Browse
Submit Data
Databases
API
Help

Dataset Information

27 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

On using electronic health records to improve optimal treatment rules in randomized trials.

ABSTRACT: Individualized treatment rules (ITRs) tailor medical treatments according to patient-specific characteristics in order to optimize patient outcomes. Data from randomized controlled trials (RCTs) are used to infer valid ITRs using statistical and machine learning methods. However, RCTs are usually conducted under specific inclusion/exclusion criteria, thus limiting their generalizability to a broader patient population in real-world practice settings. Because electronic health records (EHRs) document treatment prescriptions in the real world, transferring information in EHRs to RCTs, if done appropriately, could potentially improve the performance of ITRs, in terms of precision and generalizability. In this work, we propose a new domain adaptation method to learn ITRs by incorporating information from EHRs. Unless we assume that there is no unmeasured confounding in EHRs, we cannot directly learn the optimal ITR from the combined EHR and RCT data. Instead, we first pretrain "super" features from EHRs that summarize physician treatment decisions and patient observed benefits in the real world, as these are likely to be informative of the optimal ITRs. We then augment the feature space of the RCT and learn the optimal ITRs by stratifying by super features using subjects enrolled in RCT. We adopt Q-learning and a modified matched-learning algorithm for estimation. We present heuristic justification of our method and conduct simulation studies to demonstrate the performance of super features. Finally, we apply our method to transfer information learned from EHRs of patients with type 2 diabetes to learn individualized insulin therapies from RCT data.

SUBMITTER: Wu P

PROVIDER: S-EPMC7786287 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records.

Project description:Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle challenges with EHRs and propose a machine learning approach based on matching (M-learning) to estimate optimal ITRs from EHRs. This new learning method performs matching instead of inverse probability weighting as commonly used in many existing methods for estimating ITRs to more accurately assess individuals' treatment responses to alternative treatments and alleviate confounding. Matching-based value functions are proposed to compare matched pairs under a unified framework, where various types of outcomes for measuring treatment response (including continuous, ordinal, and discrete outcomes) can easily be accommodated. We establish the Fisher consistency and convergence rate of M-learning. Through extensive simulation studies, we show that M-learning outperforms existing methods when propensity scores are misspecified or when unmeasured confounders are present in certain scenarios. Lastly, we apply M-learning to estimate optimal personalized second-line treatments for type 2 diabetes patients to achieve better glycemic control or reduce major complications using EHRs from New York Presbyterian Hospital.

| S-EPMC7539620 | biostudies-literature

Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.

Project description:ObjectivesTo develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes.MethodsAn enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms-on a training set of 127 axSpA cases and 423 non-cases-and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only.ResultsNLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80-0.87).ConclusionAlgorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

| S-EPMC7850056 | biostudies-literature

Publication bias in clinical trials of electronic health records.

Project description:ObjectiveTo measure the rate of non-publication and assess possible publication bias in clinical trials of electronic health records.MethodsWe searched ClinicalTrials.gov to identify registered clinical trials of electronic health records and searched the biomedical literature and contacted trial investigators to determine whether the results of the trials were published. Publications were judged as positive, negative, or neutral according to the primary outcome.ResultsSeventy-six percent of trials had publications describing trial results; of these, 74% were positive, 21% were neutral, and 4% were negative (harmful). Of unpublished studies for which the investigator responded, 43% were positive, 57% were neutral, and none were negative; the lower rate of positive results was significant (p<0.001).ConclusionThe rate of non-publication in electronic health record studies is similar to that in other biomedical studies. There appears to be a bias toward publication of positive trials in this domain.

| S-EPMC3662474 | biostudies-literature

Using Electronic Health Records to Derive Control Arms for Early Phase Single-Arm Lung Cancer Trials: Proof-of-Concept in Randomized Controlled Trials.

Project description:Oncology drug development increasingly relies on single-arm clinical trials. External controls (ECs) derived from electronic health record (EHR) databases may provide additional context. Patients from a US-based oncology EHR database were aligned with patients from randomized controlled trials (RCTs) and trial-specific eligibility criteria were applied to the EHR dataset. Overall survival (OS) in the EC-derived control arm was compared with OS in the RCT experimental arm. The primary outcome was OS, defined as time from randomization or treatment initiation (EHR) to death. Cox regression models were used to obtain effect estimates using EHR data. EC-derived hazard ratio estimates aligned closely with those from the corresponding RCT with one exception. Comparing log HRs among all RCT and EC results gave a Pearson correlation coefficient of 0.86. Properly selected control arms from contemporaneous EHR data could be used to put single-arm trials of OS in advanced non-small cell lung cancer into context.

| S-EPMC7006884 | biostudies-literature

Weight Change and the Onset of Cardiovascular Diseases: Emulating Trials Using Electronic Health Records.

Project description:BackgroundCross-sectional measures of body mass index (BMI) are associated with cardiovascular disease (CVD) incidence, but less is known about whether weight change affects the risk of CVD.MethodsWe estimated the effect of 2-y weight change interventions on 7-y risk of CVD (CVD death, myocardial infarction, stroke, hospitalization from coronary heart disease, and heart failure) by emulating hypothetical interventions using electronic health records. We identified 138,567 individuals with 45-69 years of age without chronic disease in England from 1998 to 2016. We performed pooled logistic regression, using inverse-probability weighting to adjust for baseline and time-varying confounders. We categorized each individual into a weight loss, maintenance, or gain group.ResultsAmong those of normal weight, both weight loss [risk difference (RD) vs. weight maintenance = 1.5% (0.3% to 3.0%)] and gain [RD = 1.3% (0.5% to 2.2%)] were associated with increased risk for CVD compared with weight maintenance. Among overweight individuals, we observed moderately higher risk of CVD in both the weight loss [RD = 0.7% (-0.2% to 1.7%)] and the weight gain group [RD = 0.7% (-0.1% to 1.7%)], compared with maintenance. In the obese, those losing weight showed lower risk of coronary heart disease [RD = -1.4% (-2.4% to -0.6%)] but not of stroke. When we assumed that chronic disease occurred 1-3 years before the recorded date, estimates for weight loss and gain were attenuated among overweight individuals; estimates for loss were lower among obese individuals.ConclusionAmong individuals with obesity, the weight-loss group had a lower risk of coronary heart disease but not of stroke. Weight gain was associated with increased risk of CVD across BMI groups. See video abstract at, http://links.lww.com/EDE/B838.

| S-EPMC8318567 | biostudies-literature

Using Electronic Health Records to Support Clinical Trials: A Report on Stakeholder Engagement for EHR4CR.

Project description:The conduct of clinical trials is increasingly challenging due to greater complexity and governance requirements as well as difficulties with recruitment and retention. Electronic Health Records for Clinical Research (EHR4CR) aims at improving the conduct of trials by using existing routinely collected data, but little is known about stakeholder views on data availability, information governance, and acceptable working practices.Senior figures in healthcare organisations across Europe were provided with a description of the project and structured interviews were subsequently conducted to elicit their views.37 structured interviewees in Germany, UK, Switzerland, and France indicated strong support for the proposed EHR4CR platform. All interviewees reported that using the platform for assessing feasibility would enhance the conduct of clinical trials and the majority also felt it would reduce workloads. Interviewees felt the platform could enhance trial recruitment and adverse event reporting but also felt it could raise either ethical or information governance concerns in their country.There was clear support for EHR4CR and a belief that it could reduce workloads and improve the conduct and quality of trials. However data security, privacy, and information governance issues would need to be carefully managed in the development of the platform.

| S-EPMC4619877 | biostudies-literature

Different approaches to improve cohort identification using electronic health records: X-linked hypophosphatemia as an example.

Project description:Electronic Health Records (EHRs) represent a source of high value data which is often underutilized because exploiting the information contained therein requires specialized techniques unavailable to the end user i.e. the physician or the investigator. Here I describe four simple and practical avenues that will allow the standard EHR end user to identify patient cohorts: the use of diagnostic codes from different international catalogues; a search in reports from complementary tests (e.g. radiographs or lab tests) for any result of interest; a free text search; or a drug prescription search in the patient's electronic prescription record. This medical approach is acquiring great importance in the field of rare diseases, and here I demonstrate its application with X-linked hypophosphatemia. The use of these four EHR questioning approaches makes finding a cohort of patients of any condition or disease feasible and manageable, and once each case record is checked, a well-defined cohort can be assembled.

| S-EPMC7882088 | biostudies-literature

High-throughput genetic analyses of drug intolerances using electronic health records

Project description:High-throughput genetic analyses of drug intolerances using electronic health records

| PRJNA683675 | ENA

Detecting Associations between Major Depressive Disorder Treatment and Essential Hypertension using Electronic Health Records.

Project description:In this observational study, we investigate the correlation between depression and hypertension on a cohort of patients treated for major depressive disorder using Selective Serotonin Reuptake Inhibitors (SSRIs) and assess the effect of depression treatment on the diagnoses and treatment for essential hypertension. Our results indicate that the positive effect of successful depression treatment can be discovered and estimated from electronic health record (EHR) data even for a small sample size. We have also successfully detected differences in the effect of depression treatment in hypertensive patients between the two phenotypes representing successful treatment outcomes-response and remission- concluding that achieving remission has a longer lasting effect than response.

| S-EPMC4419773 | biostudies-literature

A predictor model of treatment resistance in schizophrenia using data from electronic health records

Project description:Objectives To develop a prognostic tool of treatment resistant schizophrenia (TRS) in a large and diverse clinical cohort, with comprehensive coverage of patients using mental health services in four London boroughs. Methods We used the Least Absolute Shrinkage and Selection Operator (LASSO) for time-to-event data, to develop a risk prediction model from the first antipsychotic prescription to the development of TRS, using data from electronic health records. Results We reviewed the clinical records of 1,515 patients with a schizophrenia spectrum disorder and observed that 253 (17%) developed TRS. The Cox LASSO survival model produced an internally validated Harrel’s C index of 0.60. A Kaplan-Meier curve indicated that the hazard of developing TRS remained constant over the observation period. Predictors of TRS were: having more inpatient days in the three months before and after the first antipsychotic, more community face-to-face clinical contact in the three months before the first antipsychotic, minor cognitive problems, and younger age at the time of the first antipsychotic. Conclusions Routinely collected information, readily available at the start of treatment, gives some indication of TRS but is unlikely to be adequate alone. These results provide further evidence that earlier onset is a risk factor for TRS.

| S-EPMC9484642 | 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