Patient, Provider, and Clinic Characteristics Associated with Opioid and Non-Opioid Pain Prescriptions for Patients Receiving Low Back Imaging in Primary Care.

BACKGROUND: To describe characteristics of patients, providers, and clinics associated with opioid or non-opioid pain medication prescribing patterns for patients who received lower spine imaging in primary care clinics. METHODS: In these secondary analyses of the Lumbar Imaging with Reporting of Epidemiology (LIRE) study, a randomized controlled trial conducted in 4 health systems in the United States, we evaluated characteristics associated with receipt of pain medication prescriptions. The outcomes were receipt of prescriptions for opioid or, separately, non-opioid pain medications within 90 days after imaging. Among patients who received opioid or non-opioid prescriptions, we evaluated receipt of multiple prescriptions in the year following imaging. Mixed models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Compared with whites, patients identified as Asian (OR, 0.53; 95% CI, 0.51-0.56), Native Hawaiian/Pacific Islander (OR, 0.73; 95% CI, 0.64-0.83), multiracial (OR, 0.84; 95% CI, 0.71-0.98) or Black (OR, 0.92; 95% CI, 0.89-0.96) had significantly reduced odds for receiving prescriptions for opioids within 90 days. Patients identified as Native American/Alaska Native had greater odds for receiving prescriptions for non-opioid pain medications within 90 days (OR, 1.12; 95% CI, 1.01-1.24). Receipt of pain prescriptions 120 days before imaging was strongly predictive of subsequent receipt of pain prescriptions across all categories. CONCLUSIONS: After adjusting for factors that could affect prescribing, the strongest differences observed in pain-medication prescribing were across racial categories and for patients with previous pain prescriptions. Further research is needed to understand these differences and to optimize prescribing.

Expected Organizational Costs for Inserting Prevalence Information Into Lumbar Spine Imaging Reports.

BACKGROUND: Modifying physician behavior to more closely align with guideline-based care can be challenging. Few effective strategies resulting in appropriate spine-related health care have been reported. The Lumbar Imaging With Reporting of Epidemiology (LIRE) intervention did not result in reductions in spine care but did in opioid prescriptions written. OBJECTIVES: To estimate organizational resource needs and costs associated with implementing a pragmatic, decision support-type intervention that inserted age- and modality-matched prevalence information for common lumbar spine imaging findings, using site-based resource use data from the LIRE trial. RESEARCH DESIGN: Time and cost estimation associated with implementing the LIRE intervention in a health organization. SUBJECTS: Providers and patients assessed in the LIRE trial. MEASURES: Expected personnel costs required to implement the LIRE intervention. RESULTS: Annual salaries were converted to daily average per person costs, ranging from $400 to $2,200 per day (base case) for personnel (range: $300-$2,600). Estimated total average cost for implementing LIRE was $5,009 (range: $2,651-$12,020), including conducting pilot testing with providers. Costs associated with a small amount of time for a radiologist (6-12 hours) and imaging-ordering providers (1-8 hours each) account for approximately 75% of the estimated total cost. CONCLUSIONS: The process of implementing an intervention for lumbar spine imaging reports containing age- and modality-appropriate epidemiological benchmarks for common imaging findings required radiologists, imaging-ordering providers, information technology specialists, and limited testing and monitoring. The LIRE intervention seems to be a relatively low-cost, evidence-based, complementary tool that can be easily integrated into the reporting of spine imaging.

Providing Epidemiological Data in Lumbar Spine Imaging Reports Did Not Affect Subsequent Utilization of Spine Procedures: Secondary Outcomes from a Stepped-Wedge Randomized Controlled Trial.

OBJECTIVE: To evaluate the effect of inserting epidemiological information into lumbar spine imaging reports on subsequent nonsurgical and surgical procedures involving the thoracolumbosacral spine and sacroiliac joints. DESIGN: Analysis of secondary outcomes from the Lumbar Imaging with Reporting of Epidemiology (LIRE) pragmatic stepped-wedge randomized trial. SETTING: Primary care clinics within four integrated health care systems in the United States. SUBJECTS: 238,886 patients ≥18 years of age who received lumbar diagnostic imaging between 2013 and 2016. METHODS: Clinics were randomized to receive text containing age- and modality-specific epidemiological benchmarks indicating the prevalence of common spine imaging findings in people without low back pain, inserted into lumbar spine imaging reports (the "LIRE intervention"). The study outcomes were receiving 1) any nonsurgical lumbosacral or sacroiliac spine procedure (lumbosacral epidural steroid injection, facet joint injection, or facet joint radiofrequency ablation; or sacroiliac joint injection) or 2) any surgical procedure involving the lumbar, sacral, or thoracic spine (decompression surgery or spinal fusion or other spine surgery). RESULTS: The LIRE intervention was not significantly associated with subsequent utilization of nonsurgical lumbosacral or sacroiliac spine procedures (odds ratio [OR] = 1.01, 95% confidence interval [CI] 0.93-1.09; P = 0.79) or any surgical procedure (OR = 0.99, 95 CI 0.91-1.07; P = 0.74) involving the lumbar, sacral, or thoracic spine. The intervention was also not significantly associated with any individual spine procedure. CONCLUSIONS: Inserting epidemiological text into spine imaging reports had no effect on nonsurgical or surgical procedure utilization among patients receiving lumbar diagnostic imaging.

Effects of Including Epidemiologic Data in Lumbar Spine Imaging Reports on Prescribing Non-Opioid Medications for Pain.

BACKGROUND: Information on the prevalence of common imaging findings among patients without back pain in spine imaging reports might affect pain medication prescribing for patients with back pain. Prior research on inserting this text suggested a small reduction in opioid prescribing. OBJECTIVE: To evaluate the effect of epidemiologic information in spine imaging reports on non-opioid pain medication prescribing for primary care patients with back pain. DESIGN: Post hoc analysis of the Lumbar Imaging with Reporting of Epidemiology cluster-randomized trial. PARTICIPANTS: A total of 170,680 patients aged ≥ 18 years from four healthcare systems who received thoracolumbar, lumbar, or lumbosacral spine imaging from 2013 to 2016 and had not received a prescription for non-opioid pain medication in the preceding 120 days. INTERVENTION: Text of age- and modality-specific epidemiologic benchmarks indicating the prevalence of common findings in people without back pain inserted into thoracolumbar, lumbar, or lumbosacral spine imaging reports at intervention clinics. MAIN MEASURES: Primary outcomes: any non-opioid prescription within 90 days after index imaging, overall, and by sub-class (skeletal muscle relaxants, NSAIDs, gabapentinoids, tricyclic antidepressants, benzodiazepines, duloxetine). SECONDARY OUTCOMES: count of non-opioid prescriptions within 90 days, overall, and by sub-class. KEY RESULTS: The intervention was not associated with the likelihood of patients receiving at least one prescription for new non-opioid pain-related medications, overall (adjusted OR, 1.02; 95% CI, 0.97-1.08) or by sub-class. The intervention was not associated with the number of prescriptions for any non-opioid medication (adjusted incidence rate ratio [IRR], 1.02; 95% CI, 0.99-1.04). However, the intervention was associated with more new prescriptions for NSAIDs (IRR, 1.12) and tricyclic antidepressants (IRR, 1.11). CONCLUSIONS: Inserting epidemiologic text in spine imaging reports had no effect on whether new non-opioid pain-related medications were prescribed but was associated with the number of new prescriptions for certain non-opioid sub-classes. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02015455.

The Effect of Including Benchmark Prevalence Data of Common Imaging Findings in Spine Image Reports on Health Care Utilization Among Adults Undergoing Spine Imaging: A Stepped-Wedge Randomized Clinical Trial.

Importance: Lumbar spine imaging frequently reveals findings that may seem alarming but are likely unrelated to pain. Prior work has suggested that inserting data on the prevalence of imaging findings among asymptomatic individuals into spine imaging reports may reduce unnecessary subsequent interventions. Objective: To evaluate the impact of including benchmark prevalence data in routine spinal imaging reports on subsequent spine-related health care utilization and opioid prescriptions. Design, Setting, and Participants: This stepped-wedge, pragmatic randomized clinical trial included 250 401 adult participants receiving care from 98 primary care clinics at 4 large health systems in the United States. Participants had imaging of their backs between October 2013 and September 2016 without having had spine imaging in the prior year. Data analysis was conducted from November 2018 to October 2019. Interventions: Either standard lumbar spine imaging reports (control group) or reports containing age-appropriate prevalence data for common imaging findings in individuals without back pain (intervention group). Main Outcomes and Measures: Health care utilization was measured in spine-related relative value units (RVUs) within 365 days of index imaging. The number of subsequent opioid prescriptions written by a primary care clinician was a secondary outcome, and prespecified subgroup analyses examined results by imaging modality. Results: We enrolled 250 401 participants (of whom 238 886 [95.4%] met eligibility for this analysis, with 137 373 [57.5%] women and 105 497 [44.2%] aged >60 years) from 3278 primary care clinicians. A total of 117 455 patients (49.2%) were randomized to the control group, and 121 431 patients (50.8%) were randomized to the intervention group. There was no significant difference in cumulative spine-related RVUs comparing intervention and control conditions through 365 days. The adjusted median (interquartile range) RVU for the control group was 3.56 (2.71-5.12) compared with 3.53 (2.68-5.08) for the intervention group (difference, -0.7%; 95% CI, -2.9% to 1.5%; P = .54). Rates of subsequent RVUs did not differ between groups by specific clinical findings in the report but did differ by type of index imaging (eg, computed tomography: difference, -29.3%; 95% CI, -42.1% to -13.5%; magnetic resonance imaging: difference, -3.4%; 95% CI, -8.3% to 1.8%). We observed a small but significant decrease in the likelihood of opioid prescribing from a study clinician within 1 year of the intervention (odds ratio, 0.95; 95% CI, 0.91 to 1.00; P = .04). Conclusions and Relevance: In this study, inserting benchmark prevalence information in lumbar spine imaging reports did not decrease subsequent spine-related RVUs but did reduce subsequent opioid prescriptions. The intervention text is simple, inexpensive, and easily implemented. Trial Registration: ClinicalTrials.gov Identifier: NCT02015455.

Association of Allergic Rhinitis With Change in Nasal Congestion in New Continuous Positive Airway Pressure Users.

Importance: Nasal congestion occurring after continuous positive airway pressure (CPAP) treatment initiation impairs CPAP adherence. Allergic rhinitis is associated with worsening nasal congestion in patients who are exposed to nonallergic triggers. Use of CPAP presents potential nonallergic triggers (eg, humidity, temperature, pressure, and airflow). Objective: To compare nasal congestion among CPAP users with allergic rhinitis, nonallergic rhinitis, and no rhinitis. We hypothesize that CPAP patients with baseline allergic rhinitis are more likely to experience a worsening of nasal congestion (or less improvement in nasal congestion) compared with patients with no baseline rhinitis. Design, Setting, and Participants: This prospective cohort study included consecutive patients newly diagnosed with obstructive sleep apnea in a tertiary sleep center who were using CPAP therapy 3 months after diagnosis. Baseline rhinitis status was assigned as allergic rhinitis, nonallergic rhinitis, or no rhinitis, based on questionnaire responses and past allergy testing. Data were collected from 2004 to 2008 and analyzed from July 2019 to February 2020. Main Outcomes and Measures: At baseline before CPAP exposure and again 3 months later, subjective nasal congestion was measured with the Nasal Obstruction Symptom Evaluation (NOSE) scale and a visual analog scale (VAS), each scored from 0 to 100 (100 = worst congestion). Changes in nasal congestion were tested over 3 months for the whole cohort, within each rhinitis subgroup (paired t test), and between rhinitis subgroups (multivariate linear regression). Results: The study cohort comprised 102 participants, of whom 61 (60%) were male and the mean (SD) age was 50 (13). The study included 23 (22.5%) participants with allergic rhinitis, 67 (65.7%) with nonallergic rhinitis, and 12 (11.8%) with no rhinitis. Nasal congestion improved from baseline to 3 months in the whole cohort (mean [SD] NOSE score, 38 [26] to 27 [23], mean [SD] change, -10 [23]; 95% CI, -15 to -6; mean [SD] VAS score, 41 [27] to 32 [28]; mean [SD] change, -10 [26]; 95% CI, [-15 to -4]) and in each rhinitis subgroup. Adjusted improvement in nasal congestion at 3 months was significantly less in the allergic rhinitis subgroup compared with the no rhinitis subgroup (positive difference means less improvement) compared with baseline: NOSE score 14 (95% CI, 1 to 28) and VAS score 15 (95% CI, 0 to 30). Conclusions and Relevance: Initiation of CPAP was associated with improved subjective nasal congestion, but less improvement in patients with baseline allergic rhinitis. Baseline allergic rhinitis may predict which patients are more vulnerable to potential congestive effects of CPAP.

Development of a Sleep Apnea-Specific Health State Utility Algorithm.

Importance: With the increasing emphasis on economic evaluations, there is a need for additional methods of measuring patient utility in the obstructive sleep apnea population. Objective: To develop and validate a utility scoring algorithm for a sleep apnea-specific quality-of-life instrument. Design, Setting, and Participants: Development and validation were conducted at 2 tertiary referral sleep centers and associated sleep clinics and included patients with newly diagnosed obstructive sleep apnea from a randomized clinical trial and an associated observational cohort study. Baseline participants were randomly divided into a model development group (60%) and a cross-validation group (40%). Main Outcomes and Measures: Utility scoring of the Symptoms of Nocturnal Obstruction and Related Events (SNORE-25) was mapped from the SF-6D utility index through multiple linear regression in the development sample using the Akaike information criterion to determine the best model. Results: A total of 500 participants (development, n = 300; validation, n = 200) were enrolled; the analyzed sample of 500 participants included 295 men (59%), and the mean (SD) age was 48.6 (12.8) years, with a range of 18 to 90 years. The mean (SD) SF-6D utility among participants with untreated sleep apnea was 0.61 (0.08; range, 0.40-0.85) with similar utility across sleep apnea severity groups. The best-fit model (the SNORE Utility Index) was the natural log conversion of the instrument subscales (r2 = 0.32 in the development sample). The SNORE Utility Index retained this association within the validation sample (r2 = 0.33). Conclusions and Relevance: The SNORE Utility Index provides a validated, disease-specific, preference-weighted utility instrument that can be used in future studies of patients with obstructive sleep apnea.

Long-term outcomes of a large, prospective observational cohort of older adults with back pain.

BACKGROUND CONTEXT: Although back pain is common among older adults, there is relatively little research on the course of back pain in this age group. PURPOSE: Our primary goals were to report 2-year outcomes of older adults initiating primary care for back pain and to examine the relative importance of patient factors versus medical interventions in predicting 2-year disability and pain. STUDY DESIGN/SETTING: This study used a predictive model using data from a prospective, observational cohort from a primary care setting. PATIENT SAMPLE: The study included patients aged ≥65 years at the time of new primary care visits for back pain. OUTCOME MEASURES: Self-reported 2-year disability (Roland-Morris Disability Questionnaire [RDQ]) and back pain (0-10 numerical rating scale [NRS]). METHODS: We developed our models using a machine learning least absolute shrinkage and selection operator approach. We evaluated the predictive value of baseline characteristics and the incremental value of interventions that occurred between 0 and 90 days, and the change in patient disability and pain from 0 to 90 days. Limitations included confounding by indication and unmeasured confounding. RESULTS: Of 4,665 patients (89%) with follow-up, both RDQ (from mean 9.6 [95% confidence interval {CI} 9.4-9.7] to mean 8.3 [95% CI 8.0-8.5]) and back pain NRS (from mean 5.0 [95% CI 4.9-5.1] to mean 3.5 [95% CI 3.4-3.6]) scores improved slightly. Only 16% (15%-18%) reported no back pain-related disability or back pain at 2 years after initial visits. Regression model parameters explained 40% of the variation (R2) in 2-year RDQ scores, and the addition of 0- to 3-month change in RDQ score and pain improved prediction (R2=51%). The most consistent predictors of 2-year RDQ scores and back pain NRS scores were 0- to 90-day change in each respective outcome and patient confidence in improvement. Patients experienced 50% and 43% improvement in back pain and disability, respectively, 2 years after their initial visit. However, fewer than 20% of patients had complete resolution of their back pain and disability at that time. CONCLUSIONS: Baseline patient factors were more important than early interventions in explaining disability and pain after 2 years.

Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes.

Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52-0.82), specificity 404/408 = 0.99 (0.97-1.0), precision (positive predictive value) 35/39 = 0.90 (0.75-0.97), negative predictive value 404/419 = 0.96 (0.94-0.98), and F1-score 0.79 (0.43-1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity.

Pragmatic clinical trials embedded in healthcare systems: generalizable lessons from the NIH Collaboratory.

BACKGROUND: The clinical research enterprise is not producing the evidence decision makers arguably need in a timely and cost effective manner; research currently involves the use of labor-intensive parallel systems that are separate from clinical care. The emergence of pragmatic clinical trials (PCTs) poses a possible solution: these large-scale trials are embedded within routine clinical care and often involve cluster randomization of hospitals, clinics, primary care providers, etc. Interventions can be implemented by health system personnel through usual communication channels and quality improvement infrastructure, and data collected as part of routine clinical care. However, experience with these trials is nascent and best practices regarding design operational, analytic, and reporting methodologies are undeveloped. METHODS: To strengthen the national capacity to implement cost-effective, large-scale PCTs, the Common Fund of the National Institutes of Health created the Health Care Systems Research Collaboratory (Collaboratory) to support the design, execution, and dissemination of a series of demonstration projects using a pragmatic research design. RESULTS: In this article, we will describe the Collaboratory, highlight some of the challenges encountered and solutions developed thus far, and discuss remaining barriers and opportunities for large-scale evidence generation using PCTs. CONCLUSION: A planning phase is critical, and even with careful planning, new challenges arise during execution; comparisons between arms can be complicated by unanticipated changes. Early and ongoing engagement with both health care system leaders and front-line clinicians is critical for success. There is also marked uncertainty when applying existing ethical and regulatory frameworks to PCTS, and using existing electronic health records for data capture adds complexity.

Predicting CPAP Use and Treatment Outcomes Using Composite Indices of Sleep Apnea Severity.

STUDY OBJECTIVES: Measures of baseline sleep apnea disease burden (apnea-hypopnea index, Epworth Sleepiness Scale) predict continuous positive airway pressure (CPAP) adherence, but composite indices of sleep apnea severity (Sleep Apnea Severity Index, Modified Sleep Apnea Severity Index) may be more robust measures of disease burden. We tested the relative prognostic ability of each measure of sleep apnea disease burden to predict subsequent CPAP adherence and subjective sleep outcomes. METHODS: Prospective cohort study at a tertiary academic sleep center. Patients (n = 323) underwent initial diagnostic polysomnography for suspected obstructive sleep apnea and 6 mo of subsequent CPAP therapy. RESULTS: Baseline apnea-hypopnea index and both composite indices predicted adherence to CPAP therapy at 6 mo in multivariate analyses (all p ≤ 0.001). Baseline Epworth Sleepiness Scale did not predict CPAP adherence (p = 0.22). Both composite indices were statistically stronger predictors of CPAP adherence at 6 mo than apnea-hypopnea index (p < 0.001). In multivariate analyses, baseline apnea-hypopnea index (p < 0.05) and both composite indices (both p < 0.04) predicted change in Pittsburgh Sleep Quality Index, whereas only the composite indices predicted changes in Sleep Apnea Quality of Life Index (both p < 0.001). Adjustment for treatment adherence did not affect the relationship of the composite indices with change in Sleep Apnea Quality of Life Index (both p ≤ 0.005). CONCLUSIONS: Composite indices of baseline sleep apnea severity better predict objective CPAP adherence and subjective treatment outcomes than baseline apnea-hypopnea index and baseline Epworth Sleepiness Scale.

Lumbar Imaging With Reporting Of Epidemiology (LIRE)--Protocol for a pragmatic cluster randomized trial.

BACKGROUND: Diagnostic imaging is often the first step in evaluating patients with back pain and likely functions as a "gateway" to a subsequent cascade of interventions. However, lumbar spine imaging frequently reveals incidental findings among normal, pain-free individuals suggesting that treatment of these "abnormalities" may not be warranted. Our prior work suggested that inserting the prevalence of imaging findings in patients without back pain into spine imaging reports may reduce subsequent interventions. We are now conducting a pragmatic cluster randomized clinical trial to test the hypothesis that inserting this prevalence data into lumbar spine imaging reports for studies ordered by primary care providers will reduce subsequent spine-related interventions. METHODS/DESIGN: We are using a stepped wedge design that sequentially randomizes 100 primary care clinics at four health systems to receive either standard lumbar spine imaging reports, or reports containing prevalence data for common imaging findings in patients without back pain. We capture all outcomes passively through the electronic medical record. Our primary outcome is spine-related intervention intensity based on Relative Value Units (RVUs) during the following year. Secondary outcomes include subsequent prescriptions for opioid analgesics and cross-sectional lumbar spine re-imaging. DISCUSSION: If our study shows that adding prevalence data to spine imaging reports decreases subsequent back-related RVUs, this intervention could be easily generalized and applied to other kinds of testing, as well as other conditions where incidental findings may be common. Our study also serves as a model for cluster randomized trials that are minimal risk and highly pragmatic.

Composite severity indices reflect sleep apnea disease burden more comprehensively than the apnea-hypopnea index.

OBJECTIVE: To compare 2 composite indices of sleep apnea disease burden with the commonly used apnea-hypopnea index with regard to baseline measurement of subjective and objective disease burden. STUDY DESIGN: Cross-sectional study. SETTING: Tertiary academic medical center sleep laboratory. SUBJECTS AND METHODS: Patients with suspected diagnosis of sleep apnea undergoing first diagnostic polysomnography. Subjective data were collected via validated questionnaires; objective data were obtained by standardized physical examination, chart extraction, and polysomnography. Four subjective (patient-reported) disease burden measures and 3 objective (anatomic and physiologic) disease burden measures were used for validation. Associations between composite indices or apnea-hypopnea index and these 7 construct validation measures were compared using bootstrapped correlation coefficients. RESULTS: Two hundred sixteen subjects were included in the final analysis. Both composite disease burden indices showed clinically important or nearly important associations with 3 of 4 subjective validation measures and all 3 objective validation measures, whereas the apnea-hypopnea index was associated only with the objective validation measures. CONCLUSION: Composite indices of sleep apnea disease burden may capture the breadth of baseline sleep apnea disease burden, particularly subjective disease burden, better than the apnea-hypopnea index.

Comparison of anatomic, physiological, and subjective measures of the nasal airway.

BACKGROUND: Studies comparing different categories of nasal measures have reported inconsistent results. We sought to compare validated measures of the nasal airway: anatomic (acoustic rhinometry), physiological (nasal peak inspiratory flow), and subjective experience (Nasal Obstruction Symptom Evaluation Scale and a visual analog scale [VAS]). METHODS: This prospective cross-sectional study of 290 nonrhinologic patients included upright and supine rhinometry (minimum cross sectional area [MCA] and volume) and flow (mean and maximum) measurements, as well as subjective measures. Associations between measures were evaluated with Spearman correlations and multivariate linear regression, adjusting for age, sex, race, body mass index, and smoking history. RESULTS: Correlations between objective (rhinometry and flow) and subjective categories of nasal measures ranged from -0.16 to 0.03 (mean correlation, -0.07 +/- 0.05), with 0 significant correlations of 16 tested. Correlations between anatomic (rhinometry) and physiological (flow) categories ranged from 0.04 to 0.15 (mean correlation, 0.10 +/- 0.03), with 0 significant correlations of 16 tested. In contrast, within each category (rhinometry, flow, and subjective), all correlations were significant (13 correlations, all p < 0.001) and ranged from 0.62 to 0.99. Of 16 adjusted associations between objective and subjective measures, 14 were not significant (p > 0.05); only upright and supine MCAs were significantly associated with the VAS (both, p < 0.05). CONCLUSION: Validated anatomic, physiological, and subjective nasal measures may assess different aspects of the nasal airway and provide complementary information. Future studies should be directed at developing a composite measure including components from all three categories of nasal measurement.