Validity and reliability of Patient-Reported Outcomes Measurement Information System Global Health scale in adults with atopic dermatitis.

BACKGROUND: Patient-Reported Outcomes Measurement Information System Global Health (PGH) was validated to assess health-related quality of life in several diseases. Little is known about its measurement properties in adult atopic dermatitis. OBJECTIVE: Examine the measurement properties of PGH in adult atopic dermatitis. METHODS: A prospective dermatology practice-based study of 994 atopic dermatitis patients (18-97 years). RESULTS: PGH physical and mental health 4-item and abridged 2-item T scores, as well as mapped EuroQol-5D score, showed strong to very strong correlation with one another and moderate to strong Spearman correlations with Patient-Oriented Scoring Atopic Dermatitis, Patient-Health Questionnaire-9, Patient-Reported Outcomes Measurement Information System sleep disturbance and related impairment, Eczema Area and Severity Index, objective Scoring Atopic Dermatitis; and weak to moderate correlations with Patient Oriented Eczema Measure, numeric rating scale worst itch and average itch, and Scoring Atopic Dermatitis. The Dermatology Life Quality Index (DLQI) had stronger correlations with Patient Oriented Eczema Measure, Patient-Oriented Scoring Atopic Dermatitis, numeric rating scale worst itch and average itch, Eczema Area and Severity Index, and Scoring Atopic Dermatitis, but weaker correlations with Patient-Health Questionnaire-9 and Patient-Reported Outcomes Measurement Information System sleep disturbance and related impairment (convergent/divergent validity). PGH and DLQI scores had similarly poor ability to differentiate between levels of self-reported global atopic dermatitis severity (known-groups validity). No floor or ceiling effects were observed. No PGH or DLQI items had differential item functioning by demographics. PGH and DLQI scores showed fair to good responsiveness. Finally, PGH and DLQI showed similarly good test-retest reliability. LIMITATIONS: Single-center study. CONCLUSION: PGH scores had sufficient validity and reliability to assess health-related quality of life in atopic dermatitis.

Comparison of Patient-Oriented Eczema Measure and Patient-Oriented Scoring Atopic Dermatitis vs Eczema Area and Severity Index and other measures of atopic dermatitis: A validation study.

BACKGROUND: Little is known about the measurement properties of Patient-Oriented Scoring Atopic Dermatitis (PO-SCORAD) in adults with atopic dermatitis (AD). Even less is known about how PO-SCORAD performs compared with the Patient-Oriented Eczema Measure (POEM). OBJECTIVE: To examine the measurement properties of PO-SCORAD and compare them with those of POEM. METHODS: A prospective dermatology practice-based study of 291 patients with AD (age range, 18-72 years). RESULTS: PO-SCORAD and POEM were moderately correlated with each other (Spearman ρ = 0.56) and had weak-moderate correlations with the Numeric Rating Scale (NRS) worst itch and average itch, Dermatology Life Quality Index (DLQI), ItchyQOL, Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance (SD) and Sleep-Related Impairment (SRI), Patient Health Questionnaire-9 (PHQ-9), and Eczema Area and Severity Index (EASI) (P < .001). POEM had significantly stronger correlations with DLQI, ItchyQOL, and EASI than did PO-SCORAD. PO-SCORAD and POEM had fair discriminant validity. Changes from baseline in PO-SCORAD and POEM were moderately correlated with each other; were weakly to strongly correlated with NRS worst itch and average itch, DLQI, ItchyQOL, PROMIS SD, PROMIS SRI, PHQ-9, and EASI; and had good test-retest reliability. There was no differential item functioning of items or floor or ceiling effects for PO-SCORAD or POEM. The thresholds for meaningful change for PO-SCORAD and POEM were -15.5 and -5.0, respectively. Median completion times for PO-SCORAD and POEM were 3 minutes and 1 minute, respectively. CONCLUSION: PO-SCORAD and POEM had good construct and cross-cultural validity, reliability, and responsiveness in adults with AD and were feasible for use in clinical trials and practice. However, POEM had better measurement properties than PO-SCORAD.

Association of itch triggers with atopic dermatitis severity and course in adults.

BACKGROUND: Atopic dermatitis (AD) is associated with heterogeneous triggers of itch, which may affect AD course and severity. OBJECTIVE: To characterize the triggers of itch in adult AD. METHODS: This was a prospective dermatology practice-based study using questionnaires and evaluation by a dermatologist (n = 587). Thirteen itch triggers were assessed using the patient-reported outcomes measurement information system Itch-Triggers. RESULTS: Overall, 381 (64.9%) patients reported greater than or equal to 1 itch trigger in the past week and 212 (36.1%) reported greater than or equal to 3 itch triggers. The most commonly reported triggers were stress (35.4%), sweat (30.5%), weather change (24.7%), dry air (24.4%), and heat (24.0%). In multivariable Poisson regression models, the number of itch triggers was associated with more severe patient-reported global AD severity, Numeric Rating Scale worst itch, Patient-Oriented Eczema Measure, Scoring Atopic Dermatitis sleep, Numeric Rating Scale skin pain, Eczema Area and Severity Index, and objective Scoring Atopic Dermatitis. The seasonality of AD was associated with distinct itch triggers. In multivariable logistic regression models, the number of itch triggers was associated with less than or equal to 3 months of AD remission during the year, greater than or equal to 2 AD flares, and AD being worse during some seasons. Four patterns of itch triggers were identified using latent class analysis, each associated with different clinical characteristics. CONCLUSION: Itch triggers are common and affect the course of AD. Itch triggers are an important end point to assess in patients with AD.

Validation of four single-item patient-reported assessments of sleep in adult atopic dermatitis patients.

BACKGROUND: The optimal approaches for monitoring sleep disturbances in adults with atopic dermatitis (AD) is not established. Multiple patient-reported outcome measures for AD and itch have sleep-related items. These items have not been validated previously. OBJECTIVE: Assess the measurement properties of sleep-related items from the Patient-Oriented Eczema Measure (POEM), SCORing AD (SCORAD), 5-dimensions of itch (5D), and ItchyQOL in adults with AD. METHODS: We performed a prospective dermatology practice-based study using questionnaires and evaluation by a dermatologist (n = 115). RESULTS: There was modest overlap and weak-moderate concordance of responses to the different assessments. Regarding concurrent validity, POEM-sleep, SCORAD-sleep, 5D-sleep, and ItchyQOL-sleep showed moderate correlations with each other. Regarding convergent validity, all items showed moderate correlation with total POEM, but weak correlations with Eczema Area and Severity Index (EASI), objective and total SCORAD, moderate to strong correlations with mean ItchyQOL and Dermatology Life Quality Index (DLQI), but poor or no significant correlation with Numeric Rating Scale (NRS) for worst or average itch. Regarding discriminant validity, all items showed significant and stepwise increases with increasing self-reported and physician-reported AD severity (Kruskal-Wallis, P < .01 for all). Floor effects were observed for POEM-sleep (n = 53, 46.1%), SCORAD-sleep (n = 28, 24.4%), 5D-sleep (n = 41, 35.7%), and ItchyQOL-sleep (n = 33, 28.7%); no ceiling effects were observed. Change in sleep-related item scores showed moderate strong correlations with change in POEM, 5Ditch, mean ItchyQOL, DLQI, objective and total SCORAD, and EASI, but inconsistent correlations with change of itch severity. CONCLUSION: Sleep-related items from POEM, SCORAD, 5D and ItchyQOL showed good validity and responsiveness to monitor sleep disturbances in adult AD patients.

Placebo responses in randomized controlled trials for systemic therapy in atopic dermatitis: A systematic review and meta-analysis.

