Dataset Information

Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

ABSTRACT: PURPOSE:A barrier to chemoprevention uptake among high-risk women is the lack of routine breast cancer risk assessment in the primary care setting. We calculated breast cancer risk using the Breast Cancer Surveillance Consortium (BCSC) model, accounting for age, race/ethnicity, first-degree family history of breast cancer, benign breast disease, and mammographic density, using data collected from the electronic health records (EHRs) and self-reports. PATIENTS AND METHODS:Among women undergoing screening mammography, we enrolled those age 35 to 74 years without a prior history of breast cancer. Data on demographics, first-degree family history, breast radiology, and pathology reports were extracted from the EHR. We assessed agreement between the EHR and self-report on information about breast cancer risk. RESULTS:Among 9,514 women with known race/ethnicity, 1,443 women (15.2%) met high-risk criteria based upon a 5-year invasive breast cancer risk of 1.67% or greater according to the BCSC model. Among 1,495 women with both self-report and EHR data, more women with a first-degree family history of breast cancer (14.6% v 4.4%) and previous breast biopsies (21.3% v 11.3%) were identified by self-report versus EHR, respectively. However, more women with atypia and lobular carcinoma in situ were identified from the EHR. There was moderate agreement in identification of high-risk women between EHR and self-report data (?, 0.48; 95% CI, 0.42-0.54). CONCLUSION:By using EHR data, we determined that 15% of women undergoing screening mammography had a high risk for breast cancer according to the BCSC model. There was moderate agreement between information on breast cancer risk derived from the EHR and self-report. Examining EHR data may serve as an initial screen for identifying women eligible for breast cancer chemoprevention.

SUBMITTER: Jiang X

PROVIDER: S-EPMC6874029 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

Jiang Xinyi X McGuinness Julia E JE Sin Margaret M Silverman Thomas T Kukafka Rita R Crew Katherine D KD

JCO clinical cancer informatics 20190301

<h4>Purpose</h4>A barrier to chemoprevention uptake among high-risk women is the lack of routine breast cancer risk assessment in the primary care setting. We calculated breast cancer risk using the Breast Cancer Surveillance Consortium (BCSC) model, accounting for age, race/ethnicity, first-degree family history of breast cancer, benign breast disease, and mammographic density, using data collected from the electronic health records (EHRs) and self-reports.<h4>Patients and methods</h4>Among wom ...[more]

PMID: 30869999

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Project description:BackgroundSuicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum.ObjectiveThis study aimed to leverage the information in clinical notes using deep neural networks (DNNs) to (1) improve the identification of patients treated for intentional self-harm and (2) predict future self-harm events.MethodsWe extracted clinical text notes from electronic health records (EHRs) of 835 patients with International Classification of Diseases (ICD) codes for intentional self-harm and 1670 matched controls who never had any intentional self-harm ICD codes. The data were divided into training and holdout test sets. We tested a number of algorithms on clinical notes associated with the intentional self-harm codes using the training set, including several traditional bag-of-words-based models and 2 DNN models: a convolutional neural network (CNN) and a long short-term memory model. We also evaluated the predictive performance of the DNNs on a subset of patients who had clinical notes 1 to 6 months before the first intentional self-harm event. Finally, we evaluated the impact of a pretrained model using Word2vec (W2V) on performance.ResultsThe area under the receiver operating characteristic curve (AUC) for the CNN on the phenotyping task, that is, the detection of intentional self-harm in clinical notes concurrent with the events was 0.999, with an F1 score of 0.985. In the predictive task, the CNN achieved the highest performance with an AUC of 0.882 and an F1 score of 0.769. Although pretraining with W2V shortened the DNN training time, it did not improve performance.ConclusionsThe strong performance on the first task, namely, phenotyping based on clinical notes, suggests that such models could be used effectively for surveillance of intentional self-harm in clinical text in an EHR. The modest performance on the predictive task notwithstanding, the results using DNN models on clinical text alone are competitive with other reports in the literature using risk factors from structured EHR data.

Project description:ObjectivesRecent sepsis studies have defined patients as "infected" using a combination of culture and antibiotic orders rather than billing data. However, the accuracy of these definitions is unclear. We aimed to compare the accuracy of different established criteria for identifying infected patients using detailed chart review.DesignRetrospective observational study.SettingSix hospitals from three health systems in Illinois.PatientsAdult admissions with blood culture or antibiotic orders, or Angus International Classification of Diseases infection codes and death were eligible for study inclusion as potentially infected patients. Nine-hundred to 1,000 of these admissions were randomly selected from each health system for chart review, and a proportional number of patients who did not meet chart review eligibility criteria were also included and deemed not infected.InterventionsNone.Measurements and main resultsThe accuracy of published billing code criteria by Angus et al and electronic health record criteria by Rhee et al and Seymour et al (Sepsis-3) was determined using the manual chart review results as the gold standard. A total of 5,215 patients were included, with 2,874 encounters analyzed via chart review and a proportional 2,341 added who did not meet chart review eligibility criteria. In the study cohort, 27.5% of admissions had at least one infection. This was most similar to the percentage of admissions with blood culture orders (26.8%), Angus infection criteria (28.7%), and the Sepsis-3 criteria (30.4%). Sepsis-3 criteria was the most sensitive (81%), followed by Angus (77%) and Rhee (52%), while Rhee (97%) and Angus (90%) were more specific than the Sepsis-3 criteria (89%). Results were similar for patients with organ dysfunction during their admission.ConclusionsPublished criteria have a wide range of accuracy for identifying infected patients, with the Sepsis-3 criteria being the most sensitive and Rhee criteria being the most specific. These findings have important implications for studies investigating the burden of sepsis on a local and national level.

Project description:OBJECTIVE:Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. DESIGN:We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. SETTING:These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. PARTICIPANTS:The codelist was used to estimate the frequency of shortness of breath in a cohort of 28?216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). RESULTS:Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13?091 episodes of shortness of breath were identified in the cohort of 28?216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. CONCLUSIONS:Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.

Dataset Information

Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

Publications

Identifying Women at High Risk for Breast Cancer Using Data From the Electronic Health Record Compared With Self-Report.

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