Dissertations / Theses on the topic 'Prognosing'

The master’s thesis deals with the usage of statistical analysis in the evaluation of the financial situation of the chosen company. The author focuses on the analysis of economic indocators, that are used in business practice for the assessment of the company financial situation. Summarized economic data are analyzed by financial and statistical analysis. The data comparism results to recommendations that may be impemented for company improvement. On the basis of historical data and trends prognosis the company gets specific picture about future situation and the effects on the market.

Introduction: The general purpose of this thesis was to study prognosis in traumatic brain injury (TBI) patients, with the aim of providing useful and practical information in clinical practice and clinical research. The specific objectives were: to develop and validate practical prognostic models for TBI patients and to assess the validity of the Modified Oxford Handicap Scale (mOHS) for predicting disability at six months. Methods: A survey was first conducted to understand the importance of prognostic information among physicians. A systematic review of prognostic models for TBI patients was then carried out. Prognostic models were developed using data from a cohort of 10,008 TBI patients (CRASH trial) and validated in a cohort of 8,509 TBI patients (IMPACT study). Two focus groups and a survey were conducted to develop a paper-based prognostic score card. The correlation between the mOHS and the Glasgow Outcome Scale (GOS) was assessed, the validity of different mOHS dichotomies was assessed, and the discriminative ability of the mOHS to predict GOS was evaluated. Results: Doctors considered prognostic information to be very important in the clinical management of TBI patients, and believed that an accurate prognostic model would change their current clinical practice. Many prognostic models for TBI have been published, but they have many methodological flaws which limit their validity. Valid prognostic models for patients from high income countries and low & middle income .countries were developed and made available as a web calculator, and as a paper based score card. The mOHS was strongly correlated with and was predictive of GOS at six months. Conclusion: The prognostic models developed are valid and practical to use in the clinical setting. The association between mOHS and GOS suggest that the mOHS could be used for interim analysis in randomised clinical trials in TBI patients, for dealing with loss to follow-up, or could be used as simple tool to inform patients and relatives about their prognosis at hospital discharge.

Die vorliegende Untersuchung gibt einen Ausblick auf Prognosemöglichkeiten mit dynamischen Strukturgleichungsmodellen. Die Analyse komplexer Systeme mit umfangreichen Datensätzen und die Erkennung relevanter Muster erfordern die Verwendung moderner statistischer Verfahren. DPLS-Modelle, eine Variante der Strukturgleichungsmodelle mit Latenten Variablen, werden methodisch erweitert, um mehrere zeitliche Verzögerungsstufen gleichzeitig modellieren zu können. Die Modelle versuchen, zahlreiche latente Einflussfaktoren und ihre Wechselwirkungen zu identifizieren. Als Daten werden rund 80 Indikatoren aus 20 Quellen verwendet, um Stimmungen, Erwartungen und wirtschaftlich relevanten Größen zu operationalisieren und zeitliche Prognosemöglichkeiten zu evaluieren. Für kürzere Zeiträume von sechs Monaten sind Stimmungen und Erwartungen die besten verfügbaren Prognosevariablen. Dieser Beitrag ist entstanden im Rahmen eines Vortrages im gemeinsamen Forschungsseminar mit der Staatlichen Universität für Wirtschaft und Finanzen Sankt Petersburg Finec im Dezember 2010.

The five studies presented in this thesis consider different aspects of the long-term prognosis of epilepsy and febrile seizures. The studies were: 1). A systemic review to examine a) how the risk of premature mortality in an individual with epilepsy changes over time and b) whether population mortality rates due to epilepsy have changed over time. (Study 1) 2). An extension of the National General Practice Study of Epilepsy (NGPSE), a prospective community-based incident cohort study to examine a) long-term seizure prognosis and mortality in people with epilepsy and b) seizure prognosis in children with febrile seizures (Study 2) 3). Two retrospective hospital-based cohort studies examining prognosis of chronic epilepsy with regard to a) frequency of different seizure patterns (Study 3) and mediumterm seizure outcome following anti-epileptic drug changes (Study 4). 4). A systematic review examining the impact of aetiology and others factors on outcome in status epilepticus (Study 5). Amongst the findings were: 1): There is no conclusive evidence that either the overall standardised mortality ratio (SMR) or the mortality rate of people with epilepsy has changed significantly over time. The SMR is highest soon after diagnosis and subsequently decreases with a possible late increase after 10 years. In the NGPSE cohort the SMR remains significantly elevated after 20 years despite over 80% currently being in terminal remission. 2): 6.7% (95% CI 4, 11%) of children with febrile seizures developed epilepsy after 20 years. 3): Approximately one-third of people with chronic epilepsy have a history of at least one significant period of seizure freedom (two or more years) while a comparable number with apparent drug-resistant epilepsy attain at least one year of seizure freedom after medication change, although approximately half subsequently relapse. 4). Aetiology and, to a lesser extent, age are the primary determinants of prognosis in status epilepticus.

Background: Amyloidosis is a rare disorder of protein folding in which a variety of proteins deposit as fibrils in the extracellular space. The two most commonly affected organs are the kidneys and heart. Deposition of amyloid in these two critical organs is of major prognostic importance. Aims: To identify the burden of systemic amyloidosis in the English population. To characterise the phenotype and diagnostic features of wild type transthyretin amyloidosis and identify the type and frequency of arrhythmic activity in cardiac amyloidosis. To evaluate outcome in renal amyloidosis, and assess the role of renal replacement therapy. Results and Conclusions: Amyloidosis was mentioned in 0.58/1000 deaths in England between 2000 and 2008. Sensitivity of death certificates in identifying patients with amyloidosis was 79%. The estimated true incidence of the disease is ~1/100000 population in England. Wild type transthyretin amyloidosis (ATTRwt) is increasingly diagnosed in the UK. Age of the patient at diagnosis and N T-proB N P level can aid in distinguishing ATTRwt from cardiac AL amyloidosis. Median survival is significantly better in ATTRwt than in cardiac AL amyloidosis. A positive troponin T, a pacemaker and NYHA class IV symptoms are all associated with worse outcome. Complex ventricular arrhythmias are seen more frequently on Holter monitoring in patients with transthyretin cardiac amyloidosis compared to cardiac AL which are in turn more frequent than patients without myocardial amyloid infiltration. There does not appear to be an association between the frequency of complex ventricular arrhythmi as and disease severity in cardiac amyl oi dosi s. Renal and overall outcome in AL amyl oi dosi s are both strongly associated with FLC response and are best among patients who achieve >90% suppression of the monoclonal component of the FLC. Survival on dialysis is improving. Outcome following renal transplantation is dependent on the amyloid fibril type and suppression of the precursor protein.

