Markers of systemic inflammation in diagnostics and in prediction of outcome of community-acquired infection

Sepsis is associated with a systemic inflammatory response. It is characterised by an early proinflammatory response and followed by a state of immunosuppression. In order to improve the outcome of patients with infection and sepsis, novel therapies that influence the systemic inflammatory response are being developed and utilised. Thus, an accurate and early diagnosis of infection and evaluation of immune state are crucial. In this thesis, various markers of systemic inflammation were studied with respect to enhancing the diagnostics of infection and of predicting outcome in patients with suspected community-acquired infection. A total of 1092 acutely ill patients admitted to a university hospital medical emergency department were evaluated, and 531 patients with a suspicion of community-acquired infection were included for the analysis. Markers of systemic inflammation were determined from a blood sample obtained simultaneously with a blood culture sample on admission to hospital. Levels of phagocyte CD11b/CD18 and CD14 expression were measured by whole blood flow cytometry. Concentrations of soluble CD14, interleukin (IL)-8, and soluble IL-2 receptor α (sIL-2Rα) were determined by ELISA, those of sIL-2R, IL-6, and IL-8 by a chemiluminescent immunoassay, that of procalcitonin by immunoluminometric assay, and that of C-reactive protein by immunoturbidimetric assay. Clinical data were collected retrospectively from the medical records. No marker of systemic inflammation, neither CRP, PCT, IL-6, IL-8, nor sIL-2R predicted bacteraemia better than did the clinical signs of infection, i.e., the presence of infectious focus or fever or both. IL-6 and PCT had the highest positive likelihood ratios to identify patients with hidden community-acquired infection. However, the use of a single marker failed to detect all patients with infection. A combination of markers including a fast-responding reactant (CD11b expression), a later-peaking reactant (CRP), and a reactant originating from inflamed tissues (IL-8) detected all patients with infection. The majority of patients (86.5%) with possible but not verified infection showed levels exceeding at least one cut-off limit of combination, supporting the view that infection was the cause of their acute illness. The 28-day mortality of patients with community-acquired infection was low (3.4%). On admission to hospital, the low expression of cell-associated lipopolysaccharide receptor CD14 (mCD14) was predictive for 28-day mortality. In the patients with severe forms of community-acquired infection, namely pneumonia and sepsis, high levels of soluble CD14 alone did not predict mortality, but a high sCD14 level measured simultaneously with a low mCD14 raised the possibility of poor prognosis. In conclusion, to further enhance the diagnostics of hidden community-acquired infection, a combination of inflammatory markers is useful; 28-day mortality is associated with low levels of mCD14 expression at an early phase of the disease.

Modeling ecological and evolutionary processes in spatially structured populations

Many species inhabit fragmented landscapes, resulting either from anthropogenic or from natural processes. The ecological and evolutionary dynamics of spatially structured populations are affected by a complex interplay between endogenous and exogenous factors. The metapopulation approach, simplifying the landscape to a discrete set of patches of breeding habitat surrounded by unsuitable matrix, has become a widely applied paradigm for the study of species inhabiting highly fragmented landscapes. In this thesis, I focus on the construction of biologically realistic models and their parameterization with empirical data, with the general objective of understanding how the interactions between individuals and their spatially structured environment affect ecological and evolutionary processes in fragmented landscapes. I study two hierarchically structured model systems, which are the Glanville fritillary butterfly in the Åland Islands, and a system of two interacting aphid species in the Tvärminne archipelago, both being located in South-Western Finland. The interesting and challenging feature of both study systems is that the population dynamics occur over multiple spatial scales that are linked by various processes. My main emphasis is in the development of mathematical and statistical methodologies. For the Glanville fritillary case study, I first build a Bayesian framework for the estimation of death rates and capture probabilities from mark-recapture data, with the novelty of accounting for variation among individuals in capture probabilities and survival. I then characterize the dispersal phase of the butterflies by deriving a mathematical approximation of a diffusion-based movement model applied to a network of patches. I use the movement model as a building block to construct an individual-based evolutionary model for the Glanville fritillary butterfly metapopulation. I parameterize the evolutionary model using a pattern-oriented approach, and use it to study how the landscape structure affects the evolution of dispersal. For the aphid case study, I develop a Bayesian model of hierarchical multi-scale metapopulation dynamics, where the observed extinction and colonization rates are decomposed into intrinsic rates operating specifically at each spatial scale. In summary, I show how analytical approaches, hierarchical Bayesian methods and individual-based simulations can be used individually or in combination to tackle complex problems from many different viewpoints. In particular, hierarchical Bayesian methods provide a useful tool for decomposing ecological complexity into more tractable components.

