Multivariable Prediction Models for Traumatic Spinal Cord Injury: A Systematic Review.

Background: Traumatic spinal cord injuries (TSCI) greatly affect the lives of patients and their families. Prognostication may improve treatment strategies, health care resource allocation, and counseling. Multivariable clinical prediction models (CPMs) for prognosis are tools that can estimate an absolute risk or probability that an outcome will occur. Objectives: We sought to systematically review the existing literature on CPMs for TSCI and critically examine the predictor selection methods used. Methods: We searched MEDLINE, PubMed, Embase, Scopus, and IEEE for English peer-reviewed studies and relevant references that developed multivariable CPMs to prognosticate patient-centered outcomes in adults with TSCI. Using narrative synthesis, we summarized the characteristics of the included studies and their CPMs, focusing on the predictor selection process. Results: We screened 663 titles and abstracts; of these, 21 full-text studies (2009-2020) consisting of 33 distinct CPMs were included. The data analysis domain was most commonly at a high risk of bias when assessed for methodological quality. Model presentation formats were inconsistently included with published CPMs; only two studies followed established guidelines for transparent reporting of multivariable prediction models. Authors frequently cited previous literature for their initial selection of predictors, and stepwise selection was the most frequent predictor selection method during modelling. Conclusion: Prediction modelling studies for TSCI serve clinicians who counsel patients, researchers aiming to risk-stratify participants for clinical trials, and patients coping with their injury. Poor methodological rigor in data analysis, inconsistent transparent reporting, and a lack of model presentation formats are vital areas for improvement in TSCI CPM research.

Enabling knowledge translation: implementation of a web-based tool for independent walking prediction after traumatic spinal cord injury.

Introduction: Several clinical prediction rules (CPRs) have been published, but few are easily accessible or convenient for clinicians to use in practice. We aimed to develop, implement, and describe the process of building a web-based CPR for predicting independent walking 1-year after a traumatic spinal cord injury (TSCI). Methods: Using the published and validated CPR, a front-end web application called "Ambulation" was built using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. A survey was created using QualtricsXM Software to gather insights on the application's usability and user experience. Website activity was monitored using Google Analytics. Ambulation was developed with a core team of seven clinicians and researchers. To refine the app's content, website design, and utility, 20 professionals from different disciplines, including persons with lived experience, were consulted. Results: After 11 revisions, Ambulation was uploaded onto a unique web domain and launched (www.ambulation.ca) as a pilot with 30 clinicians (surgeons, physiatrists, and physiotherapists). The website consists of five web pages: Home, Calculation, Team, Contact, and Privacy Policy. Responses from the user survey (n = 6) were positive and provided insight into the usability of the tool and its clinical utility (e.g., helpful in discharge planning and rehabilitation), and the overall face validity of the CPR. Since its public release on February 7, 2022, to February 28, 2023, Ambulation had 594 total users, 565 (95.1%) new users, 26 (4.4%) returning users, 363 (61.1%) engaged sessions (i.e., the number of sessions that lasted 10 seconds/longer, had one/more conversion events e.g., performing the calculation, or two/more page or screen views), and the majority of the users originating from the United States (39.9%) and Canada (38.2%). Discussion: Ambulation is a CPR for predicting independent walking 1-year after TSCI and it can assist frontline clinicians with clinical decision-making (e.g., time to surgery or rehabilitation plan), patient education and goal setting soon after injury. This tool is an example of adapting a validated CPR for independent walking into an easily accessible and usable web-based tool for use in clinical practice. This study may help inform how other CPRs can be adopted into clinical practice.

Beneficial Effects of Preoperative Exercise on the Outcomes of Lumbar Fusion Spinal Surgery.