Background: This study aimed to evaluate the incremental prognostic value of infarct size, microvascular obstruction (MO), myocardial salvage index (MSI), and left ventricular ejection fraction (LV-EFCMR) assessed by cardiac magnetic resonance imaging (CMR) in comparison to traditional outcome markers in patients with ST-elevation myocardial infarction (STEMI) reperfused by primary percutaneous intervention (PCI). Methods: STEMI patients reperfused by primary PCI (n = 278) within 12 hours after symptom onset underwent CMR three days after the index event (interquartile range [IQR] two to four). Infarct size and MO were measured 15 minutes after gadolinium injection. T2-weighted and contrast-enhanced CMR were used to calculate MSI. In addition, traditional outcome markers such as ST-segment resolution, pre- and post-PCI Thrombolysis In Myocardial Infarction (TIMI)-flow, maximum level of creatine kinase-MB, TIMI-risk score, and left ventricular ejection fraction assessed by echocardiography were determined in all patients. Clinical follow-up was conducted after 19 months (IQR 10 to 27). The primary endpoint was defined as a composite of death, myocardial reinfarction, and congestive heart failure (MACE). Results: In multivariable Cox regression analysis, adjusting for all traditional outcome parameters significantly associated with the primary endpoint in univariable analysis, MSI was identified as an independent predictor for the occurrence of MACE (Hazard ratio 0.94, 95% CI 0.92 to 0.96, P <0.001). Further, C-statistics comparing a model including only traditional outcome markers to a model including CMR parameters on top of traditional outcome markers revealed an incremental prognostic value of CMR parameters (0.74 versus 0.94, P <0.001). Conclusions: CMR parameters such as infarct size, MO, MSI, and LV-EFCMR add incremental prognostic value above traditional outcome markers alone in acute reperfused STEMI.

Texte remanié de: Diss.--Fakultät Geschichts-, Sozial- und Wirtschaftswissenschaften--Universität Stuttgart, 2000. Titre de soutenance : "Entscheidende Faktoren" in Analysen der "Invasion" 1944. Zur Entstehung und Verwendung dezisionistischer und ökonomisch-deterministischer Erklärungsmuster.
Bibliogr. p. 239-269.

This thesis investigates the management and prognosis of endometrial hyperplasia. The literature on conservative therapies for endometrial hyperplasia is systematically reviewed and a meta-analysis is performed to identify the most effective treatment. Further meta-analysis is performed for young women with severe endometrial hyperplasia or cancer to explore the effectiveness of fertility-sparing treatment. A national survey of Gynaecologists is performed to evaluate current and the need for further research. A large cohort study is included that defines the regression and relapse of endometrial hyperplasia with two popular conservative therapies, the Levonorgestrel-releasing intrauterine system (LNG-IUS) and oral progestogens. The LNG-IUS is found to induce regression more often with fewer events of relapse than oral progestogens. A prediction model based on clinical characteristics and biomarkers finds that morbid obesity is an independent predictor for relapse. This research has major implications for clinical practice and a national guideline in process is based on its findings.

Prognosis, literally translated from its Greek roots, means "fore-knowing." It is one of the three classic responsibilities of a physician, the others being diagnosis and therapy. Although the breadth and detail of scientific evidence to support medical practice has expanded significantly in recent decades, there is a case to be made that evidence about prognosis has lagged behind evidence for physicians' other work. Clinical questions in perinatal epidemiology demonstrate several important issues related to the conduct of prognostic research. Using examples from the study of prognosis for infants born with congenital diaphragmatic hernia, a condition for which estimates of survival vary widely, we illustrate the importance of selecting and specifying appropriate prognostic categories and contexts (e.g., time and place) to promote the appropriate interpretation of prognosis. In a study of extremely preterm birth--another condition with wide variation in available survival statistics--we show why decisions made by patients and physicians need to be accounted for in prognostic research. By revealing several potential pitfalls of prognostic research, each of the projects described in this thesis also illuminates important opportunities for the better conduct and interpretation of such work. Making predictions about the future and providing this information to patients may always be difficult work for physicians. But better scientific evidence and interpretation of that evidence can at least make predictions more accurate. The aim of this thesis is to advance our knowledge about how to achieve such improvements.

Introduction: Prognosis in Traumatic Brain Injury (TBI) can be made using prognostic models (the IMPACT and CRASH models) or brain injury biomarkers (S100B). Current prognostic models are derived from historic datasets recruited from heterogeneous countries in terms of trauma care and for the purpose of clinical trials. Objective: To construct a prognostic model suitable for British trauma care, to compare the prognostic performance of prognostic models with S100B and to assess the combination of prognosticators from the constructed models with S100B. Methods: A dataset of 802 TBI cases from the Trauma Audit and Research Network (TARN), Manchester, UK was used to construct the prognostic models.. During the modelling, criteria for well-developed models as per the literature review were followed such as the dataset being large, the variables being selected from the literature and missing information being imputed. A further dataset of TBI cases was used to validate these models Moreover, the resulting models were run on a dataset of 100 TBI cases who had their serum S100B recorded at 24 hours to compare their performance with S100B. Results: Two prognostic models were constructed (models A and B) to predict the discharge survival. Both models share age, admission Glasgow Coma Scale (GCS), admission pupillary reactivity and presence/absence of hypoxia and lowblood pressure (on admission) and brain stem injury. However, model A includes Injury Severity Score (ISS) which is replaced with cause of injury, extracranial injury, brain swelling and interaction of cause of injury and age in model B. Both models have high performance either on the derivation dataset (Area Under the ROC Curve (AUC) of model A: 0.92 and AUC of model B: 0.93) or the external validation set from a later time period in TARN (AUC of model A: 0.92 and AUC of model B: 0.82). Furthermore, in the S100B dataset, it appears that the performance of prognostic models is not significantly different to that of S100B (for example, AUC of model A in this dataset: 0.64 versus 0.69 of the model just including S100B for survival prediction). A combination of S100B and models prognosticators improved performance and S100 improved the performance of models A and B. Discussion: The proposed prognostic models have very high AUCs and since they have been validated on a different TBI dataset from TARN, they are valid to be used for the purpose of the British trauma care benchmarking. Unfortunately, the results of the analysis on the small S100B dataset are not adequately powerful to be conclusive. However, these findings highlight the importance of future research on this topic in larger datasets. Conclusion: Two prognostic models have been constructed which can be used for the British TBI patients.

Chronic lymphocytic leukemia (CLL) exhibits remarkable clinical heterogeneity likely reflecting the underlying biological heterogeneity. The genetic landscape of CLL has been recently enriched with mutations within a number of genes proposed as novel prognostic markers. Mounting evidence also supports the pivotal role of the clonotypic B-cell receptor immunoglobulin (BcR IG) in the natural history of CLL. Interestingly, almost 30% of all CLL patients can be assigned to different patient subsets, each defined by expression of a distinct stereotyped BcR IG. Whether stereotyped subsets exhibit distinct clinical behavior is still an issue of debate. The aim of this thesis was to evaluate the prognostic relevance of recurrent gene mutations and to assess the clinicobiological associations and clinical impact of BcR IG stereotypy in CLL. In a cohort of 3490 patients, NOTCH1, SF3B1 and TP53 mutations were enriched within clinically aggressive cases carrying unmutated IGHV genes (U-CLL), frequently co-occurring with trisomy 12, del(11q) and del(17p), respectively. Of note, SF3B1 mutations increased in parallel with increasing timespan between diagnosis and mutational screening. NOTCH1 mutations, SF3B1 mutations and TP53 abnormalities (TP53abs, deletions and/or mutations) correlated with shorter time-to-first-treatment among early stage cases, while in multivariate analysis, only SF3B1 mutations and TP53abs retained independent significance. In a series of 8593 CLL patients, stereotyped subsets showed marked differences in demographics, clinical presentation, cytogenetic aberrations and gene mutational spectrum. Patients within a specific subset generally followed similar clinical courses, whereas patients in different stereotyped subsets—even when displaying similar IG somatic hypermutation status— experienced significantly different clinical outcome. In particular, subset #2 (IGHV3-21/IGLV3-21), the largest overall, was found to exhibit (i) a remarkably high incidence of SF3B1 mutations (44%), alluding to subset-biased acquisition of genomic aberrations, in the context of particular antigenic stimulation; and, (ii) a dismal clinical outcome, distinct from the remaining IGHV3-21 CLL. Our findings strongly support the adverse clinical impact of SF3B1 mutations in CLL in addition to TP53abs. BcR IG stereotypy also emerges as prognostically relevant, further highlighting that an immunogenetic sub-classification of CLL based on BcR IG configuration could refine patient risk stratification.

Hodgkin lymphoma (HL) is characterised histologically by a minority of malignant Hodgkin and Reed-Sternberg (HRS) cells surrounded by benign cells, and clinically by a relatively good prognosis. The treatment, however, leads to a risk of serious side effects. Knowledge about the biology of the disease, particularly the interaction between the HRS cells and the surrounding cells, is essential in order to improve diagnosis and treatment.

HL patients with abundant eosinophils in the tumours have a poor prognosis, therefore the eosinophil derived protein eosinophil cationic protein (ECP) was studied. Serum-ECP (S-ECP) was elevated in most HL patients. It correlated to number of tumour eosinophils, nodular sclerosis (NS) histology, and the negative prognostic factors high erythrocyte sedimentation rate (ESR) and blood leukocyte count (WBC). A polymorphism in the ECP gene (434(G>C)) was identified and the 434GG genotype correlated to NS histology and high ESR.

The poor prognosis in patients with abundant eosinophils in the tumours has been proposed to depend on HRS cell stimulation by the eosinophils via a CD30 ligand (CD30L)-CD30 interaction. However, CD30L mRNA and protein were detected in mast cells and the predominant CD30L expressing cell in HL is the mast cell. Mast cells were shown to stimulate HRS cell lines via CD30L-CD30 interaction. The number of mast cells in HL tumours correlated to worse relapse-free survival, NS histology, high WBC, and low blood haemoglobin.

Survival in patients with early and intermediate stage HL, diagnosed between 1985 and 1992, was generally favourable and comparatively limited treatment was sufficient to produce acceptable results for most stages. The majority of relapses could be salvaged. Patients treated with a short course of chemotherapy and radiotherapy had an excellent outcome.

In conclusion prognosis is favourable in early and intermediate stages and there are possibilities for further improvements based on the fact that mast cells and eosinophils affect the biology and prognosis of HL.

Thyroid cancer constitutes about 1% of all malignant tumours and the incidence is increasing in Sweden. It is rare in children before the age of 10. During puberty the female to male ratio increases to be two to three times more common in females. The ratio remains constant until menopause and thereafter declines. The etiology of this gender-dependent incidence difference is unclear. Ionizing radiation is the only well-established risk factor for the disease, while the impact of other etiological factors is not clear. A retrospective study based upon medical records of 218 females and 91 males with papillary, mixed or follicular types of thyroid cancer was conducted. Prognostic factors were compared by multivariate analysis using Cox's semiparametric hazard model. Differences in prognosis between women and men were found. There was a higher relapse rate and mortality risk among men. Distant metastasis, age >50 years, regional lymph node metastasis, low or moderate differentiation, and tumour related symptoms at diagnosis were also independent factors related to increased tumour mortality risk. A population-based case-control study including 180 cases and 360 controls was carried out to identify risk factors for thyroid cancer. Information on exposure was obtained by mailed questionnaires. The first part of the study investigated connections between medical ionizing radiation and thyroid cancer. The results showed that diagnostic X rays were a significant risk factor for papillary thyroid cancer in women between 20 and 50 years at diagnosis. Exposure to iodine-131 caused no increased risk for thyroid cancer. The result supports that external radiotherapy is a risk factor for thyroid cancer in women. The second part of the case-control study dealt with occupation and different exposures. Work with diagnostic X-ray investigations and work as a lineman was associated with thyroid cancer. Exposure to impregnating agents increased the risk. The third part of this study showed that one pregnancy increased the risk for papillary thyroid cancer. A medical history of asthma or allergy decreased the risk. Another case-control study using medical records as the source for assessment of exposure gave a non significantly increased risk for thyroid cancer in patients who had been treated with external radiotherapy including the thyroid gland.

Diss. (sammanfattning) Umeå : Umeå universitet, 1994, härtill 5 uppsatser.

digitalisering@umu

The capability of predicting failure events of systems and components can provide benefits in equipment operation and maintenance. For this reason, the subject of failure prognosis is gaining greater attention from academia and industry over the last years. This work presents novel contributions related to the development and performance evaluation of failure prognosis solutions. One important step in failure prognosis is the estimation of the health state of the monitored equipment and its trend. Here, Sigma-Point Kalman Filter (SPKF) algorithms are employed for this purpose and their performance is compared to Particle Filter (PF) algorithms which are commonly cited in literature for this kind of application. Once the health state and its trend are estimated, in order to proceed with the failure prognosis, it is necessary to use this information to predict the remaining useful life (RUL) of the equipment. The RUL estimate is commonly yielded on the form of a probability distribution. A novel method, based on the Unscented Transform (UT), is presented and evaluated for this purpose. Results indicate that this approach may provide benefits when compared to the usual Monte Carlo based method. Finally, after a failure prognosis solution is developed, it is necessary to adequately evaluate its performance. This work also comprises the proposition of a novel method for such assessment, based on the use of the Probability Integral Transform (PIT). Such new method provides a measure of how adequately the proposed RUL probability distributions fit the available set of ground truth validation data. Also, additional proposed features make it possible to take into consideration the impact of the size of the validation data set into the uncertainty of the resulting metrics.

