Dataset Information

Real-World Assessment of Asthma Control and Severity in Children, Adolescents, and Adults with Asthma: Relationships to Care Settings and Comorbidities.

ABSTRACT: BACKGROUND:Little is known about how patient-level factors and care settings relate to asthma outcomes in real-world settings. OBJECTIVE:We therefore examined the rates and relative contributions of comorbidities and care settings in terms of asthma severity and control among pediatric and adolescent/adult patients in a large national sample. METHODS:We examined deidentified patient data from 28,508 unique encounters documented in the Asthma Specialist Tool to Help Manage Asthma and Improve Quality database, obtaining patient-level factors (demographics, asthma characteristics, comorbidities), care setting (primary care physician [PCP] vs specialist physician [allergist or pulmonologist]), and guideline-defined levels of asthma control/severity. Rates of comorbidities were identified by asthma severity and control and by care setting. We calculated odds ratios for asthma control and severity based on each comorbidity. RESULTS:Baseline demographic data indicated that patients seen by specialists versus PCPs were older, and had more severe and poorly controlled asthma (P < .05). Patients cared for by specialists also had more comorbid conditions, including gastroesophageal reflux disease (GERD; P < .01), rhinosinusitis (P < .01), and obstructive sleep apnea (adolescents/adults only: P < .01). GERD, smoke exposure, depression (adolescents/adults), rhinosinusitis (children), and African American race were associated with uncontrolled asthma. Smoke exposure (children), rhinosinusitis, and African American race were associated with severe disease. CONCLUSIONS:We identified several demographics and comorbidities that are independently associated with the specialist care setting, persistent asthma, and poor asthma control. Awareness of these relationships may be helpful for clinicians caring for patients with asthma.

SUBMITTER: Nyenhuis SM

PROVIDER: S-EPMC7064399 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Real-World Assessment of Asthma Control and Severity in Children, Adolescents, and Adults with Asthma: Relationships to Care Settings and Comorbidities.

Nyenhuis Sharmilee M SM Akkoyun Esra E Liu Li L Schatz Michael M Casale Thomas B TB

The journal of allergy and clinical immunology. In practice 20191107 3

<h4>Background</h4>Little is known about how patient-level factors and care settings relate to asthma outcomes in real-world settings.<h4>Objective</h4>We therefore examined the rates and relative contributions of comorbidities and care settings in terms of asthma severity and control among pediatric and adolescent/adult patients in a large national sample.<h4>Methods</h4>We examined deidentified patient data from 28,508 unique encounters documented in the Asthma Specialist Tool to Help Manage A ...[more]

PMID: 31707065

Similar Datasets

Project description:BackgroundPsoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current lack of reliable tools for objective severity assessments is overcome. Psoriasis Area and Severity Index (PASI) has a prominent level of subjectivity and is rarely used in real practice, although it is the most widely accepted metric for measuring psoriasis severity currently.ObjectiveThis study aimed to develop an image-artificial intelligence (AI)-based validated system for severity assessment with the explicit intention of facilitating long-term management of patients with psoriasis.MethodsA deep learning system was trained to estimate the PASI score by using 14,096 images from 2367 patients with psoriasis. We used 1962 patients from January 2015 to April 2021 to train the model and the other 405 patients from May 2021 to July 2021 to validate it. A multiview feature enhancement block was designed to combine vision features from different perspectives to better simulate the visual diagnostic method in clinical practice. A classification header along with a regression header was simultaneously applied to generate PASI scores, and an extra cross-teacher header after these 2 headers was designed to revise their output. The mean average error (MAE) was used as the metric to evaluate the accuracy of the predicted PASI score. By making the model minimize the MAE value, the model becomes closer to the target value. Then, the proposed model was compared with 43 experienced dermatologists. Finally, the proposed model was deployed into an app named SkinTeller on the WeChat platform.ResultsThe proposed image-AI-based PASI-estimating model outperformed the average performance of 43 experienced dermatologists with a 33.2% performance gain in the overall PASI score. The model achieved the smallest MAE of 2.05 at 3 input images by the ablation experiment. In other words, for the task of psoriasis severity assessment, the severity score predicted by our model was close to the PASI score diagnosed by experienced dermatologists. The SkinTeller app has been used 3369 times for PASI scoring in 1497 patients from 18 hospitals, and its excellent performance was confirmed by a feedback survey of 43 dermatologist users.ConclusionsAn image-AI-based psoriasis severity assessment model has been proposed to automatically calculate PASI scores in an efficient, objective, and accurate manner. The SkinTeller app may be a promising alternative for dermatologists' accurate assessment in the real world and chronic disease self-management in patients with psoriasis.

