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Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury. A machine learning algorithm, developed to detect occlusion myocardial infarction with no-ST elevation from electrocardiogram, outperforms clinicians in diagnostic assessments.

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain. Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.

Homozygous familial hypercholesterolemia is a serious disorder resulting in early-onset vascular disease. This preliminary study explored a new therapy, an inhibitor of microsomal triglyceride transfer protein, which was very effective in lowering low-density lipoprotein cholesterol levels. Practical application of this therapy may be limited, however, by substantial accumulation of hepatic fat and elevation of liver aminotransferase levels. This preliminary study explored a new therapy, an inhibitor of microsomal triglyceride transfer protein, which was very effective in lowering low-density lipoprotein cholesterol levels. Homozygous familial hypercholesterolemia is caused by loss-of-function mutations in both alleles of the low-density lipoprotein (LDL) receptor gene. 1 – 3 Patients with the disease have plasma cholesterol levels of more than 500 mg per deciliter (12.9 mmol per liter); if untreated, patients have cardiovascular disease before 20 years of age and generally do not survive past 30 years of age. 1 – 3 Patients with homozygous familial hypercholesterolemia also have a poor response to conventional drug therapy, 1 – 3 which generally lowers LDL cholesterol levels through up-regulation of the hepatic LDL receptor. The current standard of care for these patients is LDL apheresis. This . . .

Electrocardiographic (ECG) monitoring plays an important role in the management of patients with atrial fibrillation (AF). Automated real-time AF detection algorithm is an integral part of ECG monitoring during AF therapy. Before and after antiarrhythmic drug therapy and surgical procedures require ECG monitoring to ensure the success of AF therapy. This article reports our experience in developing a real-time AF monitoring algorithm and techniques to eliminate false-positive AF alarms. We start by designing an algorithm based on R-R intervals. This algorithm uses a Markov modeling approach to calculate an R-R Markov score. This score reflects the relative likelihood of observing a sequence of R-R intervals in AF episodes versus making the same observation outside AF episodes. Enhancement of the AF algorithm is achieved by adding atrial activity analysis. P-R interval variability and a P wave morphology similarity measure are used in addition to R-R Markov score in classification. A hysteresis counter is applied to eliminate short AF segments to reduce false AF alarms for better suitability in a monitoring environment. A large ambulatory Holter database (n = 633) was used for algorithm development and the publicly available MIT-BIH AF database (n = 23) was used for algorithm validation. This validation database allowed us to compare our algorithm performance with previously published algorithms. Although R-R irregularity is the main characteristic and strongest discriminator of AF rhythm, by adding atrial activity analysis and techniques to eliminate very short AF episodes, we have achieved 92% sensitivity and 97% positive predictive value in detecting AF episodes, and 93% sensitivity and 98% positive predictive value in quantifying AF segment duration.

Forensic investigators of automobile collisions are commonly tasked with determining whether physical evidence observed on restraint systems is consistent with the occupant’s use or non-use of the seat belt restraint. The characteristics of collision-induced markings generated on seat belt systems are not solely dependent on the belted status of the occupant, but also the technological features incorporated in the seat belt assembly. As the state-of-the-art for seat belt assemblies has changed over time, so has the constellation of physical evidence typically created on seat belt restraint systems. Pretensioner deployment can leave physical evidence on restraint system hardware in the absence of occupant loading. This study presents examples of physical evidence collected from seat belt systems involved in real-world collisions, which were initially alleged to affirm proper belt use, but were ultimately proven to be evidence of non-use. Several laboratory demonstrations were conducted to investigate physical evidence created on restraint system hardware as a result of pretensioner deployments of non-used seat belts in a variety of incompletely stowed conditions. The demonstrations show the initial positions of the seat belt assemblies necessary to produce the distinct physical evidence documented in the real-world samples. The physical evidence observed on the real-world restraint systems were consistent with the markings produced in the laboratory demonstrations. The presence of physical evidence on restraint system hardware and webbing is inadequate to conclude that a seat belt restraint was in use. Careful consideration of restraint system physical evidence combined with restraint system geometry and incorporated technological features can distinguish physical evidence related to seat belt use or non-use during a collision event. These examples will be useful to investigators for the assessment of physical evidence and diagnosis of seat belt use.

Automated measurements of electrocardiographic (ECG) intervals are widely used by clinicians for individual patient diagnosis and by investigators in population studies. We examined whether clinically significant systematic differences exist in ECG intervals measured by current generation digital electrocardiographs from different manufacturers and whether differences, if present, are dependent on the degree of abnormality of the selected ECGs. Measurements of RR interval, PR interval, QRS duration, and QT interval were made blindly by 4 major manufacturers of digital electrocardiographs used in the United States from 600 XML files of ECG tracings stored in the US FDA ECG warehouse and released for the purpose of this study by the Cardiac Safety Research Consortium. Included were 3 groups based on expected QT interval and degree of repolarization abnormality, comprising 200 ECGs each from (1) placebo or baseline study period in normal subjects during thorough QT studies, (2) peak moxifloxacin effect in otherwise normal subjects during thorough QT studies, and (3) patients with genotyped variants of congenital long QT syndrome (LQTS). Differences of means between manufacturers were generally small in the normal and moxifloxacin subjects, but in the LQTS patients, differences of means ranged from 2.0 to 14.0 ms for QRS duration and from 0.8 to 18.1 ms for the QT interval. Mean absolute differences between algorithms were similar for QRS duration and QT intervals in the normal and in the moxifloxacin subjects (mean ≤6 ms) but were significantly larger in patients with LQTS. Small but statistically significant group differences in mean interval and duration measurements and means of individual absolute differences exist among automated algorithms of widely used, current generation digital electrocardiographs. Measurement differences, including QRS duration and the QT interval, are greatest for the most abnormal ECGs.

Muraglitazar, a Novel Dual (α/γ) Peroxisome Proliferator–Activated Receptor Activator, Improves Diabetes and Other Metabolic Abnormalities and Preserves β-Cell Function in db/db Mice Thomas Harrity 1 , Dennis Farrelly 1 , Aaron Tieman 1 , Cuixia Chu 1 , Lori Kunselman 1 , Liqun Gu 1 , Randolph Ponticiello 1 , Michael Cap 1 , Fucheng Qu 2 , Chunning Shao 2 , Wei Wang 2 , Hao Zhang 2 , William Fenderson 3 , Sean Chen 2 , Pratik Devasthale 2 , Yoon Jeon 2 , Ramakrishna Seethala 1 , Wen-Pin Yang 3 , Jimmy Ren 1 , Min Zhou 1 , Denis Ryono 2 , Scott Biller 2 , Kasim A. Mookhtiar 1 , John Wetterau 1 , Richard Gregg 1 , Peter T. Cheng 2 and Narayanan Hariharan 1 1 Department of Metabolic Diseases Biology, Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton, New Jersey 2 Department of Metabolic Diseases Chemistry, Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton, New Jersey 3 Department of Applied Genomics, Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton, New Jersey Address correspondence and reprint requests to Narayanan Hariharan, PhD, Metabolic Diseases, HWP-21-2.02, Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton, NJ 08543. E-mail: narayanan.hariharan{at}bms.com Abstract Muraglitazar, a novel dual (α/γ) peroxisome proliferator–activated receptor (PPAR) activator, was investigated for its antidiabetic properties and its effects on metabolic abnormalities in genetically obese diabetic db/db mice. In db/db mice and normal mice, muraglitazar treatment modulates the expression of PPAR target genes in white adipose tissue and liver. In young hyperglycemic db/db mice, muraglitazar treatment (0.03–50 mg · kg −1 · day −1 for 2 weeks) results in dose-dependent reductions of glucose, insulin, triglycerides, free fatty acids, and cholesterol. In older hyperglycemic db/db mice, longer-term muraglitazar treatment (30 mg · kg −1 · day −1 for 4 weeks) prevents time-dependent deterioration of glycemic control and development of insulin deficiency. In severely hyperglycemic db/db mice, muraglitazar treatment (10 mg · kg −1 · day −1 for 2 weeks) improves oral glucose tolerance and reduces plasma glucose and insulin levels. In addition, treatment increases insulin content in the pancreas. Finally, muraglitazar treatment increases abnormally low plasma adiponectin levels, increases high–molecular weight adiponectin complex levels, reduces elevated plasma corticosterone levels, and lowers elevated liver lipid content in db/db mice. The overall conclusions are that in db/db mice, the novel dual (α/γ) PPAR activator muraglitazar 1 ) exerts potent and efficacious antidiabetic effects, 2 ) preserves pancreatic insulin content, and 3 ) improves metabolic abnormalities such as hyperlipidemia, fatty liver, low adiponectin levels, and elevated corticosterone levels. ACO, acyl coenzyme-A oxidase FFA, free fatty acid HMW, high molecular weight LMW, low molecular weight MMW, medium molecular weight PPAR, peroxisome proliferator–activated receptor WAT, white adipose tissue Footnotes S.B. is currently affiliated with Novartis Institute Bio-Med Research, Cambridge, Massachusetts. K.A.M. is currently affiliated with Advinus Therapeutics, Pune, India. J.W. is currently affiliated with the Department of Cardiovascular Pharmaceuticals, Pfizer, Ann Arbor, Michigan. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted September 26, 2005. Received May 23, 2005. DIABETES

The details of digital recording and computer processing of a 12-lead electrocardiogram (ECG) remain a source of confusion for many health care professionals. A better understanding of the design and performance tradeoffs inherent in the electrocardiograph design might lead to better quality in ECG recording and better interpretation in ECG reading. This paper serves as a tutorial from an engineering point of view to those who are new to the field of ECG and to those clinicians who want to gain a better understanding of the engineering tradeoffs involved. The problem arises when the benefit of various electrocardiograph features is widely understood while the cost or the tradeoffs are not equally well understood. An electrocardiograph is divided into 2 main components, the patient module for ECG signal acquisition and the remainder for ECG processing which holds the main processor, fast printer, and display. The low-level ECG signal from the body is amplified and converted to a digital signal for further computer processing. The Electrocardiogram is processed for display by user selectable filters to reduce various artifacts. A high-pass filter is used to attenuate the very low frequency baseline sway or wander. A low-pass filter attenuates the high-frequency muscle artifact and a notch filter attenuates interference from alternating current power. Although the target artifact is reduced in each case, the ECG signal is also distorted slightly by the applied filter. The low-pass filter attenuates high-frequency components of the ECG such as sharp R waves and a high-pass filter can cause ST segment distortion for instance. Good skin preparation and electrode placement reduce artifacts to eliminate the need for common usage of these filters.

Abetalipoproteinemia is a human genetic disease that is characterized by a defect in the assembly or secretion of plasma very low density lipoproteins and chylomicrons. The microsomal triglyceride transfer protein (MTP), which is located in the lumen of microsomes isolated from the liver and intestine, has been proposed to function in lipoprotein assembly. MTP activity and the 88-kilodalton component of MTP were present in intestinal biopsy samples from eight control individuals but were absent in four abetalipoproteinemic subjects. This finding suggests that a defect in MTP is the basis for abetalipoproteinemia and that MTP is indeed required for lipoprotein assembly.

The importance of glucokinase (GK; EC 2.7.1.12) in glucose homeostasis has been demonstrated by the association of GK mutations with diabetes mellitus in humans and by alterations in glucose metabolism in transgenic and gene knockout mice. Liver GK activity in humans and rodents is allosterically inhibited by GK regulatory protein (GKRP). To further understand the role of GKRP in GK regulation, the mouse GKRP gene was inactivated. With the knockout of the GKRP gene, there was a parallel loss of GK protein and activity in mutant mouse liver. The loss was primarily because of posttranscriptional regulation of GK, indicating a positive regulatory role for GKRP in maintaining GK levels and activity. As in rat hepatocytes, both GK and GKRP were localized in the nuclei of mouse hepatocytes cultured in low-glucose-containing medium. In the presence of fructose or high concentrations of glucose, conditions known to relieve GK inhibition by GKRP in vitro, only GK was translocated into the cytoplasm. In the GKRP-mutant hepatocytes, GK was not found in the nucleus under any tested conditions. We propose that GKRP functions as an anchor to sequester and inhibit GK in the hepatocyte nucleus, where it is protected from degradation. This ensures that glucose phosphorylation is minimal when the liver is in the fasting, glucose-producing phase. This also enables the hepatocytes to rapidly mobilize GK into the cytoplasm to phosphorylate and store or metabolize glucose after the ingestion of dietary glucose. In GKRP-mutant mice, the disruption of this regulation and the subsequent decrease in GK activity leads to altered glucose metabolism and impaired glycemic control.