Description, Classification, and Prediction of Dengue Illnesses in a Thai Pediatric Cohort: A Dissertation

Dengue fever (DF) and dengue hemorrhagic fever (DHF) are emerging infectious diseases which are endemic in many regions of the globe, many of which are resource-poor areas. DHF and DF impose a severe economic health burden in tropical and subtropical areas. Dengue virus causes an acute febrile illness that can be a self-limited febrile illness, as seen in most cases of DF, or a life-threatening illness with plasma leakage and shock, as seen in cases of DHF. A systematic review of the literature revealed gaps in the knowledge base of clinical laboratory findings of dengue illness with regards to longitudinal dynamics and classification and predictive modeling of disease severity. The objective of this thesis was to investigate the utility of clinical laboratory variables for classification and prediction of disease outcomes. The data used in this investigation was derived from a prospective study of Thai children presenting to either of two study hospitals within 72 hours of onset of an acute febrile illness. Systematic data collection, including clinical laboratory parameters, and routine clinical management continued each day until 24 hours after the fever had subsided. A final diagnosis of DHF, DF, or other febrile illness (OFI) was assigned by an expert physician after chart review. The first research objective of this study was to describe the temporal dynamics of clinical laboratory parameters among subjects with DHF, DF, or OFI. Data were analyzed using lowess curves and population-average models. Quadratic functions of clinical variables over time were established and demonstrated significantly divergent patterns between the various diagnostic groups. The second research objective was to establish and validate tools for classification of illness severity using easily obtained clinical laboratory measures. Bivariate logistic regression models were established using data from one hospital in an urban area of Thailand as a training data set and validated with a second data set from a hospital in a rural area of Thailand. The validated models maintained a high sensitivity and specificity in distinguishing severe dengue illnesses without using the hallmark indicators of plasma leakage. The third research objective used classification and regression tree (CART) analysis to established diagnostic decisions trees using data obtained on the day of study enrollment, within the first 3 days of acute illness. Decision trees with high sensitivity were established for severe dengue defined either as: 1) DHF with evidence of shock (dengue shock syndrome, DSS); or 2) DSS or dengue with significant pleural effusion. This study expands existing knowledge of the potential utility of clinical laboratory variables during different phases of dengue illness. The application of the results of these studies should lead to promising opportunities in the fields of epidemiological research and disease surveillance to reduce the health burden, and improve the clinical management, of dengue illness. Future directions involve application of these algorithms to different study populations and age groups. Additionally, other analytical techniques, such as those involving CART analysis, can be explored with these data

Towards patient-tailored care for soft tissue sarcoma

Over the past few decades, there has been a paradigm shift in cancer research from focusing on the homogeneity within a patient population to emphasizing the diversity or heterogeneity in presentation and clinical outcomes among patients. This concept has been commonly referred to as personalised medicine. The foundation of personalised medicine lies in delivering effective care tailored to each individual patient. In this thesis, we aimed to contribute to a more personalised and patient-tailored approach in the management of patients with soft tissue sarcoma (STS), a rare type of cancer. We achieved this goal by developing and validating different prediction tools and assessing how prediction tools could play a role in the clinical decision making process for STS. The following three main questions were addressed in this thesis:1. PART I: Given the current practice,what is the variation in clinical presentation and oncological outcome of patients with STS? which factors influence this variation in oncological outcome?2. PART II: How to better identify patients at risk and predict oncological outcome in patients with STS?3. PART III: How could prognostic tools play a role in the clinical decision making and management of STS?<br/

A palliative care approach for people with advanced heart failure : recognition of need, transitions in care and impact on patients, family carers and clinicians

Background: Despite international and national consensus guidelines, patients with advanced heart failure (HF) have significant unmet palliative care needs. UK policy recommends identification of those requiring palliative care based on prognosis (last year of life). However, HF has an unpredictable course, and clinicians might not discuss a palliative approach for fear of causing alarm and destroying hope.Aim: To explore aspects of a palliative care approach for people with advanced HF: recognition of need, transitions in care and impact on patients, family carers and clinicians.Methods: Mixed method study with integration of findings. Systematic literature review of prognostic variables associated with the last year of life in HF. Analysis of General Practice Research Database (GPRD) records to compare recognition of the need for palliative care between cancer and HF patients. Qualitative semi-structured interviews with patients receiving a palliative approach to care, their carers and clinicians.Findings: GPRD data demonstrated gross inequity between documented recognition of the need for a palliative care approach; HF patients were poorly represented on the palliative care register, and those that were, were registered close to death. Prognostic markers, identified in both the systematic review and GPRD, had limited clinical usefulness for identifying the last year of life. From interview data, clinicians appeared reluctant to discuss a palliative care approach without clear irreversible deterioration of the patient. However, patients welcomed, and some initiated, conversations regarding the change in focus of care. Following such discussion all involved found this approach beneficial, even with subsequent periods of stability or improvement. Other barriers included lack of recognition of symptoms by clinicians and difficulties in delivering proactive care.Conclusions: A palliative care approach before the very end of life is beneficial in this group. A problem-based flexible approach to recognising the need for palliative care, rather than prognosis is recommended

Endometrial carcinoma; can biomarkers aid in the prediction of aggressive disease? A study with focus on preoperative tumour markers

Background: Although endometrial cancer in general has a good prognosis, 15-20% recurs. Surgery is the main treatment with lymph node sampling increasingly advocated as compulsory for adequate staging. In metastatic disease, there is limited effect from systemic therapies including chemotherapy or antihormonal treatment. No other targeted therapies are yet available in a routine clinical setting. To improve and individualise therapy for this patient group, improved tools for identification of highrisk patients, to tailor surgery in particular, and identification of targetable molecular alterations for development of more effective systemic therapies, are urgently needed. Several biomarkers including hormone receptor status, TP53 and Stathmin expression have been found to be of prognostic importance in retrospective studies. The PI3Kinase signalling pathway is over-expressed in aggressive endometrial carcinomas and PI3kinase inhibitors are entering clinical trials for treatment of metastatic disease. Main objectives: The main objective was to evaluate if biomarkers, particularly examined in a preoperative setting, could identify aggressive endometrial carcinomas, especially those with lymph node metastasis. An additional aim was to evaluate immunohistochemical markers potentially applicable as markers for response to antihormonal therapy and PI3Kinase-inhibitors. Also, we wanted to study changes in treatment strategy in relation to survival for endometrial carcinoma patients during a 30-year period in a population based setting. Materials and methods: To evaluate potential biomarkers related to PI3Kinase signalling, a population based cohort was investigated for immunohistochemical expression of AKT, Phospho-AKT and Stathmin in hysterectomy specimens. These markers were also related to level of PI3Kinase signalling based on mRNA expression score in a prospective series of 76 patients (Paper I). The prospective international multicenter study MoMaTEC; Molecular Markers in Treatment of Endometrial Cancer, recruited clinical data, tissue and blood samples from 1192 endometrial cancer patients treated at 10 different centres during 2001-2010. Preoperative curettage specimens and blood samples have been investigated for expression of a panel of potential biomarkers; Stathmin, Estrogen Receptor (ER), Progesterone Receptor (PR), TP53 and GDF-15 (Paper II, III and IV). Changes in clinicopathological features and treatment were related to survival in a population based cohort of endometrial cancer patients from Hordaland County, Norway over the last 30 years (Paper V). Results: Stathmin overexpression in hysterectomy specimens was strongly correlated with characteristics for aggressive disease and poor survival. PI3Kinase signalling activation was significantly associated with overexpression of Stathmin. Neither AKT nor phospho-AKT expression showed any significant correlations with clinicopathological factors nor PI3Kinase signalling levels (Paper I). Overexpression of Stathmin validated to be correlated with aggressive disease in the large prospective multicentre setting (Paper II). Stathmin staining in curettage specimens was an independent predictor of lymph node metastases and overexpression of Stathmin estimated in curettage and hysterectomy specimens were both independent predictors of poor survival. High preoperative plasma GDF-15 level was significantly associated with aggressive disease. Adjusting for age and histological risk factors detected in preoperative biopsies, plasma GDF-15 independently predicted risk of lymph node metastasis. GDF-15 level also independently predicted poor prognosis (Paper III). Pathologic expression of ER, PR and TP53 in preoperative curettage specimen correlated significantly with high age at diagnosis, high FIGO stage, nonendometrioid histology, high grade, metastatic nodes and poor prognosis in a large prospective multicenter setting. Double negative ER-PR independently predicted lymph node metastasis and poor survival. Even for the most favourable group of lymph node negative endometrioid tumours, ER-PR negative status influenced survival independent of tumour grade (Paper IV). The number of endometrial cancer patients from Hordaland County increased significantly from 1981 through 2010 (Paper V), with a simultaneous increase in body mass index and decrease in disease stage at diagnosis. Routinely performed pelvic lymph node sampling increased, adjuvant radiotherapy was reduced and survival increased significantly during the same period. Conclusions: Stathmin immunohistochemical staining is superior to AKT and phospho-AKT staining in detecting PI3Kinase signalling activation and endometrial carcinomas with poor outcome (Paper I). Stathmin staining has been validated to identify endometrial carcinomas with aggressive clinic-pathological features in a large multicenter setting. Immunohistochemical staining for Stathmin in preoperative biopsies (curettage) independently predicts lymph node metastasis and poor survival (Paper II). Plasma GDF-15 has been documented as elevated in two independent patient cohorts of endometrial cancer patients compared to controls. High preoperative GDF-15 plasma level was significantly correlated with aggressive subtypes and a significant and independent predictor for lymph node metastasis and poor survival (Paper III). Double negative hormone receptor status (ER and PR negative) in preoperative endometrial cancer curettage has been validated to identify patients with poor prognosis in a prospective multicenter setting. ER-PR status independently predicts lymph node metastasis (Paper IV). During the 30-year period 1981 through 2010, a reduction in adjuvant radiotherapy and increase in routine pelvic lymphadenectomy and curative surgery with advanced disease, are associated with improved disease-specific- and overall survival in a population-based study of endometrial carcinoma patients with steadily increasing body mass index (Paper V)

