Dataset Information

Electronic health record-based assessment of cardiovascular health: The stroke prevention in healthcare delivery environments (SPHERE) study.

ABSTRACT: < 3% of Americans have ideal cardiovascular health (CVH). The primary care encounter provides a setting in which to conduct patient-provider discussions of CVH. We implemented a CVH risk assessment, visualization, and decision-making tool that automatically populates with electronic health record (EHR) data during the encounter in order to encourage patient-centered CVH discussions among at-risk, yet under-treated, populations. We quantified five of the seven CVH behaviors and factors that were available in The Ohio State University Wexner Medical Center's EHR at baseline (May-July 2013) and compared values to those ascertained at one-year (May-July 2014) among intervention (n = 109) and control (n = 42) patients. The CVH of women in the intervention clinic improved relative to the metrics of body mass index (16% to 21% ideal) and diabetes (62% to 68% ideal), but not for smoking, total cholesterol, or blood pressure. Meanwhile, the CVH of women in the control clinic either held constant or worsened slightly as measured using those same metrics. Providers need easy-to-use tools at the point-of-care to help patients improve CVH. We demonstrated that the EHR could deliver such a tool using an existing American Heart Association framework, and we noted small improvements in CVH in our patient population. Future work is needed to assess how to best harness the potential of such tools in order to have the greatest impact on the CVH of a larger patient population.

SUBMITTER: Foraker RE

PROVIDER: S-EPMC4959947 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Electronic health record-based assessment of cardiovascular health: The stroke prevention in healthcare delivery environments (SPHERE) study.

Foraker Randi E RE Shoben Abigail B AB Kelley Marjorie M MM Lai Albert M AM Lopetegui Marcelo A MA Jackson Rebecca D RD Langan Michael A MA Payne Philip R O PR

Preventive medicine reports 20160713

< 3% of Americans have ideal cardiovascular health (CVH). The primary care encounter provides a setting in which to conduct patient-provider discussions of CVH. We implemented a CVH risk assessment, visualization, and decision-making tool that automatically populates with electronic health record (EHR) data during the encounter in order to encourage patient-centered CVH discussions among at-risk, yet under-treated, populations. We quantified five of the seven CVH behaviors and factors that were ...[more]

PMID: 27486559

Similar Datasets

Project description:ImportanceSecondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.ObjectiveTo develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.DesignCohort study with patients enrolled between 2003-2016 with follow-up through 2022.SettingPrimary care practices affiliated with two academic medical centers.ParticipantsIndividuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).ExposuresPredictors of PSCI were ascertained from the electronic health record.Main outcomeThe outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.ResultsThe analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.Conclusions and relevanceFive-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.

Project description:Many individuals at higher risk for cardiovascular disease (CVD) do not receive recommended treatments. Prior interventions using personalized risk information to promote prevention did not test clinic-wide effectiveness.To perform a 9-month cluster-randomized trial, comparing a strategy of electronic health record-based identification of patients with increased CVD risk and individualized mailed outreach to usual care.Patients of participating physicians with a Framingham Risk Score of at least 5 %, low-density lipoprotein (LDL)-cholesterol level above guideline threshold for drug treatment, and not prescribed a lipid-lowering medication were included in the intention-to-treat analysis.Patients of physicians randomized to the intervention group were mailed individualized CVD risk messages that described benefits of using a statin (and controlling hypertension or quitting smoking when relevant).The primary outcome was occurrence of a LDL-cholesterol level, repeated in routine practice, that was at least 30 mg/dl lower than prior. A secondary outcome was lipid-lowering drug prescribing. Clinicaltrials.gov identifier: NCT01286311.Fourteen physicians with 218 patients were randomized to intervention, and 15 physicians with 217 patients to control. The mean patient age was 60.7 years and 77% were male. There was no difference in the primary outcome (11.0 % vs. 11.1 %, OR 0.99, 95 % CI 0.56-1.74, P?=?0.96), but intervention group patients were twice as likely to receive a prescription for lipid-lowering medication (11.9 %, vs. 6.0 %, OR 2.13, 95 % CI 1.05-4.32, p?=?0.038). In post hoc analysis with extended follow-up to 18 months, the primary outcome occurred more often in the intervention group (22.5 % vs. 16.1 %, OR 1.59, 95 % CI 1.05-2.41, P?=?0.029).In this effectiveness trial, individualized mailed CVD risk messages increased the frequency of new lipid-lowering drug prescriptions, but we observed no difference in proportions lowering LDL-cholesterol after 9 months. With longer follow-up, the intervention's effect on LDL-cholesterol levels was apparent.

Project description:BACKGROUND:Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structured real-world evidence databases seldom include the severity. The aim of this study was to use machine learning models to impute NIHSS scores for all patients with newly diagnosed stroke from multi-institution electronic health record (EHR) data. METHODS:NIHSS scores available in the Optum© de-identified Integrated Claims-Clinical dataset were extracted from physician notes by applying natural language processing (NLP) methods. The cohort analyzed in the study consists of the 7149 patients with an inpatient or emergency room diagnosis of ischemic stroke, hemorrhagic stroke, or transient ischemic attack and a corresponding NLP-extracted NIHSS score. A subset of these patients (n?=?1033, 14%) were held out for independent validation of model performance and the remaining patients (n?=?6116, 86%) were used for training the model. Several machine learning models were evaluated, and parameters optimized using cross-validation on the training set. The model with optimal performance, a random forest model, was ultimately evaluated on the holdout set. RESULTS:Leveraging machine learning we identified the main factors in electronic health record data for assessing stroke severity, including death within the same month as stroke occurrence, length of hospital stay following stroke occurrence, aphagia/dysphagia diagnosis, hemiplegia diagnosis, and whether a patient was discharged to home or self-care. Comparing the imputed NIHSS scores to the NLP-extracted NIHSS scores on the holdout data set yielded an R2 (coefficient of determination) of 0.57, an R (Pearson correlation coefficient) of 0.76, and a root-mean-squared error of 4.5. CONCLUSIONS:Machine learning models built on EHR data can be used to determine proxies for stroke severity. This enables severity to be incorporated in studies of stroke patient outcomes using administrative and EHR databases.

Dataset Information

Electronic health record-based assessment of cardiovascular health: The stroke prevention in healthcare delivery environments (SPHERE) study.

Publications

Electronic health record-based assessment of cardiovascular health: The stroke prevention in healthcare delivery environments (SPHERE) study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets