Predictive value of the five-repetition sit-to-stand test for outcomes after surgery for lumbar disc herniation: prospective study.

OBJECTIVE: It is unknown whether presence of pre-operative objective functional impairment (OFI) can predict post-operative outcomes in patients with lumbar disc herniation (LDH). We aimed to determine whether pre-operative OFI measured by the five-repetition sit-to-stand test (5R-STS) could predict outcomes at 12-months post-discectomy. METHODS: Adult patients with LDH scheduled for surgery were prospectively recruited from a Dutch short-stay spinal clinic. The 5R-STS time and patient reported outcome measures (PROMs) including Oswestry Disability Index, Roland-Morris Disability Questionnaire, Visual Analogue Scale (VAS) for back and leg pain, EQ-5D-3L health-related quality of life, EQ5D-VAS and ability to work were recorded pre-operatively and at 12-months. A 5R-STS time cut-off of ≥ 10.5 s was used to determine OFI. Mann-Whitney and Chi-square tests were employed to determine significant differences in post-operative outcomes between groups stratified by presence of pre-operative OFI. RESULTS: We recruited 134 patients in a prospective study. Twelve-month follow-up was completed by 103 (76.8%) patients. Mean age was 53.2 ± 14.35 years and 50 (48.5%) patients were female. Pre-operatively, 53 (51.5%) patients had OFI and 50 (48.5%) did not. Post-operatively, patients with OFI experienced a significantly greater mean change (p < 0.001) across all PROMs compared to patients without OFI, except leg pain (p = 0.176). There were no significant differences in absolute PROMs between groups at 12-months (all p > 0.05). CONCLUSIONS: The presence of OFI based on 5R-STS time does not appear to decrease a patient's likelihood of experiencing satisfactory post-operative outcomes. The 5R-STS cannot predict how a patient with LDH will respond to surgery at 12-month follow-up.

Calculation of the minimum clinically important difference (MCID) using different methodologies: case study and practical guide.

INTRODUCTION: Establishing thresholds of change that are actually meaningful for the patient in an outcome measurement instrument is paramount. This concept is called the minimum clinically important difference (MCID). We summarize available MCID calculation methods relevant to spine surgery, and outline key considerations, followed by a step-by-step working example of how MCID can be calculated, using publicly available data, to enable the readers to follow the calculations themselves. METHODS: Thirteen MCID calculations methods were summarized, including anchor-based methods, distribution-based methods, Reliable Change Index, 30% Reduction from Baseline, Social Comparison Approach and the Delphi method. All methods, except the latter two, were used to calculate MCID for improvement of Zurich Claudication Questionnaire (ZCQ) Symptom Severity of patients with lumbar spinal stenosis. Numeric Rating Scale for Leg Pain and Japanese Orthopaedic Association Back Pain Evaluation Questionnaire Walking Ability domain were used as anchors. RESULTS: The MCID for improvement of ZCQ Symptom Severity ranged from 0.8 to 5.1. On average, distribution-based methods yielded lower MCID values, than anchor-based methods. The percentage of patients who achieved the calculated MCID threshold ranged from 9.5% to 61.9%. CONCLUSIONS: MCID calculations are encouraged in spinal research to evaluate treatment success. Anchor-based methods, relying on scales assessing patient preferences, continue to be the "gold-standard" with receiver operating characteristic curve approach being optimal. In their absence, the minimum detectable change approach is acceptable. The provided explanation and step-by-step example of MCID calculations with statistical code and publicly available data can act as guidance in planning future MCID calculation studies.

Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning.

OBJECTIVES: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). METHODS: Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s-indicating presence of OFI. K-means clustering-an unsupervised ML algorithm-was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. RESULTS: We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI-with female gender, lower body mass index, and less smokers as Type I hallmarks. CONCLUSIONS: Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics.

Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease.

BACKGROUND: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). METHODS: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity. RESULTS: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. CONCLUSIONS: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.

Predictors of five-repetition sit-to-stand test performance in patients with lumbar degenerative disease.

