ROBOTICSS - Robotic-Assisted Pedicle Screw Placement in Spine Surgery: A Multicenter Prospective Observational Study
Conditions: Pedicle Screw Fixation of Spine
Sponsor: Uppsala University

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Synopsis 
Pedicle screw placement is a key component in spinal procedures such as fusions, deformity 
correction, and fracture repair. Accurate screw positioning is critical for spinal stability and to 
avoid complications like neural or vascular injury. Misplacement is a leading cause of early 
reoperations. 
Traditional freehand techniques vary in accuracy, while navigation and robotic-assisted systems 
offer enhanced accuracy. Robotic systems may also support minimally invasive approaches and 
improve surgical planning and workflow. Despite promising early results, evidence remains 
limited regarding their impact on outcomes and cost-effectiveness. Accordingly, 
“Medicintekniska produktrådet, Sveriges Kommuner och Landsting” (“Medical Devices Products 
council, Swedish Association for Local Authorities and Regions”) has advised against their 
routine use in Sweden. 
This multicenter, observational non-randomized study includes patients of all ages undergoing 
surgery with pedicle screws for a spinal disorder. The intervention group receives robotic-
assisted screw placement, while controls undergo non-robotic-assisted (freehand or navigation-
assisted) surgery. Outcomes include accuracy, surgery time, reoperations, complications, 
patient-reported outcomes, precision, hospital stay, and cost-effectiveness. All hospitals 
performing spine surgery in Sweden will participate.  
The study has three main objectives, studied in three separate substudies: To study screw 
placement accuracy, learning curve, and reoperations.  
The following protocol is based on the Standard Protocol Items: Recommendations for 
Interventional Trials (SPIRIT) checklist [1].  
 1. Title 
ROBOTICSS – Robotic-Assisted Pedicle Screw Placement in Spine Surgery: A Multicenter 
Prospective Observational Study 
Date: 2025-12-01. 
 2. Trial Registration 
This study will be registered at clinicaltrials.gov after approval of the Swedish Ethical Review 
Authority, which was received 2026-02-02.   
3. Protocol Version 
1.01. 
 4. Funding 
The study is currently unfunded. Funding will be applied for. Treatment is part of standard 
clinical care and does not require funding. Funding will be needed to monitor, oversee data 
collection, analyze data and finalize reports.  

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5. Roles and Responsibilities 
Principal Investigators: Paul Gerdhem and Freyr Gauti Sigmundsson   
Co-investigators: Anders Joelson, Lukas Bobinski, Nikos Schizas, Catharina Parai, Anastasios 
Charalampidis, Ludvig Vavruch, Ted Eneqvist, Håkan Löfgren, Tobias Kyrk, Erik Vestberg, Kardo 
Raouf.   
Collaborating Institutions: Uppsala University, Uppsala University Hospital, Örebro University, 
Örebro University Hospital, Umeå University, Umeå University Hospital, Karolinska Institutet, 
Karolinska University Hospital, Södersjukhuset, Gothenburg University, Sahlgrenska University 
Hospital, Linköping University, Linköping University Hospital, Lund University, Skåne University 
Hospital, Länssjukhuset Ryhov, Jönköping county, Länssjukhuset Kalmar, Kalmar.  
Study coordinators: Catharina Strömstedt, and others to be appointed. 
Roles and responsibilities for each investigator and study coordinator. 
All investigators are responsible for study introduction, and to oversee data collection, and 
arrange any necessary hospital permits.  
 6. Background and Rationale 
Surgical techniques have continuously evolved, with advances enabling more precise and 
effective interventions. A cornerstone in addressing spinal instability or deformity is pedicle 
screw fixation—widely used in procedures for deformities, fractures, and degenerative 
conditions requiring stabilization. 
Achieving high accuracy in pedicle screw placement is critical. Misplaced screws can lead to 
complications such as neural injury, vascular damage, implant loosening, and instrumentation 
failure—often necessitating revision surgery [2]. Accuracy is therefore essential to reduce risks 
and improve outcomes. 
Improvements such as intraoperative fluoroscopy and advanced navigation have enhanced 
screw placement accuracy while reducing the need for extensive anatomical exposure. This shift 
toward minimally invasive surgery has reduced surgical trauma and recovery time, supporting a 
broader trend toward optimizing patient outcomes through smaller incisions and improved 
techniques [3]. 
Robotic-assisted surgery (RAS) represents a further step in this evolution. These systems, which 
use a navigated robotic arm to assist screw placement, aim to enhance accuracy and reduce 
variability—which may be especially beneficial for less experienced surgeons. RAS may also 
streamline intraoperative workflow and support minimally invasive approaches. While early 
results are promising, there remains limited evidence on the broader clinical benefits, especially 
during the early learning phase of implementation [4]. Robotic-assisted spine surgery is not 
sanctioned by the Swedish Association of Authorities and Regions, unless performed in clinical 
trials. 

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This study aims to evaluate the clinical introduction of robotic systems in spine surgery, focusing 
on their impact on reoperations, screw placement accuracy, complications, outcome, and 
intraoperative workflow. 
 7. Objectives 
This study has three main objectives, analysed in three different substudies: To study screw 
placement accuracy, learning curve, and reoperations.  
Secondary Objectives: To assess complications, patient-reported outcomes, length of stay, 
surgical time, and cost-effectiveness. 
 8. Trial Design 
Multicenter, non-randomized observational cohort study with consecutive patient inclusion. 
 9. Study Setting 
All hospitals in Sweden performing spinal surgery will participate. 
 10. Eligibility Criteria 
Inclusion Criteria:   
- Treatment with pedicle screws in the cervical, thoracic, lumbar spine and/or sacrum. 
- All ages and spinal diagnoses 
Exclusion Criteria:   
- Treatment without pedicle screws 
Consent 
-All patients will be treated according to each hospital's routine. Patient consent will therefore 
not be needed.  
 11. Interventions 
Experimental Group: Robotic-assisted pedicle screw insertion using available equipment. 
Currently available systems are Brainlab Cirq and Mazor Robotics, but others systems may be 
used. 
Control Group: Patients undergoing non-robotic-assisted (freehand or navigation-assisted) 
pedicle screw placement. 
 12. Outcomes 
 
Primary objectives and outcomes 
Substudy 1 primary outcome - Screw placement accuracy (binary per-patient outcome) 
- Proportion of patients in which at least one screw is not accurately placed.  

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Substudy 2 primary outcome- Learning curve (surgical time) 
-To evaluate when time in surgery is no longer decreasing in robotic-assisted surgery.  
Substudy 3 primary outcome - Reoperation rate at 2 years  
-The primary outcome is the occurrence of reoperations of any cause within 2 years from the 
index surgery in patients undergoing robotic-assisted surgery compared to non-robotic-assisted 
(navigation-assisted or free hand) surgery.    
Secondary Outcomes 
- Intraoperative (screw placement precision, including comparisons preoperative and 
postoperative positioning for screws and rods, surgical time; skin-to-skin as well as wheel-in 
wheel-out time, estimated patient radiation exposure, intraoperative bleeding)   
- Postoperative outcomes (length of stay, patient-reported outcome measures (PROMs), time to 
return to work)   
- Cost-effectiveness and procedural resource use   
- Complications (care related infections, thrombosis, emboli, cardiovascular, mortality)   
 
Table 1 
Table over data collection and source.  
 
Radiographic data 
Reoperatio
ns 
Intraoperative metrics 
Postoperative 
outcomes; 
complications, PROMs 
Data 
source 
PACS 
SPOR, 
Swespine, 
NPR 
SPOR/Surg planning 
program/Electronic 
health records 
Swespine, SFR, SPOR, 
Electronic health 
records 
Data origin 
Radiology 
department 
Surgeon, 
Anesthesiol
ogist, 
Hospital 
Surgeon/Anesthesiolo
gist 
Patient 
Need for 
separate 
assessment 
Yes, radiographic 
assessment by the 
investigators 
No 
No 
No 
Time 
points for 
data 
collection 
Preoperative, 
intraoperative, 
postoperative, up to 
2 years 
Postoperati
ve, within 2 
years 
Intraoperative 
Preop and up to 2 years 
postoperative 
Swespine: Swedish Spine Register 
SFR: Swedish Fracture Register 
SPOR: Swedish PeriOperative Register 
 

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 13. Participant Timeline 
Figure 1. Participant timeline. Data will be collected up to 5 years postoperative.  
 
