Generic versus disease-specific adverse event reporting: a comparison of the NSQIP and SAVES databases for the identification of acute care adverse events in adult spine surgery
Bibliographic record
Abstract
OBJECTIVE: The accurate identification and reporting of adverse events (AEs) is crucial for quality improvement. A myriad of AE systems are utilized. There is a lack of understanding of the differences between prospective versus retrospective, disease-specific versus generic, and point-of-care versus chart-abstracted systems. The objective of this study was to compare the benefits and limitations between the prospective, disease-specific, point-of-care Spine Adverse Events Severity System (SAVES) and the retrospective, generic, and chart-abstracted National Surgical Quality Improvement Program (NSQIP) for the identification and reporting of AEs in adult patients undergoing spinal surgery. METHODS: The authors conducted an observational ambidirectional cohort study of adult patients undergoing spine surgery other than for trauma between 2011 and 2019 in a quaternary spine center. Patients were identified using Current Procedural Terminology codes in the NSQIP database and matched using unique medical record numbers to their corresponding record in SAVES. The incidence of AEs and per-patient AEs as recorded in NSQIP and SAVES was the primary outcome of interest. Comparable AEs were identified by matching NSQIP AEs to equivalent ones in SAVES. Chi-square tests were used to test for significant differences in the incidence of overall and comparable AEs between the databases. RESULTS: There were 2198 patients identified in NSQIP, of whom 2033 also had complete records in SAVES. SAVES identified 5342 individual AEs in 1484 patients (73%) compared with 1291 individual AEs in 807 patients (39.7%) with the NSQIP database (p < 0.001). SAVES identified 250 intraoperative and 422 postoperative spine-specific AEs that NSQIP did not record. NSQIP captured a greater number of AEs beyond 30 days, including prolonged length of stay > 30 days, unplanned readmission, unplanned reoperation, and death later than 30 days after surgery compared with SAVES. CONCLUSIONS: SAVES captures a greater incidence of peri- and intraoperative spine-specific AEs than NSQIP, while NSQIP identifies a greater number of AEs beyond 30 days. While a prospective, disease-specific, point-of-care AE system such as SAVES is specific for guiding quality improvement in spine surgery, it incurs greater time and financial costs. Conversely, a retrospective, generic, and chart-abstracted system such as NSQIP provides equivocal cross-institutional comparability with reduced time and financial costs. Specific contextual and aim-specific needs should guide the choice and implementation of an AE system.
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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 | 0.043 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".