Background: Previously, we had shown that elevation of cerebral perfusion pressure, using pressors, improved short-term outcomes after traumatic brain injury and hemorrhagic shock in swine. The current study evaluates outcomes after resuscitation with diaspirin cross-linked hemoglobin (DCLHb)-a hemoglobin-based oxygen carrier with pressor activity-in the same swine model of traumatic brain injury and hemorrhagic shock. Methods: Anesthetized and ventilated swine received traumatic brain injury via cortical fluid percussion (6-8 atm) followed by 45% blood volume hemorrhage. One hour later, animals were randomized to either a control group (SAL) resuscitated with normal saline equal to three times shed blood volume or to one of two experimental groups resuscitated with DCLHb. The two experimental groups consisted of a low-dose group, resuscitated with 250 mL of DCLHb (Hb1), and a high-dose group, resuscitated with 500 mL of DCLHb (Hb2). Animals were observed for 210 minutes postresuscitation. Outcomes evaluated were cerebral oxygenation by measuring partial pressure and saturation of oxygen in cerebrovenous blood; cerebral function by evaluating the preservation and magnitude of cerebrovascular carbon dioxide reactivity; and brain structural damage by semiquantitatively assessing beta amyloid precursor protein positive axons. Results: Postresuscitation, cerebral perfusion pressure was higher in the DCLHb groups (p < 0.05, Hb1 and Hb2 vs. SAL), and intracranial pressure was lower in the Hb2 group (p < 0.05 vs. SAL). Cerebrovenous oxygen level was similar in all groups (p > 0.05). At baseline, 5% carbon dioxide evoked a 16 ± 1% increase in cerebrovenous oxygen saturation, indicating vasodilatation. At 210 minutes, this response was nearly absent in SAL (4 ± 4%) (p < 0.05 vs. baseline) and Hb1 (1 ± 5%), but was partially preserved in Hb2 (9 ± 5%). There was no intergroup difference in beta amyloid precursor protein positive axons. Five of 20 SAL and 0 of 13 DCLHb animals developed brain death (flat electroencephalogram) (p = 0.05, SAL vs. DCLhb). Postresuscitation, DCLHb animals maintained higher mean pulmonary arterial pressure (28 ± 1 mm Hg, SAL; 42 ± mm Hg, Hb1; 45 ± 1 mm Hg, Hb2) (p < 0.05, Hb1 and Hb2 vs. SAL) and lower cardiac output (3.9 ± 1.6 L/min, SAL; 2.6 ± 0.1 L/min, Hb1; 2.7 ± 0.1 L/min, Hb2) (p < 0.05, Hb1 and Hb2 vs. SAL). Three Hb2 animals died as a result of cardiac failure, and one SAL animal died as a result of irreversible shock. Conclusion: In this swine model of traumatic brain injury and hemorrhagic shock, resuscitation with DCLHb maintained a higher cerebral perfusion pressure. Low-dose DCLHb (minimal increase in oxygen carriage) failed to significantly improve short-term outcome. With high-dose DCLHb (significant improvement in oxygen carriage), intracranial pressure was lower and cerebrovascular carbon dioxide reactivity was partially preserved; however, this was at the cost of poorer cardiac performance secondary to high afterload.

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京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13167号 / 論医博第2154号 / 新制||医||1029(附属図書館) / (主査)教授 宮本 享, 教授 小泉 昭夫, 教授 柳田 素子 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

No description available.

No description available.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24797号 / 医博第4989号 / 新制||医||1066(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 花川 隆, 教授 髙橋 良輔, 教授 高橋 淳 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Intracranial aneurysms represent a potentially life-threatening condition and occur in 3–5% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each affected individual, it is utterly important to estimate the rupture risk of the respective aneurysm. However, clinically applied decision tools, such as the PHASES score, remain insufficient. Therefore, a machine learning approach assessing the rupture risk of intracranial aneurysms is proposed in our study. For training and evaluation of the algorithm, data from a single neurovascular center was used, comprising 446 aneurysms (221 ruptured, 225 unruptured). The machine learning model was then compared with the PHASES score and proved superior in accuracy (0.7825), F1-score (0.7975), sensitivity (0.8643), specificity (0.7022), positive predictive value (0.7403), negative predictive value (0.8404), and area under the curve (0.8639). The frequency distributions of the predicted rupture probabilities and the PHASES score were analyzed. A symmetry can be observed between the rupture probabilities, with a symmetry axis at 0.5. A feature importance analysis reveals that the body mass index, consumption of anticoagulants, and harboring vessel are regarded as the most important features when assessing the rupture risk. On the other hand, the size of the aneurysm, which is weighted most in the PHASES score, is regarded as less important. Based on our findings we discuss the potential role of the model for clinical practice in geographically confined aneurysm patients.

Cerebral hemodynamics monitoring is vital in the neuroscience intensive care unit to assess brain health. Diffuse optical methods using near-infrared light, e.g., near-infrared spectroscopy (NIRS) and diffuse correlation spectroscopy (DCS), allow for non-invasive prolonged monitoring of cerebral hemoglobin oxygenation and blood flow. For patients suffering from cerebral fluid or tissue volume buildup, intracranial pressure (ICP) is monitored invasively as its elevation compromises cerebral perfusion. The critical closing pressure (CrCP) is a transcranial doppler (TCD) derived non-invasive parameter that correlates with ICP; however, its use is limited due to discomfort during extended TCD measurement. I expanded on Sutin’s preliminary study using DCS to estimate CrCP and found high correlations between DCS obtained CrCP against TCD (R2: 0.77-0.83) in stroke patients. The use of DCS to monitor CrCP is advantageous because its sensors are comfortable to wear and easy to use continuously without the need of a specialized operator. However, the low DCS signal-to-noise ratio (SNR) limits the depth sensitivity and temporal resolution of CrCP measures. Following these encouraging results, I built a low-cost wireless cerebral oximeter based on multi-distance continuous wave NIRS called FlexNIRS, which exhibits high SNR (NEP < 70 fw/Hz0.5) and high sampling rate (266 Hz). This device not only quantifies cerebral oxygenation but resolves the pulsatile blood volume signal at large source-detector separations (33 mm). Using the relationship between blood flow and volume, I augmented pulsatile DCS blood flow measurements with FlexNIRS pulsatile signals. I experimentally demonstrated the high fidelity (R2: 0.98) and > 50-fold SNR improvement of the method, resulting in a one order of magnitude increase in the temporal resolution of CrCP estimates. / 2024-06-12T00:00:00Z

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Intracranial Aneurysms are a prevalent vascular pathology present in 3-4% of the population with an inherent risk of rupture. The growing accessibility of angiography has led to a rising incidence of detected aneurysms. An accurate assessment of the rupture risk is of utmost importance for the very high disability and mortality rates in case of rupture and the non-negligible risk inherent to surgical treatment. However, human evaluation is rather subjective, and current treatment guidelines, such as the PHASES score, remain inefficient. Therefore we aimed to develop an automatic machine learning-based rupture prediction model. Our study utilized 686 CTA scans, comprising 844 intracranial aneurysms. Among these aneurysms, 579 were classified as ruptured, while 265 were categorized as non-ruptured. Notably, the CTAs of ruptured aneurysms were obtained within a week after rupture, during which negligible morphological changes were observed compared to the aneurysm’s pre-rupture shape, as established by previous research. Based on this observation, our rupture risk assessment focused on the models’ ability to classify between ruptured and unruptured IAs. In our investigation, we implemented an automated vessel and aneurysm segmentation, vessel labeling, and feature extraction framework. The rupture risk prediction involved the use of deep learning-based vessel and aneurysm shape features, along with a combination of demographic features (patient sex and age) and morphological features (aneurysm location, size, surface area, volume, sphericity, etc.). An ablation-type study was conducted to evaluate these features. Eight different machine learning models were trained with the objective of identifying ruptured aneurysms. The best performing model achieved an area under the receiver operating characteristic curve (AUC) of 0.833, utilizing a random forest algorithm with feature selection based on Spearman’s rank correlation thresholding, which effectively eliminated highly correlated and anti-correlated features...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliography / Intrakranielle Aneurysmen sind eine weit verbreitete vaskuläre Pathologie, die bei 3 bis 4% der Bevölkerung auftritt und ein inhärentes Rupturrisiko birgt. Mit der zunehmenden Verfügbarkeit von Angiographie wird eine steigende Anzahl von Aneurysmen entdeckt. Angesichts der sehr hohen permanenten Beeinträchtigungs- und Sterblichkeitsraten im Falle einer Ruptur und des nicht zu vernachlässigenden Risikos einer chirurgischen Behandlung ist eine genaue Bewertung des Rupturrisikos von größter Bedeutung. Die Beurteilung durch den Menschen ist jedoch sehr subjektiv, und die derzeitigen Behandlungsrichtlinien, wie der PHASES-Score, sind nach wie vor ineffizient. Daher wollten wir ein automatisches, auf maschinellem Lernen basierendes Modell zur Rupturvorhersage entwickeln. Für unsere Studie wurden 686 CTA-Scans von 844 intrakraniellen Aneurysmen verwendet, von denen 579 rupturiert waren und 265 nicht rupturiert waren. Dabei ist zu beachten, dass die CTAs der rupturierten Aneurysmen innerhalb einer Woche nach der Ruptur gewonnen wurden, in der im Vergleich zur Form des Aneurysmas vor der Ruptur nur geringfügige morphologische Veränderungen zu beobachten waren, wie in vorhergegangenen Studient festgestellt wurde. Im Rahmen unserer Untersuchung haben wir eine automatische Segmentierung von Adern und Aneurysmen, ein Aderlabeling und eine Merkmalsextraktion implementiert. Für die Vorhersage des Rupturrisikos wurden auf Deep Learning basierende Ader- und Aneurysmaformmerkmale zusammen mit einer Kombination aus demografischen Merkmalen (Geschlecht und Alter des Patienten) und morphologischen Merkmalen (u. A. Lage, Größe, Oberfläche, Volumen, Sphärizität des Aneurysmas) verwendet. Zur Bewertung dieser Merkmale wurde eine Ablationsstudie durchgeführt. Acht verschiedene maschinelle Lernmodelle wurden mit dem Ziel trainiert, rupturierte Aneurysmen zu erkennen...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliography