record
Abstract
BACKGROUND: The Pediatric National Surgical Quality Improvement Program (P-NSQIP) samples surgical procedures for benchmarking and quality improvement. While generally comprehensive, P-NSQIP does not collect intraoperative physiologic data, despite potential impact on outcomes. AIMS: The aims of this study were (a) to describe a methodology to augment P-NSQIP with vital signs data and (b) demonstrate its utility by exploring relationships that intraoperative hypothermia and hypotension have with P-NSQIP outcomes. METHODS: Vital signs from 2012 to 2016 were available in a research databank. Episodes of hypotension and hypothermia were extracted and recorded alongside local P-NSQIP data. Multivariable regression analyses were performed to explore associations with undesired outcomes, including: surgical site infection, wound disruption, unplanned return to the operating room, and blood transfusion. Model variables were selected with the Akaike information criterion using 2012-2014 as the training set and validated with receiver operating characteristics analysis using 2015-2016 as the testing set. RESULTS: Data from 6737 patients were analyzed, with 43.9% female, median [interquartile range] age 5.8 [1.3-12.4] years, undergoing procedures lasting 118 [75-193] minutes. Hypothermia, observed in 45% of cases, was associated with wound disruption (odds ratio 1.75, 95% CI 1.1-2.83). Hypotension, observed in 60% of cases, was associated with unplanned returns (odds ratio 1.58, 95% CI 1.02-2.51), and transfusions (odds ratio 1.95, 95% CI 1.14-3.52). Surgical site infection, wound disruption, unplanned return, and transfusion models had areas under the receiver operating characteristic curve of 0.69/0.67, 0.59/0.63, 0.78/0.79, and 0.92/0.93 for validation models including hypothermia/hypotension respectively. CONCLUSION: Adding intraoperative vital signs to P-NSQIP data allowed identification of two modifiable risk factors: hypothermia was associated with increased wound disruption, and hypotension with increased blood transfusions and unplanned returns to the operating room. These findings may motivate prospective studies and prompt other centers and P-NSQIP to augment outcome data with intraoperative physiological data.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".