 
 
 14. Sample size 
Substudy 1- Primary outcome- Screw placement accuracy (binary per-patient outcome)  
Accurate screw placement is defined as a pedicle screw that remains entirely within the cortical 
boundary of the pedicle, or with up to 2 mm breach of the pedicle wall (corresponds to 
Gertzbein-Robbins Grade A and B) [5]. To evaluate screw placement accuracy, we will define a 
binary per-patient outcome: whether at least one screw is not accurately placed. 
Based on previous literature, robotic-assisted systems may achieve clinically acceptable 
accuracy per screw (Gertzbein-Robbins Grade A and B) of approximately 97%, while navigated 
systems achieve approximately 93% [6], and freehand 84-90% [7, 8].   
Assuming a minimum of 4 screws per patient, the estimated probability of at least one non-
clinically acceptable screw is 12% in the robotic group and 25% in the navigated and free hand 
group. 
Using a two-sided alpha of 0.05, 80% power, and a 1:6 group ratio, the required sample size is 
71 patients in the robotic-assisted surgery group and 426 patients in the non-robotic-assisted 
surgery group, for a total of 497 patients. We added patients to 75 in the robotic-assisted 
surgery group, for a total of 501 patients, to be able to include an equal number of patients 
from Örebro, Umeå and Uppsala. This estimate is conservative, as most patients are expected to 
receive more than 4 screws, which would increase the likelihood of detecting placement 
differences.  
The primary analysis will use either mixed-effects logistic regression or logistic regression with 
cluster-robust standard errors to account for intra-patient correlation of screw outcomes. 
A secondary analysis of robot-assisted surgeries after the first 501 patients may be performed.  

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Substudy 2 - Primary outcome - Learning curve (surgical time) 
To evaluate the learning curve associated with robotic-assisted surgery, we will model operative 
time as a function of case number. Operative time is expected to decrease with experience, then 
plateau. We will use mixed-effects piece-wise linear regression with the surgeon as a random 
effect to estimate the case number at which operative time levels off. We will look both at 
wheel-in wheel-out time (primary), and skin-to-skin time (secondary). We expect to do this 
analysis after each center has performed 100 robot-assisted spine surgeries, in total 300 
surgeries. 
We have not conducted a traditional power analysis for this substudy, as the objective is not to 
test a binary hypothesis but to estimate when operative time stabilizes. If no clear inflection 
point is observed, this will be interpreted as evidence that the learning curve is either very short 
(already completed), very gradual, or inconsistent. Such a result would itself be informative, and 
will require us to compare operative times with today’s standard of care, navigated or free hand 
surgery.   
Substudy 3- Primary outcome – Reoperation rate at 2 years 
The primary outcome is the occurrence of reoperations of any cause within 2 years from the 
index surgery. The expected reoperation rate in the non-robotic group is likely to be higher than 
3-5% over a 2-year period [9–11], and may in the robotic-assisted group be 1% or less [12]. A 
difference of 2 percentage points or greater is considered clinically meaningful. Power analyses 
(80% power, alfa 0.05) have been made in two scenarios, with reoperation rates of 3% vs 1%, or 
5% vs 3%. We estimate a ratio of 1:6 for robotic-assisted surgery vs navigation-assisted or free 
hand surgeries. 
Comparing 3% and 1% reoperation rates, 420 robotic-assisted surgery patients and 2,520 non-
robotic surgery patients are needed (yielding a total sample size of 2,940 patients). In case 
reoperation rates are 5% and 3%, 866 robotic-assisted surgery patients and 5,196 non-robotic 
surgery patients are needed (yielding a total sample size of 6,062 patients). An increased 
proportion of robotic-assisted surgeries or a larger difference in reoperation rates will decrease 
the total sample size. 
An estimated 1800 patients per year are treated with pedicle screw instrumentation at the 
hospitals, totaling 7,200 patients in 4 years. If we after 4 years deem that the sample size will 
not be reached in a reasonable time frame, the study will be stopped. 
No patients will be lost to follow-up, as outcomes will be ascertained through linkage with 
national quality, health and surgical registries, requiring no active participation from patients or 
clinical teams beyond standard registration. If a patient moves abroad before the 2-year period 
has come to an end, their existing data will be used. 

