Development of a patient-specific model for stroke risk assessment in atrial fibrillation patients

Atrial fibrillation is associated with a five-fold increase in the risk of cerebrovascular events,being responsible of 15-18% of all strokes.The morphological and functional remodelling of the left atrium caused by atrial fibrillation favours blood stasis and, consequently, stroke risk. In this context, several clinical studies suggest that stroke risk stratification could be improved by using haemodynamic information on the left atrium (LA) and the left atrial appendage (LAA). The goal of this study was to develop a personalized computational fluid-dynamics (CFD) model of the left atrium which could clarify the haemodynamic implications of atrial fibrillation on a patient specific basis. The developed CFD model was first applied to better understand the role of LAA in stroke risk. Infact, the interplay of the LAA geometric parameters such as LAA length, tortuosity, surface area and volume with the fluid-dynamics parameters and the effects of the LAA closure have not been investigated. Results demonstrated the capabilities of the CFD model to reproduce the real physiological behaviour of the blood flow dynamics inside the LA and the LAA. Finally, we determined that the fluid-dynamics parameters enhanced in this research project could be used as new quantitative indexes to describe the different types of AF and open new scenarios for the patient-specific stroke risk stratification.

Body ares networks for human motor assessment

Healthcare, Human Computer Interfaces (HCI), Security and Biometry are the most promising application scenario directly involved in the Body Area Networks (BANs) evolution. Both wearable devices and sensors directly integrated in garments envision a word in which each of us is supervised by an invisible assistant monitoring our health and daily-life activities. New opportunities are enabled because improvements in sensors miniaturization and transmission efficiency of the wireless protocols, that achieved the integration of high computational power aboard independent, energy-autonomous, small form factor devices. Application’s purposes are various: (I) data collection to achieve off-line knowledge discovery; (II) user notification of his/her activities or in case a danger occurs; (III) biofeedback rehabilitation; (IV) remote alarm activation in case the subject need assistance; (V) introduction of a more natural interaction with the surrounding computerized environment; (VI) users identification by physiological or behavioral characteristics. Telemedicine and mHealth [1] are two of the leading concepts directly related to healthcare. The capability to borne unobtrusiveness objects supports users’ autonomy. A new sense of freedom is shown to the user, not only supported by a psychological help but a real safety improvement. Furthermore, medical community aims the introduction of new devices to innovate patient treatments. In particular, the extension of the ambulatory analysis in the real life scenario by proving continuous acquisition. The wide diffusion of emerging wellness portable equipment extended the usability of wearable devices also for fitness and training by monitoring user performance on the working task. The learning of the right execution techniques related to work, sport, music can be supported by an electronic trainer furnishing the adequate aid. HCIs made real the concept of Ubiquitous, Pervasive Computing and Calm Technology introduced in the 1988 by Marc Weiser and John Seeley Brown. They promotes the creation of pervasive environments, enhancing the human experience. Context aware, adaptive and proactive environments serve and help people by becoming sensitive and reactive to their presence, since electronics is ubiquitous and deployed everywhere. In this thesis we pay attention to the integration of all the aspects involved in a BAN development. Starting from the choice of sensors we design the node, configure the radio network, implement real-time data analysis and provide a feedback to the user. We present algorithms to be implemented in wearable assistant for posture and gait analysis and to provide assistance on different walking conditions, preventing falls. Our aim, expressed by the idea to contribute at the development of a non proprietary solutions, driven us to integrate commercial and standard solutions in our devices. We use sensors available on the market and avoided to design specialized sensors in ASIC technologies. We employ standard radio protocol and open source projects when it was achieved. The specific contributions of the PhD research activities are presented and discussed in the following. • We have designed and build several wireless sensor node providing both sensing and actuator capability making the focus on the flexibility, small form factor and low power consumption. The key idea was to develop a simple and general purpose architecture for rapid analysis, prototyping and deployment of BAN solutions. Two different sensing units are integrated: kinematic (3D accelerometer and 3D gyroscopes) and kinetic (foot-floor contact pressure forces). Two kind of feedbacks were implemented: audio and vibrotactile. • Since the system built is a suitable platform for testing and measuring the features and the constraints of a sensor network (radio communication, network protocols, power consumption and autonomy), we made a comparison between Bluetooth and ZigBee performance in terms of throughput and energy efficiency. Test in the field evaluate the usability in the fall detection scenario. • To prove the flexibility of the architecture designed, we have implemented a wearable system for human posture rehabilitation. The application was developed in conjunction with biomedical engineers who provided the audio-algorithms to furnish a biofeedback to the user about his/her stability. • We explored off-line gait analysis of collected data, developing an algorithm to detect foot inclination in the sagittal plane, during walk. • In collaboration with the Wearable Lab – ETH, Zurich, we developed an algorithm to monitor the user during several walking condition where the user carry a load. The remainder of the thesis is organized as follows. Chapter I gives an overview about Body Area Networks (BANs), illustrating the relevant features of this technology and the key challenges still open. It concludes with a short list of the real solutions and prototypes proposed by academic research and manufacturers. The domain of the posture and gait analysis, the methodologies, and the technologies used to provide real-time feedback on detected events, are illustrated in Chapter II. The Chapter III and IV, respectively, shown BANs developed with the purpose to detect fall and monitor the gait taking advantage by two inertial measurement unit and baropodometric insoles. Chapter V reports an audio-biofeedback system to improve balance on the information provided by the use centre of mass. A walking assistant based on the KNN classifier to detect walking alteration on load carriage, is described in Chapter VI.

