Commentary: Sense and sensibility: the role of specialists in health care reform.

How to redesign the incentives structure in the United States to reward effective coordinated care rather than production volume is a staggering public health policy challenge. In the mind of the public, there is a fine distinction between health care rationing and rational health care. Specialists have a vital but underappreciated role in reining in health care costs, but specific incentives to elicit behavior change with positive social outcomes remain ambiguous. It is imperative, therefore, that redesigning the incentives structure is thoughtfully considered, modeled, and tested prior to implementation, lest an inferior-quality model is inadvertently adopted and costs are only marginally contained. Quality metrics need to be universal and reflect real patient outcomes instead of the degree of investment by the institution in the reporting tools. Still, specialists should take immediate action to implement safe and efficient procedures and to assess their long-term impact on patients' quality of life. Scientific evaluations should guide both the assessment of the appropriateness and the safe delivery of care. Investment in high-quality data architecture and the science of health delivery implementation is an imperative if health care reform is to achieve its goals. Coordination and collaboration between specialists and primary care physicians is essential to this enterprise. Specialists can champion these efforts as they pertain to their areas of expertise by considering their care episodes in the context of the patient as a whole, working closely with generalists, and returning to the mindset of the specialist as a family doctor.

Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death.

OBJECTIVE: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD). BACKGROUND: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI) cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data. METHODS: Patients were enrolled (N = 397) in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%). Brief ECGs were recorded (15 min) and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn) and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF). Out-of-hospital AD was determined by modified Hinkle-Thaler criteria. RESULTS: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p 100 (p 11.4 (p

Risk stratification for arrhythmic death in an emergency department cohort: a new method of nonlinear PD2i analysis of the ECG.

Heart rate variability (HRV) reflects both cardiac autonomic function and risk of sudden arrhythmic death (AD). Indices of HRV based on linear stochastic models are independent risk factors for AD in postmyocardial infarction (MI) cohorts. Indices based on nonlinear deterministic models have a higher sensitivity and specificity for predicting AD in retrospective data. A new nonlinear deterministic model, the automated Point Correlation Dimension (PD2i), was prospectively evaluated for prediction of AD. Patients were enrolled (N = 918) in 6 emergency departments (EDs) upon presentation with chest pain and being determined to be at risk of acute MI (AMI) >7%. Brief digital ECGs (>1000 heartbeats, approximately 15 min) were recorded and automated PD2i results obtained. Out-of-hospital AD was determined by modified Hinkle-Thaler criteria. All-cause mortality at 1 year was 6.2%, with 3.5% being ADs. Of the AD fatalities, 34% were without previous history of MI or diagnosis of AMI. The PD2i prediction of AD had sensitivity = 96%, specificity = 85%, negative predictive value = 99%, and relative risk >24.2 (p ≤ 0.001). HRV analysis by the time-dependent nonlinear PD2i algorithm can accurately predict risk of AD in an ED cohort and may have both life-saving and resource-saving implications for individual risk assessment.