Common variants associated with changes in levels of circulating free fatty acids after administration of glucose-insulin-potassium (GIK) therapy in the IMMEDIATE trial.

Glucose-insulin-potassium (GIK) therapy may promote a shift from oxygen-wasteful free fatty acid (FFA) metabolism to glycolysis, potentially reducing myocardial damage during ischemia. Genetic variation associated with FFA response to GIK was investigated in an IMMEDIATE (Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care) sub-study (n=117). In patients with confirmed acute coronary syndromes, associations between 132 634 variants and 12-h circulating FFA response were assessed. Between initial and 6-h measurements, three LINGO2 variants were associated with increased levels of total FFA (P-value for 2 degree of freedom test, P2df ⩽5.51 × 10-7). Lead LINGO2 single-nucleotide polymorphism, rs12003487, was nominally associated with reduced 30-day ejection fraction (P2df=0.03). Several LINGO2 signals were linked to alterations in epigenetic profile and gene expression levels. Between 6 and 12 h, rs7017336 nearest to IMPA1/FABP12 showed an association with decreased saturated FFAs (P2df=5.47 × 10-7). Nearest to DUSP26, rs7464104 was associated with a decrease in unsaturated FFAs (P2df=5.51 × 10-7). Genetic variation may modify FFA response to GIK, potentially conferring less beneficial outcomes.

Genetic variation at glucose and insulin trait loci and response to glucose-insulin-potassium (GIK) therapy: the IMMEDIATE trial.

The mechanistic effects of intravenous glucose, insulin and potassium (GIK) in cardiac ischemia are not well understood. We conducted a genetic sub-study of the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care (IMMEDIATE) Trial to explore effects of common and rare glucose and insulin-related genetic loci on initial to 6-h and 6- to 12-h change in plasma glucose and potassium. We identified 27 NOTCH2/ADAM30 and 8 C2CD4B variants conferring a 40-57% increase in glucose during the first 6 h of infusion (P<5.96 × 10(-6)). Significant associations were also found for ABCB11 and SLC30A8 single-nucleotide polymorphisms (SNPs) and glucose responses, and an SEC61A2 SNP with a potassium response to GIK. These studies identify genetic factors that may impact the metabolic response to GIK, which could influence treatment benefits in the setting of acute coronary syndromes (ACS).

Genetic modifiers of response to glucose-insulin-potassium (GIK) infusion in acute coronary syndromes and associations with clinical outcomes in the IMMEDIATE trial.

Modifiers of response to glucose, insulin and potassium (GIK) infusion may affect clinical outcomes in acute coronary syndromes (ACS). In an Immediate Myocardial Metabolic Enhancement During Initial Assessment And Treatment In Emergency Care (IMMEDIATE) trial's sub-study (n = 318), we explored effects of 132,634 genetic variants on plasma glucose and potassium response to 12-h GIK infusion. Associations between metabolite-associated variants and infarct size (n = 84) were assessed. The 'G' allele of rs12641551, near ACSL1, as well as the 'A' allele of XPO4 rs2585897 were associated with a differential glucose response (P for 2 degrees of freedom test, P2df ⩽ 4.75 × 10(-7)) and infarct size with GIK (P2df < 0.05). Variants within or near TAS1R3, LCA5, DNAH5, PTPRG, MAGI1, PTCSC3, STRADA, AKAP12, ARFGEF2, ADCYAP1, SETX, NDRG4 and ABCB11 modified glucose response, and near CSF1/AHCYL1 potassium response (P2df ⩽ 4.26 × 10(-7)), but not outcomes. Gene variants may modify glucose and potassium response to GIK infusion, contributing to cardiovascular outcomes in ACS.

PIPELINEs: Creating Comparable Clinical Knowledge Efficiently by Linking Trial Platforms.

Adaptive, seamless, multisponsor, multitherapy clinical trial designs executed as large scale platforms, could create superior evidence more efficiently than single-sponsor, single-drug trials. These trial PIPELINEs also could diminish barriers to trial participation, increase the representation of real-world populations, and create systematic evidence development for learning throughout a therapeutic life cycle, to continually refine its use. Comparable evidence could arise from multiarm design, shared comparator arms, and standardized endpoints-aiding sponsors in demonstrating the distinct value of their innovative medicines; facilitating providers and patients in selecting the most appropriate treatments; assisting regulators in efficacy and safety determinations; helping payers make coverage and reimbursement decisions; and spurring scientists with translational insights. Reduced trial times and costs could enable more indications, reduced development cycle times, and improved system financial sustainability. Challenges to overcome range from statistical to operational to collaborative governance and data exchange.

Projects and priorities in cardiovascular modeling.

Studies that either 1) model the likelihood of clinical outcomes as a function of patient characteristics, or 2) examine the factors underlying a specific medical decision are discussed. Although the currently available models represent important contributions, the working group that met during the 1987 Regenstrief Conference identified several important areas for further attention. Described are discussions on ways to improve standardization, accessibility, validation and dissemination of decision models.

Hospital mortality in women and men with acute cardiac ischemia: a prospective multicenter study.

OBJECTIVES: This study sought to determine gender differences in hospital mortality in patients with acute cardiac ischemia. BACKGROUND: It is unclear why women experience higher mortality from acute myocardial infarction (AMI) than men and whether this applies to all patients with acute ischemia. METHODS: We analyzed data from a prospective multicenter study involving patients presenting to the emergency department (ED) with symptoms suggestive of acute ischemia. RESULTS: Of 10,783 patients, 5,221 (48.4%) were women. Mean age was 60.5 years for women and 56.9 for men (p < 0.001). Women had more hypertension (54.6% vs. 45.9%, p < 0.001) and diabetes (23.3% vs. 17.0%, p < 0.001) than men but fewer previous AMIs (21.1% vs. 28.9%, p < 0.001). Acute ischemia was confirmed in 1,090 women (20.8%) and 1,451 men (26.1%, p < 0.001), including AMI in 322 women (6.2%) and 572 men (10.3%, p < 0.001). Women with an AMI were in a higher Killip class than men: class I in 60.3% versus 72.2%, class II in 19.3% versus 16%, class III in 15.5% versus 8.7% and class IV in 5% versus 3.1%, respectively (p = 0.001). There was no significant difference in mortality from acute ischemia between genders (4.0% vs. 3.5%, p = 0.6), but there was a trend for higher AMI mortality in women (10.3% vs. 7.4%, p = 0.1). After controlling for age, diabetes, heart failure and presenting blood pressure, gender did not predict mortality from acute ischemia (odds ratio 0.9, 95% confidence interval 0.5 to 1.4, p = 0.5). CONCLUSIONS: Among patients presenting to the ED with acute cardiac ischemia, gender does not appear to be an independent predictor of hospital mortality. The trend for higher mortality in women from AMI can be explained by their older age, greater frequency of diabetes and higher Killip class on presentation.

A national survey of emergency department chest pain centers in the United States.

Although chest pain centers are promoted as improving emergency cardiac care, no data exist on their structure and processes. This national study determines the 1995 prevalence rate for emergency department (ED)-based chest pain centers in the United States and compares organizational differences of EDs with and without such centers. A mail survey was directed to 476 EDs randomly selected from the American Hospital Association's database of metropolitan hospitals (n = 2,309); the response rate was 63%. The prevalence of chest pain centers was 22.5% (95% confidence interval 18% to 27%), which yielded a projection of 520 centers in the United States in 1995. EDs with centers had higher overall patient volumes, greater use of high-technology testing, lower treatment times for thrombolytic therapy, and more advertising (all p <0.05). Hospitals with centers had greater market competition and more beds per annual admissions, cardiac catheterization, and open heart surgery capability (all p <0.05). Logistic regression identified open heart surgery, high-admission volumes, and nonprofit status as independent predictors of hospitals having chest pain centers. Thus, chest pain centers have a moderate prevalence, offer more services and marketing efforts than standard EDs, and tend to be hosted by large nonprofit hospitals.

Prediction of the infarct-related artery in acute myocardial infarction by a scoring system using summary ST-segment and T-wave changes.

We developed a scoring system to predict the artery responsible for an acute myocardial infarction (AMI) using ST-segment and T-wave changes on the initial electrocardiogram (ECG) using data from 228 patients (development set) with symptoms compatible with AMI and tested in a similar group of 223 patients (test set) from the Thrombolysis and Angioplasty in Myocardial Infarction (TAMI-5) Trial. Using stepwise logistic regression we were able to accurately predict the left anterior descending (LAD), right, or left circumflex (LC) coronary artery as the infarct-related artery using 2 variables: (1) the summation of the ST-segment elevation in leads V1 to V4; and (2) the summation of the T-wave negativity in leads I, aVL, and V5. In the development set, these 2 variables demonstrated respective sensitivity and specificity of 98% and 90% for LAD lesions, 82% and 85% for right narrowings, and 82% and 84% for LC narrowings. In the test set, the sensitivity and specificity were 97% and 95% for LAD lesions, 85% and 86% for right lesions, and 73% and 60% for LC coronary artery lesions. Information easily obtained on the ECG can accurately predict the likelihood of the LAD, right, or LC artery as the infarct-related artery. This may be useful in the decision to administer thrombolytic treatment.

Do patients' coronary risk factor reports predict acute cardiac ischemia in the emergency department? A multicenter study.

The objective of the present study was to determine whether the presence of the classical coronary risk factors increases the likelihood of acute cardiac ischemia beyond that expected from clinical presentation and electrocardiogram. Clinical data and reports of classical coronary risk factors were collected prospectively from 1743 patients without clinically obvious coronary disease. Patients were selected from 5773 emergency department patients at 6 hospitals who presented with symptoms suggesting acute ischemia. We used logistic regression to determine the relative risk of each risk factor report for acute ischemia. In women, the presence of classical risk factor reports does not increase the risk of acute ischemia. In men, only diabetes and family history of myocardial infarction significantly increase the risk (p less than 0.05). The relative risks are 2.4 and 2.1, respectively, and are small compared to those conferred by chest pain (12.1), an abnormal ST segment (8.7), or an abnormal T wave (5.3). For a patient presenting to the emergency department, the classical coronary risk factors convey minimal risk for acute cardiac ischemia, especially when compared to the overwhelming importance of the chief complaint and the ECG.

A logistic regression model when some events precede treatment: the effect of thrombolytic therapy for acute myocardial infarction on the risk of cardiac arrest.

When outcomes occur in clinical trials before treatment can be given, neither intent-to-treat nor according-to-protocol analyses give optimal estimates of the treatment effect. A better approach employs a time-dependent variable for treatment. Intent-to-treat analyses are conservative, biasing against treatment; according-to-protocol analyses bias in favor of treatment. We show how to measure the effect of a time-dependent variable in a logistic regression using person-time intervals as units of measurement and describe appropriate methods for reporting model performance. The method is applied to develop a model to predict the probability that a patient with a myocardial infarction will have a sudden cardiac arrest within 48 hours of presentation to emergency medical services both when treated with thrombolysis and when not treated. We use a time-dependent treatment variable because many patients went into cardiac arrest while awaiting treatment. This technique has been programmed into an electrocardiograph for real-time use in an emergency department.

Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment.

When treating individual patients, physicians may face difficulties using the evidence from center-based randomized control trials (RCTs) due to limitations in these studies generalizability. Therefore, they often perform their own "informal" tests of treatment effectiveness. Single patient ("N-of-1") trials provide a structured design for more rigorous assessment of medical treatments of chronic diseases, but are applied only to the index patient. We present a hierarchical Bayesian random effects model to combine N-of-1 studies to obtain an estimate of treatment effectiveness for the population and to use this population information to aid in the evaluation of an individual patient's trial results. The model's treatment effect estimates are adjustments between the population estimate and the individual's observed results. This adjustment is based upon the within-patient and between-patient heterogeneity. We demonstrate this patient-focused method using published data from 23 N-of-1 trial results comparing amitriptyline and placebo for the treatment of fibromyalgia.

How did the acute ischemic heart disease predictive instrument reduce unnecessary coronary care unit admissions?

The use of the acute ischemic heart disease predictive instrument reduced coronary care unit (CCU) admissions for patients without acute ischemic heart disease by 30%. One hypothesis holds that it reinforced physicians' correctly low estimates of the probability of acute ischemia, supporting a decision against CCU admission, another that it lowered physicians' over-high probability estimates for acute ischemia so that CCU admission was felt to be unnecessary. The authors asked 86 physicians to estimate the probability of acute ischemia for each of three study cases and to decide on CCU admission. For the low-probability case, the mean of physicians' probability estimates for acute ischemia was 46%, vs. the predictive instrument's calculated probability of 19% (p less than 0.00001), a 142% over-estimation by the physicians. For the medium-probability case, the mean of physicians' estimates was 54%, vs. the calculated probability of 58% (not significant). For the high-probability case, the mean of physicians' estimates was 82%, vs. the calculated probability of 78% (not significant). All cases for which physicians considered not admitting to the CCU corresponded to their probability estimates of acute ischemia's being in a threshold range of approximately 10 to 30%. These results support the hypothesis that the mechanism by which the predictive instrument reduces unnecessary CCU admissions is by downward correction of physicians' overly-high suspicions of acute cardiac ischemia into a threshold range for which CCU admission is considered unnecessary.

The exclusion of women from clinical trials of thrombolytic therapy: implications for developing the thrombolytic predictive instrument database.

The thrombolytic predictive instrument (TPI) was developed to identify those patients most likely to benefit from thrombolytic therapy for acute myocardial infarction as well as to facilitate the earliest possible administration of this treatment. The TPI consists of predictive models derived from clinical data obtained from both clinical trials and data registries. These models are subject to potential bias due to combinations of primary data from different sources. The purpose of this investigation was to assess the influence of gender in developing the TPI database. In this database, there were 1,096 (22%) women and 3,826 (78%) men; only 38% of the women were enrolled in clinical trials, whereas 46% of the men were (p < 0.0001). Within clinical trials, there were few significant eligibility differences between women and men, as the vast majority of patients met eligibility standards for entry in these trials. However, within clinical registries, the women were older (p < 0.0001) and more often had elevated blood pressure on admission (p = 0.002). Multivariate logistic regression indicated that after adjustment for significant predictors of trial inclusion, women were 25% less likely to be included in clinical trials (odds ratio = 0.76, 95% confidence interval = 0.60, 0.96). In order to counter bias introduced by the exclusion of women from clinical trials, the TPI database included patients from non-trial settings. Carefully including patients from clinical registries or non-trial settings may be an important strategy in constructing generally applicable predictive instruments.

A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.

BACKGROUND: There is increasing interest in mathematical methods for the prediction of medical outcomes. Three methods have attracted particular attention: logistic regression, classification trees (such as ID3 and CART), and neural networks. To compare their relative performance, we used a large clinical database to develop and compare models using these methods. METHODS: Each modeling method was used to generate predictive instruments for acute cardiac ischemia (which includes acute myocardial infarction and unstable angina pectoris), using prospectivel-collected clinical data on 5773 patients, who presented over a two year period to six hospitals' emergency departments with chest pain or symptoms suggesting acute ischemia. This data set was then split into training (n = 3453) and test (n = 2320) sets. Of 200 available variables, modeling was restricted to those available within the first 10 minutes of emergency department care (history, physical exam, and electrocardiogram). RESULTS: When the number of variables was limited to eight, representing a practical number for input in the real-time clinical setting, the logistic regression's receiver-operating characteristic (ROC) curve area, as a measure of diagnostic performance, was 0.887; the classification tree model's ROC curve area was 0.858, and the neural network's ROC curve area was 0.902. When the number of variables used by a model was not limited, the logistic regression's ROC area was 0.905, the classification tree model's 0.861, and the neural network's 0.923. Among these models the neural networks had noticeably poorer calibration. When the outputs from each of these unrestricted models were presented to each of the other methods as an additional independent variable, the ROC areas of the new "hybrid" models were not significantly better than the original unlimited models (ROC areas 0.858 to 0.920). CONCLUSIONS: Logistic regression, classification tree, and neural network models all can provide excellent predictive performance of medical outcomes for clinical decision aids and policy models. Their ultimate limitations seem due to the availability of the information in data (a "data barrier") rather than their respective intrinsic properties. Choices between these methods would seem to be most appropriately based on the needs of the specific application, rather than on the premise that any one of these methods is intrinsically more powerful.

Physicians' views on capitated payment for medical care: does familiarity foster acceptance?

Physicians' attitudes toward capitated payment have not been quantified. We sought to assess physicians' views on capitated payment and to compare the views of those who did and did not participate in such payment. A written survey was given to 200 physicians with admitting privileges at a 600-bed Ohio hospital; 82 (41%) responded and were included in this study. Among respondents, 21 (26%) were primary care physicians, 18 (22%) were medical subspecialists, and 18 (22%) were surgeons. Fifty-eight (71%) were providers for managed care plans, and 35 (43%) participated in capitated payment arrangements. Among physicians who did not participate in capitated care, 100% believed that there was a conflict of interest in capitated payment, and 77% (23 physicians) believed that participation in plans that reduce physician income in proportion to medical expenditures is not acceptable. Among those who did participate in capitated payment contracts, 95% (41 physicians) believed these plans posed a conflict of interest, and 72% (31 physicians) said this was not acceptable (P = 0.4 and 0.66 for each comparison). There was no trend toward the opinion that capitated payment arrangements are acceptable with greater levels of experience in capitated care (P = 0.5 by Spearman test). There were trends suggesting that compared with those who were not receiving capitated payments, those who received capitated payment were 50% more likely to have never discussed capitated payment with any patient (63% versus 42%, P = 0.08), were 70% more likely to very strongly oppose the use of capitation to pay their own family's physicians (49% versus 29%, P = 0.07), and were 30% more likely to believe that it is impossible to stay in the practice of medicine without participating in capitated payment plans (84% versus 65%, P = 0.06). None of the respondents reported that they had a contractual "gag clause," but 34% (27 physicians) said they would not speak publicly about any perceived risks of capitated payments anyway. Among this sample of physicians, those who participated in existing capitated payment managed care plans had views that were as negative, or more negative, on the acceptability of capitated payment as did those of nonparticipating physicians. Many were participating in capitated payment plans in spite of these negative views because they feared that to do otherwise would force them out of medical practice. The hypotheses generated by this study must be tested in larger, national studies.

Insurance type and the transportation to emergency departments of patients with acute cardiac ischemia: the ACI-TIPI Trial Insurance Study.

The relationship of insurance type to treatment-seeking behavior (ie, the transportation to emergency departments of patients with symptoms suggestive of acute cardiac ischemia) was evaluated. The focus was on comparing patients belonging to a health maintenance organization (HMO) with patients who had indemnity insurance. Data were collected prospectively on 10,783 patients presenting to emergency departments of 10 adult care hospitals in the Eastern and Midwestern United States between April and December 1993 as part of a clinical trial. A total of 6,604 patients presented within 24 hours of symptom onset. Although these patients as a group had a wide range of demographic and clinical characteristics, persons belonging to an HMO and those with indemnity insurance were very similar. The main outcome measures were whether the patient was transported by ambulance and the duration of time from symptom onset to emergency department arrival. A hospital-matched sample of HMO-insured and indemnity-insured patients allowed multivariable regression: HMO membership was not associated with a different rate of ambulance use (odds ratio = 1.0; 95% confidence interval = 0.73, 1.35) or duration of time from symptom onset to emergency department presentation (6 minutes less, P = 0.8). HMO participation was not related to treatment-seeking behavior, as reflected by ambulance use and duration of time from symptom onset to emergency department arrival. However, studies of more constrained managed care organizations and of broader ranges of patients are needed.

Causes of chest pain and symptoms suggestive of acute cardiac ischemia in African-American patients presenting to the emergency department: a multicenter study.

This study examines whether race is a significant determinant of the diagnoses of acute myocardial infarction or angina pectoris in patients with symptoms suggestive of acute cardiac ischemia. The study population was comprised of 3401 (34%) African-American and 6600 (66%) white patients who presented to emergency departments with symptoms suggestive of acute cardiac ischemia. The main outcome measure was a diagnosis of acute myocardial infarction or angina pectoris. African Americans were younger, predominantly female, and more often had hypertension, diabetes mellitus, or smoked. The diagnosis of acute myocardial infarction was confirmed in 6% of African-American and 12% of white men, and in 4% of African-American and 8% of white women. After adjusting for age, gender, medical history, signs and symptoms, and hospital, African Americans were half as likely to develop acute myocardial infarction and were 60% as likely to have acute cardiac ischemia. Despite having less acute cardiac ischemia, African Americans in this study had high risk levels for coronary artery disease.

A proposal for integrated efficacy-to-effectiveness (E2E) clinical trials.

We propose an "efficacy-to-effectiveness" (E2E) clinical trial design, in which an effectiveness trial would commence seamlessly upon completion of the efficacy trial. Efficacy trials use inclusion/exclusion criteria to produce relatively homogeneous samples of participants with the target condition, conducted in settings that foster adherence to rigorous clinical protocols. Effectiveness trials use inclusion/exclusion criteria that generate heterogeneous samples that are more similar to the general patient spectrum, conducted in more varied settings, with protocols that approximate typical clinical care. In E2E trials, results from the efficacy trial component would be used to design the effectiveness trial component, to confirm and/or discern associations between clinical characteristics and treatment effects in typical care, and potentially to test new hypotheses. An E2E approach may improve the evidentiary basis for selecting treatments, expand understanding of the effectiveness of treatments in subgroups with particular clinical features, and foster incorporation of effectiveness information into regulatory processes.