Search

Purpose Using administrative codes and minimal physiologic and laboratory data, we sought a high-specificity identification strategy for patients whose sepsis initially appeared during their ICU stay. Materials and methods We studied all patients discharged from an academic hospital between September 1, 2013 and October 31, 2014. Administrative codes and minimal physiologic and laboratory criteria were used to identify patients at high risk of developing the onset of sepsis in the ICU. Two clinicians then independently reviewed the patient record to verify that the screened-in patients appeared to become septic during their ICU admission. Results Clinical chart review verified sepsis in 437/466 ICU stays (93.8%). Of these 437 encounters, only 151 (34.6%) were admitted to the ICU with neither SIRS nor evidence of infection and therefore appeared to become septic during their ICU stay. Conclusions Selected administrative codes coupled to SIRS criteria and applied to patients admitted to ICU can yield up to 94% authentic sepsis patients. However, only 1/3 of patients thus identified appeared to become septic during their ICU stay. Studies that depend on high-intensity monitoring for description of the time course of sepsis require clinician review and verification that sepsis initially appeared during the monitoring period.

Purpose Complex autonomic communication can be assessed by autonomic information flow (AIF) functions. The objective was to evaluate new complexity measures involving physiologically relevant time scales to predict the length of stay (LOS) in the hospital after abdominal aortic surgery (AAS). Our hypothesis was that AAS disrupts AIF, that restoration of AIF is necessary for recovery from major surgery, and that measures of AIF are superior to conventional heart rate variability (HRV) measures and equivalent to APACHE IV score in predicting LOS. Materials and Methods Twenty-four–hour Holter recordings were analyzed in 94 patients after AAS for standard time, frequency domain, and several complexity measures of different time scales derived from AIF functions. The risk of staying in the hospital for longer than 7 days as a function of HRV measures and APACHE IV score was modeled by logistic regression. The area under the curve (AUC) of receiver operating characteristic with 95% confidence interval as measure of predictive accuracy was calculated and internally cross-validated. Results The long-term dacay of AIF over 100s (LD100) with cross-validated AUC = 0.67 (0.56-0.79) nearly reached the predictive accuracy of the APACHE IV score with AUC = 0.69 (0.58-0.79). None of the traditional time and frequency domain HRV measures remained in the multivariate models. The LD100 adjusted for ventilatory support with AUC = 0.70 (0.59-0.81) was equivalent to the APACHE IV score in this patient group. Although the strongest correlation between AIF measures and the APACHE IV score was found for LD100, r was only −0.37. Conclusions Results confirm the hypothesis that AIF measures characterize pathophysiologic autonomic communication better than traditional HRV measures and may have similar predictive value to the APACHE IV score for LOS. The relative independence of the APACHE IV score and AIF measures suggests that AIF measures could add to clinical risk prediction.

Background Continuous renal replacement therapy (CRRT) is an important tool in the care of critically ill patients. However, the impact of a specific CRRT machine type on the successful delivery of CRRT is unclear. The purpose of this study was to evaluate the effectiveness of CRRT delivery with an intensive care unit (ICU) bedside nurse delivery model for CRRT while comparing circuit patency and circuit exchange rates in 2 Food and Drug Administration–approved CRRT devices. This article presents the data comparing circuit exchange rates for 2 different CRRT machines. Materials and Methods A group of ICU nurses were selected to undergo expanded training in CRRT operation and empowered to deliver all aspects of CRRT. The ICU nurses then provided all aspects of CRRT on 2 Food and Drug Administration–approved CRRT devices for 6 months. Each device was used exclusively in the designated ICU for a 2-week run-in period followed by 3-month data collection period. The primary end point for the study was the differences in average number of filter exchanges per day during each CRRT event. Results A total of 45 unique patients who underwent 64 separate CRRT treatment periods were included. Four CRRT events were excluded (see text for details). Twenty-eight CRRT events occurred in the NxStage System One arm (NxStage Medical, Lawrence, Mass) and 32 events in the Gambro Prismaflex arm (Gambro Renal Products, Boulder, Colo). Average (SD) filter exchanges per day was 0.443 (0.60) for the NxStage System One machine and 0.553 (0.65) for Gambro Prismaflex machine (P = .09). Conclusions There was no demonstrable difference in circuit patency as defined by the rate of filter exchanges per day of CRRT therapy.