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To reduce confounding by indication in this non-randomized design, we will apply both 
propensity score matching (PSM) and inverse probability weighting (IPW). The propensity score 
will be estimated using logistic regression, incorporating relevant baseline covariates including 
age, sex, BMI, smoking status, diagnosis, number of levels fused, revision status, surgical 
approach (minimally invasive vs. open), interbody device use, and fixed effects for hospital site. 
In the primary analysis, 1:2 nearest-neighbor matching without replacement will be used, with a 
caliper of 0.2 standard deviations of the logit of the propensity score. Covariate balance after 
matching will be evaluated using standardized mean differences (SMDs), with SMD < 0.1 
considered acceptable. Outcome analysis will be performed using Cox proportional hazards 
regression with robust standard errors to account for matched pairs. 
As a secondary analysis, IPW will be used to create a pseudo-population in which treatment 
assignment is independent of observed covariates. Stabilized weights will be applied, and 
outcome analysis will be conducted using weighted regression models, adjusted for baseline 
covariates to enhance robustness. 
Although both methods are suitable for dealing with moderate imbalances in group size, it is 
important to note that reducing the size of the non-robotic group may limit the number of 
matches available during PSM and could increase variance in the IPW analysis if extreme 
weights arise. These risks will be mitigated through careful diagnostics, including evaluation of 
match yield, standardized mean differences, and weight distributions. Overlap weighting or 
trimming will be considered if weight instability is detected. 
While the study is powered at 80% based on the reduced cohort, adjusted analyses using 
propensity score methods may yield a slightly reduced effective sample size due to matching 
inefficiency or variance inflation. However, both approaches are expected to preserve sufficient 
power to detect the prespecified effect size, given the projected event rate and available sample 
size. 
Multiple imputation will be used to handle missing baseline data, and all patients will be 
included in the final analysis. An increased proportion of robotic surgeries relative to non-
robotic surgeries will decrease the overall sample size requirement to detect the same effect. 
Secondary outcome - Screw placement precision 
We will evaluate screw placement precision as the deviation between preoperative planning 
and actual placement in robotic-assisted surgery cases. For each screw, linear deviation (at the 
tip and tail) perpendicular from the planned position, to the actual position, and angular 
deviation between the planned position and the actual position will be measured. This 
comparison will be made on an overlay of the preoperative planning made on a preoperative 
computed tomography, and compared with the computed tomography performed intra- or 
postoperative for assessment of pedicle screw position.  

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Stratified comparisons (e.g., by spinal level or surgeon experience) may be performed to identify 
patterns of deviation. We plan to use the first 60 cases in the study in each of 3 centers that 
perform robotic-assisted spine surgery for this analysis, and for comparison divide them into a 
first (n=30*3) and second group (n=30*3), in all 180 cases.  
Other secondary outcomes 
Other complications 
Thromboembolic, cardiovascular complications and mortality will be assessed as secondary 
safety outcomes. These events are rare in spine surgery, and although the study is not powered 
to detect small differences in such outcomes, comparisons between robotic and non-robotic 
cohorts will be reported.  
Other outcomes 
Length of stay, time to return to work will be described, but no power calculation has been 
made. 
Health economic analysis 
A health economic analysis will be performed and resource use described. No power calculation 
has been made.  
 
Patient reported outcomes (PROMs) 
Patient reported outcomes will be collected through the Swedish Spine Register (Swespine) and 
the Swedish Fracture Register (SFR). Response rates at 1 year is around 75-80%. Response rates 
in the SFR are lower. The majority of patients will be registered in Swespine (only fracture 
patients are registered in the SFR). Depending on diagnoses different questionnaires are used. 
The only questionnaires in Swespine and SFR that are distributed irrespective of diagnoses are 
EQ-VAS running from 0 (worst) to 100 (best possible health) and EQ-5D. Other questionnaires 
are Oswestry Disability Index (ODI), Scoliosis Research Society-22revised (SRS-22r), Early Onset 
Scoliosis Questionnaire-24 (EOSQ-24) and Short Musculoskeletal Function Assessment (SMFA), 
but are only answered by subpopulations. Several studies indicate that 2 year data does not 
result in additional information compared to 1 year data, so our primary analysis will use 1 year 
data, which are available in both Swespine and SFR. 
Continuous outcome analysis 
EQ-VAS will be analyzed as a continuous variable. We consider a 5-point difference between 
groups as clinically relevant at the group level, corresponding to a small-to-moderate effect size 
(Cohen’s d appr. 0.25), with an expected standard deviation of 20 points, power 80% and alfa 

