Improving Interpretation of the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Scale for Specific Tasks in Community-Dwelling Older Adults

Background and purpose: New generic patient-reported outcomes like the Patient-Reported Outcomes Measurement Information System (PROMIS) are available to physical therapists to assess physical function. However, the interpretation of the PROMIS Physical Function (PF) T-score is abstract because it references the United States average and not specific tasks. The purposes of this study were to (1) determine convergent validity of the PROMIS PF scale with physical performance tests; (2) compare predicted performance test values to normative data; and (3) identify sets of PROMIS PF items similar to performance tests that also scale in increasing difficulty and align with normative data. Methods: Community-dwelling older adults (n = 45; age = 77.1 ± 4.6 years) were recruited for this cross-sectional analysis of PROMIS PF and physical performance tests. The modified Physical Performance Test (mPPT), a multicomponent test of mostly timed items, was completed during the same session as the PROMIS PF scale. Regression analysis examined the relationship of mPPT total and component scores (walking velocity, stair ascent, and 5 times sit to stand) with the PROMIS PF scale T-scores. Normative data were compared with regression-predicted mPPT timed performance across PROMIS PF T-scores. The PROMIS PF items most similar to walking, stair ascent, or sit to stand were identified and then PROMIS PF model parameter-calibrated T-scores for these items were compared alongside normative data. Results and discussion: There were statistically significant correlations (r = 0.32-0.64) between PROMIS PF T-score and mPPT total and component scores. Regression-predicted times for walking, stair ascent, and sit-to-stand tasks (based on T-scores) aligned with published normative values for older adults. Selected PF items for stair ascent and walking scaled well to discriminate increasing difficulty; however, sit-to-stand items discriminated only lower levels of functioning. Conclusions: The PROMIS PF T-scores showed convergent validity with physical performance and aligned with published normative data. While the findings are not predictive of individual performance, they improve clinical interpretation by estimating a range of expected performance for walking, stair ascent, and sit to stand. These findings support application of T-scores in physical therapy testing, goal setting, and wellness plans of care for community-dwelling older adults

Implementing an application programming interface for PROMIS measures at three medical centers

BACKGROUND: There is an increasing body of literature advocating for the collection of patient-reported outcomes (PROs) in clinical care. Unfortunately, there are many barriers to integrating PRO measures, particularly computer adaptive tests (CATs), within electronic health records (EHRs), thereby limiting access to advances in PRO measures in clinical care settings. OBJECTIVE: To address this obstacle, we created and evaluated a software integration of an Application Programming Interface (API) service for administering and scoring Patient-Reported Outcomes Measurement Information System (PROMIS) measures with the EHR system. METHODS: We created a RESTful API and evaluated the technical feasibility and impact on clinical workflow at three academic medical centers. RESULTS: Collaborative teams (i.e., clinical, information technology [IT] and administrative staff) performed these integration efforts addressing issues such as software integration as well as impact on clinical workflow. All centers considered their implementation successful based on the high rate of completed PROMIS assessments (between January 2016 and January 2021) and minimal workflow disruptions. CONCLUSION: These case studies demonstrate not only the feasibility but also the pathway for the integration of PROMIS CATs into the EHR and routine clinical care. All sites utilized diverse teams with support and commitment from institutional leadership, initial implementation in a single clinic, a process for monitoring and optimization, and use of custom software to minimize staff burden and error

Do Patient Sociodemographic Factors Impact the PROMIS Scores Meeting the Patient-Acceptable Symptom State at the Initial Point of Care in Orthopaedic Foot and Ankle Patients?

