The Elder Patient with Suspected Acute Coronary Syndromes in the Emergency Department

Objectives To describe the evaluation and outcomes of elder patients with suspected acute coronary syndromes (ACS) presenting to the emergency department (ED). Methods This was a post hoc analysis of the Internet Tracking Registry for Acute Coronary Syndromes (i^?trACS) registry, which had 17,713 ED visits for suspected ACS. First visits from the United States with nonmissing patient demographics, 12‐lead electrocardiogram results, and clinical history were included in the analysis. Those who used cocaine or amphetamines or left the ED against medical advice were excluded. Elder was defined as age 75 years or older. ACS was defined by 30‐day revascularization, Diagnosis‐related Group codes, or death within 30 days with positive cardiac biomarkers at index hospitalization. Multivariable logistic regression analyses were performed to determine the association between being elder and 1) 30‐day all‐cause mortality, 2) ACS, 3) diagnostic tests ordered, and 4) disposition. Multivariable logistic regression was also performed to determine which clinical variables were associated with ACS in elder and nonelder patients. Results A total of 10,126 patients with suspected ACS presenting to the ED were analyzed. For patients presenting to the ED, being elder was independently associated with ACS and all‐cause 30‐day mortality, with adjusted odds ratios of 1.8 (95% confidence interval [CI] = 1.5 to 2.2) and 2.6 (95% CI = 1.6 to 4.3), respectively. Elder patients were more likely to be admitted to the hospital (adjusted odds ratio, 2.2; 95% CI = 1.8 to 2.6), but there were no differences in the rates of cardiac catheterization and noninvasive stress cardiac imaging. Different clinical variables were associated with ACS in elder and nonelder patients. Chest pain as chief complaint, typical chest pain, and previous history of coronary artery disease were significantly associated with ACS in nonelder patients but were not associated with ACS in elder patients. Male gender and left arm pain were associated with ACS in both elder and nonelder patients. Conclusions Elder patients who present to the ED with suspected ACS represent a population at high risk for ACS and 30‐day mortality. Elders are more likely to be admitted to the hospital, but despite an increased risk for adverse events, they have similar odds of receiving a diagnostic test, such as stress cardiac imaging or cardiac catheterization, compared with nonelder patients. Different clinical variables are associated with ACS, and clinical prediction rules utilizing presenting symptoms should consider the effect modification of age.     

Using data mining techniques to characterize participation in observational studies

Data mining techniques are gaining in popularity among health researchers for an array of purposes, such as improving diagnostic accuracy, identifying high‐risk patients and extracting concepts from unstructured data. In this paper, we describe how these techniques can be applied to another area in the health research domain: identifying characteristics of individuals who do and do not choose to participate in observational studies. In contrast to randomized studies where individuals have no control over their treatment assignment, participants in observational studies self‐select into the treatment arm and therefore have the potential to differ in their characteristics from those who elect not to participate. These differences may explain part, or all, of the difference in the observed outcome, making it crucial to assess whether there is differential participation based on observed characteristics. As compared to traditional approaches to this assessment, data mining offers a more precise understanding of these differences. To describe and illustrate the application of data mining in this domain, we use data from a primary care‐based medical home pilot programme and compare the performance of commonly used classification approaches – logistic regression, support vector machines, random forests and classification tree analysis (CTA) – in correctly classifying participants and non‐participants. We find that CTA is substantially more accurate than the other models. Moreover, unlike the other models, CTA offers transparency in its computational approach, ease of interpretation via the decision rules produced and provides statistical results familiar to health researchers. Beyond their application to research, data mining techniques could help administrators to identify new candidates for participation who may most benefit from the intervention.     

Motivational interviewing-based health coaching as a chronic care intervention

Objective  To evaluate the impact of motivational interviewing-based health coaching on a chronically ill group of participants compared with non-participants. Specifically, measures that could be directly attributed to a health coaching intervention on chronic illness were assessed.
Design  Quasi-experimental study design.
Setting  A large medical university in the north-west United States.
Methods  One hundred and six chronically ill programme participants completed a health risk survey instrument prior to enrolment and again at approximately 8 months. Outcomes were compared with 230 chronically ill non-participants who completed the survey twice over a similar time frame. Inverse probability of treatment weights were used in conjunction with the propensity score to correct for selection bias.
Results  Compared with non-participants, programme participants improved their self-efficacy ( P  = 0.01), patient activation ( P  = 0.02), lifestyle change score ( P  = 0.01) and perceived health status ( P  = 0.03). Fewer participants increased their stages of change risk over time than non-participants ( P  < 0.01), and more participants decreased their stages of change risk over time than non-participants ( P  = 0.03).
Conclusion  These results support motivational interviewing-based health coaching as an effective chronic care management intervention in impacting outcome measures that could also serve well as a proxy in the absence of other clinical or cost indices.