Dataset Information

The relationship between stress and vitiligo: Evaluating perceived stress and electronic medical record data.

ABSTRACT: Vitiligo is a T-cell mediated skin disorder characterized by progressive loss of skin color. In individuals genetically predisposed to the disease, various triggers contribute to the initiation of vitiligo. Precipitating factors can stress the skin, leading to T-cell activation and recruitment. Though hereditary factors are implicated in the pathogenesis of vitiligo, it is unknown whether precipitating, stressful events play a role in vitiligo. To understand this, we utilized a validated perceived stress scale (PSS) to measure this parameter in vitiligo patients compared to persons without vitiligo. Additionally, we probed a clinical database, using a knowledge linking software called ROCKET, to gauge stress-related conditions in the vitiligo patient population. From a pool of patients in an existing database, a hundred individuals with vitiligo and twenty-five age- and sex-matched comparison group of individuals without vitiligo completed an online survey to quantify their levels of perceived stress. In parallel, patients described specifics of their disease condition, including the affected body sites, the extent, duration and activity of their vitiligo. Perceived stress was significantly higher among vitiligo individuals compared to those without vitiligo. ROCKET analyses suggested signs of metabolic-related disease (i.e., 'stress') preceding vitiligo development. No correlation was found between perceived stress and the stage or the extent of disease, suggesting that elevated stress may not be a consequence of pigment loss alone. The data provide further support for stress as a precipitating factor in vitiligo development.

SUBMITTER: Henning SW

PROVIDER: S-EPMC6984686 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The relationship between stress and vitiligo: Evaluating perceived stress and electronic medical record data.

Henning Steven W SW Jaishankar Dinesh D Barse Levi W LW Dellacecca Emilia R ER Lancki Nicola N Webb Kirsten K Janusek Linda L Mathews Herbert L HL Price Ronald N RN Le Poole I Caroline IC

PloS one 20200127 1

Vitiligo is a T-cell mediated skin disorder characterized by progressive loss of skin color. In individuals genetically predisposed to the disease, various triggers contribute to the initiation of vitiligo. Precipitating factors can stress the skin, leading to T-cell activation and recruitment. Though hereditary factors are implicated in the pathogenesis of vitiligo, it is unknown whether precipitating, stressful events play a role in vitiligo. To understand this, we utilized a validated perceiv ...[more]

PMID: 31986193

Similar Datasets

Project description:Background:Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenemia, oligo-anovulation, and numerous ovarian cysts. Hospital electronic medical records provide an avenue for investigating polycystic ovary morphology commonly seen in PCOS at a large scale. The purpose of this study was to develop and evaluate the performance of two machine learning text algorithms, for classification of polycystic ovary morphology (PCOM) in pelvic ultrasounds. Methods:Pelvic ultrasound reports from patients at Boston Medical Center between October 1, 2003 and December 12, 2016 were included for analysis, which resulted in 39,093 ultrasound reports from 25,535 unique women. Following the 2003 Rotterdam Consensus Criteria for polycystic ovary syndrome, 2000 randomly selected ultrasounds were expert labeled for PCOM status as present, absent, or unidentifiable (not able to be determined from text alone). An ovary was marked as having PCOM if there was mention of numerous peripheral follicles or if the volume was greater than 10?ml in the absence of a dominant follicle or other confounding pathology. Half of the labeled data was used to develop and refine the algorithms, and the other half was used as a test set for evaluating its accuracy. Results:On the evaluation set of 1000 random US reports, the accuracy of the classifiers were 97.6% (95% CI: 96.5, 98.5%) and 96.1% (94.7, 97.2%). Both models were more adept at identifying PCOM-absent ultrasounds than either PCOM-unidentifiable or PCOM-present ultrasounds. The two classifiers estimated prevalence of PCOM within the whole set of 39,093 ultrasounds to be 44% PCOM-absent, 32% PCOM-unidentifiable, and 24% PCOM-present. Conclusions:Although accuracy measured on the test set and inter-rater agreement between the two classifiers (Cohen's Kappa?=?0.988) was high, a major limitation of our approach is that it uses the ultrasound report text as a proxy and does not directly count follicles from the ultrasound images themselves.

Project description:BackgroundComplex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access patterns to identify and then highlight the most relevant data for each patient.ObjectivesWe used insights from literature and feedback from potential users to inform the design of an EMR display capable of highlighting relevant information.MethodsWe used a review of relevant literature to guide the design of preliminary paper prototypes of the LEMR user interface. We observed five ICU physicians using their current EMR systems in preparation for morning rounds. Participants were interviewed and asked to explain their interactions and challenges with the EMR systems. Findings informed the revision of our prototypes. Finally, we conducted a focus group with five ICU physicians to elicit feedback on our designs and to generate ideas for our final prototypes using participatory design methods.ResultsParticipating physicians expressed support for the LEMR system. Identified design requirements included the display of data essential for every patient together with diagnosis-specific data and new or significantly changed information. Respondents expressed preferences for fishbones to organize labs, mouseovers to access additional details, and unobtrusive alerts minimizing color-coding. To address the concern about possible physician overreliance on highlighting, participants suggested that non-highlighted data should remain accessible. Study findings led to revised prototypes, which will inform the development of a functional user interface.ConclusionIn the feedback we received, physicians supported pursuing the concept of a LEMR system. By introducing novel ways to support physicians' cognitive abilities, such a system has the potential to enhance physician EMR use and lead to better patient outcomes. Future plans include laboratory studies of both the utility of the proposed designs on decision-making, and the possible impact of any automation bias.

Project description:BACKGROUND:The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. METHODS:Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. RESULTS:A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). CONCLUSIONS:This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Dataset Information

The relationship between stress and vitiligo: Evaluating perceived stress and electronic medical record data.

Publications

The relationship between stress and vitiligo: Evaluating perceived stress and electronic medical record data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets