Erik M. Holzer, Emin Aghayev, Dave O’Riordan, Tamas F. Fekete, Dezső J. Jeszenszky, Daniel Haschtmann, Francois Porchet, Frank S. Kleinstueck, Tim Pigott, Everard Munting, Andrea Luca, Anne F. Mannion

January 2021, Volume 30, Issue 1, pp 1 -12 Original Article Read Full Article 10.1007/s00586-020-06651-w

First Online: 24 November 2020

Validation of a surgical invasiveness index in patients with lumbar spinal disorders registered in the Spine Tango registry


Being able to quantify the invasiveness of a surgical procedure is important to weigh up its associated risks, since invasiveness governs the blood loss, operative time and likelihood of complications. Mirza et al. (Spine (Phila Pa 1976) 33:2651–2661, 2008) published an invasiveness index for spinal surgery. We evaluated the validity of a modified version of the Mirza invasiveness index (mMII), adapted for use with registry data.


A cross-sectional analysis was performed with data acquired from the Spine Tango registry including 21,634 patients. The mMII was calculated as the sum of six possible interventions on each vertebral level: decompression, fusion and stabilization either on anterior or posterior structures. The association between the mMII and blood loss, operative time and complications was evaluated using multiple regression, adjusting for possible confounders.


The mean (± SD) mMII was 3.9 ± 5.0 (range 0–40). A 1-point increase in the mMII was associated with an additional blood loss of 12.8% (95% CI 12.6–13.0; p < 0.001) and an increase of operative time of 10.4 min (95% CI 10.20–10.53; p < 0.001). The R2 for the blood loss model was of 43% and for operative time, 47%. The mean mMII was significantly (p < 0.001) higher in patients with surgical complications (4.5 ± 5.6) and general medical complications (6.5 ± 7.0) compared to those without (3.8 ± 4.9). Our results were comparable to those reported in the original publication of Mirza et al.


The mMII appeared to be a valid measure of surgical invasiveness in our study population. It can be used in predictor models and to adjust for surgical case-mix when comparing outcomes in different studies or different hospitals/surgeons in a registry.

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