The weight of the most common biological variables in the prediction of adverse events after adult spinal deformity surgery


Pizones J., Nunez-Pereira S., Haddad S., Gomez-Rice A., Moreno-Manzanaro L., Charles Y. P., ...Daha Fazla

EUROPEAN SPINE JOURNAL, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s00586-026-09923-z
  • Dergi Adı: EUROPEAN SPINE JOURNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet

Özet

Purpose Although patient biological factors have gained importance in predicting complications following adult spinal deformity (ASD) surgery, the individual predictive contribution of each biological factor is still unknown. Methods Retrospective analysis from a multicenter database. Operated ASD patients with minimum 2-year follow-up were included. Independent biological variables were chronological age, BMI, Charlson Comorbidity Index-CCI, and ASD-Frailty Index. Correlations and collinearity (variance inflation factor-VIF) between them were tested. Four different adverse events were predicted: major complications, mechanical complications, reinterventions, readmissions. Univariate analyses and multivariable logistic regression models weighted their predictive ability after min-max normalization. Results 1133 patients were studied. Median age 63 (49; 72), BMI 25.1 (22.1; 28.5), CCI = 3 (2; 5), ASD-FI = 0.39 (0.3; 0.48). VIF < 3 and Spearman-Rho R-2 < 0.18, indicated very low collinearity between age, BMI, ASD-FI, (moderate collinearity between age and CCI; R-2 0.61,VIF 2.8). All biological variables associated with all four adverse events (p < 0.01) in the univariate analyses. For major complications and mechanical complications only age, BMI, and ASD-FI were selected as independent predictors in the regression analysis (p < 0.001). CCI joined them for reinterventions and readmission. The strongest predictor was chronological age, followed by ASD-FI for all models. However, overall PseudoR(2) was modest (< 0.1), with AUC values < 0.64. Conclusion Age, BMI, ASD-FI, and CCI measured different biological domains with very low collinearity. All influenced the occurrence of adverse events, especially chronological age over frailty, but the overall weight in the models was modest. This confirms the importance of biological factors in risk stratification, but highlights the need to identify new markers with greater discriminative power.