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0.05. The minimum required number of answering patients in the robotic-assisted group would 
be 147 and in the non-robotic-assisted group 882. We will exceed this number and achieve a 
quite reliable estimate if all patients are included in this analysis, also when considering that 
30%-40% may not respond to questionnaires in Swespine and SFR.   
PASS (Patient Acceptable Symptom State) 
We will report the proportion of patients reaching a Patient Acceptable Symptom State (PASS), 
defined as EQ-VAS ≥75 for elective cases and ≥65 for non-elective cases. A PASS threshold of 10 
percentage point difference in the proportion achieving PASS is considered meaningful.  The 
minimum required number of answering patients in the robotic-assisted group would be 226 
and in the non-robotic-assisted group 1,356 if power is 80% and alfa 0.05. We expect the sample 
size to provide sufficient power to detect any difference. 
15. Recruitment 
Patients will be included consecutively at each site during the enrollment window which is 
planned to be four years (2025–2029). Currently robotic-assisted spine surgery is available in 
two hospitals (Umeå and Örebro), with a third hospital waiting for their equipment to be 
installed (Uppsala). If other hospitals install robotic-assisted surgery during the study period this 
will just result in more patients involved in the robotic-assisted arm of the trial, which would 
result in a potentially faster inclusion of the needed amount of patients. We still believe that 
during the study period more patients will be operated with non-robotic-assisted methods than 
with robotic-assisted methods.  
16–17. Allocation and blinding 
No randomization or blinding will be performed. If a planned robotic-assisted surgery is 
abandoned after the start of surgery, the patient will be included in the robotic-assisted group 
(i.e intention to treat principle). 
 18. Data collection methods 
Data will be collected through national registries (Swespine, the Swedish Fracture Register, the 
Swedish PeriOperative Register, hospital systems (electronic health records, surgical planning 
software, Picture Archiving and Communication System; PACS and others). PROMs will be 
recorded preoperatively, and at follow-ups (Swespine: 1, 2 and 5 years; the Swedish Fracture 
Register: 1 year). Images will be retrieved using standardized CT protocols and assessment will 
be made by at least two researchers independently. 
 19. Data management 
Data will be collected and managed in compliance with GDPR. Regular validation and updates 
will occur every 6–12 months. Final datasets will be stored at Uppsala University for a minimum 
of 15 years. 
 20. Statistical methods 
Statistical analysis will compare outcomes using appropriate parametric or non-parametric tests, 

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including survival methods. Baseline characteristics will be adjusted for using multivariable 
models. Adjustment variables include age, diagnosis, comorbidities, previous spine surgery, 
number of levels, number of screws, robotic system type, use of navigation or free-hand surgery 
and hospital. 
Analysis is currently planned to be performed in R. Interim analyses will be performed when half 
of the patients have been included. Sensitivity analyses will be performed as described earlier.  
 21. Data monitoring 
If funding is available a formal Data Monitoring Committee will be appointed. All data are 
collected within the regular health care system which means that the data is subject to regular 
clinical monitoring as part of standard practice. Reporting of deviations (clinical practice or 
complications) will be done within regular hospital systems.  
 22. Harms 
Harms will be tracked via registry data and clinical records. These include infections, 
reoperations, and other adverse events, including mortality. 
 23. Auditing 
Study sites may be subject to independent audits by the hospital, external parts contracted by 
the hospital or governmental agencies. Records will be maintained in compliance with Good 
Clinical Practice. 
 24. Ethics Approval 
Ethical approval will be obtained from the Swedish National Ethical Review Authority prior to 
initiation. 
 25. Protocol Amendments 
All protocol changes will be documented. If protocol changes require submission for ethical 
approval this will be done prior to implementation, except in urgent safety situations. 
 26. Consent and confidentiality 
Informed consent is not required since all interventions are standard of care. After data 
extraction and before analyses, all data will be pseudonymized and handled in accordance with 
national privacy legislation. 
 27. Declaration of interests 
Paul Gerdhem has received lecture fees from DePuySynthes, Johnson and Johnson Medtech (to 
himself). Gauti Sigmundsson has received lecture fees from DePuySynthes, Johnson and Johnson 
Medtech (to himself). Lukas Bobinski has received teaching honoraria and a research grant from 
Medtronic. All other investigators and collaborators declare they have no conflict of interest.  
 28. Access to data 
The research group (investigators and collaborators) will have access to the final dataset. 

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 29. Ancillary and post-trial care 
No specific post-trial care beyond standard clinical and national quality and health register-
based follow-up is planned. 
 30. Dissemination policy 
The results will be submitted for peer-reviewed publication and presented at scientific 
conferences. Summary findings will be posted in public trial registries. 
31. Use of generative AI 
Generative AI (Chat GPT 4o) has been used for assistance in coding the preliminary power 
analyses and for grammar correction. Power analyses has then independently been recalculated 
by statistician Jonas Selling at “Statistikakademin”, Uppsala, Sweden. 
Figure 2. Study timeline. Years are indicated.  
 
 
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Gertzbein SD, Robbins SE (1990) Accuracy of Pedicular Screw Placement In Vivo: Spine 
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