Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Reweighting to address nonparticipation and missing data bias in a longitudinal electronic health record study.

ABSTRACT:

Purpose

We examined whether weighting techniques could account for longitudinal differences in disease activity by race/ethnicity between research participants and nonparticipants with rheumatoid arthritis (RA).

Methods

We included 377 patients with RA from a public hospital in San Francisco, CA. We estimated the probability of not enrolling in a research study by constructing weights using inverse probability weighting. Disease activity over time by race/ethnicity was analyzed across the entire patient population and among research participants only using multivariable mixed-effects models.

Results

There were no differences in RA disease activity scores between research participants and nonparticipants at baseline; however, longitudinal differences in disease activity between research participants and nonparticipants were found by race/ethnicity. Weighting research participants in accordance with sociodemographic and clinical characteristics of the nonparticipant population did not result in any meaningful changes in disease activity by race/ethnicity over time.

Conclusions

In our study of patients with RA, inverse probability weighting using select sociodemographic and clinical variables was not sufficient to account for longitudinal disease activity differences by race/ethnicity between research participants and nonparticipants.

SUBMITTER: Gianfrancesco MA

PROVIDER: S-EPMC7541711 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS.

Project description:Electronic health records (EHRs) have become a vital source of patient outcome data but the widespread prevalence of missing data presents a major challenge. Different causes of missing data in the EHR data may introduce unintentional bias. Here, we compare the effectiveness of popular multiple imputation strategies with a deeply learned autoencoder using the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT). To evaluate performance, we examined imputation accuracy for known values simulated to be either missing completely at random or missing not at random. We also compared ALS disease progression prediction across different imputation models. Autoencoders showed strong performance for imputation accuracy and contributed to the strongest disease progression predictor. Finally, we show that despite clinical heterogeneity, ALS disease progression appears homogenous with time from onset being the most important predictor.

| S-EPMC5144587 | biostudies-literature

Characterizing Bias Due to Differential Exposure Ascertainment in Electronic Health Record Data.

Project description:Data derived from electronic health records (EHR) are heterogeneous with availability of specific measures dependent on the type and timing of patients' healthcare interactions. This creates a challenge for research using EHR-derived exposures because gold-standard exposure data, determined by a definitive assessment, may only be available for a subset of the population. Alternative approaches to exposure ascertainment in this case include restricting the analytic sample to only those patients with gold-standard exposure data available (exclusion); using gold-standard data, when available, and using a proxy exposure measure when the gold standard is unavailable (best available); or using a proxy exposure measure for everyone (common data). Exclusion may induce selection bias in outcome/exposure association estimates, while incorporating information from a proxy exposure via either the best available or common data approaches may result in information bias due to measurement error. The objective of this paper was to explore the bias and efficiency of these three analytic approaches across a broad range of scenarios motivated by a study of the association between chronic hyperglycemia and five-year mortality in an EHR-derived cohort of colon cancer survivors. We found that the best available approach tended to mitigate inefficiency and selection bias resulting from exclusion while suffering from less information bias than the common data approach. However, bias in all three approaches can be severe, particularly when both selection bias and information bias are present. When risk of either of these biases is judged to be more than moderate, EHR-based analyses may lead to erroneous conclusions.

| S-EPMC8336686 | biostudies-literature

Impact of longitudinal data-completeness of electronic health record data on risk score misclassification.

Project description:BackgroundElectric health record (EHR) discontinuity, that is, receiving care outside of a given EHR system, can lead to substantial information bias. We aimed to determine whether a previously described EHR-continuity prediction model can reduce the misclassification of 4 commonly used risk scores in pharmacoepidemiology.MethodsThe study cohort consists of patients aged ≥ 65 years identified in 2 US EHR systems linked with Medicare claims data from 2007 to 2017. We calculated 4 risk scores, CHAD2DS2-VASc, HAS-BLED, combined comorbidity score (CCS), claims-based frailty index (CFI) based on information recorded in the 365 days before cohort entry, and assessed their misclassification by comparing score values based on EHR data alone versus the linked EHR-claims data. CHAD2DS2-VASc and HAS-BLED were assessed in atrial fibrillation (AF) patients, whereas CCS and CFI were assessed in the general population.ResultsOur study cohort included 204 014 patients (26 537 with nonvalvular AF) in system 1 and 115 726 patients (15 529 with nonvalvular AF) in system 2. Comparing the low versus high predicted EHR continuity in system 1, the proportion of patients with misclassification of ≥2 categories improved from 55% to 16% for CHAD2DS2-VASc, from 55% to 12% for HAS-BLED, from 37% to 16% for CCS, and from 10% to 2% for CFI. A similar pattern was found in system 2.ConclusionsUsing a previously described prediction model to identify patients with high EHR continuity may significantly reduce misclassification for the commonly used risk scores in EHR-based comparative studies.

| S-EPMC9196679 | biostudies-literature

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification.

Project description:Electronic health records (EHR) are not designed for population-based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow-up to the methodological work, we demonstrate how to apply these methods for two real-world case studies, and we evaluate their performance. In both examples, we use individual patient-level data collected through the University of Michigan Health System and various external population-based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age-related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR-based research.

| S-EPMC9826451 | biostudies-literature

Safety huddles to proactively identify and address electronic health record safety.

Project description:ObjectiveMethods to identify and study safety risks of electronic health records (EHRs) are underdeveloped and largely depend on limited end-user reports. "Safety huddles" have been found useful in creating a sense of collective situational awareness that increases an organization's capacity to respond to safety concerns. We explored the use of safety huddles for identifying and learning about EHR-related safety concerns.DesignData were obtained from daily safety huddle briefing notes recorded at a single midsized tertiary-care hospital in the United States over 1 year. Huddles were attended by key administrative, clinical, and information technology staff. We conducted a content analysis of huddle notes to identify what EHR-related safety concerns were discussed. We expanded a previously developed EHR-related error taxonomy to categorize types of EHR-related safety concerns recorded in the notes.ResultsOn review of daily huddle notes spanning 249 days, we identified 245 EHR-related safety concerns. For our analysis, we defined EHR technology to include a specific EHR functionality, an entire clinical software application, or the hardware system. Most concerns (41.6%) involved " EHR technology working incorrectly, " followed by 25.7% involving " EHR technology not working at all. " Concerns related to "EHR technology missing or absent" accounted for 16.7%, whereas 15.9% were linked to " user errors ."ConclusionsSafety huddles promoted discussion of several technology-related issues at the organization level and can serve as a promising technique to identify and address EHR-related safety concerns. Based on our findings, we recommend that health care organizations consider huddles as a strategy to promote understanding and improvement of EHR safety.

| S-EPMC5391729 | biostudies-literature

Leveraging the Electronic Health Record to Address the COVID-19 Pandemic.

Project description:The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to "omics" data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.

| S-EPMC8059945 | biostudies-literature

Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events.

Project description:ObjectiveTo propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events.Materials and methodsThis retrospective study explores the efficacy of machine learning algorithms in classifying clinical deterioration events among patients in intensive care units using sequences of timestamps of vital sign measurements, flowsheets comments, order entries, and nursing notes. We design a data pipeline to partition events into discrete, regular time bins that we refer to as timesteps. Logistic regressions, random forest classifiers, and recurrent neural networks are trained on datasets of different length of timesteps, respectively, against a composite outcome of death, cardiac arrest, and Rapid Response Team calls. Then these models are validated on a holdout dataset.ResultsA total of 6720 intensive care unit encounters meet the criteria and the final dataset includes 830 578 timestamps. The gated recurrent unit model utilizes timestamps of vital signs, order entries, flowsheet comments, and nursing notes to achieve the best performance on the time-to-outcome dataset, with an area under the precision-recall curve of 0.101 (0.06, 0.137), a sensitivity of 0.443, and a positive predictive value of 0. 092 at the threshold of 0.6.Discussion and conclusionThis study demonstrates that our recurrent neural network models using only timestamps of longitudinal electronic health record data that reflect healthcare processes achieve well-performing discriminative power.

| S-EPMC8363809 | biostudies-literature

An Electronic Health Record-Based Strategy to Address Child Tobacco Smoke Exposure.

Project description:IntroductionA high proportion of children presenting to pediatric urgent cares are exposed to tobacco smoke. An electronic health record-based clinical decision support system for nurses to facilitate guideline-based tobacco smoke exposure screening and counseling for caregivers who smoke was designed and evaluated.DesignA mixed-methods, 3-month, prospective study that began in November 2015, data were analyzed in June 2016.Setting/participantsFive urgent cares that were part of a large children's hospital in Cincinnati, OH. Participants were urgent care nurses.InterventionThe clinical decision support system prompted nurses to Ask, Advise, Assess, and Assist caregivers to quit smoking. Monthly feedback reports were also provided.Main outcome measureClinical decision support system use rates, nurses' attitudes towards tobacco smoke exposure intervention, and percentage of children screened and caregivers counseled.ResultsAll nurses used the clinical decision support system. Compared with Month 1, nurses were twice as likely to advise and assess during Months 2 and 3. There was significant improvement in nurses feeling prepared to assist caregivers in quitting. Nurses reported that feedback reports motivated them to use the clinical decision support system, and that it was easy to use. Almost 65% of children were screened for tobacco smoke exposure; 19.5% screened positive. Of caregivers identified as smokers, 26% were advised to quit and 29% were assessed for readiness to quit. Of those assessed, 67% were interested in quitting, and of those, 100% were assisted.ConclusionsA clinical decision support system increased rates of tobacco smoke exposure screening and intervention in pediatric urgent cares. Rates might further improve by incorporating all components of the clinical decision support system into the electronic health record.Trial registrationThis study is registered at www.clinicaltrials.gov NCT02489708.

| S-EPMC5736447 | biostudies-literature

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction.

Project description:Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice - American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.

| S-EPMC6345960 | biostudies-literature

Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.

Project description:Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for analyzing EHR data is limited and specific efficacy for postoperative complication detection is unclear. Several data imputation methods were used to develop data models for automated detection of three types (i.e., superficial, deep, and organ space) of surgical site infection (SSI) and overall SSI using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) Registry 30-day SSI occurrence data as a reference standard. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values. Missing data imputation appears to be an effective means for improving postoperative SSI detection using EHR clinical data.

| S-EPMC5474942 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data