Hypercholesterolemia and Myocardial function evaluated via Tissue Doppler Imaging

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Hyperlipidemia and Atherosclerotic Lesion Development in Ldlr-Deficient Mice on a Long-Term High-Fat Diet

BACKGROUND: Mice deficient in the LDL receptor (Ldlr(-/-) mice) have been widely used as a model to mimic human atherosclerosis. However, the time-course of atherosclerotic lesion development and distribution of lesions at specific time-points are yet to be established. The current study sought to determine the progression and distribution of lesions in Ldlr(-/-) mice. METHODOLOGY/PRINCIPAL FINDINGS: Ldlr-deficient mice fed regular chow or a high-fat (HF) diet for 0.5 to 12 months were analyzed for atherosclerotic lesions with en face and cross-sectional imaging. Mice displayed significant individual differences in lesion development when fed a chow diet, whereas those on a HF diet developed lesions in a time-dependent and site-selective manner. Specifically, mice subjected to the HF diet showed slight atherosclerotic lesions distributed exclusively in the aortic roots or innominate artery before 3 months. Lesions extended to the thoracic aorta at 6 months and abdominal aorta at 9 months. Cross-sectional analysis revealed the presence of advanced lesions in the aortic sinus after 3 months in the group on the HF diet and in the innominate artery at 6 to 9 months. The HF diet additionally resulted in increased total cholesterol, LDL, glucose, and HBA1c levels, along with the complication of obesity. CONCLUSIONS/SIGNIFICANCE: Ldlr-deficient mice on the HF diet tend to develop site-selective and size-specific atherosclerotic lesions over time. The current study should provide information on diet induction or drug intervention times and facilitate estimation of the appropriate locations of atherosclerotic lesions in Ldlr(-/-) mice

A methodological approach to validate pneumonia encounters from radiology reports using Natural Language Processing (NLP)

Introduction: Pneumonia is caused by microbes that establish an infectious process in the lungs. The gold standard for pneumonia diagnosis is radiologist-documented pneumonia-related features in radiology notes that are captured in electronic health records in an unstructured format. Objective: The study objective was to develop a methodological approach for assessing validity of a pneumonia diagnosis based on identifying presence or absence of key radiographic features in radiology reports with subsequent rendering of diagnostic decisions into a structured format. Methods: A pneumonia-specific Natural Language Processing (NLP) pipeline was strategically developed applying cTAKES to validate pneumonia diagnoses following development of a pneumonia feature-specific lexicon. Radiographic reports of study-eligible subjects identified by International Classification of Diseases (ICD) codes were parsed through the NLP pipeline. Classification rules were developed to assign each pneumonia episode into one of three categories: positive , negative or not classified: requires manual review based on tagged concepts that support or refute diagnostic codes. Results: A total of 91,998 pneumonia episodes diagnosed in 65,904 patients were retrieved retrospectively. Approximately 89% (81,707/91,998) of the total pneumonia episodes were documented by 225,893 chest x-ray reports. NLP classified and validated 33% (26,800/81,707) of pneumonia episodes classified as \u27Pneumonia-positive\u27, 19% as (15401/81,707) as \u27Pneumonia-negative\u27 and 48% (39,209/81,707) as \u27\u27episode classification pending further manual review\u27. NLP pipeline performance metrics included accuracy (76.3%), sensitivity (88%), and specificity (75%). Conclusion: The pneumonia-specific NLP pipeline exhibited good performance comparable to other pneumonia-specific NLP systems developed to date

Identifying pneumonia sub-types from electronic health records using rule-based algorithms

Background: International Classification of Disease (ICD) coding for pneumonia classification is based on causal organism or use of general pneumonia codes, creating challenges for epidemiological evaluations, where pneumonia is standardly subtyped by settings, exposures and time of emergence. Pneumonia subtype classification requires data available in electronic health records (EHR), frequently in non-structured formats including radiological interpretation or clinical notes that complicate electronic classification. Objective: The current study undertook development of a rule-based pneumonia subtyping algorithm for stratifying pneumonia by the setting in which it emerged using information documented in the EHR. Methods: Pneumonia subtype classification was developed by interrogating patient information within the EHR of a large private Health System. ICD coding was mined in the EHR applying requirements for \u27rule of two\u27 pneumonia-related codes or one ICD code and radiologically-confirmed pneumonia validated by natural language processing and/or documented antibiotic prescriptions. A rule-based algorithm flow chart was created to support sub-classification based on features including symptomatic patient point of entry into the healthcare system timing of pneumonia emergence and identification of clinical, laboratory or medication orders that informed definition of the pneumonia sub-classification algorithm. Results: Data from 65,904 study-eligible patients with 91,998 episodes of pneumonia diagnoses documented by 380,509 encounters were analyzed, while 8,611 episodes were excluded following NLP classification of pneumonia status as \u27negative\u27 or \u27unknown\u27. Subtyping of 83,387 episodes identified: community acquired (54.5%), hospital-acquired (20%), aspiration-related (10.7%), healthcare-acquired (5%), ventilator-associated (0.4%) cases, and 9.4% were not classifiable by the algorithm. Conclusion: Study outcome indicated capacity to achieve electronic pneumonia subtype classification based on interrogation of big data available in the EHR. Examination of portability of the algorithm to achieve rule-based pneumonia classification in other health systems remains to be explored

Impact of prior statin therapy on arrhythmic events in patients with acute coronary syndromes (from the Global Registry of Acute Coronary Events [GRACE])

Animal models of myocardial ischemia have demonstrated reduction in arrhythmias using statins. It was hypothesized that previous statin therapy before hospitalization might be associated with reductions of in-hospital arrhythmic events in patients with acute coronary syndromes. In this multinational, prospective, observational study (the Global Registry of Acute Coronary Events [GRACE]), data from 64,679 patients hospitalized for suspected acute coronary syndromes (from 1999 to 2007) were analyzed. The primary outcome of interest was in-hospital arrhythmic events in previous statin users compared with nonusers. The 2 primary end points were atrial fibrillation and the composite end point of ventricular tachycardia, ventricular fibrillation, and/or cardiac arrest. In-hospital death was also examined. Of the 64,679 patients, 17,636 (27%) had received previous statin therapy. Those taking statins had higher crude rates of histories of angina (69% vs 46%), diabetes (34% vs 22%), heart failure (15% vs 8.4%), hypertension (74% vs 58%), atrial fibrillation (9.3% vs 7.0%), and dyslipidemia (85% vs 35%). Patients previously taking statins were less likely to have in-hospital arrhythmias. In propensity-adjusted multivariable models, previous statin use was associated with a lower risk for ventricular tachycardia, ventricular fibrillation, or cardiac arrest (odds ratio 0.81, 95% confidence interval 0.72 to 0.96, p = 0.002); atrial fibrillation (odds ratio 0.81, 95% confidence interval 0.73 to 0.89, p \u3c0.0001); and death (odds ratio 0.82, 95% confidence interval 0.70 to 0.95, p = 0.010). In conclusion, patients previously taking statins had a lower incidence of in-hospital arrhythmic events after acute coronary syndrome than those not previously taking statins. Our study suggests another possible benefit from appropriate primary and secondary prevention therapy with statins