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. Graphic abstract: These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.

Healthcare utilization and satisfaction with treatment before and after direct discharge from the Emergency Department of simple stable musculoskeletal injuries in the Netherlands

PURPOSE: To evaluate healthcare utilization and satisfaction with treatment before and after implementing direct discharge (DD) from the Emergency Department (ED) of patients with simple, stable musculoskeletal injuries. METHODS: Patients with simple, stable musculoskeletal injuries were included in two Dutch hospitals, both level-2 trauma centers: OLVG and Sint Antonius (SA), before (pre-DD-cohort) and after implementing DD (DD-cohort). With DD, no routine follow-up appointments are scheduled after the ED visit, supported by information leaflets, a smartphone application and a telephone helpline. Outcomes included: secondary healthcare utilization (follow-up appointments and X-ray/CT/MRI); satisfaction with treatment (scale 1-10); primary healthcare utilization (general practitioner (GP) or physiotherapist visited, yes/no). Linear regression was used to compare secondary healthcare utilization for all patients and per injury subgroup. Satisfaction and primary healthcare utilization were analyzed descriptively. RESULTS: A total of 2033 (OLVG = 1686; SA = 347) and 1616 (OLVG = 1396; SA = 220) patients were included in the pre-DD-cohort and DD-cohort, respectively. After DD, the mean number of follow-up appointments per patient reduced by 1.06 (1.13-0.99; p < 0.001) in OLVG and 1.07 (1.02-0.93; p < 0.001) in SA. Follow-up appointments reduced significantly for all injury subgroups. Mean number of follow-up X-rays per patient reduced by 0.17 in OLVG (p < 0.001) and 0.18 in SA (p < 0.001). Numbers of CT/MRI scans were low and comparable. In OLVG, mean satisfaction with treatment was 8.1 (pre-DD-cohort) versus 7.95 (DD-cohort), versus 7.75 in SA (DD-cohort only). In OLVG, 23.6% of pre-DD-cohort patients visited their GP, versus 26.1% in the DD-cohort, versus 13.3% in SA (DD-cohort only). Physiotherapist use was comparable. CONCLUSION: This study performed in a large population and additional hospital confirms earlier pilot results, i.e., that DD has the potential to effectively reduce healthcare utilization, while maintaining high levels of satisfaction. LEVEL OF EVIDENCE: II

Normative data of a smartphone app-based 6-minute walking test, test-retest reliability, and content validity with patient-reported outcome measures

OBJECTIVE The 6-minute walking test (6WT) is used to determine restrictions in a subject's 6-minute walking distance (6WD) due to lumbar degenerative disc disease. To facilitate simple and convenient patient self-measurement, a free and reliable smartphone app using Global Positioning System coordinates was previously designed. The authors aimed to determine normative values for app-based 6WD measurements. METHODS The maximum 6WD was determined three times using app-based measurement in a sample of 330 volunteers without previous spine surgery or current spine-related disability, recruited at 8 centers in 5 countries (mean subject age 44.2 years, range 16-91 years; 48.5% male; mean BMI 24.6 kg/m2, range 16.3-40.2 kg/m2; 67.9% working; 14.2% smokers). Subjects provided basic demographic information, including comorbidities and patient-reported outcome measures (PROMs): visual analog scale (VAS) for both low-back and lower-extremity pain, Core Outcome Measures Index (COMI), Zurich Claudication Questionnaire (ZCQ), and subjective walking distance and duration. The authors determined the test-retest reliability across three measurements (intraclass correlation coefficient [ICC], standard error of measurement [SEM], and mean 6WD [95% CI]) stratified for age and sex, and content validity (linear regression coefficients) between 6WD and PROMs. RESULTS The ICC for repeated app-based 6WD measurements was 0.89 (95% CI 0.87-0.91, p < 0.001) and the SEM was 34 meters. The overall mean 6WD was 585.9 meters (95% CI 574.7-597.0 meters), with significant differences across age categories (p < 0.001). The 6WD was on average about 32 meters less in females (570.5 vs 602.2 meters, p = 0.005). There were linear correlations between average 6WD and VAS back pain, VAS leg pain, COMI Back and COMI subscores of pain intensity and disability, ZCQ symptom severity, ZCQ physical function, and ZCQ pain and neuroischemic symptoms subscores, as well as with subjective walking distance and duration, indicating that subjects with higher pain, higher disability, and lower subjective walking capacity had significantly lower 6WD (all p < 0.001). CONCLUSIONS This study provides normative data for app-based 6WD measurements in a multicenter sample from 8 institutions and 5 countries. These values can now be used as reference to compare 6WT results and quantify objective functional impairment in patients with degenerative diseases of the spine using z-scores. The authors found a good to excellent test-retest reliability of the 6WT app, a low area of uncertainty, and high content validity of the average 6WD with commonly used PROMs

The European Robotic Spinal Instrumentation (EUROSPIN) study: protocol for a multicentre prospective observational study of pedicle screw revision surgery after robot-guided, navigated and freehand thoracolumbar spinal fusion

Introduction Robotic guidance (RG) and computer-assisted navigation (NV) have seen increased adoption in instrumented spine surgery over the last decade. Although there exists some evidence that these techniques increase radiological pedicle screw accuracy compared with conventional freehand (FH) surgery, this may not directly translate to any tangible clinical benefits, especially considering the relatively high inherent costs. As a non-randomised, expertise-based study, the European Robotic Spinal Instrumentation Study aims to create prospective multicentre evidence on the potential comparative clinical benefits of RG, NV and FH in a real-world setting. Methods and analysis Patients are allocated in a non-randomised, non-blinded fashion to the RG, NV or FH arms. Adult patients that are to undergo thoracolumbar pedicle screw instrumentation for degenerative pathologies, infections, vertebral tumours or fractures are considered for inclusion. Deformity correction and surgery at more than five levels represent exclusion criteria. Follow-up takes place at 6 weeks, as well as 12 and 24 months. The primary endpoint is defined as the time to revision surgery for a malpositioned or loosened pedicle screw within the first postoperative year. Secondary endpoints include patient-reported back and leg pain, as well as Oswestry Disability Index and EuroQOL 5-dimension questionnaires. Use of analgesic medication and work status are recorded. The primary analysis, conducted on the 12-month data, is carried out according to the intention-to-treat principle. The primary endpoint is analysed using crude and adjusted Cox proportional hazards models. Patient-reported outcomes are analysed using baseline-adjusted linear mixed models. The study is monitored according to a prespecified monitoring plan. Ethics and dissemination The study protocol is approved by the appropriate national and local authorities. Written informed consent is obtained from all participants. The final results will be published in an international peer-reviewed journal. Trial registration number Clinical Trials.gov registry NCT03398915; Pre-results, recruiting stage

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

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