BACKGROUND: Atopic dermatitis (AD) has a variable disease course and intermittent triggers, and responses to topical therapy vary, potentially affecting the magnitude of the placebo response in AD trials. OBJECTIVE: To determine the predictors of increased placebo response in randomized controlled trials of AD. METHODS: We performed a systematic review and meta-analysis of randomized controlled trials for systemic therapy in AD published during 2007-2018. We searched the Cochrane Library, Medline, Embase, Global Resource for EczemA Trials (GREAT), Literature of the Latin American and Caribbean Health Sciences (LILACS), and Scopus. Two authors performed study selection and data extraction. Multivariable mixed models were constructed for Cohen D of Eczema Area and Severity Index (EASI), SCORing Atopic Dermatitis (SCORAD), numeric rating scale (NRS)-itch and visual analog scale (VAS)-itch, and Dermatology Life Quality Index (DLQI). RESULTS: Overall, 64 trials were included. Use of concomitant topical therapy prescriptions, study duration ≥3 months, and fewer treatment arms were associated with an increased placebo response for EASI, NRS- and VAS-itch, and DLQI. For EASI, the placebo response was increased in studies with a higher proportion of male patients, mild-moderate mean baseline EASI scores, and no blinding. For NRS-itch, and VRS-itch, higher placebo responses were associated with higher proportions of male patients and moderate-severe mean itch scores at baseline. CONCLUSION: Placebo responses can be reduced in clinical trials of systemic therapy in AD by incorporating double- and triple-blinding, balancing the sex distribution of patients, disallowing concomitant use of prescription topical therapy, and having shorter study durations.

Association of atopic dermatitis severity with cognitive function in adults.

BACKGROUND: Atopic dermatitis (AD) is associated with itch, pain, and sleep disturbance, all of which may contribute toward cognitive dysfunction. OBJECTIVE: To determine the relationship of AD severity and cognitive function in adults. METHODS: We performed a prospective dermatology practice-based study using questionnaires and evaluation by a dermatologist (n = 386). Cognitive function was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Cognitive Function 8-item Short-Form. RESULTS: At baseline, 118 patients (58.1%) reported ≥1 symptoms of cognitive dysfunction in the past 4 weeks, with 29 (14.3%) having mild, 11 (5.4%) moderate, and 4 (2.0%) severe PROMIS Cognitive Function T-scores. In propensity score-weighted regression models, PROMIS Cognitive Function T-scores were inversely associated with patient-reported global AD severity, Patient Oriented Eczema Measure (POEM), Numeric Rating Scale worst itch and skin pain, SCORing Atopic Dermatitis (SCORAD)-sleep, POEM-sleep, Eczema Area and Severity Index, and SCORAD, with stepwise decreases of cognitive function with worsening AD severity. At all AD severity levels, cognitive dysfunction was associated with increased Dermatology Life Quality Index and ItchyQoL scores. Changes from baseline in PROMIS Cognitive Function T-scores were weakly to moderately inversely correlated with changes from baseline in multiple AD outcomes. LIMITATIONS: Single-center study without non-AD controls. CONCLUSION: Cognitive dysfunction is associated with AD severity. Cognitive function may be an important end point for monitoring treatment response in AD.

Measurement properties of the Patient-Reported Outcomes Measurement Information System Itch Questionnaire item banks in adults with atopic dermatitis.

BACKGROUND: The Patient-Reported Outcomes Measurement Information System (PROMIS) Itch Questionnaire (PIQ) was recently developed. OBJECTIVE: To validate PIQ short forms in adults with AD. METHODS: Self-administered questionnaires and skin examinations were performed in 239 adults with atopic dermatitis (AD) in a dermatology practice setting. RESULTS: PIQ items had good content validity. PIQ item bank T-scores strongly correlated with each other, moderately correlated with numeric and verbal rating scales for worst or average itch and with itch frequency, moderately to strongly correlated with patient-oriented eczema measure, and weakly to moderately correlated with the Eczema Area and Severity Index and Objective-Scoring AD (Spearman correlations, P < .0001). There were significant and stepwise increases of T-scores for all item banks with increasing patient-reported global severity (Wilcoxon rank sum test, P < .0001). However, there was limited ability to discriminate between the lowest or highest 2 levels of AD or itch severity. Item banks showed good internal consistency (Cronbach α, 0.91-0.95). No differential item functioning was identified by age, sex, race/ethnicity, or educational level. There were floor effects for total scores, particularly in almost clear/mild AD or itch. LIMITATIONS: Single-center study. CONCLUSIONS: PIQ item bank short forms showed good content and construct validity and are feasible for potential use in clinical trials and practice.

Efficacy of bleach baths in reducing severity of atopic dermatitis: A systematic review and meta-analysis.

BACKGROUND: Bleach baths have been proposed as a treatment for decreasing the severity of atopic dermatitis (AD). However, conflicting results have been found regarding their efficacy. OBJECTIVE: To determine the efficacy of bleach vs water baths at decreasing AD severity. METHODS: We performed a systematic review of all studies evaluating the efficacy of bleach baths for AD. Cochrane, EMBASE, GREAT, LILACS, MEDLINE, and Scopus were searched. Two authors independently performed study selection and data extraction. RESULTS: Five studies were included in the review. Four studies reported significantly decreased AD severity in patients treated with bleach on at least 1 time point. However, of 4 studies comparing bleach with water baths, only 2 found significantly greater decreases in AD severity with bleach baths, 1 found greater decreases with water baths, and 1 found no significant differences. In pooled analyses, there were no significant differences observed between bleach vs water baths at 4 weeks vs baseline for the Eczema Area and Severity Index (I2 = 98%; random effect regression model, P = .16) or body surface area (I2 = 96%; P = .36). CONCLUSION: Although bleach baths are effective in decreasing AD severity, they do not appear to be more effective than water baths alone. Future larger-scale, well-designed randomized controlled trials are needed.

Burden of skin pain in atopic dermatitis.

BACKGROUND: Atopic dermatitis (AD) is associated with itch, skin inflammation and barrier disruption, and scratching, all of which may be associated with skin pain. OBJECTIVE: To characterize the patient burden of skin pain in AD. METHODS: We performed a prospective dermatology practice-based study using questionnaires and evaluation by a dermatologist. RESULTS: Overall, 305 patients (age range, 13-97 years) were included in the study, with 564 encounters. The cohort included 195 females (63.9%) and 193 whites (63.7%). The mean (SD) age at enrollment was 42.3 (18.1) years, and the mean (SD) age of patient-reported AD onset was 29.6 (31.9) years. At baseline, 144 patients (42.7%) reported skin pain in the past week, with 42 (13.8%) reporting severe or very severe pain. Twenty-four (16.8%) thought the skin pain was part of their itch, 16 (11.2%) from scratching, and 77 (72.0%) from both. Patients with skin pain were more likely to describe their itch using terms that resembled neuropathic pain. Prevalence of skin pain was increased in patients with vs without excoriations (72.6% vs 57.6%; χ2 test P = .02) but not other morphologic characteristics. Skin pain severity was most strongly correlated with the Patient-Oriented Eczema Measure (Spearman ρ = 0.54), followed by ItchyQOL (ρ = 0.52), 5-dimensions of itch scale (ρ = 0.47), Dermatology Life Quality Index (ρ = 0.45), numeric rating scale for itch (ρ = 0.43) and sleep (ρ = 0.36), Patient Health Questionnaire 9 (ρ = 0.36), patient-reported global AD severity (ρ = 0.34), Eczema Area and Severity Index (ρ = 0.23), and objective Scoring AD index (ρ = 0.20) (P < .001 for all). Patients with both severe itch and pain vs those with only one or neither symptom being severe had significant increases in all these measures. CONCLUSION: Skin pain is a common and burdensome symptom in AD. Skin pain severity should be assessed with itch severity in AD patients and may be an important end point for monitoring treatment response.

Stepwise Transfer Learning for Expert-Level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning (DL) pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001-December 2015) from a national brain tumor consortium (n = 184; median age, 7 years (range: 1-23 years); 94 male) and a pediatric cancer center (n = 100; median age, 8 years (range: 1-19 years); 47 male) to develop and evaluate DL neural networks for pediatric low-grade glioma segmentation using a novel stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally-tested on an independent test set and subjected to randomized, blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain, stepwise transfer learning (median DSC: 0.88 [IQR 0.72-0.91] versus 0.812 [0.56-0.89] for baseline model; P = .049). On external testing, AI model yielded excellent accuracy using reference standards from three clinical experts (Expert-1: 0.83 [0.75-0.90]; Expert-2: 0.81 [0.70-0.89]; Expert-3: 0.81 [0.68-0.88]; mean accuracy: 0.82)). On clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score: median 9 [IQR 7-9]) versus 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% versus 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level, automated pediatric brain tumor auto-segmentation and volumetric measurement with a high level of clinical acceptability. ©RSNA, 2024.

Noninvasive Molecular Subtyping of Pediatric Low-Grade Glioma with Self-Supervised Transfer Learning.

Purpose To develop and externally test a scan-to-prediction deep learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pediatric low-grade glioma. Materials and Methods This retrospective study included two pediatric low-grade glioma datasets with linked genomic and diagnostic T2-weighted MRI data of patients: Dana-Farber/Boston Children's Hospital (development dataset, n = 214 [113 (52.8%) male; 104 (48.6%) BRAF wild type, 60 (28.0%) BRAF fusion, and 50 (23.4%) BRAF V600E]) and the Children's Brain Tumor Network (external testing, n = 112 [55 (49.1%) male; 35 (31.2%) BRAF wild type, 60 (53.6%) BRAF fusion, and 17 (15.2%) BRAF V600E]). A deep learning pipeline was developed to classify BRAF mutational status (BRAF wild type vs BRAF fusion vs BRAF V600E) via a two-stage process: (a) three-dimensional tumor segmentation and extraction of axial tumor images and (b) section-wise, deep learning-based classification of mutational status. Knowledge-transfer and self-supervised approaches were investigated to prevent model overfitting, with a primary end point of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, a novel metric, center of mass distance, was developed to quantify the model attention around the tumor. Results A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest classification performance with an AUC of 0.82 (95% CI: 0.72, 0.91), 0.87 (95% CI: 0.61, 0.97), and 0.85 (95% CI: 0.66, 0.95) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively, on internal testing. On external testing, the pipeline yielded an AUC of 0.72 (95% CI: 0.64, 0.86), 0.78 (95% CI: 0.61, 0.89), and 0.72 (95% CI: 0.64, 0.88) for BRAF wild type, BRAF fusion, and BRAF V600E, respectively. Conclusion Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pediatric low-grade glioma mutational status prediction in a limited data scenario. Keywords: Pediatrics, MRI, CNS, Brain/Brain Stem, Oncology, Feature Detection, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Noninvasive molecular subtyping of pediatric low-grade glioma with self-supervised transfer learning.

Purpose: To develop and externally validate a scan-to-prediction deep-learning pipeline for noninvasive, MRI-based BRAF mutational status classification for pLGG. Materials and Methods: We conducted a retrospective study of two pLGG datasets with linked genomic and diagnostic T2-weighted MRI of patients: BCH (development dataset, n=214 [60 (28%) BRAF fusion, 50 (23%) BRAF V600E, 104 (49%) wild-type), and Child Brain Tumor Network (CBTN) (external validation, n=112 [60 (53%) BRAF-Fusion, 17 (15%) BRAF-V600E, 35 (32%) wild-type]). We developed a deep learning pipeline to classify BRAF mutational status (V600E vs. fusion vs. wildtype) via a two-stage process: 1) 3D tumor segmentation and extraction of axial tumor images, and 2) slice-wise, deep learning-based classification of mutational status. We investigated knowledge-transfer and self-supervised approaches to prevent model overfitting with a primary endpoint of the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we developed a novel metric, COMDist, that quantifies the accuracy of model attention around the tumor. Results: A combination of transfer learning from a pretrained medical imaging-specific network and self-supervised label cross-training (TransferX) coupled with consensus logic yielded the highest macro-average AUC (0.82 [95% CI: 0.70-0.90]) and accuracy (77%) on internal validation, with an AUC improvement of +17.7% and a COMDist improvement of +6.4% versus training from scratch. On external validation, the TransferX model yielded AUC (0.73 [95% CI 0.68-0.88]) and accuracy (75%). Conclusion: Transfer learning and self-supervised cross-training improved classification performance and generalizability for noninvasive pLGG mutational status prediction in a limited data scenario.

Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learning.

Purpose: Artificial intelligence (AI)-automated tumor delineation for pediatric gliomas would enable real-time volumetric evaluation to support diagnosis, treatment response assessment, and clinical decision-making. Auto-segmentation algorithms for pediatric tumors are rare, due to limited data availability, and algorithms have yet to demonstrate clinical translation. Methods: We leveraged two datasets from a national brain tumor consortium (n=184) and a pediatric cancer center (n=100) to develop, externally validate, and clinically benchmark deep learning neural networks for pediatric low-grade glioma (pLGG) segmentation using a novel in-domain, stepwise transfer learning approach. The best model [via Dice similarity coefficient (DSC)] was externally validated and subject to randomized, blinded evaluation by three expert clinicians wherein clinicians assessed clinical acceptability of expert- and AI-generated segmentations via 10-point Likert scales and Turing tests. Results: The best AI model utilized in-domain, stepwise transfer learning (median DSC: 0.877 [IQR 0.715-0.914]) versus baseline model (median DSC 0.812 [IQR 0.559-0.888]; p<0.05). On external testing (n=60), the AI model yielded accuracy comparable to inter-expert agreement (median DSC: 0.834 [IQR 0.726-0.901] vs. 0.861 [IQR 0.795-0.905], p=0.13). On clinical benchmarking (n=100 scans, 300 segmentations from 3 experts), the experts rated the AI model higher on average compared to other experts (median Likert rating: 9 [IQR 7-9]) vs. 7 [IQR 7-9], p<0.05 for each). Additionally, the AI segmentations had significantly higher (p<0.05) overall acceptability compared to experts on average (80.2% vs. 65.4%). Experts correctly predicted the origins of AI segmentations in an average of 26.0% of cases. Conclusions: Stepwise transfer learning enabled expert-level, automated pediatric brain tumor auto-segmentation and volumetric measurement with a high level of clinical acceptability. This approach may enable development and translation of AI imaging segmentation algorithms in limited data scenarios.

Automated temporalis muscle quantification and growth charts for children through adulthood.

Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.

Measurement Properties of Patient Health Questionnaire 9 and Patient Health Questionnaire 2 in Adult Patients With Atopic Dermatitis.

BACKGROUND: Few outcome measures were validated for assessing depressive symptoms in AD. Patient Health Questionnaire-9 (PHQ9) and the abridged PHQ2 are established patient-reported outcome measures of depressive symptoms. OBJECTIVE: We sought to examine the measurement properties of PHQ9 and PHQ2 in adult AD. A prospective dermatology-practice based study of 458 AD patients (age 18-97 years) was conducted. RESULTS: PHQ9 strongly correlated with Dermatology Life Quality Index, Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep-Disturbance and Sleep-Related Impairment, and PROMIS Itch Questionnaire Mood and Sleep (PIQ-MS), and moderately correlated with Patient-Oriented Eczema Measure, Numeric Rating Scale (NRS) average-itch, NRS-sleep, Eczema Area and Severity Index, Scoring AD and Rajka-Langeland scores. PHQ2 had significantly weaker correlations than PHQ9 with PROMIS SD, SRI and PIQ-MS, but similar correlations with other outcomes. PHQ9 and PHQ2 had good discriminant validity. Changes from baseline in PHQ9 and PHQ2 were poorly or weakly correlated with changes of the other outcome measures. There was no differential item functioning of PHQ items. PHQ9 showed good reliability (intraclass correlation coefficient range: 0.80-0.87). PHQ2 had slightly lower reliability (0.76-0.82). CONCLUSIONS: PHQ9 and PHQ2 had similar measurement properties, but PHQ2 was more feasible to assess depressive symptoms in AD.

Measurement Properties of 4 Patient-Reported Outcome Measures to Assess Sleep Disturbance in Adults With Atopic Dermatitis.

BACKGROUND: The ideal patient-reported outcome measure to assess sleep disturbance in atopic dermatitis (AD) has not been determined. OBJECTIVE: We sought to determine the measurement properties of the Patient-Reported Outcomes Measurement Information System (PROMIS) Itch Questionnaire Mood and Sleep (PIQ-MS), Sleep Disturbance (SD), Sleep-Related Impairment (SRI), and Epworth Sleepiness Scale (ESS) in adults with AD. METHODS: A prospective dermatology practice-based study was performed using questionnaires and evaluation by a dermatologist (n=611). RESULTS: PIQ-MS, PROMIS SD, SRI, and ESS had good convergent validity with intensity and frequency of sleep disturbance, Patient-Oriented Eczema Measure, Eczema Area and Severity Index, total and objective-Scoring AD, Numerical Rating Scale of worst-itch and average-itch, and Dermatology Life Quality Index. PIQ-MS had significantly better correlations with other severity measures than the other sleep measures (Fisher z-scores, P≤0.007). PIQ-MS, and to lesser extent PROMIS SD, PROMIS SRI and ESS had good discriminant validity. All four sleep assessments showed fair responsiveness to change of severity of sleep-disturbance, AD and itch. PIQ-MS had the best reliability. PIQ-MS, PROMIS SD, SRI and ESS showed good internal consistency and were feasible for use in clinical practice. CONCLUSIONS: PIQ-MS, followed by PROMIS SD, had the best construct validity and reliability in adult AD.

Assessing the severity of atopic dermatitis in clinical trials and practice.

There is a tremendous need for accurate and reproducible scoring systems for the grading of skin disease to further the development of research and standards of care. There are presently greater than 60 measures that have been used to assess the severity of atopic dermatitis. These assessments vary considerably with respect to content, scale, instructions, validity, and concordance. This contribution reviews the available scoring systems of atopic dermatitis signs based on their design and merit in specific settings. These scores assess lesional intensity and/or extent, symptoms, disease course, and epidermal function. Scoring atopic dermatitis, Investigator Global Assessment, and Eczema Area and Severity Index are the most commonly used assessments of atopic dermatitis signs. Eczema Area and Severity Index has emerged as the preferred outcome measure of atopic dermatitis signs for use in clinical trials. Unfortunately, Eczema Area and Severity Index can be cumbersome in clinical practice. Itch intensity (visual analog or numeric rating scales) and Patient-Oriented Eczema Measure have emerged as the preferred patient-reported outcome in clinical trials. Clinicians' gestalt global assessment of severity, Patient-Oriented Eczema Measure, and intensity of itch may be feasible for clinical practice.

Clinical characteristics and outcomes of patients with multiple simultaneous superficial vein thrombi.

BACKGROUND: Although unprovoked superficial venous thrombosis (SVT) has traditionally been considered a local, benign disorder, recent studies demonstrate that patients with SVT are at significant risk for deep venous thrombosis, pulmonary embolism, and other venous thromboembolism (VTE) events. Nevertheless, clinical management remains widely inconsistent. Moreover, patients with multiple, unprovoked SVTs of noncommunicating anatomic sites have not been previously described, and they may be at even increased risk for adverse outcomes. The objective of this study was to describe the clinical characteristics and outcomes of patients with multiple, unprovoked SVTs to elucidate whether this subset of patients possesses a higher risk of thrombophilia, cancer, recurrent VTE, or death compared with patients with unprovoked SVT at a single location. METHODS: Twenty-four patients with multiple, unprovoked SVTs were enrolled. Blood tests and computed tomography scans were performed to detect thrombophilia and malignant disease. Patients were followed up with duplex ultrasound and clinical examination for at least 3 months. The prevalence of recurrent VTE and clinical outcomes were compared with a control group of 39 patients with unprovoked SVT in a single vein. RESULTS: Cancer was detected in five patients (20.8%) and thrombophilia in 10 patients (41.7%). During the follow-up period, nine patients (37.5%) exhibited recurrent VTEs, and five patients (16.2%) died. The VTE recurrence rate was significantly greater than in controls (P = .03). Patients with a coexisting thrombophilia or cancer had elevated thrombotic load (4.08 vs 2.27 separate vein segments; P = .0096) and an increase in VTE recurrence (P = .038) compared with patients without any such findings. CONCLUSIONS: The results of this study warrant further investigation into this subset of patients through a larger multicenter design, as patients with multiple SVTs are at greater risk for thrombophilia, cancer, recurrent VTE events, and death compared with patients with isolated SVT.