The Prevalence and Incidence of Atrial Fibrillation in Patients with Acute Pulmonary Embolism

Background

Symptomatic pulmonary embolism (PE) is a major cause of cardiovascular death and morbidity. Estimated prevalence and incidence of atrial fibrillation (AF) in developed countries are between 388–661 per 100,000, and 90–123 per 100,000 person-years respectively. However, the prevalence and incidence of AF in patients presenting with an acute PE and its predictors are not clear.

Methods

Individual patient clinical details were retrieved from a database containing all confirmed acute PE presentations to a tertiary institution from 2001–2012. Prevalence and incidence of AF was tracked from a population registry by systematically searching for AF during any hospital admission (2000–2013) based on International Classification of Disease (ICD-10) code.

Results

Of the 1,142 patients included in this study, 935 (81.9%) had no AF during index PE admission whilst 207 patients had documented baseline AF (prevalence rate 18,126 per 100,000; age-adjusted 4,672 per 100,000). Of the 935 patients without AF, 126 developed AF post-PE (incidence rate 2,778 per 100,000 person-years; age-adjusted 984 per 100,000 person-years). Mean time from PE to subsequent AF was 3.4 ± 2.9 years. Total mortality (mean follow-up 5.0 ± 3.7 years) was 42% (n = 478): 35% (n = 283), 59% (n = 119) and 60% (n = 76) in the no AF, baseline AF and subsequent AF cohorts respectively. Independent predictors for subsequent AF after acute PE include age (hazard ratio [HR] 1.06, 95% confidence interval [CI] 1.04–1.08, p<0.001), history of congestive cardiac failure (HR 1.88, 95% CI 1.12–3.16, p = 0.02), diabetes (HR 1.72, 95% CI 1.07–2.77, p = 0.02), obstructive sleep apnea (HR 4.83, 1.48–15.8, p = 0.009) and day-1 serum sodium level during index PE admission (HR 0.94, 95% CI 0.90–0.98, p = 0.002).

Conclusions

Patients presenting with acute PE have a markedly increased age-adjusted prevalence and subsequent incidence of AF. Screening for AF may be of importance post-PE.     

Effects of Intra-Operative Total Intravenous Anaesthesia with Propofol versus Inhalational Anaesthesia with Sevoflurane on Post-Operative Pain in Liver Surgery: A Retrospective Case-Control Study

Background

Patients receiving total intravenous anesthesia (TIVA) with propofol have been shown to experience less postoperative pain. We evaluated the post-operative analgesic effects of propofol compared with sevoflurane maintenance of anesthesia in liver surgery. This study was registered at ClinicalTrials.gov ({"type":"clinical-trial","attrs":{"text":"NCT02179437","term_id":"NCT02179437"}}NCT02179437).

Methods

In this retrospective study, records of patients who underwent liver surgery between 2010 and 2013 were reviewed. Ninety-five patients anesthetized with propofol TIVA were matched with 95 patients anesthetized with sevoflurane. Numeric pain rating scale (NRS) pain scores, postoperative morphine consumption, side effects and patients’ satisfaction with pain relief were evaluated.

Results

The TIVA group reported lower NRS pain scores during coughing on postoperative days 1 and 2 but not 3 (p = 0.0127, p = 0.0472, p = 0.4556 respectively). They also consumed significantly less daily (p = 0.001 on day 1, p = 0.0231 on day 2, p = 0.0004 on day 3), accumulative (p = 0.001 on day 1, p<0.0001 on day 2 and p = 0.0064 on day 3) and total morphine (p = 0.03) when compared with the sevoflurane group. There were no differences in total duration of intravenous patient controlled analgesia (PCA) morphine use and patient satisfaction. No difference was found in reported side effects.

Conclusion

Patients anesthetized with propofol TIVA reported less pain during coughing and consumed less daily, accumulative and total morphine after liver surgery.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

Lysosomal membrane permeabilization is involved in oxidative stress-induced apoptotic cell death in LAMP2-deficient iPSCs-derived cerebral cortical neurons

Patients with Danon disease may suffer from severe cardiomyopathy, skeletal muscle dysfunction as well as varying degrees of mental retardation, in which the primary deficiency of lysosomal membrane-associated protein-2 (LAMP2) is considerably associated. Owing to the scarcity of human neurons, the pathological role of LAMP2 deficiency in neural injury of humans remains largely elusive. However, the application of induced pluripotent stem cells (iPSCs) may shed light on overcoming such scarcity.In this study, we obtained iPSCs derived from a patient carrying a mutated LAMP2 gene that is associated with Danon disease. By differentiating such LAMP2-deficient iPSCs into cerebral cortical neurons and with the aid of various biochemical assays, we demonstrated that the LAMP2-deficient neurons are more susceptible to mild oxidative stress-induced injury.The data from MTT assay and apoptotic analysis demonstrated that there was no notable difference in cellular viability between the normal and LAMP2-deficient neurons under non-stressed condition. When exposed to mild oxidative stress (10 μM H₂O₂), the LAMP2-deficient neurons exhibited a significant increase in apoptosis. Surprisingly, we did not observe any aberrant accumulation of autophagic materials in the LAMP2-deficient neurons under such stress condition.Our results from cellular fractionation and inhibitor blockade experiments further revealed that oxidative stress-induced apoptosis in the LAMP2-deficient cortical neurons was caused by increased abundance of cytosolic cathepsin L. These results suggest the involvement of lysosomal membrane permeabilization in the LAMP2 deficiency associated neural injury.