Dataset Information

Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.

ABSTRACT: To demonstrate the use of aggregated and de-identified electronic health record (EHR) data for multivariate post-marketing pharmacosurveillance in a case study of azathioprine (AZA).Using aggregated, standardized, normalized, and de-identified, population-level data from the Explore platform (Explorys, Inc.) we searched over 10 million individuals, of which 14,580 were prescribed AZA based on RxNorm drug orders. Based on logical observation identifiers names and codes (LOINC) and vital sign data, we examined the following side effects: anemia, cell lysis, fever, hepatotoxicity, hypertension, nephrotoxicity, neutropenia, and neutrophilia. Patients prescribed AZA were compared to patients prescribed one of 11 other anti-rheumatologic drugs to determine the relative risk of side effect pairs.Compared to AZA case report trends, hepatotoxicity (marked by elevated transaminases or elevated bilirubin) did not occur as an isolated event more frequently in patients prescribed AZA than other anti-rheumatic agents. While neutropenia occurred in 24% of patients (RR 1.15, 95% CI 1.07-1.23), neutrophilia was also frequent (45%) and increased in patients prescribed AZA (RR 1.28, 95% CI 1.22-1.34). After constructing a pairwise side effect network, neutropenia had no dependencies. A reduced risk of neutropenia was found in patients with co-existing elevations in total bilirubin or liver transaminases, supporting classic clinical knowledge that agranulocytosis is a largely unpredictable phenomenon. Rounding errors propagated in the statistically de-identified datasets for cohorts as small as 40 patients only contributed marginally to the calculated risk.Our work demonstrates that aggregated, standardized, normalized and de-identified population level EHR data can provide both sufficient insight and statistical power to detect potential patterns of medication side effect associations, serving as a multivariate and generalizable approach to post-marketing drug surveillance.

SUBMITTER: Patel VN

PROVIDER: S-EPMC4002671 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.

Patel Vishal N VN Kaelber David C DC

Journal of biomedical informatics 20131028

<h4>Objective</h4>To demonstrate the use of aggregated and de-identified electronic health record (EHR) data for multivariate post-marketing pharmacosurveillance in a case study of azathioprine (AZA).<h4>Methods</h4>Using aggregated, standardized, normalized, and de-identified, population-level data from the Explore platform (Explorys, Inc.) we searched over 10 million individuals, of which 14,580 were prescribed AZA based on RxNorm drug orders. Based on logical observation identifiers names and ...[more]

PMID: 24177317

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Project description:ImportanceNew-onset retinal vascular occlusion (RVO) occurring acutely after messenger RNA (mRNA) COVID-19 vaccination has been described in recent literature. Because RVO can cause vision loss or blindness, an epidemiologic investigation evaluating this potential association is of great importance to public health.ObjectiveTo investigate how often patients are diagnosed with new RVO acutely after the mRNA COVID-19 vaccine compared with influenza and tetanus, diphtheria, pertussis (Tdap) vaccines.Design, setting, and participantsA retrospective population-based cohort design using the TriNetX Analytics platform, a federated, aggregated electronic health record (EHR) research network containing the deidentified EHR data of more than 103 million patients, was used to examine aggregate EHR data. Data were collected and analyzed on October 20, 2022. Data on patients within the TriNetX Analytics platform were searched for the presence of vaccination Common Procedural Technology codes, and instances of newly diagnosed RVO within 21 days of vaccination were recorded and reported. Propensity score matching based on demographic characteristics (age, sex, race and ethnicity) and comorbidities (diabetes, hypertension, and hyperlipidemia) was performed between vaccination groups for evaluation of relative risks (RRs).Main outcomes and measuresThe appearance of a new-encounter diagnosis of RVO within 21 days of the mRNA COVID-19 vaccination was the primary outcome. Historical comparison cohorts of patients receiving influenza and Tdap vaccinations allowed for evaluation of the RRs for RVO.ResultsOf 3 108 829 patients (mean [SD] age at vaccination, 50.7 [20.4] years; 56.4% women) who received the mRNA COVID-19 vaccine, 104 (0.003%; 95% CI, 0.003%-0.004%) patients had a new diagnosis of RVO within 21 days of vaccination. After propensity score matching, the RR for new RVO diagnosis after the first dose of COVID-19 vaccination was not significantly different from that after influenza (RR, 0.74; 95% CI, 0.54-1.01) or Tdap (RR, 0.78; 95% CI, 0.44-1.38) vaccinations, but was greater when compared with the second dose of the COVID-19 vaccination (RR, 2.25; 95% CI, 1.33-3.81).Conclusions and relevanceThe findings of this study suggest that RVO diagnosed acutely after mRNA COVID-19 vaccination occurs extremely rarely at rates similar to those of 2 different historically used vaccinations, the influenza and Tdap vaccines. No evidence suggesting an association between the mRNA COVID-19 vaccination and newly diagnosed RVO was found.

Project description:ImportanceAccurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation.ObjectiveTo develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital.Design, setting, and participantsWe performed a retrospective study of hospitalizations at an academic medical center. Hospitalizations for patients 18 years or older who were admitted after January 1, 2013, and discharged before February 28, 2015, were included. From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least 1 of 3 characteristics: heart failure on problem list, inpatient loop diuretic, or brain natriuretic peptide level of 500 pg/mL or higher; (3) logistic regression of 30 clinically relevant structured data elements; (4) machine-learning approach using unstructured notes; and (5) machine-learning approach using structured and unstructured data.Main outcomes and measuresHeart failure diagnosis based on discharge diagnosis and physician review of sampled medical records.ResultsA total of 47 119 hospitalizations were included in this study (mean [SD] age, 60.9 [18.15] years; 23 952 female [50.8%], 5258 black/African American [11.2%], and 3667 Hispanic/Latino [7.8%] patients). Of these hospitalizations, 6549 (13.9%) had a discharge diagnosis of heart failure. Inclusion of heart failure on the problem list (algorithm 1) had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96 for heart failure identification. Algorithm 2 improved sensitivity to 0.77 at the expense of a PPV of 0.64. Algorithms 3, 4, and 5 had areas under the receiver operating characteristic curves of 0.953, 0.969, and 0.974, respectively. With a PPV of 0.9, these algorithms had associated sensitivities of 0.68, 0.77, and 0.83, respectively.Conclusions and relevanceThe problem list is insufficient for real-time identification of hospitalized patients with heart failure. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.

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Dataset Information

Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.

Publications

Using aggregated, de-identified electronic health record data for multivariate pharmacosurveillance: a case study of azathioprine.

Similar Datasets

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