Development of a model to predict the probability of incurring a complication during spine surgery.
Complications
Degenerative spine
Patient outcome
Prediction model
Spine surgery
Journal
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
ISSN: 1432-0932
Titre abrégé: Eur Spine J
Pays: Germany
ID NLM: 9301980
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
received:
03
10
2020
accepted:
16
02
2021
pubmed:
10
3
2021
medline:
6
7
2021
entrez:
9
3
2021
Statut:
ppublish
Résumé
Predictive models in spine surgery are of use in shared decision-making. This study sought to develop multivariable models to predict the probability of general and surgical perioperative complications of spinal surgery for lumbar degenerative diseases. Data came from EUROSPINE's Spine Tango Registry (1.2012-12.2017). Separate prediction models were built for surgical and general complications. Potential predictors included age, gender, previous spine surgery, additional pathology, BMI, smoking status, morbidity, prophylaxis, technology used, and the modified Mirza invasiveness index score. Complete case multiple logistic regression was used. Discrimination was assessed using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). Plots were used to assess the calibration of the models. Overall, 23'714/68'111 patients (54.6%) were available for complete case analysis: 763 (3.2%) had a general complication, with ASA score being strongly predictive (ASA-2 OR 1.6, 95% CI 1.20-2.12; ASA-3 OR 2.98, 95% CI 2.19-4.07; ASA-4 OR 5.62, 95% CI 3.04-10.41), while 2534 (10.7%) had a surgical complication, with previous surgery at the same level being an important predictor (OR 1.9, 95%CI 1.71-2.12). Respectively, model AUCs were 0.74 (95% CI, 0.72-0.76) and 0.64 (95% CI, 0.62-0.65), and calibration was good up to predicted probabilities of 0.30 and 0.25, respectively. We developed two models to predict complications associated with spinal surgery. Surgical complications were predicted with less discriminative ability than general complications. Reoperation at the same level was strongly predictive of surgical complications and a higher ASA score, of general complications. A web-based prediction tool was developed at https://sst.webauthor.com/go/fx/run.cfm?fx=SSTCalculator .
Identifiants
pubmed: 33686535
doi: 10.1007/s00586-021-06777-5
pii: 10.1007/s00586-021-06777-5
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1337-1354Références
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