Search

Abstract: Purpose: Preference-based summary scores are used to quantify values, differences, and changes in health-related quality of life (HRQoL) that can be used for cost-effectiveness analyses. The PROMIS-Preference (PROPr) measure is a preference-based summary score comprised of 7 PROMIS domains. The PROMIS-16 is a new PROMIS profile instrument. We evaluated the measurement properties of PROPr generated from the widely used PROMIS-29 + 2 compared with the PROMIS-16. Methods: We performed a secondary analysis of data from an online survey of the general US population, with a longitudinal subsample who reported back pain. The survey included both the PROMIS-16 and the PROMIS-29 + 2 profiles. PROPr scores were calculated from each profile and compared by the distribution of scores, overall mean scores, product-moment correlations with pain measure scores (Oswestry Disability Index, Roland-Morris Disability Questionnaire, Pain Intensity, Interference with Enjoyment of Life, Interference with General Activity Scale, and Graded Chronic Pain Scale), and difference in mean scores in subgroups with 13 chronic health conditions (Cohen’s d). Results: Of the 4,115 participants in the baseline survey, 1,533 with any reported back pain were invited for the 6-month follow-up survey and 1,256 completed it. At baseline, the overall mean (SD) PROPr score was 0.532 (0.240) from PROMIS-16 and 0.535 (0.250) from PROMIS 29 + 2. At both time points, the correlations of PROPr scores with physical and mental health summary scores from the PROMIS-29 and 4 pain scales were within 0.01 between profiles. Using subgroups with chronic health conditions and comparing between profiles, Cohen’s d estimates of the difference in effect size were small (

BackgroundVenous thromboembolism (VTE) and atrial fibrillation (AF) disproportionately affect older adults, who are at increased risk of bleeding from treatment with anticoagulant therapy. The impact of bleeding on older adults' quality of life (QoL) is poorly understood due to the lack of a validated measure of their experience. This study's purpose is to describe the first evidence-based steps in developing a new condition-specific patient-reported outcome measure (PROM) for the effect of anticoagulant-related bleeding on older adults' QoL.MethodsAdults aged 65 years and older with VTE or AF at the University of Utah who were eligible for anticoagulation were recruited. We purposely sampled by age, sex, race/ethnicity, diagnosis, bleeding history and anticoagulant. In accordance with evidence-based guidelines for PROM development, focus groups were conducted to refine a hypothesized conceptual model of bleeding-related QoL and analyzed according to hybrid inductive and deductive thematic analysis methods. Focus group results were used to write a draft PROM. Semi-structured qualitative interviews were conducted to revise items on the draft PROM and were analyzed using thematic analysis to generate a final bleeding-related QoL PROM.ResultsTwenty individuals participated in interviews, and nine in two focus groups. Median age was 81 (range 69-95), 52% were women and 69% were White (10% Asian, 10% Hispanic, 7% Black, 3% Native Hawaiian/Pacific Islander). Through inductive and deductive thematic analysis of focus groups, we identified 19 themes (e.g., specific bleeding symptoms, adaptations in relationships, fatigue) that fell into 5 domains: 1) bleeding symptoms, 2) healthcare experiences, 3) social function, 4) emotional function, 5) physical function. These 19 themes and 5 domains formed the basis of the draft PROM. We conducted semi-structured cognitive interviews, and we performed thematic analysis to revise the drafted measure for clarity and relevance. The data resulted in a new PROM for bleeding-related QoL in older adults.ConclusionEmploying evidence-based methods for PROM development, we found that bleeding can have a marked impact on everyday activities, emotional wellbeing and interpersonal relations for older adults. We incorporated these distinct aspects of wellbeing and function into a novel PROM for bleeding-related QoL that can inform clinical care and research after subsequent psychometric validation.

Simple and validated physical function measures are needed for kidney transplant candidates because pretransplant low physical function is a common and potentially modifiable risk factor. This single-center retrospective study investigated the associations between pretransplant physical function assessed by the Patient-Reported Outcomes Measurement Information System® Physical Function (PROMIS-PF) computer adaptive testing and early posttransplant outcomes. We analyzed 154 adult kidney-alone transplant recipients. The median pretransplant PROMIS-PF score was 43 (interquartile range, 39–47). Patient characteristics were not significantly different across the score category (normal, score ≥45; mild, score of 40–45; and moderate/severe, score

With more disease- and symptom-specific measures available and research pointing to increased usefulness, patient-reported outcome measures (PROMs) can be routinely used in clinical care. PROMs increase efficiency in healthcare, improve the clinician-patient relationship, and increase patient satisfaction with their care. PROMs can be administered before, during, and after clinic visits using paper-and-pencil, mobile phones, tablets, and computers. Herein, we combine available literature with expert views to discuss overcoming barriers and helping dermatologists incorporate PROMs into routine patient-centered care. We believe dermatology patients will benefit from broader PROM implementation and routine clinical use. However, a few major barriers exist: (1) cost to implement the technology, (2) selecting the right PROMs for each disease, and (3) helping both patients and clinicians understand how PROMs add to and complement their current clinical experience. We provide recommendations to assist dermatologists when considering whether to implement PROMs in their practices.

Background: Patient-reported outcome measures (PROMs) correlate with heart failure (HF) severity among adults and are adjunct tools in clinical care. Limited data exist regarding the validity of PROMs in pediatric HF. Hypothesis: Patient-Reported Outcome Measurement Information System (PROMIS) Pediatric Fatigue correlates with HF severity, measured by the New York University Pediatric Heart Failure Index (NYU PHFI). Methods: Children ≥8 and

BackgroundApproximately 5% of cancer patients in the United States presented with metastatic bone disease (MBD) at diagnosis. Current study explores the disparities in survival for patients with MBD.MethodsPatients with the diagnosis of MBD at presentation for the five most common primary anatomical sites were extracted from Surveillance, Epidemiology, and End Results Census tract-level dataset (2010-2016). Kaplan-Meier and Cox Proportional Hazard models were used to evaluate survival, and prognostic factors for each cohort. Prognostic significance of socioeconomic status (SES) and insurance status were ascertained.ResultsThe five most common anatomical-sites with MBD at presentation included "lung" (n = 59 739), "prostate" (n = 19 732), "breast" (n = 16 244), "renal and urothelium" (n = 7718) and "colon" (n= 3068). Lower SES was an independent risk factor for worse disease-specific survival (DSS) for patients with MBD originating from lung, prostate, breast and colon. Lack of insurance was an independent risk factor for worse DSS for MBD patients with primary tumors in lung and breast.ConclusionsMBD patients from the five most common primary sites demonstrated SES and insurance-related disparities in disease-specific survival. This is the first and largest study to explore SES and insurance-related disparities among patients specifically afflicted with MBD. Our findings highlight vulnerability of patients with MBD across multiple primary sites to financial toxicity.

BackgroundWe have analyzed sex, race/ethnicity or socioeconomic disparities in the incidence of metastatic bone disease (MBD).MethodsPatients with the diagnosis of MBD at presentation for five most common primary anatomical sites was extracted from Surveillance, Epidemiology, and End Results Census tract-level dataset. Mean incidence of MBD for different sex, racial/ethnic and socioeconomic groups were compared.ResultsThe five most common anatomical sites with MBD at presentation include "lung: (n = 59 739), "prostate" (n = 19 732), "breast" (n = 16 244), "renal" (n = 7718) and "colon" (n = 3068). There was an increase in incidence of MBD among cancers originating from prostate (annual percentage change [APC] 4.94), renal (APC 2.55), and colon (APC 3.21) (p

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions.

Abstract Background Lumbar spinal stenosis (LSS) is a common degenerative condition that contributes to back and back-related leg pain in older adults. Most patients with symptomatic LSS initially receive non-operative care before surgical consultation. However, there is a scarcity of data regarding prognosis for patients seeking non-surgical care. The overall goal of this project is to develop and evaluate a clinically useful model to predict long-term physical function of patients initiating non-surgical care for symptomatic LSS. Methods This is a protocol for an inception cohort study of adults 50 years and older who are initiating non-surgical care for symptomatic LSS in a secondary care setting. We plan to recruit up to 625 patients at two study sites. We exclude patients with prior lumbar spine surgeries or those who are planning on lumbar spine surgery. We also exclude patients with serious medical conditions that have back pain as a symptom or limit walking. We are using weekly, automated data pulls from the electronic health records to identify potential participants. We then contact patients by email and telephone within 21 days of a new visit to determine eligibility, obtain consent, and enroll participants. We collect data using telephone interviews, web-based surveys, and queries of electronic health records. Participants are followed for 12 months, with surveys completed at baseline, 3, 6, and 12 months. The primary outcome measure is the 8-item PROMIS Physical Function (PF) Short Form. We will identify distinct phenotypes using PROMIS PF scores at baseline and 3, 6, and 12 months using group-based trajectory modeling. We will develop and evaluate the performance of a multivariable prognostic model to predict 12-month physical function using the least absolute shrinkage and selection operator and will compare performance to other machine learning methods. Internal validation will be conducted using k-folds cross-validation. Discussion This study will be one of the largest cohorts of individuals with symptomatic LSS initiating new episodes of non-surgical care. The successful completion of this project will produce a cross-validated prognostic model for LSS that can be used to tailor treatment approaches for patient care and clinical trials.