Therapeutic hypothermia after cardiac arrest : Studies on neurological and cardiological outcome and prediction of outcome in hypothermia-treated patients resuscitated from out-of-hospital cardiac arrest

The outcome of the successfully resuscitated patient is mainly determined by the extent of hypoxic-ischemic cerebral injury, and hypothermia has multiple mechanisms of action in mitigating such injury. The present study was undertaken from 1997 to 2001 in Helsinki as a part of the European multicenter study Hypothermia after cardiac arrest (HACA) to test the neuroprotective effect of therapeutic hypothermia in patients resuscitated from out-of-hospital ventricular fibrillation (VF) cardiac arrest (CA). The aim of this substudy was to examine the neurological and cardiological outcome of these patients, and especially to study and develop methods for prediction of outcome in the hypothermia-treated patients. A total of 275 patients were randomized to the HACA trial in Europe. In Helsinki, 70 patients were enrolled in the study according to the inclusion criteria. Those randomized to hypothermia were actively cooled externally to a core temperature 33 ± 1ºC for 24 hours with a cooling device. Serum markers of ischemic neuronal injury, NSE and S-100B, were sampled at 24, 36, and 48 hours after CA. Somatosensory and brain stem auditory evoked potentials (SEPs and BAEPs) were recorded 24 to 28 hours after CA; 24-hour ambulatory electrocardiography recordings were performed three times during the first two weeks and arrhythmias and heart rate variability (HRV) were analyzed from the tapes. The clinical outcome was assessed 3 and 6 months after CA. Neuropsychological examinations were performed on the conscious survivors 3 months after the CA. Quantitative electroencephalography (Q-EEG) and auditory P300 event-related potentials were studied at the same time-point. Therapeutic hypothermia of 33ºC for 24 hours led to an increased chance of good neurological outcome and survival after out-of-hospital VF CA. In the HACA study, 55% of hypothermia-treated patients and 39% of normothermia-treated patients reached a good neurological outcome (p=0.009) at 6 months after CA. Use of therapeutic hypothermia was not associated with any increase in clinically significant arrhythmias. The levels of serum NSE, but not the levels of S-100B, were lower in hypothermia- than in normothermia-treated patients. A decrease in NSE values between 24 and 48 hours was associated with good outcome at 6 months after CA. Decreasing levels of serum NSE but not of S-100B over time may indicate selective attenuation of delayed neuronal death by therapeutic hypothermia, and the time-course of serum NSE between 24 and 48 hours after CA may help in clinical decision-making. In SEP recordings bilaterally absent N20 responses predicted permanent coma with a specificity of 100% in both treatment arms. Recording of BAEPs provided no additional benefit in outcome prediction. Preserved 24- to 48-hour HRV may be a predictor of favorable outcome in CA patients treated with hypothermia. At 3 months after CA, no differences appeared in any cognitive functions between the two groups: 67% of patients in the hypothermia and 44% patients in the normothermia group were cognitively intact or had only very mild impairment. No significant differences emerged in any of the Q-EEG parameters between the two groups. The amplitude of P300 potential was significantly higher in the hypothermia-treated group. These results give further support to the use of therapeutic hypothermia in patients with sudden out-of-hospital CA.

Acute renal failure in critically ill patients : With special reference to prediction of outcome

Acute renal failure (ARF) is a clinical syndrome characterized by rapidly decreasing glomerular filtration rate, which results in disturbances in electrolyte- and acid-base homeostasis, derangement of extracellular fluid volume, and retention of nitrogenous waste products, and is often associated with decreased urine output. ARF affects about 5-25% of patients admitted to intensive care units (ICUs), and is linked to high mortality and morbidity rates. In this thesis outcome of critically ill patients with ARF and factors related to outcome were evaluated. A total of 1662 patients from two ICUs and one acute dialysis unit in Helsinki University Hospital were included. In study I the prevalence of ARF was calculated and classified according to two ARF-specific scoring methods, the RIFLE classification and the classification created by Bellomo et al. (2001). Study II evaluated monocyte human histocompatibility leukocyte antigen-DR (HLA-DR) expression and plasma levels of one proinflammatory (interleukin (IL) 6) and two anti-inflammatory (IL-8 and IL-10) cytokines in predicting survival of critically ill ARF patients. Study III investigated serum cystatin C as a marker of renal function in ARF and its power in predicting survival of critically ill ARF patients. Study IV evaluated the effect of intermittent hemodiafiltration (HDF) on myoglobin elimination from plasma in severe rhabdomyolysis. Study V assessed long-term survival and health-related quality of life (HRQoL) in ARF patients. Neither of the ARF-specific scoring methods presented good discriminative power regarding hospital mortality. The maximum RIFLE score for the first three days in the ICU was an independent predictor of hospital mortality. As a marker of renal dysfunction, serum cystatin C failed to show benefit compared with plasma creatinine in detecting ARF or predicting patient survival. Neither cystatin C nor plasma concentrations of IL-6, IL-8, and IL-10, nor monocyte HLA-DR expression were clinically useful in predicting mortality in ARF patients. HDF may be used to clear myoglobin from plasma in rhabdomyolysis, especially if the alkalization of diuresis does not succeed. The long-term survival of patients with ARF was found to be poor. The HRQoL of those who survive is lower than that of the age- and gender-matched general population.