Purpose: To determine whether there was an association between self-reported preoperative exercise and postoperative outcomes after lumbar fusion spinal surgery. Method: We performed a retrospective multivariable analysis of the prospective Canadian Spine Outcomes and Research Network (CSORN) database of 2,203 patients who had elective single-level lumbar fusion spinal surgeries. We compared adverse events and hospital length of stay between patients who reported regular exercise (twice or more per week) prior to surgery ("Regular Exercise") to those exercising infrequently (once or less per week) ("Infrequent Exercise") or those who did no exercise ("No Exercise"). For all final analyses, we compared the Regular Exercise group to the combined Infrequent Exercise or No Exercise group. Results: After making adjustments for known confounding factors, we demonstrated that patients in the Regular Exercise group had fewer adverse events (adjusted odds ratio 0.72; 95% CI: 0.57, 0.91; p = 0.006) and significantly shorter lengths of stay (adjusted mean 2.2 vs. 2.5 d, p = 0.029) than the combined Infrequent Exercise or No Exercise group. Conclusions: Patients who exercised regularly twice or more per week prior to surgery had fewer postoperative adverse events and significantly shorter hospital lengths of stay compared to patients that exercised infrequently or did no exercise. Further study is required to determine effectiveness of a targeted prehabilitation programme.

Objectif : déterminer s'il y avait une association entre les exercices préopératoires autodéclarés et les résultats postopératoires après une chirurgie de fusion lombaire. Méthodologie : analyse multivariable rétrospective de la base de données prospective Canadian Spine Outcomes and Research Network (CSORN) composée de 2 203 patients qui avaient subi une chirurgie de fusion lombaire univertébrale non urgente. Les chercheurs ont comparé les événements indésirables et la durée du séjour hospitalier entre les patients qui déclaraient faire de l'exercice régulier (au moins deux fois par semaine) avant l'opération (« exercice régulier ¼) à ceux qui n'en faisaient pas souvent (une fois ou moins par semaine; « exercice peu fréquent ¼) et qui n'en faisaient pas du tout (« absence d'exercice ¼). Pour toutes les analyses définitives, ils ont comparé le groupe qui faisait de l'exercice régulier aux groupes combinés d'exercice peu fréquent et d'absence d'exercice. Résultats : après correction pour tenir compte des facteurs confusionnels connus, les chercheurs ont démontré que les patients du groupe faisant de l'exercice régulier présentaient moins d'événements indésirables (rapport de cotes rajusté 0,72; IC à 95 % : 0,57, 0,91; p = 0,006) et leur séjour à l'hôpital était significativement plus court (moyenne corrigée 2,2 jours par rapport à 2,5 jours, p = 0,029) que dans le groupe combiné d'exercice peu fréquent et d'absence d'exercice. Conclusions : les patients qui faisaient de l'exercice régulièrement au moins deux fois par semaine avant l'opération présentaient moins d'événements indésirables après l'opération et étaient hospitalisés beaucoup moins longtemps que ceux qui ne faisaient pas beaucoup d'exercice ou n'en faisaient pas du tout. Il faudra réaliser d'autres études pour déterminer l'efficacité d'un programme de préréadaptation ciblé.

Morphable models of the lumbar spine to vary geometry based on pathology, demographics, and anatomical measurements.

The shape of the lumbar spine influences its function and dysfunction. Yet examining the influence of geometric differences associated with pathology or demographics on lumbar biomechanics is challenging in vivo where these effects cannot be isolated, and the use of simple anatomical measurements does not fully capture the complex three-dimensional geometry. The goal of this work was to develop and share morphable models of the lumbar spine that allow geometry to be varied according to pathology, demographics, or anatomical measurements. Partial least squares regression was used to generate statistical shape models that quantify geometric differences associated with pathology, demographics, and anatomical measurements from the lumbar spines of 87 patients. To determine if the morphable models detected meaningful geometric differences, the ability of the morphable models to classify spines was compared with models generated from random labels. The models for disc herniation (p < 0.04), spondylolisthesis (p < 0.001), and sex (p < 0.01) all performed significantly better than the random models. Age was predicted with a root mean square error of 14.1 years using the age-based model. The morphable models for anatomical measurements were able to produce instances with root mean square errors less than 0.8°, 0.3 cm2, and 0.7 mm between desired and resulting measurements. This method can be used to produce morphable models that enable further analysis of the relationship among shape, pathology, demographics, and function through computational simulations. The morphable models and code are available at https://github.com/aclouthier/morphable-lumbar-model.

Using Clinical Vignettes and a Modified Expert Delphi Panel to Determine Parameters for Identifying Non-Traumatic Spinal Cord Injury in Health Administrative and Electronic Medical Record Databases.

OBJECTIVE: To obtain expert consensus on the parameters and etiologic conditions required to retrospectively identify cases of non-traumatic spinal cord injury (NTSCI) in health administrative and electronic medical record (EMR) databases based on the rating of clinical vignettes. DESIGN: A modified Delphi process included 2 survey rounds and 1 remote consensus panel. The surveys required the rating of clinical vignettes, developed after chart reviews and expert consultation. Experts who participated in survey rounds were invited to participate in the Delphi Consensus Panel. SETTING: An international collaboration using an online meeting platform. PARTICIPANTS: Thirty-one expert physicians and/or clinical researchers in the field of spinal cord injury (SCI). MAIN OUTCOME MEASURE(S): Agreement on clinical vignettes as NTSCI. Parameters to classify cases of NTSCI in health administrative and EMR databases. RESULTS: In health administrative and EMR databases, cauda equina syndromes should be considered SCI and classified as a NTSCI or TSCI based on the mechanism of injury. A traumatic event needs to be listed for injury to be considered TSCI. To be classified as NTSCI, neurologic sufficient impairments (motor, sensory, bowel, and bladder) are required, in addition to an etiology. It is possible to have both a NTSCI and a TSCI, as well as a recovered NTSCI. If information is unavailable or missing in health administrative and EMR databases, the case may be listed as "unclassifiable" depending on the purpose of the research study. CONCLUSION: The Delphi panel provided guidelines to appropriately classify cases of NTSCI in health administrative and EMR databases.

Subsidence of Spinal Fusion Cages: A Systematic Review.

BACKGROUND: Although many research studies investigating subsidence of intervertebral fusion cages have been published, to our knowledge, no study has comprehensively compared cage subsidence among all lumbar intervertebral fusion (LIF) techniques. This study aimed to review the literature reporting evidence of cage subsidence linked to LIF. The amount of subsidence was compared and associated with the procedures and corresponding implants used, and the effect of cage subsidence on clinical outcomes was investigated. METHODS: For this systematic review, the MEDLINE and PubMed databases were used to identify relevant studies. Search terms included lumbar, lumbar vertebrae, lumbar spine, cage, spinal fusion, prosthesis, prosthesis implantation, implantation, implants, interbody, spacer, and subsidence. Studies included in this review were those having more than 10 patients and reporting the amount of subsidence observed using computed tomography or x-ray imaging after surgery and at follow-up visits after a minimum of 6 weeks postsurgery. Data and scale definitions related to subsidence were extracted from articles for comparison of subsidence prevalence between the 5 LIF surgical procedures. RESULTS: Forty articles were identified for inclusion. The review included data from 390 anterior lumbar intervertebral fusions (ALIFs), 2130 lateral lumbar intervertebral fusions (LLIFs), 560 posterior lumbar intervertebral fusions (PLIFs), 245 oblique lumbar intervertebral fusions (OLIFs), and 1634 transverse lumbar intervertebral fusions (TLIFs) for a total of 4959 patients who underwent LIF surgery. The minimum and maximum percentages of the number of patients having subsidence for each procedure in the included studies were as follows: ALIF stand-alone, 6% and 23.1%; LLIF stand-alone, 8.7% and 39.6%; LLIF with posterior fixation, 3.3% and 20.7%; OLIF with posterior fixation, 4.4% and 36.9%; PLIF with posterior fixation, 7.4% and 31.8%; and TLIF, 0.0% and 51.2%. CONCLUSIONS: The number of patients experiencing subsidence varied between studies within each fusion procedure. Our findings indicate that all 5 surgical methods are at risk of subsidence. Overall, ALIF without posterior fixation resulted in the lowest reported subsidence occurrence among the 5 surgical approaches. There is conflicting evidence on the association between subsidence and negative clinical outcomes. CLINICAL RELEVANCE: This review defines and compares subsidence incidence between all LIF procedures and investigates the risk of symptomatic clinical outcomes.

A scoping review of complication prediction models in spinal surgery: An analysis of model development, validation and impact.