BACKGROUND: The five-repetition sit-to-stand test (5R-STS) has recently been validated as an objective measure of functional impairment in patients with lumbar degenerative disease (LDD). Knowledge of factors influencing 5R-STS performance is useful to correct for confounders, create personalized adjusted test times, and potentially identify prognostic subgroups. We evaluate factors predicting the 5R-STS performance in patients with LDD. METHODS: Patients with LDD requiring surgery were included. Each participant performed the 5R-STS and completed a questionnaire that included their age, gender, weight, height, body mass index (BMI), smoking status, education level, employment type, ability to work, analgesic drug usage, history of previous spinal surgery, and EQ5D depression and anxiety domain. Surgical indication and index level of the spinal pathology were also recorded. Predictors of 5R-STS were identified through multivariable linear regression. RESULTS: The cohort consisted of 240 patients, 47.9% being female (mean age, 47.7 ± 13.6 years). In the final multivariable model incorporating confounders, height (regression coefficient (RC), 0.08; 95% confidence interval (CI), 0.003/0.16, p = 0.042) and being an active smoker (RC, 2.44; 95%CI, 0.56/4.32, p = 0.012) were significant predictors of worse 5R-STS performance. Full ability to work (RC, - 2.39; 95%CI, - 4.39/ - 0.39, p = 0.020) was associated with a better 5R-STS performance. Age, height, surgical indication, index level of pathology, history of previous spine surgery, history of pain, analgesic drug use, employment type, and severity of anxiety and depression symptoms demonstrated confounding effect on the 5R-STS time. CONCLUSIONS: Greater height, being an active smoker, and inability to work are significant predictors of worse 5R-STS performance in patients with LDD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03303300 and NCT03321357.

Overweight and smoking promote recurrent lumbar disk herniation after discectomy.

PURPOSE: Recurrent lumbar disk herniation (rLDH) following lumbar microdiscectomy is common. While several risk factors for primary LDH have been described, risk factors for rLDH have only sparsely been investigated. We evaluate the effect of Body mass index (BMI) and smoking on the incidence and timing of rLDH. METHODS: From a prospective registry, we identified all patients undergoing primary tubular microdiscectomy (tMD), with complete BMI and smoking data, and a minimum 12-month follow-up. We defined rLDH as reherniation at the same level and side requiring surgery. Overweight was defined as BMI > 25, and obesity as BMI > 30. Intergroup comparisons and age- and gender-adjusted multivariable regression were carried out. We conducted a survival analysis to assess the influence of BMI and smoking on time to reoperation. RESULTS: Of 3012 patients, 166 (5.5%) underwent re-microdiscectomy for rLDH. Smokers were reoperated more frequently (6.4% vs. 4.0%, p = 0.007). Similarly, rLDH was more frequent in obese (7.5%) and overweight (5.9%) than in normal-weight patients (3.3%, p = 0.017). Overweight smokers had the highest rLDH rate (7.6%). This effect of smoking (Odds ratio: 1.63, 96% CI: 1.12-2.36, p = 0.010) and BMI (Odds ratio: 1.09, 95% CI: 1.02-1.17, p = 0.010) persisted after controlling for age and gender. Survival analysis demonstrated that rLDH did not occur earlier in overweight patients and/or smokers. CONCLUSIONS: BMI and smoking may directly contribute to a higher risk of rLDH, but do not accelerate rLDH development. Smoking cessation and weight loss in overweight or obese patients ought to be recommended with discectomy to reduce the risk for rLDH.

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.

BACKGROUND: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. METHODS: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. RESULTS: Models were developed and integrated into a web-app ( https://neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59-0.74], back pain (0.72, 95%CI: 0.64-0.79), and leg pain (0.64, 95%CI: 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. CONCLUSIONS: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population.

Influence of dynamic preoperative body mass index changes on patient-reported outcomes after surgery for degenerative lumbar spine disease.