Simple and validated physical function measures are needed for kidney transplant candidates because pretransplant low physical function is a common and potentially modifiable risk factor. This single-center retrospective study investigated the associations between pretransplant physical function assessed by the Patient-Reported Outcomes Measurement Information System® Physical Function (PROMIS-PF) computer adaptive testing and early posttransplant outcomes. We analyzed 154 adult kidney-alone transplant recipients. The median pretransplant PROMIS-PF score was 43 (interquartile range, 39–47). Patient characteristics were not significantly different across the score category (normal, score ≥45; mild, score of 40–45; and moderate/severe, score <40). The PROMIS-PF score was not associated with length of transplant hospital stay, delayed graft function, 6-month and 12-month graft function, or 12-month patient and graft survival. However, a lower PROMIS-PF score was significantly associated with a higher risk of emergency room visits [adjusted odds ratios compared to normal: mild, 1.68 (95% confidence interval, 0.76–3.83); moderate/severe, 3.23 (1.34–7.79)] and rehospitalization [adjusted odds ratios: mild, 2.61 (1.16–5.90); moderate/severe, 2.53 (1.07–6.00)] within 1 month posttransplant. Results suggest that PROMIS-PF is a practical tool for assessing physical function in kidney transplant candidates. Larger studies are needed to confirm the utility of PROMIS-PF to identify transplant candidates who would benefit from pretransplant prehabilitation. [ABSTRACT FROM AUTHOR]

Background: Venous thromboembolism (VTE) and atrial fibrillation (AF) disproportionately affect older adults, who are at increased risk of bleeding from treatment with anticoagulant therapy. The impact of bleeding on older adults' quality of life (QoL) is poorly understood due to the lack of a validated measure of their experience. This study's purpose is to describe the first evidence-based steps in developing a new condition-specific patient-reported outcome measure (PROM) for the effect of anticoagulant-related bleeding on older adults' QoL. Methods: Adults aged 65 years and older with VTE or AF at the University of Utah who were eligible for anticoagulation were recruited. We purposely sampled by age, sex, race/ethnicity, diagnosis, bleeding history and anticoagulant. In accordance with evidence-based guidelines for PROM development, focus groups were conducted to refine a hypothesized conceptual model of bleeding-related QoL and analyzed according to hybrid inductive and deductive thematic analysis methods. Focus group results were used to write a draft PROM. Semi-structured qualitative interviews were conducted to revise items on the draft PROM and were analyzed using thematic analysis to generate a final bleeding-related QoL PROM. Results: Twenty individuals participated in interviews, and nine in two focus groups. Median age was 81 (range 69–95), 52% were women and 69% were White (10% Asian, 10% Hispanic, 7% Black, 3% Native Hawaiian/Pacific Islander). Through inductive and deductive thematic analysis of focus groups, we identified 19 themes (e.g., specific bleeding symptoms, adaptations in relationships, fatigue) that fell into 5 domains: 1) bleeding symptoms, 2) healthcare experiences, 3) social function, 4) emotional function, 5) physical function. These 19 themes and 5 domains formed the basis of the draft PROM. We conducted semi-structured cognitive interviews, and we performed thematic analysis to revise the drafted measure for clarity and relevance. The data resulted in a new PROM for bleeding-related QoL in older adults. Conclusion: Employing evidence-based methods for PROM development, we found that bleeding can have a marked impact on everyday activities, emotional wellbeing and interpersonal relations for older adults. We incorporated these distinct aspects of wellbeing and function into a novel PROM for bleeding-related QoL that can inform clinical care and research after subsequent psychometric validation. [ABSTRACT FROM AUTHOR]

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions. [ABSTRACT FROM AUTHOR]