Background: Predictive analytics are being used increasingly in the field of spinal surgery with the development of models to predict post-surgical complications. Predictive models should be valid, generalizable, and clinically useful. The purpose of this review was to identify existing post-surgical complication prediction models for spinal surgery and to determine if these models are being adequately investigated with internal/external validation, model updating and model impact studies. Methods: This was a scoping review of studies pertaining to models for the prediction of post-surgical complication after spinal surgery published over 10 years (2010-2020). Qualitative data was extracted from the studies to include study classification, adherence to Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and risk of bias (ROB) assessment using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). Model evaluation was determined using area under the curve (AUC) when available. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used as a basis for the search methodology in four different databases. Results: Thirty studies were included in the scoping review and 80% (24/30) included model development with or without internal validation. Twenty percent (6/30) were exclusively external validation studies and only one study included an impact analysis in addition to model development and internal validation. Two studies referenced the TRIPOD guidelines and there was a high ROB in 100% of the studies using the PROBAST tool. Conclusions: The majority of post-surgical complication prediction models in spinal surgery have not undergone standardized model development and internal validation or adequate external validation and impact evaluation. As such there is uncertainty as to their validity, generalizability, and clinical utility. Future efforts should be made to use existing tools to ensure standardization in development and rigorous evaluation of prediction models in spinal surgery.

Allogeneic blood transfusions and infection risk in lumbar spine surgery: An American College of Surgeons National Surgery Quality Improvement Program Study.

BACKGROUND: Allogenic blood transfusions can lead to immunomodulation. Our purpose was to investigate whether perioperative transfusions were associated with postoperative infections and any other adverse events (AEs), after adjusting for potential confounding factors, following common elective lumbar spinal surgery procedures. STUDY DESIGN AND METHODS: We performed a multivariate, propensity-score matched, regression-adjusted retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program database between 2012 and 2016. All lumbar spinal surgery procedures were identified (n = 174,891). A transfusion group (perioperative transfusion within 72 h before, during, or after principal surgery; n = 1992) and a control group (no transfusion; n = 1992) were formed. Following adjustment for between-group baseline features, adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) were calculated using a multivariate logistic regression model for any surgical site infection (SSI), superficial SSI, deep SSI, wound dehiscence, pneumonia, urinary tract infection, sepsis, any infection, mortality, and any AEs. RESULTS: Transfusion was associated with an increased risk of each specific infection, mortality, and any AEs. Statistically significant between-group differences were demonstrated with respect to any SSI (aOR: 1.48; 95% CI: 1.01-2.16), deep SSI (aOR: 1.66; 95% CI: 0.98-2.85), sepsis (aOR: 2.69; 95% CI: 1.43-5.03), wound dehiscence (aOR: 2.27; 95% CI: 0.86-6.01), any infection (aOR: 1.46; 95% CI: 1.13-1.88), any AEs (aOR: 1.80; 95% CI: 1.48-2.18), and mortality (aOR: 2.17; 95% CI: 0.77-6.36). CONCLUSION: We showed an association between transfusion and infection in lumbar spine surgery after adjustment for various applicable covariates. Sepsis had the highest association with transfusion. Our results reinforce a growing trend toward minimizing perioperative transfusions, which may lead to reduced infections following lumbar spine surgery.

Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and F1-score for the assessment of prognostic capability.