Psychological factors demonstrably and often massively influence outcomes of degenerative spine surgery, and one could hypothesize that preoperative weight loss may correlate with motivation and lifestyle adjustment, thus leading to potentially enhanced outcomes. We aimed to evaluate the effect of preoperative weight loss or gain, respectively, on patient-reported outcomes after lumbar spine surgery. Weight loss was defined as a BMI decrease of ≤ - 0.5 kg/m2 over a period of at least 1 month, and weight gain as a BMI increase of ≥ 0.5 kg/m2 in the same time period, respectively. The primary endpoint was set as the achievement of the minimum clinically important difference (MCID) in the ODI at 1 or 2 years postoperatively. A total of 154 patients were included. Weight loss (odds ratio (OR): 1.18, 95% confidence interval (CI): 0.52 to 2.80) and weight gain (OR: 1.03, 95% CI: 0.43 to 2.55) showed no significant influence on MCID achievement for ODI compared to a stable BMI. The same results were observed when analysing long-term NRS-BP and NRS-LP. Regression analysis showed no correlation between BMI change and PROM change scores for any of the three PROMs. Adjustment for age and gender did not alter results. Our findings suggest that both preoperative weight loss and weight gain may have no measurable effect on long-term postoperative outcome compared to a stable BMI. Weight loss preoperatively-as a potential surrogate sign of patient motivation and lifestyle change-may thus not influence postoperative outcomes.

Global adoption of robotic technology into neurosurgical practice and research.

Recent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Our aim was to carry out a worldwide survey among neurosurgeons to assess the adoption of and attitude toward robotic technology in the neurosurgical operating room and to identify factors associated with use of robotic technology. The online survey was made up of nine or ten compulsory questions and was distributed via the European Association of the Neurosurgical Societies (EANS) and the Congress of Neurological Surgeons (CNS) in February and March 2018. From a total of 7280 neurosurgeons who were sent the survey, we received 406 answers, corresponding to a response rate of 5.6%, mostly from Europe and North America. Overall, 197 neurosurgeons (48.5%) reported having used robotic technology in clinical practice. The highest rates of adoption of robotics were observed for Europe (54%) and North America (51%). Apart from geographical region, only age under 30, female gender, and absence of a non-academic setting were significantly associated with clinical use of robotics. The Mazor family (32%) and ROSA (26%) robots were most commonly reported among robot users. Our study provides a worldwide overview of neurosurgical adoption of robotic technology. Almost half of the surveyed neurosurgeons reported having clinical experience with at least one robotic system. Ongoing and future trials should aim to clarify superiority or non-inferiority of neurosurgical robotic applications and balance these potential benefits with considerations on acquisition and maintenance costs.

Association of age with incidence and timing of recurrence after microdiscectomy for lumbar disc herniation.

OBJECTIVE: Recurrent lumbar disc herniation (LDH) is the most frequent reason for reoperation after lumbar microdiscectomy. While several risk factors for recurrent LDH have been well-described, the effect of age on recurrence remains unclear, especially concerning the timing of recurrent LDH. METHODS: From a prospective registry, we identified all patients who underwent tubular microdiscectomy for LDH. Recurrent LDH was defined as reoperation for LDH at the same index level and side. The associations among age and incidence of recurrent LDH as well as on time to recurrent LDH were statistically evaluated using multivariable analysis of covariance, linear regression, and Cox proportional hazards modelling. RESULTS: Of the 3013 patients who underwent surgery for LDH, 166 (5.5%) had to undergo reoperation due to LDH recurrence. Uni- and multivariable analysis revealed no influence of age on the incidence of recurrent LDH (both p > 0.05). Linear regression indicated earlier reoperation in older patients, both with (ß = -0.248) and without (ß = -0.374) correction for confounders (both p < 0.05). An additional survival analysis found that patients aged over 35 years had recurrent LDH significantly earlier (hazard ratio 0.617, p = 0.013). CONCLUSION: In an analysis of a large prospective database of patients undergoing microdiscectomy for lumbar disc herniation, we found that younger patients do not have a higher reoperation probability than their older counterparts, even after correcting for multiple confounders. However, older patients tend to experience recurrent LDH significantly earlier after the index surgery compared to younger patients.

Magnetic resonance imaging-based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept.

