Seeking out SARI: an automated search of electronic health records.
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ABSTRACT:
SUBMITTER: O'Horo JC
PROVIDER: S-EPMC5997502 | biostudies-literature | 2018 Jun
REPOSITORIES: biostudies-literature
Publications
Seeking out SARI: an automated search of electronic health records.
O'Horo John C JC Dziadzko Mikhail M Sakusic Amra A Ali Rashid R Sohail M Rizwan MR Kor Daryl J DJ Gajic Ognjen O
Epidemiology and infection 20180418 8
The definition of severe acute respiratory infection (SARI) - a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission - has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive ...[more]
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Project description:ObjectiveTo develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities.Patients and methodsThe automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities.ResultsIn the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS.ConclusionOur results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.
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Project description:BACKGROUND:Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition. OBJECTIVES:The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics. METHODS:Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts. RESULTS:In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10-5). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both). CONCLUSIONS:A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.
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Project description:Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p?=?0.015), 0.09 (p?=?0.064), 0.13 (p?=?0.003), 0.00 (p?=?0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p?=?0.004). These h2g were lower or similar to the h2g observed by the ICCBD?+?PGCBD (0.23, p?=?3.17E-80, total N?=?33,181). However, the rg between ICCBD?+?PGCBD and the EHR-based cases were high for 95-NLP (0.66, p?=?3.69?×?10-5), coded-strict (1.00, p?=?2.40?×?10-4), and coded-broad (0.74, p?=?8.11?×?10-7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.
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Project description:ObjectiveLarge electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data.Materials and methodsUsing deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors.ResultsOf 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors.Discussion and conclusionThis automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.
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Project description:The objective was to compare case ascertainment, agreement, validity, and missing values for clinical research data obtained, processed, and linked electronically from electronic health records (EHR) compared to "manual" data processing and record abstraction in a cohort of out-of-hospital trauma patients.? This was a secondary analysis of two sets of data collected for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services (EMS) agencies transporting to 16 hospitals, from January 1, 2006, through October 2, 2007. Eighteen clinical, operational, procedural, and outcome variables were collected and processed separately and independently using two parallel data processing strategies by personnel blinded to patients in the other group. The electronic approach included EHR data exports from EMS agencies, reformatting, and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor. Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to data processing.? During the 21-month period, 418 patients underwent both data processing methods and formed the primary cohort. Agreement was good to excellent (kappa = 0.76 to 0.97; intraclass correlation coefficient [ICC] = 0.49 to 0.97), with exact agreement in 67% to 99% of cases and a median difference of zero for all continuous and ordinal variables. The proportions of missing out-of-hospital values were similar between the two approaches, although electronic processing generated more missing outcomes (87 of 418, 21%, 95% confidence interval [CI] = 17% to 25%) than the manual approach (11 of 418, 3%, 95% CI = 1% to 5%). Case ascertainment of eligible injured patients was greater using electronic methods (n = 3,008) compared to manual methods (n = 629).? In this sample of out-of-hospital trauma patients, an all-electronic data processing strategy identified more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.
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Project description:IntroductionData extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).Materials and methodsFor 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.ResultsFor the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).ConclusionOur findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.
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