Dengue is considered as an emerging infectious disease with a wide expansion to more than 100 countries and territories in the world. Dengue has great social, economic and health impacts on endemic regions causing the disease burden to the community and becoming one of the most serious public health concerns. We described clinical and virological features of paediatric dengue cases in southern Vietnam in a large prospective cohort study. We used the new classification of WHO 2009 to include dengue shock syndrome, severe bleeding and organ failure in the category of severe dengue in data analysis. Of 7544 patients enrolled into the study with complete haemobiochemical results available, 2060 patients (27.3%) had laboratory- confirmed diagnosis of dengue. The number of cases with severe dengue, dengue requiring parenteral fluids and uncomplicated dengue was 117 (5.7%), 156 (7.6%) and 1787 (86.7%) respectively. Proportions of RT-PCR positivity in the groups of severe dengue, dengue requiring parenteral fluids and uncomplicated dengue was 115/117 (98.3%), 149/156 (95.5%) and 1690/1787 (94.6%) respectively. It is a challenge for attending physicians in the outpatient settings to recognize early dengue patients. A simple tool, called Early Dengue Classifier (EDC), including information of age, white blood cell and platelet counts can be used alone or in combination with the NS 1 rapid test to make early diagnosis of dengue within 72 hours of onset illness. We demonstrate that the early diagnosis of dengue can be enhanced beyond the current standard of care using a simple evidence-based algorithm. A prognostic algorithm using vomiting, platelet count, aspartate aminotransferase and NS 1 rapid test result, called Early Severe Dengue Identifier (ESDI), can predict severe complications. Though the positive predictive value of the ESDI was low as seen with common prognostic models for a rare outcome, the results should support patient management and clinical trials of specific therapies.

L'objectif de cette thèse est de construire un système intelligent, efficace et pratique pour diagnostiquer et pronostiquer les pannes d'avions. Mes recherches portent sur "La MOdélisation, le DIagnostic et le PROnostic (MODIPRO) de pannes dans les systèmes complexes". Ce travail s'inscrit dans le cadre d'un projet FUI intitulé MODIPRO, qui est porté par Dassault Aviation, dont l’objectif est de mettre sur marché une solution logicielle. Cette solution logicielle permettra d’analyser la masse de données acquises en vol par un parc d’avions afin d'en déduire des règles de diagnostic et un pronostic de panne. Le système proposé dans cette thèse a été entièrement testé à l'aide de données expérimentales de trois trimoteurs d'avions Z1, Z2 et Z3 (fournies par Dassault Aviation). L'ensemble du système devrait être construit sur une base de données contenant environ 67 heures de vol dossiers impliquant 32 capteurs. Les solutions de diagnostic classiques deviennent de moins en moins applicables pour les avions modernes, dont les systèmes électroniques et mécaniques deviennent de plus en plus complexes. Dans l’état de l’art, la maintenance non planifiée n'a lieu qu'au moment où les pannes surviennent, ce qui est trop tard pour observer les dysfonctionnements; la maintenance planifiée est préventive et doit être réalisée périodiquement et indépendamment de l'état physique de l'avion, ce qui nécessite une quantité importante de ressources financières et humaines. Bien que les tests intégrés soient largement utilisés aujourd’hui, ils prennent également beaucoup du temps car le personnel de maintenance a besoin de se connecter à une boîte de diagnostic de l'appareil après chaque vol. Ces méthodes classiques provoquent souvent un grand nombre de fausses alarmes, par conséquent la maintenance planifiée est encore indispensable aujourd’hui. De plus, les systèmes de diagnostic et de pronostic classique, tels que le management de maintenance conditionnelle (CBM) et la gestion du pronostique de situation (PHM), n’analysent la situation des avions que lorsque ceux-ci sont au sol - en mode «hors-ligne», ils ne peuvent donc pas contrôler des avions en mission. Pour résoudre tous ces problèmes et garantir un taux élevé de participation des aéronefs, le système proposé dans cette thèse utilise des méthodes d'apprentissage automatique pour détecter, isoler et même prévoir les pannes d'avions tout en conservant la fiabilité et la sécurité. Ces recherches font appel aux techniques de traitement du signal, de la reconnaissance des formes et de la classification. D'une part, un modèle de diagnostic permet de déduire la cause « réelle » d’une panne par l’observation et le traitement de signaux acquis en vol. D'autre part, un modèle de pronostic fournit l’état d’avancement d’une dégradation et permet donc d’anticiper ou reporter la maintenance. L’exploitant du système peut utiliser le diagnostic pour identifier et localiser une panne et le pronostic pour arbitrer entre ses besoins d’exploitation, ses coûts de remise en état, les risques de défaillance et leurs conséquences. En plus, ce système peut être utilisé non seulement en mode «hors-ligne», lors de la maintenance d'un aéronef, mais aussi en mode «en ligne», lorsque celui-ci est en mission. Selon les exigences de la situation, les missions du système en ligne et du système hors ligne peuvent être différentes. Le système en ligne est chargé de détecter les pannes et d’envoyer des alarmes au pilote et à la tour de contrôle. Le système hors-ligne nécessite de localiser les pannes et de faire un rapport détaillé au centre de maintenance. En outre, le système a besoin d'analyser les données des vols effectués afin de prévoir des pannes. Afin de garantir la fiabilité du système, différentes méthodes d’apprentissage sont connectées en parallèle comme des sous-systèmes. Ces méthodes peuvent compenser les inconvénients de l'autre. Dans un premier temps, les données sont analysées et pré-classées par une approche classique et simple - l'analyse discriminante linéaire (LDA). Sur la base de ces résultats, une nouvelle approche de la classification appelée SCM est proposé d'améliorer la précision du diagnostic. SCM est différent de SVM qui exige des vecteurs de support à la frontière de chaque classe pour distinguer les différentes catégories. SCM cherche les vecteurs de support de centres et sous-centres véritables de chaque classe au cours de l'apprentissage automatique. Il peut utiliser les centres correspondants comme le modèle de la classe. La classification des données se fait simplement d'après l'éloignement des centres. En outre, SCM peut travailler pour l'analyse pronostique et parfaitement résoudre le problème même dans le cas où les données sont non linéaires. L'évolution temporelle des données de vol est analysée par chaque modèle de panne. Sur la base de l'évolution de la distance entre le nuage de données et les centres du modèle, le système calcule la tendance de l'évolution des données et prévoit les pannes probables. Au-delà d'un pronostic de panne à court terme, le système peut également être utilisé pour faire une évaluation à long terme de l'état des aéronefs. Ceci est plus convaincant et efficace par rapport aux méthodes de régression et aux méthodes statistiques, qui n'ont pas la précision d'une régression à long terme et qui nécessitent plus de temps pour l'analyse des données. Bien que les résultats de diagnostic des SCM et SVM soient déjà satisfait, avec un taux de détection correcte qui dépasse 95%, des réseaux de neurones artificiels (ANN) sont utilisés pour construire un autre sous-système, afin d’analyser l’impact des différents types des capteurs et confirmer les modèles produits par SCM et SVM. Les ANN sont une approche tout à fait différente de SCM et SVM : il s'agit d'un modèle mathématique qui est inspiré par les aspects de structure et fonctionnelles des réseaux de neurones biologiques. Un réseau de neurones est constitué d'un groupe interconnecté de neurones artificiels, et il traite les informations en utilisant une approche connexionniste de calcul. Les capteurs sont répartis en différents groupes correspondants à leur type : température, pression de l'air, etc. Ces groupes des capteurs constituent les entrées des réseaux de neurones, ainsi nous pouvons étudier l'importance de chaque type de capteur d'après leurs poids dans le réseau et les résultats du diagnostic des pannes. Avec ces résultats, on peut déterminer quels groupes des capteurs sont les plus importants pour diagnostiquer chaque type de panne. Les méthodes SCM, SVM et ANN ont besoin beaucoup de temps pour réaliser l’apprentissage, ce qui ne permet pas d’apprendre au cours des vols. Dans certains cas, il peut être nécessaire de reconstruire le système de diagnostic et de pronostic, par exemple si un capteur est perdu pendant la mission. Pour pallier à cela, nous avons ajouté des sous-systèmes basés sur des arbres de décision (DT) et des modèles des mélanges Gaussiens (GMM). L'algorithme C4. 5 apprend automatiquement le meilleur arbre de décision en effectuant une recherche dans l'ensemble des arbres possibles selon les données d'apprentissage disponibles. Donc il est capable de travailler même avec des éléments d'information manquants. Il peut être utilisé pour construire un sous-système capable de restructurer le système de diagnostic à temps si certains capteurs ou informations sont perdus. Les GMM permettent de dessiner le plan des modèles dysfonctionnelles pour surveiller l’évolution des données réelles de l'avion dans le système de pronostic. Contrairement aux systèmes d’experts ou à d'autres méthodes classiques, les méthodes développées dans cette thèse peuvent facilement intégrer de nouvelles pannes et de nouvelles règles dans la base de données : il n'y a pas de conflit entre les nouvelles et les anciennes règles. D’autre part, les capteurs sont susceptibles de tomber en panne, certaines entrées peuvent donc manquer au système. Les mesures des capteurs étant utilisées comme entrées du système, la nature des capteurs influe sur l'exactitude des résultats de diagnostic et de pronostic, ainsi que dans la confiance que l'on peut avoir dans ceux-ci. Pour traiter ces problèmes, le système doit constamment vérifier l’état des capteurs à l’aide d’un modèle physique. Si certains capteurs sont en panne, le système d'origine n'est pas applicable. Il faut alors démarrer une solution d'urgence, comme le réapprentissage rapide de l'arbre de décision afin de construire un nouveau système de diagnostic de panne temporaire. En plus de cela, l'analyse en composantes principales (PCA) et l'analyse discriminante linéaire (LDA) peuvent non seulement réduire la dimension des données d'entrée, mais permettent aussi de visualiser les données en 2D ou en 3D. Ces outils sont très utiles pour observer le pronostic de panne d’un avion ou d’une flotte d’avions à partir des données réelles. D’autre part, ces informations peuvent servir aux ingénieurs pour étudier la nature des pannes observées. Le système décrit ici n'est pas une boîte noire. Bien qu'il soit construit principalement pour les avions de combat, il peut être aussi appliqué à tous les autres types d'avions, nommément des avions civile. D'une part, le système et ses modèles détectant les dysfonctionnements potentiels peuvent être conçus pour éclairer les services de client chargés de surveiller l'état des avions afin d'assurer la sécurité des clients. D'autre part, ce système peut également accumuler les connaissances (y compris les règles de fonctionnement) pour le bureau d’études et parfaire la conception de nouveaux avions
The goal of this thesis is to build an effective and practical intelligent system to diagnose and prognose aircraft faults. My research focuses on “The MOdeling, DIagnosis and PROgnosis (MODIPRO)” faults in complex systems. This work is a part of a project entitled FUI MODIPRO which is supported by Dassault Aviation. The objective of this project is to research and develop a software solution MODIPRO Version 0 and put it on the aviation market. This software solution can analyze a huge mass of data acquired from a flight and a fleet of aircraft, and the system can deduce rules for diagnosis and prognosis of faults. The system proposed in this thesis has been fully tested by using actual experimental data from a tri-engines system of aircrafts Z1, Z2 and Z3 (supplied by Dassault Aviation). The whole system would be built on a database containing about 67 hours of flight records involving 32 sensors. With the rapid development of modern aero technology and the market demand of high- performance, aircraft systems have become more and more. Thus, the classical diagnosis methods become less available. In the state of the art, unplanned maintenance takes place only at breakdowns, which is too late to observe the faults; the planned maintenance costs too much financial resources and manpower, which needs to set a periodic interval to perform preventive maintenance regardless of the health status of a physical asset. Although Build in Test (BIT) system is used widely, it also costs too much human and financial resource. In a general way the maintenance staffs need to connect the diagnostic box to the aircraft via interface after each flight mission. Because these classical methods often cause the false alarm, the planned maintenance is also indispensable today. In addition, classical diagnostic and prognostic system, such as Condition-Based Maintenance (CBM) and Prognostic Health Management (PHM), analyze the health state of aircrafts when they are on the ground – in the "offline" mode, they can’t supervise the aircraft during the mission. In order to resolve these problems and guarantee a high ratio of attendance of aircraft, the system proposed in this thesis uses machine-learning methods to automatically detect, isolate, and even forecast aircraft faults while maintaining reliability and safety. The researches involve signals processing techniques, pattern recognition and classification. On the one hand, the diagnostic model allows the system to deduce the "real" cause of a fault by the observation and the treatment of acquired signals from flight records. On the other hand, the model can provide a progress of degradation of the health state and thus allows anticipating the faults or deferring the needless planned maintenance. The diagnosis system can locate and identify faults and the prognosis system can make the arbitration of a future maintenance plan on basis of the operating needs, the costs of rehabilitation, the risk of fault and the consequences. In addition to this, the system proposed in this thesis can be used not only in the off-line mode when aircraft maintenance occurs, but also in the on-line mode during the aircraft’s mission. According to the different situations requirements, the missions of on-line system and off-line system are different. The on-line system is tasked with detecting faults and sending the alarms to the pilot and the Aircraft Ground Center (AGC) in time. The off-line system is obliged to locate the fault(s) and make a detail report to the maintenance center. Additionally, the system needs to analyze the flight data in the past time for the sake of forecasting the fault(s). In order to ensure the reliability of the system, different methods of machine learning are used in parallel as subsystems. These methods can compensate the disadvantages of each other. At first, the data are analyzed and pre-classified by Linear Analysis Discriminant (LDA), a classical and simple approach. On basis of the results, a novel approach of classification called SCM is proposed to improve the accuracy of diagnosis. SCM is different from SVM that requires the support vectors on the boundary of every class to distinguish the categories. SCM seeks the support vectors of true centers and sub-centers of each class during the machine learning. It can make the corresponding centers as the model of the class. The classification of data is simply done by the power distances of the centers. Furthermore, SCM can work for the prognosis analysis and perfectly deal with the nonlinear problem. The evolution of flight data is supervised by each fault model. On the basis of the evolution of the distances from the cloud of data to the centers, the system estimates the tendency of the evolution of data and forecast the probable faults in the future. Beyond a short-term prognosis of faults, the system can also be used to do a long-term evaluation of aircraft healthy state. This is more convincing and efficacious compared to regression methods and statistical methods, which lack the precision of a long-term regression and which require a longer time for data analysis. Although the diagnosis results of SCM and SVM are already satisfied with a correct detection rate that exceeds 95%, Artificial Neural Networks (ANN) are used to build another sub-system, so as to analyze the impact of using different types sensors on the different fault diagnosis and confirm the results from the models SVM and SCM. ANN is a quite different AI technic from SCM and SVM. It is a mathematical model that is inspired by the structure and functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. All the sensors are divided in to different groups corresponding to different types of the sensors. Different combinations of sensors are linked to the neural networks, thus we can study the importance of different types of aircraft sensors by the weights of networks and the diagnosis results of the faults. The methods, as SCM, SVM and ANN, need much time to accomplish machine learning, which cannot do the learning during the flight mission. But, in some cases, it may be necessary to rebuild the diagnosis system, for example if some sensors are broken or lost during the mission. For overcoming this, we added sub-systems based on decision trees (DT) and Gaussian mixture models (GMM), which are easier to interpret, quicker to learn than other data-driven methods, and able to work even with missing pieces of information. The C4. 5 algorithm automatically "learns" the best decision tree by performing a search through the set of possible trees according to the available training data. Its needs less time to accomplish the machine learning, so it is also studied and improved in this thesis, and be used to build a subsystem for sake of restructuring the diagnosis system if some sensors or sensors information are lost, especially under the condition of war. GMM can also draw the plan of dysfunctional models and monitor the evolution of the health state of the aircraft in the prognosis system. Unlike expert systems or other conventional methods, the methods developed in this thesis can easily integrate new faults and new rules in the database: there isn’t any conflict between the new and old rules. Beyond that, there is another important problem to consider and resolve: some sensors might be already failed before the machine learning. The measurements via sensors in the aircraft are used as the inputs of the system. The nature of the sensors will impact the accuracy and confidence of the diagnosis and prognosis results of the system. Thus, these data should be treated above of all. First, the system needs to check the healthy state of the sensors. If some sensors are broken down, the original system is not applicable. The system will start the emergency application, like fast relearning of the decision tree in order to build a new temporary fault diagnosis system. In addition to that, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used in data mining. They can not only reduce the input data’s dimension, but also make a visualization of data in 2D or 3D. It is very useful to observe the evaluation of flux data and to realize prognosis, and it is important for engineers to study the nature of faults. The system described here is not a black box. Although the system is built mainly for combat aircraft, it can be applied to all other types of aircraft, namely civil aircraft. On one hand, the system and its dysfunction models of aircraft faults can be designed to illuminate engineering consulting services responsible for monitoring the condition of aircrafts to ensure the safety of clients. On the other hand, this system can also accumulate the knowledge for re-engineering purposes (including diagnosis operational rules) and perfect the design of new aircrafts

Background Many risk factors for stroke are well characterized and might, at least to some extent, be similar for first-ever stroke and for recurrent stroke events. However, previous studies have shown heterogeneous results on predictors and rates of stroke recurrence. Patients who survive spontaneous intracerebral hemorrhage (ICH) often have compelling indications for antithrombotic (AT) treatment (antiplatelet (AP) and/or anticoagulant (AC) treatment), but due to controversy of the decision to treat, a large proportion of these patients are untreated. In the absence of evidence from randomized controlled trials (RCTs), there is need for more high- quality observational data on the clinical impact of, and optimal timing of AT in ICH survivors. The aims of this thesis were to assess time trends in stroke recurrence, to determine the factors associated with an increased risk of stroke recurrence – including socioeconomic factors – and to determine to what extent ICH survivors with and without atrial fibrillation (AF) receive AT treatment and to determine the optimal timing (if any) of such treatment.  Methods The population-based Monitoring Trends and Determinants of Cardiovascular Disease (MONICA) stroke incidence register was used to assess the epidemiology and predictors of stroke recurrence after ischemic stroke (IS) and ICH from 1995 to 2008 in northern Sweden. Riksstroke, the Swedish stroke register, linked with the National Patient Register and the Swedish Dispensed Drug Register, made it possible to identify survivors of first-ever ICH from 2005 to 2012 with and without concomitant AF to investigate to what extent these patients were prescribed AP and AC therapy. The optimal timing of initiating treatment following ICH in patients with AF 2005–2012 was described through separate cumulative incidence functions for severe thrombotic and hemorrhagic events and for the combined endpoint “vascular death or non-fatal stroke”. Riksstroke data on first-ever stroke patients from 2001 to 2012 was linked to the Longitudinal Integration Database for Health Insurance and Labour market studies to add information on education and income to investigate the relationship between socioeconomic status and risk of recurrence. Results Comparison between the cohorts of 1995–1998 and 2004–2008 showed declining risk of stroke recurrence (hazard ratio: 0.64, 95% confidence interval (CI): 0.52-0.78) in northern Sweden. Significant factors associated with an increased risk of stroke recurrence were age and diabetes. Following ICH, a majority (62%) of recurrent stroke events were ischemic.  The nationwide Riksstroke study confirmed the declining incidence, and it further concluded that low income, primary school as highest attained level of education, and living alone were associated with a higher risk of recurrence beyond the acute phase. The inverse effects of socioeconomic status on risk of recurrence did not differ between men and women and persisted over the study period. Of Swedish ICH-survivors with AF, 8.5% were prescribed AC and 36.6% AP treatment, within 6 months of ICH. In patients with AF, predictors of AC treatment were less severe ICH, younger age, previous anticoagulation, valvular disease and previous IS. High CHA2DS2-VASc scores did not seem to correlate with AC treatment. We observed both an increasing proportion of AC treatment at time of the initial ICH (8.1% in 2006 compared with 14.6% in 2012) and a secular trend of increasing AC use one year after discharge (8.3% in 2006 versus 17.2% in 2011) (p<0.001 assuming linear trends). In patients with high cardiovascular event risk, AC treatment was associated with a reduced risk of vascular death and non-fatal stroke with no significantly increased risk of severe hemorrhage. The benefit appeared to be greatest when treatment was started 7–8 weeks after ICH. For high-risk women, the total risk of vascular death or stroke recurrence within three years was 17.0% when AC treatment was initiated eight weeks after ICH and 28.6% without any antithrombotic treatment (95% CI for difference: 1.4% to 21.8%). For high-risk men, the corresponding risks were 14.3% vs. 23.6% (95% CI for difference: 0.4% to 18.2%). Conclusion Stroke recurrence is declining in Sweden, but it is still common among stroke survivors and has a severe impact on patient morbidity and mortality. Age, diabetes and low socioeconomic status are predictors of stroke recurrence. Regarding ICH survivors with concomitant AF, physicians face the clinical dilemma of balancing the risks of thrombosis and bleeding. In awaiting evidence from RCTs, our results show that AC treatment in ICH survivors with AF was initiated more frequently over the study period, which seems beneficial, particularly in high-risk patients. The optimal timing of anticoagulation following ICH in AF patients seems to be around 7–8 weeks following the hemorrhage.

Background: Peripartum cardiomyopathy (PPCM) is an incompletely understood disease that causes significant morbidity and mortality in many parts of the world, including Northern Nigeria. The aims of this Thesis were: [1] to determine if selenium deficiency, serum ceruloplasmin and traditional birth practices are risk factors for PPCM, in Kano, Nigeria; [2] to describe the one year survival and left ventricular reverse remodeling (LVRR) in a group of patients with PPCM from three referral hospitals in Kano, Nigeria; [3] to identify potential electrocardiographic (ECG) predictors of PPCM; and [4] to assess right ventricular systolic dysfunction (RVSD) and remodelling in a cohort of PPCM patients in Kano, Nigeria. Materials and Methods: The studies were carried out in 3 referral hospitals in Kano, Nigeria. Study 1: This was a case-control study. Critically low serum selenium concentration was defined as <70μg/L. Study 2: This was a longitudinal study. LVRR was defined as absolute increase in LV ejection fraction (LVEF) by ≥10.0% and decrease in LV end-diastolic dimension indexed to body surface area (LVEDDi) ≤33.0 mm/m2, while recovered LV systolic function as LVEF ≥55%, at 12 months follow-up. Study 3: This was a case-control study. Logistic regression models and a risk score were developed to determine ECG predictors of PPCM. Study 4: This was a longitudinal study and patients were followed up for 12 months. RVSD was defined as the presence of either tricuspid annular plane systolic excursion (TAPSE) <16mm or peak systolic wave (S’) tissue Doppler velocity of RV free wall <10cm/s. Recovery of RV systolic function was defined as an improvement of reduced TAPSE to ≥16mm or S’ to ≥10cm/s, without falling to reduced levels again, during follow-up. Results: Study 1: Total of 39 PPCM patients and 50 controls were consecutively recruited after satisfying the inclusion criteria. Mean serum selenium in patients (61.7±14.9μg/L) was significantly lower than in controls (118.4±45.6μg/L) (p<0.001). The prevalence of serum selenium <70μg/L was significantly higher among patients (76.9%) than controls (22.0%) (p<0.001). The mean ceruloplasmin and prevalence of socio-economic indices, multiparity, pregnancy-induced hypertension, obesity and twin pregnancy were not different between the groups (p>0.05). Logistic regression showed that rural residency significantly increased the odds for serum selenium <70μg/L by 2.773 fold (p=0.037). Study 2: A total of 33 patients were followed-up. Of the 17 survivors at 12 months, 8 patients (47.1%) satisfied the criteria for LVRR, of whom 5 (29.4%) had recovered LV systolic function, but LVRR was not predicted by any variable in the regression models. The prevalence of normal LV diastolic function increased from 11.1% at baseline to 35.3% at twelve months (p=0.02). At one year follow-up, 41.4% of patients had died (two thirds of them within the first 6 months), but mortality wasn’t predicted by any variable including LVRR. Study 3: A total of 54 PPCM and 77 controls were studied. A rise in heart rate by 1 beat/minute increased the odds of PPCM by 6.4% (p=0.001), while presence of ST-T-wave changes increased the odds of PPCM by 12.06 fold (p<0.001). In patients, QRS duration modestly correlated (r=0.4; p<0.003) with LV dimensions and end-systolic volume index (LVESVI), and was responsible for 19.9% of the variability of the latter (R2 = 0.199; p=0.003). A risk score of ≥2 had a sensitivity of 85.2%, specificity of 64.9%, negative predictive value of 86.2% and area under the curve of 83.8% (p<0.0001) for potentially predicting PPCM. Study 4: A total of 45 patients were studied. RV systolic function recovery occurred in a total of 8 patients (8/45; 17.8%), of whom 6 (75.0%) recovered in 6 months after diagnosis. The prevalence of RVSD fell from 71.1% at baseline to 36.4% at 6 months (p=0.007) and 18.8% at one year (p=0.0008 vs baseline; p=0.41 vs 6 month). Although 83.3% of the deceased had RVSD, it didn’t predict mortality in the regression models (p>0.05). Conclusion: These studies have shown that selenium deficiency seems to be a risk factor for PPCM in Kano, Nigeria, related to rural residency. However, serum ceruloplasmin, customary birth practices and some other characteristics were not associated with PPCM in the study area. They have also shown that PPCM patients had modest LVRR but high mortality at one year. In addition, using the ECG risk score could help to streamline the diagnosis of PPCM with significant accuracy, prior to confirmatory investigations in postpartum women. Finally, RVSD and reverse remodelling were common in Nigerians with PPCM, in whom the first 6 months after diagnosis seem to be critical for RV recovery and survival.
Summary

A delamination damage prognosis system is defined using data available from surface strain field measurements and verified against experimental testing. Optical Grid Deflectometry measurements are interrogated to produce a damage detection and analysis technique. The output of this analysis is used to directly inform damage models in order to assess the effect of detectable damage on structural integrity. Damage · growth is investigated both experimentally and numerically in order to produce correlated models that can be used together with sensor outputs to predict the integrity of the damaged structure.

In the present study clinical performance of new highly cardiospecific biochemical markers of myocardial injury was investigated and these markers were compared with conventional enzymes. The multimarker risk assessment score index, including biochemical markers of myocardial injury, inflammation and haemostasis, was developed for the prognosis of patients after ACS. The prognostic value of BNP concentration changes in predicting LV remodeling and dysfunction after MI was evaluated along with other biochemical markers of myocardial injury.

Prognostic indicators, measured at diagnosis, are important in breast cancer. They help clinicians select optimal treatment, provide rational bases for stratification of treatment trials and assist analysis of response to treatment. Univariate statistical survival curves have identified many such indicators. However, they do not explain why some patients, classified as favoured by one or other factor(s), experience early treatment failure, nor why a substantial number with unfavourable signs remain recurrence-free many years later. This study was undertaken to identify independent prognostic factors with the use of multivariate regression. A Cox proportional hazards model of disease-specific survival was based on 1184 primary breast cancer patients referred to the Cancer Control Agency of B.C. between 1975 and 1981 (median follow-up 60 months). Significant univariate associations with overall survival were found for estrogen receptor concentration ([ER]), axillary nodal status (NO, Nl-3, N4+), clinical stage (TNM I, II, III, IV), histologic differentiation and confluent tumor necrosis (minimal, marked). These factors were assessed at primary diagnosis. A subset of 859 patients with complete data on these variables and also histologic type, menopausal status, age, tumor size and treatment was used to fit the multivariate model. Nodal status was the most important independent factor but three others, TNM stage, [ER] and tumor necrosis, were needed to make adequate predictions. A derived Hazard Index defined risk groups with 8-fold variation in survival. Five-year predicted survival ranged from 36% (N4+, loge[ER]=0, marked necrosis) to 96% (NO, loge[ER]=6, no necrosis) with TNM I and 0% to 70% for the same categories in TNM IV. This wide variation occurred across all stages. Study of post-recurrence survival (369 patients) yielded a model with only three independent predictors: [ER], nodal status and tumor necrosis. Survival - overall, recurrence-free and post-recurrent - is predictable by modelling a few factors measureable at diagnosis. Use of ER concentration, rather than the more common ER status (+ or -), greatly strengthens the model. Presence of ER was also shown to be increasingly important as 'protective', attenuating the effect of other factors, as risk of mortality increases.
Medicine, Faculty of
Pathology and Laboratory Medicine, Department of
Graduate

Dissertação de Mestrado Integrado em Medicina Veterinária
The acute abdomen syndrome is characterized by an acute onset of abdominal pain, usually associated with general signs such as vomiting, diarrhoea, postural and gait changes, anorexia, lethargy and shock. Success results from a proactive approach to management, including rapid stabilization of major body systems, early identification of the inciting problem(s), attention to comorbid conditions, and timely definitive therapy. Herewith comes decision making, whether to take a patient to surgery or manage the patient medically. A thorough and systematic approach requires the use of diagnostic imaging modalities, including radiology and ultrasonography, performing diagnostic peritoneal lavage or abdominal paracentesis techniques, and blood work evaluation, including complete blood count and biochemistry profiles. In some cases, the results of diagnostic tests may lead to surgical versus medical management, particularly when a patient fails to respond to medical management alone. In other cases, rapid surgical management is necessary for patient survival. Also it must take into account the capabilities and equipment of the clinic, as well as the staff skills. This may be extremely effortful, time-consuming and expensive so the owners must be informed about the prognosis for survival as they frequently face the dilemma of euthanasia. In an attempt to make more valid prognostic assessment in cases of acute abdomen syndrome, many individual predictive factors and univariable analysis where investigated. A retrospective study was carried out on 28 dogs presented with acute abdomen. Medical records were reviewed and information regarding dog signalment, history, clinical and laboratory data, surgical findings and outcome was collected. After analysis, several easily measurable parameters were found to be outcome predictors in dogs with acute abdomen, these being creatinine and alkaline phosphatase values, skin tent evaluation and dehydration.
RESUMO - ABORDAGEM, MANEIO E PREVISÃO DE PROGNÓSTICO NA SÍNDROME DE ABDÓMEN AGUDO EM CÃES. ESTUDO DE PREVISORES DE PROGNÓSTICO EM 28 CASOS - A síndrome de abdómen agudo é caracterizada por dor abdominal de início repentino, normalmente associada a sinais clínicos gerais tais como vómito, diarreia, alterações de postura e equilíbrio, anorexia, letargia e choque. O sucesso na resolução desta síndrome resulta de uma abordagem proactiva ao maneio médico, incluindo estabilização rápida dos sistemas vitais, rápida identificação, atenção a situações concomitantes e terapia definitiva realizada atempadamente. Posto isto, a decisão de submeter o paciente a cirurgia ou de o tratar conservativamente tem de ser tomada. Uma abordagem completa e sistemática passa pela imagiologia, incluindo raio-x e ecografia, lavagem peritoneal diagnóstica ou abdominocentese, e análises sanguíneas, incluindo hemograma e bioquímicas. Por vezes, os resultados dos testes diagnósticos podem levar ao maneio médico versus o cirúrgico, especialmente quando não há resposta à terapêutica conservativa por parte do paciente. Noutros casos, o maneio cirúrgico urgente é necessário para a sobrevivência do doente. É também necessário ter em consideração as capacidades e equipamento do centro de atendimento médico veterinário, bem como as competências dos veterinários responsáveis. Isto pode ser extremamente trabalhoso, demorado e caro para os proprietários, devendo estes ser informados acerca do prognóstico, visto que muitas das vezes deparam-se com o dilema da eutanásia. Na tentativa de tornar a avaliação prognóstica mais eficaz na síndrome de abdómen agudo, foram avaliados previsores individuais de prognóstico e realizada a respetiva análise univariada. Foi realizado um estudo retrospectivo em 28 cães que se apresentaram com abdómen agudo. A anamnese foi feita e a informação sobre os sinais clínicos, os dados do exame físico e laboratoriais, os achados das cirurgias e prognóstico foram colhidos. Após análise, alguns parâmetros facilmente mensuráveis, mostraram contribuir para a previsão do prognóstico em cães com abdómen agudo, sendo estes os valores de creatinina e fosfatase alcalina, a avaliação da prega de pele e a desidratação do animal.

The incidence of breast cancer is rising steadily in Sweden and the proportion of carcinoma in situ (CIS) has increased appreciably, most likely due to mammography screening. The aim of this study was twofold: (1) to examine risk factors for subsequent invasive breast carcinoma and breast cancer death after primary ductal carcinoma in situ (DCIS) and (2) to study the biology in the progress between in situ and invasive carcinoma.

In a cohort-study based on 3,398 women with a primary CIS reported to the Swedish Cancer Registry (SCR) 1980-1992, women diagnosed in 1989-1992 ran a relative risk of 0.1 (CI 95%, 0.0-0.9) from dying of breast cancer as compared with women diagnosed in 1980-1982. Women in counties with mammography screening ran a relative risk of 0.2 (CI 95%, 0.0-2.1) for breast cancer death in comparison with women in non-screening counties.

In a case-control study derived from all 4,661 women with primary CIS reported to the SCR 1960-1992, we investigated risk factors for subsequent invasive breast carcinoma (n=118) and breast cancer death (n=39). Large size and multifocality were found to increase the risk for breast cancer death. Postoperative radiotherapy and mastectomy lowered the risk for ipsilateral invasive cancer.

The standardised incidence rates (SIR) for invasive breast cancer were estimated in the cohort from 1980-1992. The SIR after primary DCIS and primary lobular carcinoma in situ (LCIS) was 4.5 (CI 95%, 3.7-5.5) and 4.0 (CI 95%, 2.1-7.5), respectively.

New histopathological classification systems for DCIS were evaluated in 195 women consecutively diagnosed with primary DCIS between 1986-1994. One group with highly differentiated lesions was defined with the EORTC classification system and had an excellent prognosis.

Histopathological grade and expression of p53, c-erbB-2, Ki 67, hormone receptors, Bcl-2 and angiogenesis were compared in 626 women with either a pure DCIS, a small invasive carcinoma or a lesion with both an invasive and in situ component. When grade was taken into account, no change in tumour markers could be detected that signalled the progression from an in situ stage to invasiveness. All tumour markers correlated to grade and their distribution was very similar in the two components of mixed lesions.