OBJECTIVE: Computed tomography scanning of the lumbar spine incurs a radiation dose ranging from 3.5 mSv to 19.5 mSv as well as relevant costs and is commonly necessary for spinal neuronavigation. Mitigation of the need for treatment-planning CT scans in the presence of MRI facilitated by MRI-based synthetic CT (sCT) would revolutionize navigated lumbar spine surgery. The authors aim to demonstrate, as a proof of concept, the capability of deep learning-based generation of sCT scans from MRI of the lumbar spine in 3 cases and to evaluate the potential of sCT for surgical planning. METHODS: Synthetic CT reconstructions were made using a prototype version of the "BoneMRI" software. This deep learning-based image synthesis method relies on a convolutional neural network trained on paired MRI-CT data. A specific but generally available 4-minute 3D radiofrequency-spoiled T1-weighted multiple gradient echo MRI sequence was supplemented to a 1.5T lumbar spine MRI acquisition protocol. RESULTS: In the 3 presented cases, the prototype sCT method allowed voxel-wise radiodensity estimation from MRI, resulting in qualitatively adequate CT images of the lumbar spine based on visual inspection. Normal as well as pathological structures were reliably visualized. In the first case, in which a spiral CT scan was available as a control, a volume CT dose index (CTDIvol) of 12.9 mGy could thus have been avoided. Pedicle screw trajectories and screw thickness were estimable based on sCT findings. CONCLUSIONS: The evaluated prototype BoneMRI method enables generation of sCT scans from MRI images with only minor changes in the acquisition protocol, with a potential to reduce workflow complexity, radiation exposure, and costs. The quality of the generated CT scans was adequate based on visual inspection and could potentially be used for surgical planning, intraoperative neuronavigation, or for diagnostic purposes in an adjunctive manner.

Machine learning-augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment.

OBJECTIVE: What is considered "abnormal" in clinical testing is typically defined by simple thresholds derived from normative data. For instance, when testing using the five-repetition sit-to-stand (5R-STS) test, the upper limit of normal (ULN) from a population of spine-healthy volunteers (10.5 seconds) is used to identify objective functional impairment (OFI), but this fails to consider different properties of individuals (e.g., taller and shorter, older and younger). Therefore, the authors developed a personalized testing strategy to quantify patient-specific OFI using machine learning. METHODS: Patients with disc herniation, spinal stenosis, spondylolisthesis, or discogenic chronic low-back pain and a population of spine-healthy volunteers, from two prospective studies, were included. A machine learning model was trained on normative data to predict personalized "expected" test times and their confidence intervals and ULNs (99th percentiles) based on simple demographics. OFI was defined as a test time greater than the personalized ULN. OFI was categorized into types 1 to 3 based on a clustering algorithm. A web app was developed to deploy the model clinically. RESULTS: Overall, 288 patients and 129 spine-healthy individuals were included. The model predicted "expected" test times with a mean absolute error of 1.18 (95% CI 1.13-1.21) seconds and R2 of 0.37 (95% CI 0.34-0.41). Based on the implemented personalized testing strategy, 191 patients (66.3%) exhibited OFI. Type 1, 2, and 3 impairments were seen in 64 (33.5%), 91 (47.6%), and 36 (18.8%) patients, respectively. Increasing detected levels of OFI were associated with statistically significant increases in subjective functional impairment, extreme anxiety and depression symptoms, being bedridden, extreme pain or discomfort, inability to carry out activities of daily living, and a limited ability to work. CONCLUSIONS: In the era of "precision medicine," simple population-based thresholds may eventually not be adequate to monitor quality and safety in neurosurgery. Individualized assessment integrating machine learning techniques provides more detailed and objective clinical assessment. The personalized testing strategy demonstrated concurrent validity with quality-of-life measures, and the freely accessible web app (https://neurosurgery.shinyapps.io/5RSTS/) enabled clinical application.

Association of maximum back and leg pain severity with objective functional impairment as assessed by five-repetition sit-to-stand testing: analysis of two prospective studies.