Project description:BACKGROUND/OBJECTIVES: There is evidence that exposure to air pollution affects asthma, but the effect of air pollution on asthma severity has not been addressed. The aim was to assess the relation between asthma severity during the past 12 months and home outdoor concentrations of air pollution. METHODS: Asthma severity over the past 12 months was assessed in two complementary ways among 328 adult asthmatics from the French Epidemiological study on the Genetics and Environment of Asthma (EGEA) examined between 1991 and 1995. The four-class severity score integrated clinical events and type of treatment. The five-level asthma score is based only on the occurrence of symptoms. Nitrogen dioxide (NO(2)), sulphur dioxide (SO(2)) and ozone (O(3)) concentrations were assigned to each residence using two different methods. The first was based on the closest monitor data from 1991 to 1995. The second consisted of spatial models that used geostatistical interpolations and then assigned air pollutants to the geo-coded residences (1998). RESULTS: Higher asthma severity score was significantly related to the 8-hour average of ozone during April-September (O(3)-8 h) and the number of days (O(3)-days) with 8-hour ozone averages above 110 microg.m(-3) (for a 36-day increase, equivalent to the interquartile range, in O(3)-days, odds ratio 2.22 (95% confidence interval 1.61 to 3.07) for one class difference in score). Adjustment for age, sex, smoking habits, occupational exposure, and educational level did not alter results. Asthma severity was unrelated to NO(2). Both exposure assessment methods and severity scores resulted in very similar findings. SO(2) correlated with severity but reached statistical significance only for the model-based assignment of exposure. CONCLUSIONS: The observed associations between asthma severity and air pollution, in particular O(3), support the hypothesis that air pollution at levels far below current standards increases asthma severity.

Project description:BackgroundDual process theories propose that the brain uses 2 types of thinking to influence behavior: automatic processing and reflective processing. Automatic processing is fast, immediate, nonconscious, and unintentional, whereas reflective processing focuses on logical reasoning, and it is slow, step by step, and intentional. Most digital psychological health interventions tend to solely target the reflective system, although the automatic processing pathway can have strong influences on behavior. Laboratory-based research has highlighted that automatic processing tasks can create behavior change; however, there are substantial gaps in the field on the design, implementation, and delivery of automatic processing tasks in real-world settings. It is important to identify and summarize the existing literature in this area to inform the translation of laboratory-based research to real-world settings.ObjectiveThis scoping review aims to explore the effectiveness of automatic training tasks, types of training tasks commonly used, mode of delivery, and impacts of gamification on automatic processing tasks designed for digital psychological health interventions in real-world settings among adults.MethodsThe scoping review methodology proposed by Arskey and O'Malley and Colquhoun was applied. A scoping review was chosen because of the novelty of the digital automatic processing field and to encompass a broad review of the existing evidence base. Electronic databases and gray literature databases were searched with the search terms "automatic processing," "computerised technologies," "health intervention," "real-world," and "adults" and synonyms of these words. The search was up to date until September 2018. A manual search was also completed on the reference lists of the included studies.ResultsA total of 14 studies met all inclusion criteria. There was a wide variety of health conditions targeted, with the most prevalent being alcohol abuse followed by social anxiety. Attention bias modification tasks were the most prevalent type of automatic processing task, and the majority of tasks were most commonly delivered over the web via a personal computer. Of the 14 studies included in the review, 8 demonstrated significant changes to automatic processes and 4 demonstrated significant behavioral changes as a result of changed automatic processes.ConclusionsThis is the first review to synthesize the evidence on automatic processing tasks in real-world settings targeting adults. This review has highlighted promising, albeit limited, research demonstrating that automatic processing tasks may be used effectively in a real-world setting to influence behavior change.

Dataset Information

Real-World Assessment of Asthma Control and Severity in Children, Adolescents, and Adults with Asthma: Relationships to Care Settings and Comorbidities.

Publications

Real-World Assessment of Asthma Control and Severity in Children, Adolescents, and Adults with Asthma: Relationships to Care Settings and Comorbidities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets