Inflammatorische Biomarker als Prädiktoren für kardiovaskuläre Ereignisse - eine retrospektive Follow-Up-Analyse

Hintergrund und Ziele: Atherosklerose ist eine chronisch-entzündliche Erkrankung, in der systemische Zytokine eine wichtige Rolle in der Pathophysiologie einnehmen. Für bestimmte Zytokine und andere inflammatorische Biomarker konnten bereits Assoziationen mit kardiovaskulären Ereignissen nachgewiesen werden. Die Bestimmung von Koronarkalk (CAC) mittels kardialer Computertomographie (CT) hat, unabhängig von klassischen Risikofaktoren, einen hohen prädiktiven Wert für kardiovaskuläre Ereignisse. Dieser Arbeit lag ein Patientenkollektiv zu Grunde, für das bereits Assoziationen zwischen hohen Kalkscores und einigen inflammatorischen Biomarkern aufgezeigt werden konnten. Kardiovaskuläre Ereignisse wurden dabei jedoch nicht miteinbezogen. Ziel war es, den prädiktiven Wert von CAC und inflammatorischen Biomarkern im Hinblick auf kardiovaskuläre Ereignisse und Mortalität in einem 10-Jahres Follow-Up retrospektiv zu untersuchen. Methoden (Patienten, Material und Untersuchungsmethoden): Das Patientenkollektiv bestand aus 411 Patienten mit niedrigem bis mittlerem kardiovaskulärem Risiko, die zwischen Juni 2006 und August 2008 eine kardiale CT mit Bestimmung von CAC sowie eine Blutentnahme zur Analyse von inflammatorischen Biomarkern erhalten hatten. Zur Quantifizierung des Koronarkalks wurde der Agatston-Score bestimmt und die Analyse der Zytokine erfolgte mithilfe einer Floureszenz-basierten Luminex-Technologie. Die Patienten wurden im Zeitraum von Januar 2017 bis Februar 2018 im Rahmen einer telefonischen Befragung kontaktiert und kardiovaskuläre Ereignisse sowie Todesfälle im Follow-Up-(FU) Zeitraum registriert. Zusätzlich erfolgte eine Recherche in den elektronischen Patientenakten. Todesfälle jeglicher Ursache, Myokardinfarkte, Revaskularisationen, ischämische Schlaganfälle sowie major adverse cardiovascular events (MACE) wurden statistisch ausgewertet. Ergebnisse und Beobachtungen: Die Follow-Up-Quote betrug 76,6 % bei einer medianen Follow-Up-Zeit von 10,8 (10,3; 11,5) Jahren. Für MACE ergaben sich 73 Ereignisse (23,2 %). In der univariablen Analyse waren hohe Plasmaspiegel von monocyte chemoattractant protein-1 (MCP-1) mit Revaskularisationen assoziiert (p = 0,036) und niedrige Plasmaspiegel von Interleukin- (IL-) 1α standen in statistisch signifikantem Zusammenhang mit MACE (p = 0,014). In der multivariablen Cox-Regression mit den Kovariaten Alter > 65 Jahre, männliches Geschlecht und Kalkscore > 0 zeigten sich MCP-1 als unabhängiger Prädiktor von Revaskularisationen (Hazard Ratio (HR) 2,34; 95 % Konfidenzintervall (KI) 1,16 - 4,71) und IL-1α als unabhängiger Prädiktor von MACE (HR 1,97; 95 % KI 1,20 - 3,23). Für IL-6, IL-8 und IL-13, die bereits signifikante Assoziationen mit CAC gezeigt hatten, fanden sich keine signifikanten Unterschiede bezüglich kardiovaskulärer Ereignisse. (Praktische) Schlussfolgerungen und Diskussion: Die Ergebnisse dieser Arbeit zeigen, dass bestimmte inflammatorische Biomarker mit kardiovaskulären Ereignissen assoziiert sind. Ein zusätzlicher diagnostischer Nutzen gegenüber der alleinigen Quantifizierung von CAC konnte jedoch nicht nachgewiesen werden. Die Bestimmung eines geeigneten „Multimarkers“ im Sinne einer kombinierten Analyse mehrerer Biomarker erscheint vielversprechender und sollte die Grundlage weiterer Forschungsprojekte sein. Zuletzt hat vor allem die pharmakologische Blockade von Zytokinen durch spezifische Antikörper an Bedeutung gewonnen, um eine gezieltere Therapie der Atherosklerose zu ermöglichen. IL-1-Blocker haben hier bereits positive Ergebnisse erzielt und könnten Einzug in den klinischen Alltag finden.Background: Atherosclerosis is a chronic inflammatory disease in which systemic cytokines play an important role in pathophysiology. For certain cytokines and other inflammatory biomarkers, associations with cardiovascular events have already been demonstrated. The determination of coronary artery calcium (CAC) by cardiac computed tomography has a high predictive value for cardiovascular events, independent of classical risk factors. This thesis was based on a patient population for which associations between high CAC levels and some inflammatory biomarkers had already been demonstrated in a previous study. However, cardiovascular events had not been included. The aim was to retrospectively investigate the predictive value of CAC and inflammatory biomarkers with regard to cardiovascular events and mortality in a 10-year follow-up. Methods: 411 patients with a low to intermediate cardiovascular risk were included in a retrospective follow-up-analysis. In the context of a previous study between June 2006 and August 2008, all patients had undergone cardiac computed tomography with determination of CAC and had received a blood sample for the analysis of systemic inflammation markers. To quantify CAC, the Agatston score was performed and cytokines were analyzed using a fluorescence-based Luminex technology. A telephone survey was conducted between January 2017 and February 2018 and cardiovascular events and deaths in the follow-up-period were recorded. Additionally, a search was carried out in the electronic health records. Deaths of any cause, myocardial infarctions, revascularizations, ischemic strokes, and major adverse cardiovascular events (MACE) were statistically evaluated. Results: The follow-up rate was 76.6 % with a median follow-up time of 10.8 (10.3; 11.5) years. For MACE, there were 73 events (23.2 %). In univariable analysis, high plasma levels of Monocyte chemoattractant protein-1 (MCP-1) were associated with revascularizations (p = 0.036) and low plasma levels of Interleukin- (IL-) 1α were associated with MACE (p = 0.014). In multivariable Cox regression with covariates age > 65 years, male sex and calcium score > 0, MCP-1 was shown to be an independent predictor of revascularizations (Hazard Ratio (HR) 2.34; 95 % confidence interval (CI) 1.16 - 4.71) and IL-1α was shown to be an independent predictor of MACE (HR 1.97; 95 % CI 1.20 - 3.23). No significant differences were found for IL-6, IL-8 and IL-13, which had shown significant associations with CAC, with respect to cardiovascular events. Conclusion: The results of this thesis demonstrate that certain inflammatory biomarkers are associated with cardiovascular events. However, an additional diagnostic benefit compared to the sole quantification of CAC could not be proven. The determination of a “multimarker” performing a combined analysis of several biomarkers seems more promising and should be the basis of further research projects. Recently, the pharmacological blockade of cytokines by specific antibodies has gained importance in order to enable a more targeted therapy of atherosclerosis. IL-1 blockers have already achieved positive results in this area and could find their way into everyday clinical practice

Reducing the manual length setting error of a passive Gough-Stewart platform for surgical template fabrication using a digital measurement system

As recently demonstrated, a passive Gough-Stewart platform (a.k.a. hexapod) can be used to create a personalized surgical template to achieve minimally invasive access to the cochlea. The legs of the hexapod are manually adjusted to the desired length, which must be read off an analog scale. Previous experiments have shown that manual length setting of the hexapod's legs is error-prone because of the imprecise readability of the analog scale. The objective of this study is to determine if integration of a linear encoder and digitally displaying the measured length help reduce the length setting error. Two experiments were conducted where users set the leg length manually. In both experiments, the users were asked to set the leg length to 20 nominal values using the whole setting range from 0 mm to 10 mm. In the first experiment, users had to rely only on the analog scale; in the second experiment, the electronic display additionally showed the user the actual leg length. Results show that the mean length setting error without using the digital display and only relying on the analog scale was (0.036 ± 0.020) mm (max: 0.107 mm) in contrast to (0.001 ± 0.000) mm (max: 0.002 mm) for the experiment with the integrated digital measurement system. The results support integration of digital length measurement systems as a promising tool to increase the accuracy of surgical template fabrication and thereby patients' safety. Future studies must be conducted to evaluate if integration of a linear encoder in each of the six legs is feasible

Better insurance could effectively mitigate the increase in economic growth losses from U.S. hurricanes under global warming

Global warming is likely to increase the proportion of intense hurricanes in the North Atlantic. Here, we analyze how this may affect economic growth. To this end, we introduce an event-based macroeconomic growth model that temporally resolves how growth depends on the heterogeneity of hurricane shocks. For the United States, we find that economic growth losses scale superlinearly with shock heterogeneity. We explain this by a disproportional increase of indirect losses with the magnitude of direct damage, which can lead to an incomplete recovery of the economy between consecutive intense landfall events. On the basis of two different methods to estimate the future frequency increase of intense hurricanes, we project annual growth losses to increase between 10 and 146% in a 2°C world compared to the period 1980–2014. Our modeling suggests that higher insurance coverage can compensate for this climate change–induced increase in growth losses

Semi-Supervised Deep Learning for Microcontroller Performance Screening

In safety-critical applications, microcontrollers must satisfy strict quality constraints and performances in terms of F_max (the maximum operating frequency). Data extracted from on-chip ring oscillators (ROs) can model the F_max of integrated circuits using machine learning models. Those models are suitable for the performance screening process. Acquiring data from the ROs is a fast process that leads to many unlabeled data. Contrarily, the labeling phase (i.e., acquiring F_max) is a time-consuming and costly task, that leads to a small set of labeled data. This paper presents deep-learning-based methodologies to cope with the low number of labeled data in microcontroller performance screening. We propose a method that takes advantage of the high number of unlabeled samples in a semi-supervised learning fashion. We derive deep feature extractor models that project data into higher dimensional spaces and use the data feature embedding to face the performance prediction problem with simple linear regression. Experiments showed that the proposed models outperformed state-of-the-art methodologies in terms of prediction error and permitted us to use a significantly smaller number of devices to be characterized, thus reducing the time needed to build ML models by a factor of six with respect to baseline approaches

A Multi-Label Active Learning Framework for Microcontroller Performance Screening

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performances constraints. It has been demonstrated that on-chip ring oscillators can be be used as speed monitors to reliably predict the performances. However, any machine-learning model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this paper, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multi-label technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random labelling, while it increases the predictive accuracy, with respect to standard single-label machine-learning models