Background Patient-reported outcome measures such as the Patient-Reported Outcomes Measurement Information System (PROMIS) allow surgeons to evaluate the most important outcomes to patients, including function, pain, and mental well-being. However, PROMIS does not provide surgeons with insight into whether patients are able to successfully cope with their level of physical and/or mental health limitations in day-to-day life; such understanding can be garnered using the Patient-acceptable Symptom State (PASS). It remains unclear whether or not the PASS status for a given patient and his or her health, as evaluated by PROMIS scores, differs based on sociodemographic factors; if it does, that could have important implications regarding interpretation of outcomes and fair delivery of care. Questions/purposes In a tertiary-care foot and ankle practice, (1) Is the PASS associated with sociodemographic factors (age, gender, race, ethnicity, and income)? (2) Do PROMIS Physical Function (PF), Pain Interference (PI), and Depression scores differ based on income level? (3) Do PROMIS PF, PI, and Depression thresholds for the PASS differ based on income level? Methods In this retrospective analysis of longitudinally obtained data, all patients with foot and ankle conditions who had new-patient visits (n = 2860) between February 2015 and December 2017 at a single tertiary academic medical center were asked to complete the PROMIS PF, PI, and Depression survey and answer the following single, validated, yes/no PASS question: “Taking into account all the activity you have during your daily life, your level of pain, and also your functional impairment, do you consider that the current state of your foot and ankle is satisfactory?” Of the 2860 new foot and ankle patient visits, 21 patient visits (0.4%) were removed initially because all four outcome measures were not completed. An additional 225 patient visits (8%) were removed because the patient chart did not contain enough information to accurately geocode them; 15 patients visits (0.5%) were removed because the census block group median income data were not available. Lastly, two patient visits (0.1%) were removed because they were duplicates. This left a total of 2597 of 2860 possible patients (91%) in our study sample who had completed all three PROMIS domains and answered the PASS question. Patient sociodemographic factors such as age, gender, race, and ethnicity were recorded. Using census block groups as part of a geocoding method, the income bracket for each patient was recorded. A chi-square analysis was used to determine whether sociodemographic factors were associated with different PASS rates, two-way ANOVA analyses with pairwise comparisons were used to determine if PROMIS scores differed by income bracket, and a receiver operating characteristic (ROC) curve analysis was performed to determine PASS thresholds for the PROMIS score by income bracket. The minimum clinically important difference (MCID) for PROMIS PF in the literature in foot and ankle patients ranges from about 7.9 to 13.2 using anchor-based approaches and 4.5 to 4.7 using the ½ SD, distribution-based method. The MCID for PROMIS PI in the literature in foot and ankle patients ranges from about 5.5 to 12.4 using anchor-based approaches and about 4.1 to 4.3 using the ½ SD, distribution-based method. Both were considered when evaluating our findings. Such MCID cutoffs for PROMIS Depression are not as well established in the foot and ankle literature. Significance was set a priori at p \u3c 0.05. Results The only sociodemographic factor associated with differences in the proportion of patients achieving PASS was age (15% [312 of 2036] of patients aged 18-64 years versus 11% [60 of 561] of patients aged ≥ 65 years; p = 0.006). PROMIS PF (45 ± 10 for the ≥ USD 100,000 bracket versus 40 ± 10 for the ≤ USD 24,999 bracket, mean difference 5 [95% CI 3 to 7]; p \u3c 0.001), PI (57 ± 8 for ≥ USD 100,000 versus 63 ± 7 for ≤ USD 24,999, mean difference -6 [95% CI -7 to -4]; p \u3c 0.001), and Depression (46 ± 8 for the ≥ USD 100,000 bracket versus 51 ± 11 for ≤ USD 24,999, mean difference -5 [95% CI -7 to -3]; p \u3c 0.001) scores were better for patients in the highest income bracket compared with those in the lowest income bracket. For PROMIS PF, the difference falls within the score change range deemed clinically important when using a ½ SD, distribution-based approach but not when using an anchor-based approach; however, the score difference for PROMIS PI falls within the score change range deemed clinically important for both approaches. The PASS threshold of the PROMIS PF for the highest income bracket was near the mean for the US population (49), while the PASS threshold of the PROMIS PF for the lowest income bracket was more than one SD below the US population mean (39). Similarly, the PASS threshold of the PROMIS PI differed by 6 points when the lowest and highest income brackets were compared. PROMIS Depression was unable to discriminate the PASS. Conclusions Discussions about functional and pain goals may need to be a greater focus of clinic encounters in the elderly population to ensure that patients understand the risks and benefits of given treatment options at their advanced age. Further, when using PASS in clinical encounters to evaluate patient satisfaction and the ability to cope at different symptom and functionality levels, surgeons should consider income status and its relationship to PASS. This knowledge may help surgeons approach patients with a better idea of patient expectations and which level of symptoms and functionality is satisfactory; this information can assist in ensuring that each patient’s health goal is included in shared decision-making discussions. A better understanding of why patients with different income levels are satisfied and able to cope at different symptom and functionality levels is warranted and may best be accomplished using an epidemiologic survey approach. Level of Evidence Level III, diagnostic study

What Does a PROMIS T-score Mean for Physical Function?

Introduction/Purpose: The use of patient-reported outcomes (PRO) continues to expand beyond research to involve standard of care assessments. Although the PROMIS physical function (PF) is normalized to a T-score it is unclear how to interpret and apply this information in the daily care of patients. The T-score is abstract and unanchored to patient abilities impairing its clinical utility when shared with the patient. Patient questions are concrete such as “when will I be able to run again after this procedure?” The purpose of this research was to link PROMIS PF T-scores with physical function activities and provide a visual map of this linkage to aid in treatment assessment and address concrete patient education

AeroStat: NASA Giovanni Tool for Statistical Intercomparison of Aerosols

No abstract availabl

Alaska Climate Trend Vulnerability Study

This report presents the findings of a study that examined three transportation projects in Alaska for potential vulnerability to climate change and extreme weather events. The study was initiated jointly by the Alaska Department of Transportation and Public Facilities (AKDOT&PF) and the United States Federal Land Management Agencies (FLMAs) through a grant provided by the Federal Highway Administration (FHWA). The focus of the study was to better understand changing climate conditions in Alaska and how this understanding could potentially lead to more informed decisions on transportation asset investments, both capital investment and operation/maintenance decisions. The three case studies included an examination of roadway exposure to permafrost thaw, airport runway exposure to sea level rise and changing wind and sea ice patterns, and slope instability related to permafrost thaw and more intense precipitation. An eleven-step process developed by FHWA for engineering vulnerability assessment was used to develop the findings for each case study. The study found that future efforts to incorporate changing climate conditions into engineering decision-making will require a coordinated effort among federal agencies, state agencies, and academic or research institutions that focus on climate forecasts. The data produced by these agencies is often not specific to a project site and thus some effort is needed to translate the more aggregate forecasts to site-specific data. In particular, defining longer term climate change exposure in Alaska would benefit from more data on transportation assets including information on surrounding environmental conditions (e.g. permafrost measurement), site conditions (e.g. elevations), construction assumptions/methods, and any noted maintenance records that focus on environmentrelated problems. The application of the eleven-step process is outside of normal engineering practice and requires significant commitment and coordination for successful application as it is a new process requiring the development of information not currently prepared for other engineering projects. Shifting to a more risk-based decision-making framework will help facilitate this process moving forward. The case studies also showed that relatively low cost options can be viable strategies for dealing with climate change-related vulnerabilities. Importantly, the process of developing input data requires significant coordination between climate scientists and engineers

A novel preference-informed complementary trial (PICT) design for clinical trial research influenced by strong patient preferences

Background: Patients and their families often have preferences for medical care that relate to wider considerations beyond the clinical effectiveness of the proposed interventions. Traditionally, these preferences have not been adequately considered in research. Research questions where patients and families have strong preferences may not be appropriate for traditional randomized controlled trials (RCTs) due to threats to internal and external validity, as there may be high levels of drop-out and non-adherence or recruitment of a sample that is not representative of the treatment population. Several preference-informed designs have been developed to address problems with traditional RCTs, but these designs have their own limitations and may not be suitable for many research questions where strong preferences and opinions are present. Methods: In this paper, we propose a novel and innovative preference-informed complementary trial (PICT) design which addresses key weaknesses with both traditional RCTs and available preference-informed designs. In the PICT design, complementary trials would be operated within a single study, and patients and/or families would be given the opportunity to choose between a trial with all treatment options available and a trial with treatment options that exclude the option which is subject to strong preferences. This approach would allow those with strong preferences to take part in research and would improve external validity through recruiting more representative populations and internal validity. Here we discuss the strengths and limitations of the PICT design and considerations for analysis and present a motivating example for the design based on the use of opioids for pain management for children with musculoskeletal injuries. Conclusions: PICTs provide a novel and innovative design for clinical trials with more than two arms, which can address problems with existing preference-informed trial designs and enhance the ability of researchers to reflect shared decision-making in research as well as improving the validity of trials of topics with strong preferences

Assessing Aerosol Mixed Layer Heights from the NASA LaRC Airborne HSRL During the DISCOVER-AQ Field Campaigns

No abstract availabl

Mixed Layer Heights Derived from the NASA Langley Research Center Airborne High Spectral Resolution Lidar

The NASA airborne High Spectral Resolution Lidar (HSRL) has been deployed on board the NASA Langley Research Center's B200 aircraft to several locations in North America from 2006 to 2012 to aid in characterizing aerosol properties for over fourteen field missions. Measurements of aerosol extinction (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) during 349 science flights, many in coordination with other participating research aircraft, satellites, and ground sites, constitute a diverse data set for use in characterizing the spatial and temporal distribution of aerosols, as well as properties and variability of the Mixing Layer (ML) height. We describe the use of the HSRL data collected during these missions for computing ML heights and show how the HSRL data can be used to determine the fraction of aerosol optical thickness within and above the ML, which is important for air quality assessments. We describe the spatial and temporal variations in ML heights found in the diverse locations associated with these experiments. We also describe how the ML heights derived from HSRL have been used to help assess simulations of Planetary Boundary Layer (PBL) derived using various models, including the Weather Research and Forecasting Chemistry (WRF-Chem), NASA GEOS-5 model, and the ECMWF/MACC models