mHealth-based Methods for Neuromotor Assessment and Rehabilitation

Recent years observed massive growth in wearable technology, everything can be smart: phones, watches, glasses, shirts, etc. These technologies are prevalent in various fields: from wellness/sports/fitness to the healthcare domain. The spread of this phenomenon led the World-Health-Organization to define the term 'mHealth' as "medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices". Furthermore, mHealth solutions are suitable to perform real-time wearable Biofeedback (BF) systems: sensors in the body area network connected to a processing unit (smartphone) and a feedback device (loudspeaker) to measure human functions and return them to the user as (bio)feedback signal. During the COVID-19 pandemic, this transformation of the healthcare system has been dramatically accelerated by new clinical demands, including the need to prevent hospital surges and to assure continuity of clinical care services, allowing pervasive healthcare. Never as of today, we can say that the integration of mHealth technologies will be the basis of this new era of clinical practice. In this scenario, this PhD thesis's primary goal is to investigate new and innovative mHealth solutions for the Assessment and Rehabilitation of different neuromotor functions and diseases. For the clinical assessment, there is the need to overcome the limitations of subjective clinical scales. Creating new pervasive and self-administrable mHealth solutions, this thesis investigates the possibility of employing innovative systems for objective clinical evaluation. For rehabilitation, we explored the clinical feasibility and effectiveness of mHealth systems. In particular, we developed innovative mHealth solutions with BF capability to allow tailored rehabilitation. The main goal that a mHealth-system should have is improving the person's quality of life, increasing or maintaining his autonomy and independence. To this end, inclusive design principles might be crucial, next to the technical and technological ones, to improve mHealth-systems usability.

Planning and assessment techniques for spine surgeries

The rate of diagnosis and treatment of degenerative spine disorders is increasing, increasing the need for surgical intervention. Posterior spine fusion is one surgical intervention used to treat various spine degeneration pathologies To minimize the risk of complications and provide patients with positive outcomes, preoperative planning and postsurgical assessment are necessary. This PhD aimed to investigate techniques for the surgical planning and assessment of spine surgeries. Three main techniques were assessed: stereophotogrammetric motion analysis, 3D printing of complex spine deformities and finite element analysis of the thoracolumbar spine. Upon reviewing the literature on currently available spine kinematics protocol, a comprehensive motion analysis protocol to measure the multi-segmental spine motion was developed. Using this protocol, the patterns of spine motion in patients before and after posterior spine fixation was mapped. The second part investigated the use of virtual and 3D printed spine models for the surgical planning of complex spine deformity correction. Compared to usual radiographic images, the printed model allowed optimal surgical intervention, reduced surgical time and provided better surgeon-patient communication. The third part assessed the use of polyetheretherketone rods auxiliary to titanium rods to reduce the stiffness of posterior spine fusion constructs. Using a finite element model of the thoracolumbar spine, the rods system showed a decrease in the overall stress of the uppermost instrumented vertebra when compared to regular fixation approaches. Finally, a retrospective biomechanical assessment of a lumbopelvic reconstruction technique was investigated to assess the patients' gait following the surgery, the implant deformation over the years and the extent of bony fusion between spine and implant. In conclusion, this thesis highlighted the need to provide surgeons with new planning and assessment techniques to better understand postsurgical complications. The methodologies investigated in this project can be used in the future to establish a patient-specific planning protocol.

Retrospective assessment of fracture risk through opportunistic radiological screening in a large modern cohort of liver transplant recipients

Objective: Liver transplantation has been associated with a high prevalence of osteoporosis, although most data rely on single-center studies with limited sample size, with most of them dating back to late 1990s and early 2000s. The present thesis aims to assess the prevalence of fragility fractures and contributing factors in a large modern cohort of liver transplant recipients managed in a referral Italian Liver Transplant Center. Design and Methods: Paper and electronic medical records of 429 consecutive patients receiving liver transplantation from 1/1/2010 to 31/12/2015 were reviewed, and 366 patients were selected. Clinically obtained electronic radiological images within 6 months from the date of liver transplant surgery, such as lateral views of spine X-rays or CT abdominal scans, were opportunistically reviewed in a blinded fashion to screen for morphometric vertebral fractures. Clinical fragility fractures reported in the medical records, along with information on etiology of cirrhosis and biochemistries at the time of liver surgery were also recorded. Results: Prevalence of fragility fractures in the whole cohort was 155/366 (42.3%), with no significant differences between sexes. Of patients with fractures, most sustained vertebral fractures (145/155, 93.5%), the majority of which were mild or moderate wedges. Multiple vertebral fractures were common (41.3%). Fracture rates were similar across different etiologies of cirrhosis and were also comparable in patients with diabetes or exposed to glucocorticoids. Kidney function was significantly worse in women with fractures. Independent of age, sex, alcohol use, eGFR, etiology of liver disease, lower BMI was the only independent risk factor for fractures (adjusted OR 1,058, 95%CI 1,001-1,118, P=0.046) in this study population. Conclusions: A considerable fracture burden was shown in a large and modern cohort of liver transplant recipients. Given the remarkably high prevalence of fractures, a metabolic bone disease screening should be implemented in every patient awaiting liver transplantation.

Impatto dell'uso dell'Edmonton Symptom Assessment System all'interno di un ambulatorio integrato di radioterapia e cure palliative sull'ottimizzazione della qualita di cura

I pazienti affetti da patologie oncologiche avanzate hanno un'esperienza di sintomi multipli spesso concomitanti tra loro. Lo scopo di questo progetto è quello di valutare come, nel contesto di un’ambulatorio integrato di Radioterapia e Cure Palliative (rep), sia possibile descrivere e quantificare i sintomi riportati dal paziente attraverso uno strumento di analisi convalidato, nello specifico l’Edmonton Symptom Assessment System (ESAS) al fine di costruire una proposta terapeutica ottimizzata per il singolo paziente. Da aprile 2016 ad aprile 2020, sono stati analizzati gli accessi al rep per un totale di 519 accessi, quindi abbiamo selezionato solo le visite dei pazienti che avevano completato totalmente lo strumento ESAS per la valutazione dei sintomi. La popolazione in studio era quindi composta da 212 pazienti, di questi 120 erano uomini, 92 erano donne, l'età media era di 72 anni, del totale il 36% era stato sottoposto a radioterapia. I principali risultati ottenuti dall'analisi dei dati relativi alla popolazione in studio sono stati i seguenti: 1) il 25% dei pazienti muore entro 2,2 mesi, il 60% entro 6 mesi; 2) la popolazione maschile ha un rischio di morte più elevato rispetto alla popolazione femminile; 3) Esiste una chiara differenza negli indici di correlazione tra la popolazione femminile e quella maschile 4) I principali sintomi correlati sono: dolore, mancanza di appetito, ansia, sonnolenza; 5) Il sintomo del dolore non è associato ad un aumento del rischio di morte, mentre la mancanza di appetito e sonnolenza sono associati ad un aumento del rischio di morte. In conclusione, i dati a nostra disposizione hanno dimostrato che un'analisi approfondita dei sintomi attraverso lo strumento ESAS è fattibile e necessaria portando informazioni fondamentali per una gestione ottimale del paziente, dandoci la possibilità di muoverci verso l'opportunità di trattamenti sempre più personalizzati e individualizzati in base alle caratteristiche dei singoli pazienti.