30 research outputs found
Chaotic Signatures of Heart Rate Variability and Its Power Spectrum in Health, Aging and Heart Failure
A paradox regarding the classic power spectral analysis of heart rate variability (HRV) is whether the characteristic high- (HF) and low-frequency (LF) spectral peaks represent stochastic or chaotic phenomena. Resolution of this fundamental issue is key to unraveling the mechanisms of HRV, which is critical to its proper use as a noninvasive marker for cardiac mortality risk assessment and stratification in congestive heart failure (CHF) and other cardiac dysfunctions. However, conventional techniques of nonlinear time series analysis generally lack sufficient sensitivity, specificity and robustness to discriminate chaos from random noise, much less quantify the chaos level. Here, we apply a ‘litmus test’ for heartbeat chaos based on a novel noise titration assay which affords a robust, specific, time-resolved and quantitative measure of the relative chaos level. Noise titration of running short-segment Holter tachograms from healthy subjects revealed circadian-dependent (or sleep/wake-dependent) heartbeat chaos that was linked to the HF component (respiratory sinus arrhythmia). The relative ‘HF chaos’ levels were similar in young and elderly subjects despite proportional age-related decreases in HF and LF power. In contrast, the near-regular heartbeat in CHF patients was primarily nonchaotic except punctuated by undetected ectopic beats and other abnormal beats, causing transient chaos. Such profound circadian-, age- and CHF-dependent changes in the chaotic and spectral characteristics of HRV were accompanied by little changes in approximate entropy, a measure of signal irregularity. The salient chaotic signatures of HRV in these subject groups reveal distinct autonomic, cardiac, respiratory and circadian/sleep-wake mechanisms that distinguish health and aging from CHF
Current Data on and Clinical Insights into the Treatment of First Episode Nonaffective Psychosis: A Comprehensive Review
Implementing the most suitable treatment strategies and making appropriate clinical decisions about individuals with a first episode of psychosis (FEP) is a complex and crucial task, with relevant impact in illness outcome. Treatment approaches in the early stages should go beyond choosing the right antipsychotic drug and should also address tractable factors influencing the risk of relapse. Effectiveness and likely metabolic and endocrine disturbances differ among second-generation antipsychotics (SGAs) and should guide the choice of the first-line treatment. Clinicians should be aware of the high risk of cardiovascular morbidity and mortality in schizophrenia patients, and therefore monitoring weight and metabolic changes across time is mandatory. Behavioral and counseling interventions might be partly effective in reducing weight gain and metabolic disturbances. Ziprasidone and aripiprazole have been described to be least commonly associated with weight gain or metabolic changes. In addition, some of the SGAs (risperidone, amisulpride, and paliperidone) have been associated with a significant increase of plasma prolactin levels. Overall, in cases of FEP, there should be a clear recommendation of using lower doses of the antipsychotic medication. If no or minimal clinical improvement is found after 2 weeks of treatment, such patients may benefit from a change or augmentation of treatment. Clinicians should provide accurate information to patients and relatives about the high risk of relapse if antipsychotics are discontinued, even if patients have been symptom free and functionally recovered on antipsychotic treatment for a lengthy period of time.This review was carried out at the Hospital Marque´s de Valdecilla, University of Cantabria, Santander, Spain, with the following Grant support: Instituto de Salud Carlos III PI020499, PI050427, PI060507, Plan Nacional de Drugs Research Grant 2005-Orden sco/3246/2004, SENY Fundacio´ Research Grant CI 2005-0308007, Fundacio´n Marque´s de Valdecilla API07/011 and CIBERSAM