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Record W4403814772 · doi:10.1097/qmh.0000000000000488

Mitigating Medical Adverse Events Following Spinal Surgery: The Effectiveness of a Postoperative Quality Improvement (QI) Care Bundle

2024· article· en· W4403814772 on OpenAlex
Eryck Moskven, Michael Craig, Daniel Banaszek, Tom Inglis, Lise Bélanger, Eric C. Sayre, Tamir Ailon, Raphaële Charest-Morin, Nicolas Dea, Marcel F. Dvorak, Charles G. Fisher, Brian Kwon, Scott Paquette, Dean R. Chittock, Donald Griesdale, John Street

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQuality Management in Health Care · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of British ColumbiaResearch Canada
Fundersnot available
KeywordsMedicineAdverse effectObservational studyIncidence (geometry)SurgeryEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.800

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.493
Teacher spread0.416 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it