The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and provides a new adjunctive dimension in patient assessment. Its interpretability and confounders remain poorly understood. We quantify the association among maximum patient-reported back (MBP) or leg pain (MLP) and OFI as measured by the 5R-STS using data from two prospective studies. Patients performed the 5R-STS test and completed visual analogue scales (VAS) for back and leg pain severity. Maximum pain severity was defined as VAS scores of 9 or 10. The association of MBP and MLP with 5R-STS test times as well as with the presence of OFI (> 10.5 s) and severe OFI (> 22.0 s) as determined by the 5R-STS baseline severity stratification was quantified by use of crude and adjusted regression models. A total of 258 patients were included. MBP and MLP were reported by 27 (10.5%) and 76 (29.5%) patients, respectively. Rates of OFI differed among patients with (92.6%) and without (55.0%) MBP (p < 0.001), while presence of MLP did not influence OFI (61.8% vs. 57.7%, p = 0.632). Similarly, severe OFI was more common among patients with MBP (33.3% vs. 9.1%, p < 0.001), but not among patients with MLP (17.1% vs. 9.3%, p = 0.119). In the adjusted logistic analysis, the influence of MBP on OFI persisted (odds ratio [OR] 10.08, confidence interval [CI] 2.73-65.74, p = 0.003), while MLP demonstrated no association (OR 0.91, CI 0.49-1.72, p = 0.780). These associations were consistent with findings pertaining to raw 5R-STS test times and in discectomy patients alone. There is a strong association of maximum back pain severity on VAS with the degree of OFI determined by 5R-STS testing. Radiculopathy with maximally strong back pain, however, does not seem to relevantly influence 5R-STS performance.Trial registration ClinicalTrials.gov : NCT03321357 & NCT03303300.

Initial classification of low back and leg pain based on objective functional testing: a pilot study of machine learning applied to diagnostics.

OBJECTIVE: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment and thus provided an adjunctive dimension in patient assessment. The clinical interpretability and confounders of the 5R-STS remain poorly understood. In clinical use, it became apparent that 5R-STS performance may differ between patients with lumbar disk herniation (LDH), lumbar spinal stenosis (LSS) with or without low-grade spondylolisthesis, and chronic low back pain (CLBP). We seek to evaluate the extent of diagnostic information contained within 5R-STS testing. METHODS: Patients were classified into gold standard diagnostic categories based on history, physical examination, and imaging. Crude and adjusted comparisons of 5R-STS performance were carried out among the three diagnostic categories. Subsequently, a machine learning algorithm was trained to classify patients into the three categories using only 5R-STS test time and patient age, gender, height, and weight. RESULTS: From two prospective studies, 262 patients were included. Significant differences in crude and adjusted test times were observed among the three diagnostic categories. At internal validation, classification accuracy was 96.2% (95% CI 87.099.5%). Classification sensitivity was 95.7%, 100%, and 100% for LDH, LSS, and CLBP, respectively. Similarly, classification specificity was 100%, 95.7%, and 100% for the three diagnostic categories. CONCLUSION: 5R-STS performance differs according to the etiology of back and leg pain, even after adjustment for demographic covariates. In combination with machine learning algorithms, OFI can be used to infer the etiology of spinal back and leg pain with accuracy comparable to other diagnostic tests used in clinical examination. These slides can be retrieved under Electronic Supplementary Material.

External validation of a prediction model for pain and functional outcome after elective lumbar spinal fusion.

OBJECTIVE: Patient-reported outcome measures following elective lumbar fusion surgery demonstrate major heterogeneity. Individualized prediction tools can provide valuable insights for shared decision-making. We externally validated the spine surgical care and outcomes assessment programme/comparative effectiveness translational network (SCOAP-CERTAIN) model for prediction of 12-month minimum clinically important difference in Oswestry Disability Index (ODI) and in numeric rating scales for back (NRS-BP) and leg pain (NRS-LP) after elective lumbar fusion. METHODS: Data from a prospective registry were obtained. We calculated the area under the curve (AUC), calibration slope and intercept, and Hosmer-Lemeshow values to estimate discrimination and calibration of the models. RESULTS: We included 100 patients, with average age of 50.4 ± 11.4 years. For 12-month ODI, AUC was 0.71 while the calibration intercept and slope were 1.08 and 0.95, respectively. For NRS-BP, AUC was 0.72, with a calibration intercept of 1.02, and slope of 0.74. For NRS-LP, AUC was 0.83, with a calibration intercept of 1.08, and slope of 0.95. Sensitivity ranged from 0.64 to 1.00, while specificity ranged from 0.38 to 0.65. A lack of fit was found for all three models based on Hosmer-Lemeshow testing. CONCLUSIONS: The SCOAP-CERTAIN tool can accurately predict which patients will achieve favourable outcomes. However, the predicted probabilities-which are the most valuable in clinical practice-reported by the tool do not correspond well to the true probability of a favourable outcome. We suggest that any prediction tool should first be externally validated before it is applied in routine clinical practice. These slides can be retrieved under Electronic Supplementary Material.