BACKGROUND: With spinal surgery rates increasing in North America, models that are able to accurately predict which patients are at greater risk of developing complications are highly warranted. However, the previously published methods which have used large, multi-centre databases to develop their prediction models have relied on the receiver operator characteristics curve with the associated area under the curve (AUC) to assess their model's performance. Recently, it has been found that a precision-recall curve with the associated F1-score could provide a more realistic analysis for these models. PURPOSE: To develop a logistic regression (LR) model for the prediction of complications following posterior lumbar spine surgery and to then assess for any difference in performance of the model when using the AUC versus the F1-score. STUDY DESIGN: Retrospective review of a prospective cohort. PATIENT SAMPLE: The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) registry was used. All patients that underwent posterior lumbar spine surgery between 2005 to 2016 with appropriate data were included. OUTCOME MEASURES: Both the AUC and F1-score were utilized to assess the prognostic performance of the prediction model. METHODS: In order to develop the LR model used to predict a complication during or following spine surgery, 19 variables were selected by three orthopedic spine surgeons from the NSQIP registry. Two datasets were developed for this analysis: (1) an imbalanced dataset, which was taken directly from the NSQIP registry, and (2) a down-sampled set. The purpose of the down-sampled set was to balance the data in order to evaluate whether balancing the data had an effect on model performance. The AUC and F1-score were applied to both of these datasets. RESULTS: Within the NSQIP database, 52,787 spine surgery cases were identified of which only 10% of these cases had complications during surgery. Applying the LR model showed a large difference between the AUC (0.69) and the F1 score (0.075) on the imbalanced dataset. However, no major differences existed between the AUC and F1-score when the data was balanced and the LR model was reapplied (0.69 and 0.62, AUC and F1-score, respectively). CONCLUSIONS: The F1-score detected a drastically lower performance for the prediction of complications when using the imbalanced data, but detected a performance similar to the AUC level when balancing techniques were utilized for the dataset. This difference is due to a low precision score when many false positive classifications are present, which is not identified when using the AUC value. This lowers the utility of the AUC score, as many of the datasets used in medicine are imbalanced. Therefore, we recommend using the F1-score on large, prospective databases when the data is imbalanced with a large amount of true negative classifications.

Preoperative Factors Predict Postoperative Trajectories of Pain and Disability Following Surgery for Degenerative Lumbar Spinal Stenosis.

STUDY DESIGN: Longitudinal analysis of prospectively collected data. OBJECTIVE: Investigate potential predictors of poor outcome following surgery for degenerative lumbar spinal stenosis (LSS). SUMMARY OF BACKGROUND DATA: LSS is the most common reason for an older person to undergo spinal surgery, yet little information is available to inform patient selection. METHODS: We recruited LSS surgical candidates from 13 orthopedic and neurological surgery centers. Potential outcome predictors included demographic, health, clinical, and surgery-related variables. Outcome measures were leg and back numeric pain rating scales and Oswestry disability index scores obtained before surgery and after 3, 12, and 24 postoperative months. We classified surgical outcomes based on trajectories of leg pain and a composite measure of overall outcome (leg pain, back pain, and disability). RESULTS: Data from 529 patients (mean [SD] age = 66.5 [9.1] yrs; 46% female) were included. In total, 36.1% and 27.6% of patients were classified as experiencing a poor leg pain outcome and overall outcome, respectively. For both outcomes, patients receiving compensation or with depression/depression risk were more likely, and patients participating in regular exercise were less likely to have poor outcomes. Lower health-related quality of life, previous spine surgery, and preoperative anticonvulsant medication use were associated with poor leg pain outcome. Patients with ASA scores more than two, greater preoperative disability, and longer pain duration or surgical waits were more likely to have a poor overall outcome. Patients who received preoperative chiropractic or physiotherapy treatment were less likely to report a poor overall outcome. Multivariable models demonstrated poor-to acceptable (leg pain) and excellent (overall outcome) discrimination. CONCLUSION: Approximately one in three patients with LSS experience a poor clinical outcome consistent with surgical non-response. Demographic, health, and clinical factors were more predictive of clinical outcome than surgery-related factors. These predictors may assist surgeons with patient selection and inform shared decision-making for patients with symptomatic LSS. LEVEL OF EVIDENCE: 2.

Capturing adverse events in elective orthopedic surgery: comparison of administrative, surgeon and reviewer reporting

Summary: Ensuring adverse event (AE) recording is standardized and accurate is paramount for patient safety. In this discussion, we outline our comparison of AE data collected by orthopedic surgeons and independent clinical reviewers using the Spine Adverse Events Severity System (SAVES) and Orthopedic Surgical Adverse Events Severity System (OrthoSAVES) against AE data recorded by hospital administrative discharge abstract coders. In 164 spine, hip, knee and shoulder patients, reviewers recorded significantly more AEs than coders, and coders recorded significantly more AEs than surgeons. The AEs were recorded similarly by reviewers using SAVES and OrthoSAVES in 48 spine patients. Despite our small sample size and use of different AE tools, we believe it is important to highlight that coders, surgeons and reviewers recorded AEs differently. While further investigations on its utility and cost-effectiveness are necessary, we assert that it is feasible to use Ortho-SAVES to prospectively record AEs across all orthopedic subspecialties.

Patients undergoing surgery for lumbar spinal stenosis experience unique courses of pain and disability: A group-based trajectory analysis.

OBJECTIVE: Identify patient subgroups defined by trajectories of pain and disability following surgery for degenerative lumbar spinal stenosis, and investigate the construct validity of the subgroups by evaluating for meaningful differences in clinical outcomes. METHODS: We recruited patients with degenerative lumbar spinal stenosis from 13 surgical spine centers who were deemed to be surgical candidates. Study outcomes (leg and back pain numeric rating scales, modified Oswestry disability index) were measured before surgery, and after 3, 12, and 24 months. Group-based trajectory models were developed to identify trajectory subgroups for leg pain, back pain, and pain-related disability. We examined for differences in the proportion of patients achieving minimum clinically important change in pain and disability (30%) and clinical success (50% reduction in disability or Oswestry score ≤22) 12 months from surgery. RESULTS: Data from 548 patients (mean[SD] age = 66.7[9.1] years; 46% female) were included. The models estimated 3 unique trajectories for leg pain (excellent outcome = 14.4%, good outcome = 49.5%, poor outcome = 36.1%), back pain (excellent outcome = 13.1%, good outcome = 45.0%, poor outcome = 41.9%), and disability (excellent outcome = 30.8%, fair outcome = 40.1%, poor outcome = 29.1%). The construct validity of the trajectory subgroups was confirmed by between-trajectory group differences in the proportion of patients meeting thresholds for minimum clinically important change and clinical success after 12 postoperative months (p < .001). CONCLUSION: Subgroups of patients with degenerative lumbar spinal stenosis can be identified by their trajectories of pain and disability following surgery. Although most patients experienced important reductions in pain and disability, 29% to 42% of patients were classified as members of an outcome trajectory subgroup that experienced little to no benefit from surgery. These findings may inform appropriate expectation setting for patients and clinicians and highlight the need for better methods of treatment selection for patients with degenerative lumbar spinal stenosis.

Management of cervical spine epidural abscess: a systematic review.

BACKGROUND: Cervical spinal epidural abscess (CSEA) is a localized infection between the thecal sac and cervical spinal column which may result in neurological deficit and death if inadequately treated. Two treatment options exist: medical management and surgical intervention. Our objective was to analyze CSEA patient outcomes in order to determine the optimal method of treatment. METHODS: An electronic literature search for relevant case series and retrospective reviews was conducted through June 2016. Data abstraction and study quality assessment were performed by two independent reviewers. A lack of available data led to a post hoc decision not to perform meta-analysis of the results; study findings were synthesized qualitatively. RESULTS: 927 studies were identified, of which 11 were included. Four studies were ranked as good quality, and seven ranked as fair quality. In total, data from 173 patients were included. Mean age was 55 years; 61.3% were male. Intravenous drug use was the most common risk factor for CSEA development. Staphylococcus aureus was the most commonly cultured pathogen. 140 patients underwent initial surgery, an additional 18 patients were surgically treated upon failure of medical management, and 15 patients were treated with antibiotics alone. CONCLUSION: The rates of medical management failure described in our review were much higher than those reported in the literature for thoracolumbar spinal epidural abscess patients, suggesting that CSEA patients may be at a greater risk for poor outcomes following nonoperative treatment. Thus, early surgery appears most viable for optimizing CSEA patient outcomes. Further research is needed in order to corroborate these recommendations.

A technique for approximating transition rates from published survival analyses.

BACKGROUND: Quality-adjusted-life-years (QALYs) are used to concurrently quantify morbidity and mortality within a single parameter. For this reason, QALYs can facilitate the discussion of risks and benefits during patient counseling regarding treatment options. QALYs are often calculated using partitioned-survival modelling. Alternatively, QALYs can be calculated using more flexible and informative state-transition models populated with transition rates estimated using multistate modelling (MSM) techniques. Unfortunately the latter approach is considered not possible when only progression-free survival (PFS) and overall survival (OS) analyses are reported. METHODS: We have developed a method that can be used to estimate approximate transition rates from published PFS and OS analyses (we will refer to transition rates estimated using full multistate methods as true transition rates). RESULTS: The approximation method is more accurate for estimating the transition rates out of health than the transition rate out of illness. The method tends to under-estimate true transition rates as censoring increases. CONCLUSIONS: In this article we present the basis for and use of the transition rate approximation method. We then apply the method to a case study and evaluate the method in a simulation study.

A General Population Utility Valuation Study for Metastatic Epidural Spinal Cord Compression Health States.

STUDY DESIGN: General population utility valuation study. OBJECTIVE: This study obtained utility valuations from a Canadian general population perspective for 31 unique metastatic epidural spinal cord compression (MESCC) health states and determined the relative importance of MESCC-related consequences on quality-of-life. SUMMARY OF BACKGROUND DATA: Few prospective studies on the treatment of MESCC have collected quality-adjusted-life-year weights (termed "utilities"). Utilities are an important summative measure which distills health outcomes to a single number that can assist healthcare providers, patients, and policy makers in decision making. METHODS: We recruited a sample of 1138 adult Canadians using a market research company. Quota sampling was used to ensure that the participants were representative of the Canadian population in terms of age, sex, and province of residence. Using the validated MESCC module for the "Self-administered Online Assessment of Preferences" (SOAP) tool, participants were asked to rate six of the 31 MESCC health states, each of which presented varying severities of five MESCC-related dysfunctions (dependent; non-ambulatory; incontinent; pain; other symptoms). RESULTS: Participants equally valued all MESCC-related dysfunctions which followed a pattern of diminishing marginal disutility (each additional consequence resulted in a smaller incremental decrease in utility than the previous). These results demonstrate that the general population values physical function equal to other facets of quality-of-life. CONCLUSION: We provide a comprehensive set of ex ante utility estimates for MESCC health states that can be used to help inform decision making. This is the first study reporting direct utility valuation for a spinal disorder. Our methodology offers a feasible solution for obtaining quality-of-life data without collecting generic health status questionnaire responses from patients. LEVEL OF EVIDENCE: 4.

Back to Bayesian: A strategy to enhance prognostication of metastatic spine disease.

AIMS: Clinicians must consider prognosis when offering treatment to patients with spine metastases. Although several prognostic indices have been developed and validated for this purpose, they may not be applicable in the current era of targeted systemic therapies. Even before the introduction of targeted therapies, these prognostic indices should not have been directly used for individual patient decision making without contextualising with other sources of data. By contextualising, we mean that prognostic estimates should not be based on these scores alone and formally incorporate clinically relevant factors not part of prognostic indices. Contextualisation requires the use of Bayesian statistics which may be unfamiliar to many readers. In this paper we show readers how to correctly apply prognostic scores to individual patients using Bayesian statistics. Through Bayesian analysis, we explore the impact of new targeted therapies on prognostic estimates obtained using the Tokuhashi score. METHODS: We provide a worked calculation for the probability of a patient surviving up to 6 months using dichotomous prognostication. We then demonstrate how to calculate a patient's expected survival using continuous prognostication. Sensitivity of the posterior distribution to prior assumptions is illustrated through effective sample size adjustment. RESULTS: When the predicted prognosis from the Tokuhashi score is contextualised with data on contemporary systemic treatments, patients previously deemed non-surgical candidates may be eligible for surgery. CONCLUSIONS: Bayesian prognostication generates intuitive results and allows multiple data points to be synthesised transparently. These techniques can extend the usefulness of existing prognostic scores in the era of targeted systemic therapies.

Highlighting discrepancies in walking prediction accuracy for patients with traumatic spinal cord injury: an evaluation of validated prediction models using a Canadian Multicenter Spinal Cord Injury Registry.

BACKGROUND CONTEXT: Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable. PURPOSE: To compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models. STUDY DESIGN: A prospective cohort study. PARTICIPANT SAMPLE: Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data. OUTCOME MEASURES: The FIM locomotor score was used to assess independent walking ability at 1-year follow-up. METHODS: Two validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC). RESULTS: In total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses. CONCLUSIONS: Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.