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Identifying optimal stratification techniques for subgrouping patients with low back pain (LBP) into treatment groups for the purpose of identifying optimal management and improving clinical outcomes is an important area for further research. Our study aimed to compare performance of the STarT Back Tool (SBT) and 3 stratification techniques involving PROMIS domain scores for use in patients presenting to a spine clinic for chronic LBP. Retrospective cohort study. Adult patients with chronic LBP seen in a spine center between November 14, 2018 and May 14, 2019 who completed patient-reported outcomes (PROs) as part of routine care, and were followed up with completed PROs 1 year later. Four stratification techniques, including SBT, and 3 PROMIS-based techniques: the NIH Task Force recommended Impact Stratification Score (ISS), symptom clusters based on latent class analysis (LCA), and SPADE symptom clusters. The 4 stratification techniques were compared according to criterion validity, construct validity, and prognostic utility. For criterion validity, overlap in characterization of mild, moderate, and severe subgroups were compared to SBT, which was considered the gold standard, using quadratic weighted kappa statistic. Construct validity compared techniques' ability to differentiate across disability groups defined by modified Oswestry LBP Disability Questionnaire (MDQ), median days in the past month unable to complete activities of daily living (ADLs), and worker's compensation using standardized mean differences (SMD). Prognostic utility was compared based on the techniques' ability to predict long-term improvement in outcomes, defined as improvement in global health and MDQ at 1-year. There were 2,246 adult patients with chronic LBP included in our study (mean age 61.0 [SD 14.0], 55.0% female, 83.4% white). All stratification techniques resulted in roughly a third of patients grouped into mild, moderate, and severe categories, with ISS and LCA demonstrating substantial agreement with SBT, while SPADE had moderate agreement. Construct validity was met for all techniques, with large effects demonstrated between mild and severe categories for differentiating MDQ, ADLs, and worker's compensation disability groups (SMD range 0.57–2.48). All stratification techniques demonstrated ability to detect improvement by 1-year, with severe groups experiencing the greatest improvement in multivariable logistic regression models. All 4 stratification techniques demonstrated validity and prognostic utility for subgrouping patients with chronic LBP based on risk of long-term disability. ISS and LCA symptom clusters may be the optimal methods given the improved feasibility of including only a few relevant PROMIS domains. Future research should investigate multidisciplinary treatment approaches to target mild, moderate, and severe patients based on these techniques. [ABSTRACT FROM AUTHOR]

Background: The EQ-5D and Health Utilities Index Mark 3 (HUI-3) are preference-based measures used in cost-effectiveness studies. The Patient Reported Outcomes Measurement Information System (PROMIS) Preference scoring system (PROPr) is a new preference-based measure. In addition, algorithms were previously developed to map PROMIS Global Health (PROMIS-GH) items to HUI-3 using linear equating (HUILE) and 3-level EQ-5D using linear (EQ5DLE). We sought to evaluate and compare estimated utilities based on PROPr and PROMIS-GH in adult stroke survivors. Methods: We performed a retrospective cohort study of adults diagnosed with 1 of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage seen in an outpatient clinic between 2015 and 2019. Patients completed PROMIS scales and other measures. We computed a modified version of PROPr (mPROPr) and compared the distributional characteristics and correlations with stroke outcomes for mPROPr, HUILE, and EQ5DLE. Results: T toal of 4,159 stroke survivors (average age 62.7 ± 14.7 y, 48.4% female, 77.6% ischemic stroke) were included. Mean utility estimates for mPROPr, EQ5DLE, and HUILE were 0.333 ± 0.244, 0.739 ± 0.201, and 0.544 ± 0.301, respectively. Correlations between the modified Rankin Scale and each of mPROPr and HUILE were both −0.48 and −0.43 for EQ5DLE. Regression analyses indicated that mPROPr scores may be too low for stroke patients in good health and that EQ5DLE scores may be too high for stroke patients in poor health. Conclusions: All 3 PROMIS-based utilities were associated with measures of stroke disability and severity, but the distributions of utilities were very different. Our study highlights the problem cost-effectiveness researchers face of valuing health states with certainty. For researchers using utilities estimated from PROMIS scales, our study indicates that mapping PROMIS-GH item scores to HUI-3 via linear equating may be most appropriate in stroke patients. Highlights: A new preference-based measure has been developed from the Patient Reported Outcomes Measurement Information System (PROMIS), known as the PROMIS-Preference (PROPr) scoring system, and published equations mapping PROMIS Global Health (PROMIS-GH) items to the Health Utilities Index Mark 3 (HUI-3) and EQ-5D-3L are available for use in cost-effectiveness studies. Our study provides distributional characteristics and comparisons of utilities estimated using a modified version of PROPr and equations mapping PROMIS-GH items to EQ-5D-3L and HUI-3 in a sample of stroke survivors. The results of our study show large differences in the distributions of utilities estimated using the different health state measures, and these differences highlight the ongoing difficulty researchers face in valuing health states with certainty. [ABSTRACT FROM AUTHOR]

Background Context: Improving prognostic stratification for patients with low back pain (LBP) outside of a primary care setting has been identified as an important area for further research.Purpose: Our study aimed to identify clinical symptom classes of patients presenting to a spine clinic based on 4 Patient Reported Outcome Measurement Information System (PROMIS) domains and evaluate demographic and clinical differences across classes.Study Design: An observational cross-sectional study of patients seen in spine centers at a large health system.Patient Sample: Adult patients with LBP seen in a spine center between November 14, 2018 and May 14, 2019 who completed patient-reported outcomes as part of routine care.Outcome Measures: PROMIS physical function, pain interference, satisfaction with social roles and activities, and fatigue.Methods: Latent class analysis identified symptom classes based on PROMIS domain scores ≥1 standard deviation worse (meaningfully worse) than the general population. A multivariable multinomial logistic regression model was constructed to evaluate differences in symptom classes based on demographics and socioeconomic characteristics. Lastly, the ability of the profiles to discriminate across levels of disability, based on the modified Oswestry Disability Questionnaire (ODI), was evaluated.Results: There were 7,144 adult patients included in the study who visited spine clinics for a primary complaint of LBP and completed all 4 PROMIS domains (age 58.7±15.9, 54% female). Three distinct classes were identified. Class 1 ("Significant Symptoms," n=3238) had PROMIS scores that were meaningfully worse than the population average for all domains. Class 2 ("Mixed Symptoms," n=1366) had meaningfully worse scores on physical function and pain interference but average scores on other domains. Class 3 ("Mild Symptoms," n=2540) had average scores across all domains. Compared to patients in Class 3, those in Class 2 were more likely older, and those in Classes 1 and 2 were more likely to be divorced, have lower household income, and no employment. Level of disability was significantly different across each class (average (SD) ODI for Classes 1-3: 53.4 (14.3), 39.9 (12.5), 22.9 (12.1), p<.01).Conclusions: Patients presenting to specialty clinics for LBP demonstrate distinct clinical symptom classes which could be utilized to inform specific symptom-based treatment. Future research should evaluate the ability of these classes to predict long-term disability. [ABSTRACT FROM AUTHOR]

As the implementation of Patient-Reported Outcomes Measurement Information System (PROMIS) scales has increased, so has the number of studies linking legacy scale scores to PROMIS scale scores. Variability in linked scores for a given PROMIS score can be considerable, leading to potential bias. An alternative method is imputation using a bridge study. We sought to compare linking to this alternative novel method in group-level analyses using linked legacy scores. Adult patients who completed PROMIS Depression and Patient Health Questionnaire (PHQ-9) were included. We randomly allocated data samples to be missing either PROMIS Depression or PHQ-9. We estimated PROMIS T scores using six methods: Linking methods by Choi et al. (2014), linking in our internal data, imputation using bridge study data from external data and from our internal data, each with and without patient demographics. Estimated mean PROMIS T scores using the linking and imputation methods were compared to actual PROMIS T scores across varying proportions of missingness and sample size. We also compared regression coefficients for the six estimation methods to a model using actual PROMIS T scores. Mean estimated versus actual PROMIS T scores varied between 1 and 4 points for the linking methods and within 0.4 points for the imputation method using internal data with patient demographics. The imputation methods had estimated regression coefficients closer to that of the model using actual scores as compared to the linking methods. For group-level analyses, imputation using a bridge study may be a feasible alternative to using linked scores or can be used as a sensitivity analysis. Public Significance Statement: Many legacy scales have been linked to associated PROMIS scales using item response theory and other linking strategies. Group-level analyses involving such linked scores sometimes result in biased parameter estimates. We propose an alternative analysis approach: Imputation using a bridge study. The study findings indicate that imputation using a bridge study may be a feasible alternative to analyzing legacy scale data scored on a PROMIS metric. [ABSTRACT FROM AUTHOR]