Machine learning in neurosurgery: a global survey.

BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. METHODS: The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). RESULTS: Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. CONCLUSIONS: This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.

Can objective functional impairment in lumbar degenerative disease be reliably assessed at home using the five-repetition sit-to-stand test? A prospective study.

PURPOSE: Objective functional tests like the five-repetition sit-to-stand test (5R-STS) can supplement an objective dimension to conventional patient-reported outcome measures. The reliability of unsupervised obtainment of 5R-STS performance is currently unknown. METHODS: We included patients with degenerative pathologies of the lumbar spine. Patients performed the 5R-STS during the initial clinical visit (supervised), as well as at home after instruction by a physiotherapist. At home, patients were first timed by a relative (unsupervised) and subsequently produced a video recording of themselves performing the 5R-STS for digital measurement (telesupervised). Two raters independently assessed the recordings. RESULTS: One hundred and twenty-one patients were recruited, of which 100 were eligible. Eighty-eight reported unsupervised results. Sixty-four returned recordings, of which 61 were ratable. Both unsupervised (r: 0.94, 95% CI 0.91-0.96, p < 0.001) and telesupervised (r: 0.90, 95% CI 0.83-0.94, p < 0.001) measurements demonstrated excellent correlation with clinical test times. Patients did not perform more slowly at home (p > 0.05). The interrater agreement for digital judgement of the telesupervised recording was excellent (ICC: 0.996, 95% CI 0.993-0.998, p < 0.001). We confirmed convergent validity with self-reported disability, back pain, and quality of life (all p < 0.05), but not with leg pain (p = 0.189). CONCLUSIONS: Unsupervised at-home assessment using the 5R-STS is highly reliable. There does not appear to be a specific need for patients to return for a supervised 5R-STS follow-up. Rather, instructions can be provided, and the test performed and rated by a partner or family member at home. This is logistically and economically advantageous for patients, clinicians, and researchers. TRIAL REGISTRY NUMBER: ClinicalTrials.gov Identifier: NCT03321357. These slides can be retrieved under Electronic Supplementary Material.

Improving recovery after elective degenerative spine surgery: 5-year experience with an enhanced recovery after surgery (ERAS) protocol.

OBJECTIVEEnhanced 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).METHODSA 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.RESULTSA 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%.CONCLUSIONSApplication 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.

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis.

OBJECTIVEPatient-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.METHODSData 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.RESULTSA 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.CONCLUSIONSPreoperative 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.

Gender differences in degenerative spine surgery: Do female patients really fare worse?

PURPOSE: Prior data has set the precedent that female patients fare somewhat worse than men after spine surgery. We aimed to evaluate the effect of gender on patient-reported outcomes after lumbar spine surgery for degenerative pathologies. METHODS: We identified a consecutive cohort of patients from a prospective registry. Absolute values, as well as change scores for back and leg pain severity (numeric rating scale [NRS]), functional disability (Oswestry disability index [ODI]), and health-related quality of life (HRQOL) as assessed by EQ-5D were compared among male and female patients. RESULTS: Of the 3279 included patients, 1543 (47%) were female. At baseline, women reported higher NRS for back and leg pain, higher ODI, but equal HRQOL (all p < 0.05). Otherwise, both groups had comparable baseline data. The absolute differences in patient-reported outcomes persisted at the 6-week, 12- and 24-months follow-up, with women now additionally reporting worse HRQOL as assessed by EQ-5D (all p < 0.05). For all outcome measures, change scores were equal among male and female patients, as were the incidences of complications and reoperations (all p > 0.05). Clinical success was achieved in 82% of men and 79% of women (p = 0.34). CONCLUSIONS: Female patients are generally scheduled for surgery with a more advanced disease state. While women seem to report more severe symptoms at long-term follow-up, the degree of improvement is equal among men and women. Female patients may thus fare worse in terms of absolute scores, but enjoy the same benefit from surgery in relative terms. These slides can be retrieved under Electronic Supplementary Material.