Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records.
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ABSTRACT: Objectives
Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred.Materials and methods
We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter.Results
We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing "STEM" has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952).Discussion and conclusion
In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.
SUBMITTER: Somani S
PROVIDER: S-EPMC8374370 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Aims. We sought for peripheral blood gene expression signatures at presentation that distinguish the ST elevation (STEMI) and non-ST elevation myocardial infarction (NSTEMI) conditions, to define a precise map of differentially regulated biological processes, unveil new distinctive traits, and help predict markers of outcome. Methods and results. Total RNA from whole blood of STEMI and NSTEMI patients was used to prepare poly(A)+ enriched libraries for paired-end RNA-Seq. Transcriptomes were reconstructed and, after adjustment for unwanted variation, we performed differential expression analysis. Enrichment analyses was performed to infer differentiating functional networks. We identified 153 differentially expressed genes, which stood correction for TnI levels at admission and included 32 putative novel genes (false discovery rate-adjusted P<0.05). We found a divergent modulation of inflammatory, immune-response, angiogenic, and mitochondrial dynamics pathways. Finally, we showed that specific gene expression patterns at admission predict troponin I (TnI) peak levels and/or GRACE risk score. Conclusions. RNA-Seq allowed identifying novel differentially expressed genes in STEMI vs. NSTEMI, which might uncover unappreciated mechanisms underlying acute MI. Overall, our results depicted a scenario characterizing different pathological traits of these two types of acute MI that could pave the way for the identification of novel, specific biomarkers for early diagnosis, risk stratification, and therapeutic decision-making.
2020-05-27 | GSE103182 | GEO
Project description:BackgroundDespite the high incidence and mortality of ST-segment elevation myocardial infarction (STEMI) among the very elderly, risk markers for this condition remain poorly defined. This study was designed to identify independent markers of STEMI among individuals carefully selected for being healthy or manifesting STEMI in < 24 h.MethodsWe enrolled participants aged 80 years or older of whom 50 were STEMI patients and 207 had never manifested cardiovascular diseases. Blood tests, medical and psychological evaluations were obtained at study admission. Odds Ratio (OR) and attributed risk (AR) were obtained by multivariate regression models using STEMI as dependent variable.ResultsLow glomerular filtration rate (GFR) [OR:4.41 (1.78-10.95); p = 0.001], reduced levels of HDL-C [OR:10.70 (3.88-29.46); p = 0.001], male gender [OR:12.08 (5.82-25.08); p = 0.001], moderate to severe depressive symptoms [OR:10.00 (2.82-35.50); p = 0.001], prior smoking [OR:2.00 (1.05-3.80); p = 0.034] and current smoking [OR:6.58 (1.99-21.70); p = 0.002] were significantly associated with STEMI. No association was found between STEMI and age, diabetes, hypertension, mild depressive symptoms, triglyceride or LDL-C.ConclusionsThis is the first case-control study carried out with very elderlies to assess STEMI risk. Our findings indicate that reduced HDL-C, GFR, male gender, smoking habits and moderate to severe depressive symptoms are markers of STEMI in this age group.General significanceIn Individuals aged 80 or more years, a greater attention must be paid to low HDL-C and GFR at the expense of conventional STEMI risk factors for younger adults such as diabetes mellitus, hypertension and high LDL-C or triglyceride.
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