Tree-Based Analysis.

Tree-based methods have become one of the most flexible, intuitive, and powerful data analytic tools for exploring complex data structures. Tree-based methods provide a natural framework for creating patient subgroups for risk classification. In this article, we review methodological and practical aspects of tree-based methods, with a focus on diagnostic classification (binary outcome) and prognostication (censored survival outcome). Creating an ensemble of trees improves prediction accuracy and addresses instability in a single tree. Ensemble methods are described that rely on resampling from the original data. Finally, we present methods to identify a representative tree from the ensemble that can be used for clinical decision-making. The methods are illustrated using data on ischemic heart disease classification, and data from the SPRINT trial (Systolic Blood Pressure Intervention Trial) on adverse events in patients with high blood pressure.

Pre-hospital Transport Times and Outcomes After Different Reperfusion Strategies for ST-Elevation Myocardial Infarction.

This study evaluated prehospital transport times and clinical outcomes after different reperfusion strategies for ST-elevation myocardial infarction in a real-world setting. We consecutively enrolled 27,205 patients who underwent percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction in Michigan from 2010 to 2016. Primary PCI was performed in 25,927 patients (95%), whereas 1,278 (5%) were treated with a pharmacoinvasive strategy. The overall use of a pharmacoinvasive strategy decreased during the study period (p <0.001). Prehospital transport times were estimated by using the Google Maps API from the centroid of each home zip code tabulation area to the zip code tabulation area for the nearest hospital with PCI capability. The estimated prehospital transport time predicted the choice of reperfusion strategy (p <0.001). Primary PCI was used in 97% of the patients living within 1 hour from a hospital with PCI capability compared with 48% with estimated transport times >1 hour. Bleeding and mortality rates were similar for patients treated with primary PCI or a pharmacoinvasive strategy (odds ratio 0.832, 95% confidence interval 0.649 to 1.067, p = 0.147). In conclusion, almost all patients in Michigan had timely access to a hospital with PCI capability and received treatment with primary PCI. The authors declare no conflicts of interests.

The effect of TIcagrelor administered through a nasogastric tube to COMAtose patients undergoing acute percutaneous coronary intervention: the TICOMA study.

AIMS: Patients in a coma after cardiac arrest may have adversely affected drug absorption and metabolism. This study, the first of its kind, aimed to investigate the early pharmacokinetic and pharmacodynamic effects of ticagrelor administered through a nasogastric tube (NGT) to patients resuscitated after an out of hospital cardiac arrest (OHCA) and undergoing primary percutaneous coronary intervention (pPCI). METHODS AND RESULTS: Blood samples were drawn at baseline and at two, four, six, eight, 12, and 24 hours and then daily for up to five days after administration of a 180 mg ticagrelor loading dose (LD), followed by 90 mg twice daily in 44 patients. The primary endpoint was the occurrence of high platelet reactivity (HPR) 12 hours after the LD. Assessment by VerifyNow (VFN) showed 96 (15.25-140.5) platelet reactivity units (PRU), and five (12%) patients exhibited HPR. Multiplate analysis showed 19 (12-29) units (U) at twelve hours, and three patients (7%) had HPR. Ticagrelor and its main metabolite AR-C124910XX concentrations were 85.2 (37.2-178.5) and 18.3 (6.4-52.4) ng/mL. Median times to sufficient platelet inhibition below the HPR limit were 3 (2-6) hours (VFN) and 4 (2-8) hours (Multiplate). CONCLUSIONS: Ticagrelor, administered as crushed tablets through a nasogastric tube, leads to sufficient platelet inhibition after 12 hours, and in many cases earlier, in the vast majority of patients undergoing pPCI and subsequent intensive care management after an OHCA.

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records.

There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors-information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.