Risk-stratification and management of pancreatic neuroendocrine tumors

Dit proefschrift beoogt de principes van de behandeling en richtlijnen van pancreas neuroendocriene tumoren (pNET) te evalueren en waar mogelijk te verbeteren. Verschillende manieren om een risicovoorspelling te maken voor metastasen zijn onderzocht in dit proefschrift. Identificatie van hoog-risico patiënten zou namelijk kunnen leiden tot een betere indicatiestelling voor opereren, follow-up en adjuvante therapie. Daarnaast is een landelijk protocol geïmplementeerd voor de conservatieve behandeling van patiënten met een pNET <2 cm, waarvan gedacht wordt dat zij een laag risico hebben op het ontwikkelen van metastasen. Ook zijn de korte en lange termijn uitkomsten van verschillende therapeutische opties voor de behandeling van pNET onderzocht. Deze uitkomsten kunnen gebruikt worden tijdens het bespreken van de behandelopties met patiënt

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Immune contexture monitoring in solid tumors focusing on Head and Neck Cancer

Forti evidenze dimostrano una stretta interazione tra il sistema immunitario e lo sviluppo biologico e la progressione clinica dei tumori solidi. L'effetto che il microambiente immunitario del tumore può avere sul comportamento clinico della malattia è indicato come "immunecontexture". Nonostante ciò, l'attuale gestione clinica dei pazienti affetti da cancro non tiene conto di alcuna caratteristica immunologica né per la stadiazione né per le scelte terapeutiche. Il tumore della testa e del collo (HNSCC) rappresenta il 7° tumore più comune al mondo ed è caratterizzato da una prognosi relativamente sfavorevole e dall'effetto negativo dei trattamenti sulla qualità della vita dei pazienti. Oltre alla chirurgia e alla radioterapia, sono disponibili pochi trattamenti sistemici, rappresentati principalmente dalla chemioterapia a base di platino-derivati o dal cetuximab. L'immunoterapia è una nuova strategia terapeutica ancora limitata al setting palliativo (malattia ricorrente non resecabile o metastatica). La ricerca di nuovi biomarcatori o possibili nuovi meccanismi target è molto rilevante quindi nel contesto clinico dell'HNSCC. In questa tesi ci si concentrerà sullo studio di tre possibili popolazioni immunitarie pro-tumorali studiate nell'HNSCC: i neutrofili tumore-associati (TAN), le cellule B intratumorali con fenotipo immunosoppressivo e i T-reg CD8+. Particolare attenzione è data all'applicazione di moderne tecniche biostatistiche e bioinformatiche per riassumere informazioni complesse derivate da variabili cliniche e immunologiche multiparametriche e per validare risultati derivati ​​in situ, attraverso dati di espressione genica derivati da dataset pubblici. Infine, la seconda parte della tesi prenderà in considerazione progetti di ricerca clinica rilevanti, volti a migliorare l'oncologia di precisione nell'HNSCC, sviluppando modelli predittivi di sopravvivenza, confrontando procedure oncologiche alternative, validando nuovi classificatori o testando l'uso di nuovi protocolli clinici come l'uso dell'immunonutrizione.Strong evidences demonstrate a close interplay between the immune system and the biological development and clinical progression of solid tumors. The effect that the tumor immune microenvironment can have on the clinical behavior of the disease is referred as the immuno contexture. Nevertheless, the current clinical management of patients affected by cancer does not take into account any immunological features either for the staging or for the treatment choices. Head and Neck Cancer (HNSCC) represents the 7th most common cancer worldwide and it is characterized by a relatively poor prognosis and detrimental effect of treatments on the quality of life of patients. Beyond surgery and radiotherapy, few systemic treatments are available, mainly represented by platinum-based chemotherapy or cetuximab. Immunotherapy is a new therapeutical strategy still limited to the palliative setting (recurrent not resectable or metastatic disease). The search for new biomarkers or possible new targetable mechanisms is meaningful especially in the clinical setting of HNSCC. In this thesis a focus will be given on the study of three possible pro-tumoral immune populations studied in HNSCC: the tumor associated neutrophils (TAN), intratumoral B-cells with a immunosuppressive phenotype and the CD8+ T-regs. Biostatistical and bioinformatical techniques are applied to summarize complex information derived from multiparametric clinical and immunological variables and to validate in-situ derived findings through gene expression data of public available datasets. Lastly, the second part of the thesis will take into account relevant clinical research projects, aimed at improving the precision oncology in HNSCC developing survival prediction models, comparing alternative oncological procedures, validating new classifiers or testing the use of novel clinical protocols as the use of immunnutrition

Nuclear morphometry, apoptotic and mitotic indices, and tubular differentiation in Libyan breast cancer

Aims of this study were to evaluate the relations of nuclear morphometry, mitotic and apoptotic indices, and tubular differentiation with clinicopathological features and survival rate in Libyan women. The data were compared with corresponding results on Finnish, and Nigerian female breast cancer patients. Histological samples of breast cancer (BC) from 131 patients were retrospectively studied. Mitotic activity indices (MAI and SMI), apoptotic index (AI), and fraction of fields with tubular differentiation (FTD) were estimated. Samples were also studied by computerized nuclear morphometry, such as mean nuclear area (MNA). Demographic and clinicopathological features were analyzed from 234 patients. The Libyan BC was dominantly premenopausal, and aggressive in behavior. There were statistically significant correlations between the mean nuclear area, fraction of fields with tubular differentiation, apoptotic index and proliferative indices, and most clinicopathological features. The highest significances were shown between lymph node status and the proliferative and apoptotic indices (p=0.003 with SMI, and p=0.005 with AI). There were significant associations between clinical stage and SMI and AI (p=0.002 and 0.009, respectively). The most significant associations with grade were observed with MNA and FTD (p<0.0001 and 0.001, respectively). The proliferative differences between Libyan, Nigerian and Finnish populations were prominent. These indices in Libyan were lower than in Nigerian, but higher than in Finnish patients. The Libyan patients’ AI is slightly higher than in Nigeria, but much higher than in Finland. The differences between countries may be associated with the known variation in the distribution of genetic markers in these populations. The results also indicated that morphometric factors can be reliable prognostic indicators in Libyan BC patients.Tämän työn tarkoituksena on arvioida tumamorfometrian, mitoosi- ja apoptoosi-indeksien ja tubulaarisen differentaation suhdetta libyalaisten rintasyöpäpotilaiden kliinispatologisiin piirteisiin ja eloonjäämiseen. Tietoja verrattiin suomalaisiin ja nigerialaisiin rintasyöpää sairastavien naisten tietoihin. Työssä tutkittiin 131 potilaan histologisia rintasyöpänäytteitä retrospektiivisesti. Mitoosiaktiviteetti-indeksit (MAI ja SMI), apop¬toottinen indeksi (AI) ja niiden mikroskooppikenttien osuus, joissa todettiin tubulaarista differentaatiota (FTD) arvioitiin. Myös kasvainsolujen keskimääräinen ala (MAI) arvioitiin tietokoneistettua morfometriaa käyttäen. Libyan rintasyöpäpotilaiden demografisia ja kliinispatologisia piirteitä analysoitiin 234 potilaasta. Libyan rintasyöpä (BC) on etupäässä premenopausaalista ja käyttäytymiseltään agressiivista. MNA, FTD, AI, MAI ja SMI olivat selvästi korrelaatiossa useimpiin kliinispatologisiin tietoihin. Merkittävin suhde todettiin imusolmukestatuksen ja proliferaatioindeksien ja apoptoottisen indeksin välillä (SMI p=0.003, AI p= 0.009). Histologinen gradus korreloi parhaiten MNA:n (p=0.001) ja FTD:n (p=0.001) kanssa. Kasvainten proliferaatioindeksit Libyassa, Nigeriassa ja Suomessa olivat selvästi erilaisia. Libyan indeksit olivat matalampia kuin Nigerian indeksit, mutta korkeampia kuin Suomen indeksit. AI oli hieman matalampi kuin Nigeriassa, mutta selvästi korkeampi kuin Suomessa. Erot maiden välillä voivat liittyä populaatioiden geneettisiin eroihin. Tulokset myös osoittavat, että morfometrisia tekijöitä voidaan käyttää libyalaisten rintasyöpäpotilaiden ennustetekijöinä.Institute of BiomedicineSiirretty Doriast