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Victor E. Staartjes and Marc L. Schröder

OBJECTIVE

Recently, objective functional tests have generated interest since they can supplement an objective dimension to clinical assessment. The five-repetition sit-to-stand (5R-STS) test is a quick and objective tool that tests movements frequently used in everyday life. The aim of this prospective study was to evaluate the validity and reliability of the 5R-STS test in patients with degenerative pathologies of the lumbar spine.

METHODS

Patients and healthy volunteers completed the standardized 5R-STS, Roland-Morris Disability Questionnaire (RMDQ), Oswestry Disability Index (ODI), visual analog scale (VAS) for back and leg pain, and EQ-5D for health-related quality of life (HRQOL). To assess convergent validity, the 5R-STS test times were correlated with these questionnaires.

RESULTS

Overall, 157 patients and 80 volunteers were enrolled. Direct correlation with RMDQ (r = 0.49), ODI (r = 0.44), and VAS for back pain (r = 0.31) and indirect correlation with the EQ-5D index (r = −0.41) were observed (p < 0.001). The 5R-STS test showed no correlation with VAS for leg pain and EQ-5D VAS (p > 0.05). In 119 individuals, the 5R-STS test demonstrated excellent test-retest reliability with an intraclass correlation coefficient of 0.98. The upper limit of normal, distinguishing patients with and without objective functional impairment, was identified as 10.35 seconds. A severity stratification classified patients with test times of 10.5–15.2, 15.3–22.0, or greater than 22.0 seconds as having mild, moderate, or severe functional impairment, respectively.

CONCLUSIONS

The 5R-STS test is a simple and effective tool to describe objective functional impairment. A patient able to perform the test in 10.4 seconds can be considered to have no relevant objective functional impairment.

Clinical trial registration no.: NCT03303300 (clinicaltrials.gov)

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Marc L. Schröder and Victor E. Staartjes

OBJECTIVE

The accuracy of robot-guided pedicle screw placement has been proven to be high, but little is known about the impact of such guidance on clinical outcomes such as the rate of revision surgeries for screw malposition. In addition, there are very few data about the impact of robot-guided fusion on patient-reported outcomes (PROs). Thus, the clinical benefit for the patient is unclear. In this study, the authors analyzed revision rates for screw malposition and changes in PROs following minimally invasive robot-guided pedicle screw fixation.

METHODS

A retrospective cohort study of patients who had undergone minimally invasive posterior lumbar interbody fusion (MI-PLIF) or minimally invasive transforaminal lumbar interbody fusion was performed. Patients were followed up clinically at 6 weeks, 12 months, and 24 months after treatment and by mailed questionnaire in March 2016 as a final follow-up. Visual analog scale (VAS) scores for back and leg pain severity, Oswestry Disability Index (ODI), screw revisions, and socio-demographic factors were analyzed. A literature review was performed, comparing the incidence of intraoperative screw revisions and revision surgery for screw malposition in robot-guided, navigated, and freehand fusion procedures.

RESULTS

Seventy-two patients fit the study inclusion criteria and had a mean follow up of 32 ± 17 months. No screws had to be revised intraoperatively, and no revision surgery for screw malposition was needed. In the literature review, the authors found a higher rate of intraoperative screw revisions in the navigated pool than in the robot-guided pool (p < 0.001, OR 9.7). Additionally, a higher incidence of revision surgery for screw malposition was observed for freehand procedures than for the robot-guided procedures (p < 0.001, OR 8.1). The VAS score for back pain improved significantly from 66.9 ± 25.0 preoperatively to 30.1 ± 26.8 at the final follow-up, as did the VAS score for leg pain (from 70.6 ± 22.8 to 24.3 ± 28.3) and ODI (from 43.4 ± 18.3 to 16.2 ± 16.7; all p < 0.001). Undergoing PLIF, a high body mass index, smoking status, and a preoperative ability to work were identified as predictors of a reduction in back pain. Length of hospital stay was 2.4 ± 1.1 days and operating time was 161 ± 50 minutes. Ability to work increased from 38.9% to 78.2% of patients (p < 0.001) at the final follow-up, and 89.1% of patients indicated they would choose to undergo the same treatment again.

CONCLUSIONS

In adults with low-grade spondylolisthesis, the data demonstrated a benefit in using robotic guidance to reduce the rate of revision surgery for screw malposition as compared with other techniques of pedicle screw insertion described in peer-reviewed publications. Larger comparative studies are required to assess differences in PROs following a minimally invasive approach in spinal fusion surgeries compared with other techniques.

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Victor E. Staartjes, Marlies P. de Wispelaere and Marc L. Schröder

OBJECTIVE

Enhanced recovery after surgery (ERAS) has led to a paradigm shift in various surgical specialties. Its application can result in substantial benefits in perioperative healthcare utilization through preoperative physical and mental patient optimization and modulation of the recovery process. Still, ERAS remains relatively new to spine surgery. The authors report their 5-year experience, focusing on ERAS application to a broad population of patients with degenerative spine conditions undergoing elective surgical procedures, including anterior lumbar interbody fusion (ALIF).

METHODS

A multimodal ERAS protocol was applied between November 2013 and October 2018. The authors analyze hospital stay, perioperative outcomes, readmissions, and adverse events obtained from a prospective institutional registry. Elective tubular microdiscectomy and mini-open decompression as well as minimally invasive (MI) anterior or posterior fusion cases were included. Their institutional ERAS protocol contains 22 pre-, intra-, and postoperative elements, including preoperative patient counseling, MI techniques, early mobilization and oral intake, minimal postoperative restrictions, and regular audits.

RESULTS

A total of 2592 consecutive patients were included, with 199 (8%) undergoing fusion. The mean hospital stay was 1.1 ± 1.2 days, with 20 (0.8%) 30-day and 36 (1.4%) 60-day readmissions. Ninety-four percent of patients were discharged after a maximum 1-night hospital stay. Over the 5-year period, a clear trend toward a higher proportion of patients discharged home after a 1-night stay was observed (p < 0.001), with a concomitant decrease in adverse events in the overall cohort (p = 0.025) and without increase in readmissions. For fusion procedures, the rate of 1-night hospital stays increased from 26% to 85% (p < 0.001). Similarly, the average length of hospital stay decreased steadily from 2.4 ± 1.2 days to 1.5 ± 0.3 days (p < 0.001), with a notable concomitant decrease in variance, resulting in an estimated reduction in nursing costs of 46.8%.

CONCLUSIONS

Application of an ERAS protocol over 5 years to a diverse population of patients undergoing surgical procedures, including ALIF, for treatment of degenerative spine conditions was safe and effective, without increase in readmissions. The data from this large case series stress the importance of the multidisciplinary, iterative improvement process to overcome the learning curve associated with ERAS implementation, and the importance of a dedicated perioperative care team. Prospective trials are needed to evaluate spinal ERAS on a higher level of evidence.

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Alessandro Siccoli, Marlies P. de Wispelaere, Marc L. Schröder and Victor E. Staartjes

OBJECTIVE

Patient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. The authors aim to evaluate the feasibility of predicting short- and long-term PROMs, reoperations, and perioperative parameters by machine learning (ML) methods.

METHODS

Data were derived from a prospective registry. All patients had undergone single- or multilevel mini-open facet-sparing decompression for LSS. The prediction models were trained using various ML-based algorithms to predict the endpoints of interest. Models were selected by area under the receiver operating characteristic curve (AUC). The endpoints were dichotomized by minimum clinically important difference (MCID) and included 6-week and 12-month numeric rating scales for back pain (NRS-BP) and leg pain (NRS-LP) severity and the Oswestry Disability Index (ODI), as well as prolonged surgery (> 45 minutes), extended length of hospital stay (> 28 hours), and reoperations.

RESULTS

A total of 635 patients were included. The average age was 62 ± 10 years, and 333 patients (52%) were male. At 6 weeks, MCID was seen in 63%, 76%, and 61% of patients for ODI, NRS-LP, and NRS-BP, respectively. At internal validation, the models predicted MCID in these variables with accuracies of 69%, 76%, and 85%, and with AUCs of 0.75, 0.79, and 0.92. At 12 months, 66%, 63%, and 51% of patients reported MCID; the observed accuracies were 62%, 74%, and 66%, with AUCs of 0.68, 0.72, and 0.79. Reoperations occurred in 60 patients (9.5%), of which 27 (4.3%) occurred at the index level. Overall and index-level reoperations were predicted with 69% and 63% accuracy, respectively, and with AUCs of 0.66 and 0.61. In 15%, a length of surgery greater than 45 minutes was observed and predicted with 78% accuracy and AUC of 0.54. Only 15% of patients were admitted to the hospital for longer than 28 hours. The developed ML-based model enabled prediction of extended hospital stay with an accuracy of 77% and AUC of 0.58.

CONCLUSIONS

Preoperative prediction of a range of clinically relevant endpoints in decompression surgery for LSS using ML is feasible, and may enable enhanced informed patient consent and personalized shared decision-making. Access to individualized preoperative predictive analytics for outcome and treatment risks may represent a further step in the evolution of surgical care for patients with LSS.

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Victor E. Staartjes, Anita M. Klukowska, Elena L. Sorba and Marc L. Schröder

OBJECTIVE

Randomized controlled trials (RCTs) form the basis of today’s evidence-based approach to medicine, and play a critical role in guidelines and the drug and device approval process. Conflicts of interest (COIs) are commonplace in medical research, but little is known about their influence. The authors aimed to evaluate the extent and influence of COIs in recent RCTs published in core neurosurgical journals using a cross-sectional analysis.

METHODS

Through review of 6 general neurosurgical journals, all interventional RCTs published from January 2009 to January 2019 were identified. Because it is difficult to objectively assess study outcome, the authors opted for a strict rating approach based on the statistical significance of unambiguously reported primary endpoints, and the reported statistical protocol.

RESULTS

A total of 129 RCTs met the inclusion criteria. During the study period, the Journal of Neurosurgery published the largest number of RCTs (n = 40, 31%). Any potential COI was disclosed by 57%, and a mean of 12% of authors had a personal COI. Nonfinancial industry involvement was reported in 10%, while 31% and 20% received external support and sponsoring, respectively. Study registration was reported by 56%, while 51% of studies were blinded. Registration showed an increasing trend from 17% to 76% (p < 0.001). The median randomized sample size was 92 (interquartile range 50–153), and 8% were designed to investigate noninferiority or equality. Sixty-three RCTs (49%) unambiguously reported a primary endpoint, of which 13% were composite primary endpoints. In 43%, study outcome was positive, which was associated with a noninferiority design (31% vs 3%, p = 0.007) and a composite primary endpoint (46% vs 9%, p = 0.002). Potential COIs were not significantly associated with study positivity (69% vs 59%, p = 0.433). In the multivariate analysis, only a composite primary endpoint remained predictive of a positive study outcome (odds ratio 6.34, 95% confidence interval 1.51–33.61, p = 0.017).

CONCLUSIONS

This analysis provides an overview of COIs and their potential influence on recent trials published in core neurosurgical journals. Reporting of primary endpoints, study registration, and uniform disclosure of COIs are crucial to ensure the quality of future neurosurgical randomized trials. COIs do not appear to significantly influence the outcome of randomized neurosurgical trials.

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Carlo Serra, Lelio Guida, Victor E. Staartjes, Niklaus Krayenbühl and Uğur Türe

The authors report on and discuss the historical evolution of the 3 intellectual and scientific domains essential for the current understanding of the function of the human thalamus: 1) the identification of the thalamus as a distinct anatomical and functional entity, 2) the subdivision of thalamic gray matter into functionally homogeneous units (the thalamic nuclei) and relative disputes about nuclei nomenclature, and 3) experimental physiology and its limitations.

Galen was allegedly the first to identify the thalamus. The etymology of the term remains unknown although it is hypothesized that Galen may have wanted to recall the thalamus of Odysseus. Burdach was the first to clearly and systematically define the thalamus and its macroscopic anatomy, which paved the way to understanding its internal microarchitecture. This structure in turn was studied in both nonhuman primates (Friedemann) and humans (Vogt and Vogt), leading to several discrepancies in the findings because of interspecies differences. As a consequence, two main nomenclatures developed, generating sometimes inconsistent (or nonreproducible) anatomo-functional correlations. Recently, considerable effort has been aimed at producing a unified nomenclature, based mainly on functional data, which is indispensable for future developments. The development of knowledge about macro- and microscopic anatomy has allowed a shift from the first galenic speculations about thalamic function (the “thalamus opticorum nervorum”) to more detailed insights into the sensory and motor function of the thalamus in the 19th and 20th centuries. This progress is mostly the result of lesion and tracing studies. Direct evidence of the in vivo function of the human thalamus, however, originates from awake stereotactic procedures only.