Towards the Use of Radar Winds in Numerical Weather Prediction

Numerical weather prediction (NWP) models provide the basis for weather forecasting by simulating the evolution of the atmospheric state. A good forecast requires that the initial state of the atmosphere is known accurately, and that the NWP model is a realistic representation of the atmosphere. Data assimilation methods are used to produce initial conditions for NWP models. The NWP model background field, typically a short-range forecast, is updated with observations in a statistically optimal way. The objective in this thesis has been to develope methods in order to allow data assimilation of Doppler radar radial wind observations. The work has been carried out in the High Resolution Limited Area Model (HIRLAM) 3-dimensional variational data assimilation framework. Observation modelling is a key element in exploiting indirect observations of the model variables. In the radar radial wind observation modelling, the vertical model wind profile is interpolated to the observation location, and the projection of the model wind vector on the radar pulse path is calculated. The vertical broadening of the radar pulse volume, and the bending of the radar pulse path due to atmospheric conditions are taken into account. Radar radial wind observations are modelled within observation errors which consist of instrumental, modelling, and representativeness errors. Systematic and random modelling errors can be minimized by accurate observation modelling. The impact of the random part of the instrumental and representativeness errors can be decreased by calculating spatial averages from the raw observations. Model experiments indicate that the spatial averaging clearly improves the fit of the radial wind observations to the model in terms of observation minus model background (OmB) standard deviation. Monitoring the quality of the observations is an important aspect, especially when a new observation type is introduced into a data assimilation system. Calculating the bias for radial wind observations in a conventional way can result in zero even in case there are systematic differences in the wind speed and/or direction. A bias estimation method designed for this observation type is introduced in the thesis. Doppler radar radial wind observation modelling, together with the bias estimation method, enables the exploitation of the radial wind observations also for NWP model validation. The one-month model experiments performed with the HIRLAM model versions differing only in a surface stress parameterization detail indicate that the use of radar wind observations in NWP model validation is very beneficial.

Exploiting ground-based measurements of the Global Positioning System for numerical weather prediction

Data assimilation provides an initial atmospheric state, called the analysis, for Numerical Weather Prediction (NWP). This analysis consists of pressure, temperature, wind, and humidity on a three-dimensional NWP model grid. Data assimilation blends meteorological observations with the NWP model in a statistically optimal way. The objective of this thesis is to describe methodological development carried out in order to allow data assimilation of ground-based measurements of the Global Positioning System (GPS) into the High Resolution Limited Area Model (HIRLAM) NWP system. Geodetic processing produces observations of tropospheric delay. These observations can be processed either for vertical columns at each GPS receiver station, or for the individual propagation paths of the microwave signals. These alternative processing methods result in Zenith Total Delay (ZTD) and Slant Delay (SD) observations, respectively. ZTD and SD observations are of use in the analysis of atmospheric humidity. A method is introduced for estimation of the horizontal error covariance of ZTD observations. The method makes use of observation minus model background (OmB) sequences of ZTD and conventional observations. It is demonstrated that the ZTD observation error covariance is relatively large in station separations shorter than 200 km, but non-zero covariances also appear at considerably larger station separations. The relatively low density of radiosonde observing stations limits the ability of the proposed estimation method to resolve the shortest length-scales of error covariance. SD observations are shown to contain a statistically significant signal on the asymmetry of the atmospheric humidity field. However, the asymmetric component of SD is found to be nearly always smaller than the standard deviation of the SD observation error. SD observation modelling is described in detail, and other issues relating to SD data assimilation are also discussed. These include the determination of error statistics, the tuning of observation quality control and allowing the taking into account of local observation error correlation. The experiments made show that the data assimilation system is able to retrieve the asymmetric information content of hypothetical SD observations at a single receiver station. Moreover, the impact of real SD observations on humidity analysis is comparable to that of other observing systems.