Analysis of heart rate (HR) dynamics before, during, and after a physiologic stress has clinical importance. For example, the celerity of heart rate recovery (HRR) after a cardiac stress test (eg, treadmill exercise test) has been shown to be an independent predictor of all-cause mortality. Heart rate dynamics are modulated, in part, by the autonomic nervous system. These dynamics are commonly abstracted using metrics of heart rate variability (HRV), which are known to be sensitive to the influence of the autonomic nervous system on HR. The patient-specific modulators of HR should be reflected both in the response to stress as well as in the recovery from stress. We therefore hypothesized that the patient-specific HR response to stress could be used to predict the HRR after the stress. We devised a Markov chain model to predict the poststress HRR dynamics using the parameters (transition matrix) calculated from HR data during the stress. The model correctly predicts the exponential shape of poststress HRR. This model features a simple analytical relationship linking poststress HRR time constant (T(off)) with a standard measure of HRV, namely the correlation coefficient of the Poincare plot (first return map) of the HR recorded during the stress. A corresponding relationship exists between the time constant (T(on)) of R-R interval decrease at the onset of stress and the correlation coefficient of the Poincare plot of prestress R-R intervals. Consequently, the model can be used for the prediction of poststress HRR using the HRV measured during the stress. This direct relationship between the event-to-event microscopic fluctuations (HRV) during the stress and the macroscopic response (HRR) after the stress terminates can be interpreted as an instance of a fluctuation-dissipation relationship. We have thus applied the fluctuation-dissipation theorem to the analysis of heart rate dynamics. The approach is specific neither to cardiac physiology nor to transitions between mechanical and free ventilation as a specific stress. It may therefore have wider applicability to physiologic systems subject to modest stresses.

In this issue of the Journal of Critical Care, Lee and colleagues present their retrospective analysis of a cohort of patients cared for in 2 intensive care units co-located within a single hospital by junior surgical and senior surgical residents. The principal finding of the analysis was that patients assigned to junior surgical residents had a significantly higher mortality than those assigned to senior surgical residents (24.7% vs 17.7%) despite the fact that both groups of trainees were supervised by a common group of attending surgeons [1]. Perhaps the most interesting comment in the article is found in the discussion, where the authors state

components, and the interactions between those components within an organizational structure that exists in dynamic equilibrium. Furthermore, the organization of biologic systems is identified as necessarily hierarchical and recursive, stressing the importance of addressing both context and concurrency in any description. The resulting category-theoretical description is general enough to abstractly describe any biologic system: the general category of biology can be expressed as an abstracted biologic computational unit (ABCU) [1,2]. Mathematical descriptions of biologic systems can be constructed from instances of ABCUs and the processes that link them. Inversely, incomplete descriptions of aspects of biologic systems (such as biochemical pathways) do not fulfill the associative requirement of a category and are, therefore, not considered as members of the ABCU set. This “completeness” requirement places a constraint on the internal structure of a category-theoretical description of a biologic system and can be seen as analogous to the fact that biologic systems must follow basic physical and chemical laws. Conclusions: Biology is more than an aggregate of chemistry and physics. Although it incorporates and uses the laws of those domains, the richness of biology cannot be effectively characterized using merely the descriptive capacity of physics or chemistry. To develop the mathematics of biology, it is necessary to present a mathematical description of the fundamental properties that set biology apart from these other domains. Given the organizational complexity of biologic systems, this goal requires the use of more abstract and powerful mathematical tools that are used to delineate the laws of physics or chemistry. Category theory is particularly well suited to this goal, and it is further suggested that being able to produce a category-theoretical description may be both necessary and sufficient to define a complete biologic system, as distinguished from either a biophysical or biochemical one. Furthermore, because category theory is also being applied to various aspects of computer science, programming languages, and logic, it may provide a means of formalizing the transition from a biologic description to a computable one.

Background Physiologic data display is essential to decision making in critical care. Current displays echo first-generation hemodynamic monitors dating to the 1970s and have not kept pace with new insights into physiology or the needs of clinicians who must make progressively more complex decisions about their patients. The effectiveness of any redesign must be tested before deployment. Tools that compare current displays with novel presentations of processed physiologic data are required. Regenerating conventional physiologic displays from archived physiologic data is an essential first step. Objectives The purposes of the study were to (1) describe the SSSI (single sensor single indicator) paradigm that is currently used for physiologic signal displays, (2) identify and discuss possible extensions and enhancements of the SSSI paradigm, and (3) develop a general approach and a software prototype to construct such "extended SSSI displays" from raw data. Results We present Multi Wave Animator (MWA) framework—a set of open source MATLAB (MathWorks, Inc., Natick, MA, USA) scripts aimed to create dynamic visualizations (eg, video files in AVI format) of patient vital signs recorded from bedside (intensive care unit or operating room) monitors. Multi Wave Animator creates animations in which vital signs are displayed to mimic their appearance on current bedside monitors. The source code of MWA is freely available online together with a detailed tutorial and sample data sets.

the use of the neutral networks is restricted to assigning IVA strains to reservoirs. All of the populations are treated as well mixed, so there is no network structure within any reservoir. That is, any agent in a given reservoir is equally likely to contact any other agent. The reservoirs are connected by weighted edges, where the weight of an edge determines how strongly the IVA strains can be passed between the different reservoirs. Each population is assigned characteristics such as birth and death rates, transmission rates, and seasonal effects. Within each population, we model every individual as an agent, where on the order of 10 agents are currently being simulated. Each agent retains a history of all previous IVA infections as well as the strains with which it is currently infected. Agents are added and removed from the populations. The trans-reservoir transmissions are done between specific agents. Two main types of evolutionary processes are incorporated in our model. The first is point mutations of the IVA strains' genotypes. Here we conceptualize each of the 8 genes to have a certain number of loci. This allows the strains to evolve over a particular reservoir's neutral network. If a strain evolves onto a network that can infect multiple reservoirs, we can have trans-reservoir infections. The second evolutionary process is the reassortment of 2 IVA strains within an agent. The genome of IVA is composed of 8 individual strands of RNA. If an agent is currently infected by more than a single strain, the RNA strands can be reassorted, allowing for the rise of novel strains. Results: We hope to demonstrate that using a multi-reservoir model can reproduce the effects of using neutral networks. In this way, the model will reproduce the phylodynamics of the IVA. We have also included the important features of multiple different species and strain reassortment for IVA evolution. Conclusions: We have examined an agent-based model of IVA evolution and epidemiology. The model includes multiple interacting reservoirs (eg, avian, swine and human) of agents. Each reservoir is assigned IVA strains by which it can be infected. One of the reservoirs acts as a bridge between other reservoirs. In this, transreservoir transmission is allowed. In addition, IVA can evolve within each reservoir through the processes of mutation and reassortment.

capabilities. The intent is not to suggest that an agent-based framework is “the” format for an executable ontology layer; the experience with classic ontologies suggests that it is impossible to produce a definitive knowledge representation tool. It is fully expected that other researches will develop similar types of tools, using different modeling paradigms. However, we believe that an ABMF demonstrates a robust, evolvable approach and will provide an essential translational step toward the goal of having as many steps as possible of the discovery process be automated. Eventually, this type of architecture would allow the automated generation of models that can be picked through by the researcher, providing a truly evolutionary solution to the synthetic aspect of biomedical research.