Our current knowledge about the function of the human thalamus is the result of a long process that occurred over several centuries and has been inextricably intermingled with the increasing accumulation of data about thalamic macro- and microscopic anatomy. Although the thalamic anatomy can currently be considered well understood, further studies are still needed to gain a deeper insight into the function of the human thalamus in vivo.

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Victor E. Staartjes, Carlo Serra, Giovanni Muscas, Nicolai Maldaner, Kevin Akeret, Christiaan H. B. van Niftrik, Jorn Fierstra, David Holzmann and Luca Regli

OBJECTIVE

Gross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate cases and by poor interrater agreement. Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes. In this pilot study, the authors aimed to evaluate the utility of using deep learning to predict GTR after transsphenoidal surgery for pituitary adenoma.

METHODS

Data from a prospective registry were used. The authors trained a deep neural network to predict GTR from 16 preoperatively available radiological and procedural variables. Class imbalance adjustment, cross-validation, and random dropout were applied to prevent overfitting and ensure robustness of the predictive model. The authors subsequently compared the deep learning model to a conventional logistic regression model and to the Knosp classification as a gold standard.

RESULTS

Overall, 140 patients who underwent endoscopic transsphenoidal surgery were included. GTR was achieved in 95 patients (68%), with a mean extent of resection of 96.8% ± 10.6%. Intraoperative high-field MRI was used in 116 (83%) procedures. The deep learning model achieved excellent area under the curve (AUC; 0.96), accuracy (91%), sensitivity (94%), and specificity (89%). This represents an improvement in comparison with the Knosp classification (AUC: 0.87, accuracy: 81%, sensitivity: 92%, specificity: 70%) and a statistically significant improvement in comparison with logistic regression (AUC: 0.86, accuracy: 82%, sensitivity: 81%, specificity: 83%) (all p < 0.001).

CONCLUSIONS

In this pilot study, the authors demonstrated the utility of applying deep learning to preoperatively predict the likelihood of GTR with excellent performance. Further training and validation in a prospective multicentric cohort will enable the development of an easy-to-use interface for use in clinical practice.

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Victor E. Staartjes, Costanza M. Zattra, Kevin Akeret, Nicolai Maldaner, Giovanni Muscas, Christiaan Hendrik Bas van Niftrik, Jorn Fierstra, Luca Regli and Carlo Serra

OBJECTIVE

Although rates of postoperative morbidity and mortality have become relatively low in patients undergoing transnasal transsphenoidal surgery (TSS) for pituitary adenoma, cerebrospinal fluid (CSF) fistulas remain a major driver of postoperative morbidity. Persistent CSF fistulas harbor the potential for headache and meningitis. The aim of this study was to investigate whether neural network–based models can reliably identify patients at high risk for intraoperative CSF leakage.

METHODS

From a prospective registry, patients who underwent endoscopic TSS for pituitary adenoma were identified. Risk factors for intraoperative CSF leaks were identified using conventional statistical methods. Subsequently, the authors built a prediction model for intraoperative CSF leaks based on deep learning.

RESULTS

Intraoperative CSF leaks occurred in 45 (29%) of 154 patients. No risk factors for CSF leaks were identified using conventional statistical methods. The deep neural network–based prediction model classified 88% of patients in the test set correctly, with an area under the curve of 0.84. Sensitivity (83%) and specificity (89%) were high. The positive predictive value was 71%, negative predictive value was 94%, and F1 score was 0.77. High suprasellar Hardy grade, prior surgery, and older age contributed most to the predictions.

CONCLUSIONS

The authors trained and internally validated a robust deep neural network–based prediction model that identifies patients at high risk for intraoperative CSF. Machine learning algorithms may predict outcomes and adverse events that were previously nearly unpredictable, thus enabling safer and improved patient care and better patient counseling.