Mitigating Medical Adverse Events Following Spinal Surgery: The Effectiveness of a Postoperative Quality Improvement (QI) Care Bundle
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Spine surgery is associated with a high incidence of postoperative medical adverse events (AEs). Many of these events are considered "minor" though their cost and effect on outcome may be underestimated. We sought to examine the clinical and cost-effectiveness of a postoperative quality improvement (QI) care bundle in mitigating postoperative medical AEs in adult surgical spine patients. METHODS: We collected 14-year prospective observational interrupted time series (ITS) with two historical cohorts: 2006 to 2008, pre-implementation of the postoperative QI care bundle; and 2009 to 2019, post-implementation of the postoperative QI care bundle. Adverse Events were identified and graded (Minor I and II) using the previously validated Spine AdVerse Events Severity (SAVES) system. Pearson Correlation tested for changes across patient and surgical variables. Adjusted segmented regression estimated the effect of the postoperative QI care bundle on the annual and absolute incidences of medical AEs between the two periods. A cost model estimated the annual cumulative cost savings through preventing these "minor" medical AEs. RESULTS: We included 13,493 patients over the study period with a mean of 964 per year (SD ± 73). Mean age, mean Charlson Comorbidity Index (CCI), and mean spine surgical invasiveness index (SSII) increased from 48.4 to 58.1 years; 1.7 to 2.6; and 15.4 to 20.5, respectively (p < 0.001). Unadjusted analysis confirmed a significant decrease in the annual number of all medical AEs (p < 0.01). When adjusting for age, CCI and SSII, segmented regression demonstrated a significant absolute reduction in the annual incidence of cardiac, pulmonary, nausea and medication-related AEs by 9.58%, 7.82%, 11.25% and 15.01%, respectively (p < 0.01). The postoperative QI care bundle was not associated with reducing the annual incidence of delirium, electrolyte levels or GI AEs. Annual projected cost savings for preventing Grade I and II medical AEs were $1,808,300 CAD and $11,961,500 CAD. CONCLUSION: Postoperative QI care bundles are effective for improving patient care and preventing medical care-related AEs, with significant cost savings. Postoperative QI care bundles should be tailored to the specific vulnerability of the surgical population for experiencing AEs.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 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".