Data mining in diagnostic charts and treatment outcome prediction for vision restoration therapy

Data Mining, Vision Restoration, Treatment outcome prediction, Self-Organising-Map

Evaluation of Electrocardiographic T-peak to T-end Interval in Subjects with Increased Epicardial Fat Tissue Thickness

Abstract Background: The association between periatrial adiposity and atrial arrhythmias has been shown in previous studies. However, there are not enough available data on the association between epicardial fat tissue (EFT) thickness and parameters of ventricular repolarization. Thus, we aimed to evaluate the association of EFT thickness with indices of ventricular repolarization by using T-peak to T-end (Tp-e) interval and Tp-e/QT ratio. Methods: The present study included 50 patients whose EFT thickness ≥ 9 mm (group 1) and 40 control subjects with EFT thickness < 9 mm (group 2). Transthoracic echocardiographic examination was performed in all participants. QT parameters, Tp-e intervals and Tp-e/QT ratio were measured from the 12-lead electrocardiogram. Results: QTd (41.1 ± 2.5 vs 38.6 ± 3.2, p < 0.001) and corrected QTd (46.7 ± 4.7 vs 43.7 ± 4, p = 0.002) were significantly higher in group 1 when compared to group 2. The Tp-e interval (76.5 ± 6.3, 70.3 ± 6.8, p < 0.001), cTp-e interval (83.1 ± 4.3 vs. 76±4.9, p < 0.001), Tp-e/QT (0.20 ± 0.02 vs. 0.2 ± 0.02, p < 0.001) and Tp-e/QTc ratios (0.2 ± 0.01 vs. 0.18 ± 0.01, p < 0.001) were increased in group 1 in comparison to group 2. Significant positive correlations were found between EFT thickness and Tp-e interval (r = 0.548, p < 0.001), cTp-e interval (r = 0.259, p = 0.01), and Tp-e/QT (r = 0.662, p < 0.001) and Tp-e/QTc ratios (r = 0.560, p < 0.001). Conclusion: The present study shows that Tp-e and cTp-e interval, Tp-e/QT and Tp-e/QTc ratios were increased in subjects with increased EFT, which may suggest an increased risk of ventricular arrhythmia.

Hemorheological and Glycemic Parameters and HDL Cholesterol for the Prediction of Cardiovascular Events

Abstract Background: Hemorheological and glycemic parameters and high density lipoprotein (HDL) cholesterol are used as biomarkers of atherosclerosis and thrombosis. Objective: To investigate the association and clinical relevance of erythrocyte sedimentation rate (ESR), fibrinogen, fasting glucose, glycated hemoglobin (HbA1c), and HDL cholesterol in the prediction of major adverse cardiovascular events (MACE) and coronary heart disease (CHD) in an outpatient population. Methods: 708 stable patients who visited the outpatient department were enrolled and followed for a mean period of 28.5 months. Patients were divided into two groups, patients without MACE and patients with MACE, which included cardiac death, acute myocardial infarction, newly diagnosed CHD, and cerebral vascular accident. We compared hemorheological and glycemic parameters and lipid profiles between the groups. Results: Patients with MACE had significantly higher ESR, fibrinogen, fasting glucose, and HbA1c, while lower HDL cholesterol compared with patients without MACE. High ESR and fibrinogen and low HDL cholesterol significantly increased the risk of MACE in multivariate regression analysis. In patients with MACE, high fibrinogen and HbA1c levels increased the risk of multivessel CHD. Furthermore, ESR and fibrinogen were significantly positively correlated with HbA1c and negatively correlated with HDL cholesterol, however not correlated with fasting glucose. Conclusion: Hemorheological abnormalities, poor glycemic control, and low HDL cholesterol are correlated with each other and could serve as simple and useful surrogate markers and predictors for MACE and CHD in outpatients.

Risk Prediction of Cardiovascular Complications in Pregnant Women With Heart Disease

Abstract Background: Heart disease in pregnancy is the leading cause of non- obstetric maternal death. Few Brazilian studies have assessed the impact of heart disease during pregnancy. Objective: To determine the risk factors associated with cardiovascular and neonatal complications. Methods: We evaluated 132 pregnant women with heart disease at a High-Risk Pregnancy outpatient clinic, from January 2005 to July 2010. Variables that could influence the maternal-fetal outcome were selected: age, parity, smoking, etiology and severity of the disease, previous cardiac complications, cyanosis, New York Heart Association (NYHA) functional class > II, left ventricular dysfunction/obstruction, arrhythmia, drug treatment change, time of prenatal care beginning and number of prenatal visits. The maternal-fetal risk index, Cardiac Disease in Pregnancy (CARPREG), was retrospectively calculated at the beginning of prenatal care, and patients were stratified in its three risk categories. Results: Rheumatic heart disease was the most prevalent (62.12%). The most frequent complications were heart failure (11.36%) and arrhythmias (6.82%). Factors associated with cardiovascular complications on multivariate analysis were: drug treatment change (p = 0.009), previous cardiac complications (p = 0.013) and NYHA class III on the first prenatal visit (p = 0.041). The cardiovascular complication rates were 15.22% in CARPREG 0, 16.42% in CARPREG 1, and 42.11% in CARPREG > 1, differing from those estimated by the original index: 5%, 27% and 75%, respectively. This sample had 26.36% of prematurity. Conclusion: The cardiovascular complication risk factors in this population were drug treatment change, previous cardiac complications and NYHA class III at the beginning of prenatal care. The CARPREG index used in this sample composed mainly of patients with rheumatic heart disease overestimated the number of events in pregnant women classified as CARPREG 1 and > 1, and underestimated it in low-risk patients (CARPREG 0).

Numerical prediction of nanoparticle formation in flames

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2012

Optimal designs for the prediction in hierarchical random coefficient regression models

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015

Likelihood inferences with interval-censored data

En l’anàlisi de la supervivència el problema de les dades censurades en un interval es tracta, usualment,via l’estimació per màxima versemblança. Amb l’objectiu d’utilitzar una expressió simplificada de la funció de versemblança, els mètodes estàndards suposen que les condicions que produeixen la censura no afecten el temps de fallada. En aquest article formalitzem les condicions que asseguren la validesa d’aquesta versemblança simplificada. Així, precisem diferents condicions de censura no informativa i definim una condició de suma constant anàloga a la derivada en el context de censura per la dreta. També demostrem que les inferències obtingudes amb la versemblançaa simplificada són correctes quan aquestes condicions són certes. Finalment, tractem la identificabilitat de la funció distribució del temps de fallada a partir de la informació observada i estudiem la possibilitat de contrastar el compliment de la condició de suma constant.

The Analysis of interval censoring and double censoring via Markov chain Monte Carlo methods

L'Anàlisi de la supervivència s'utilitza en diferents camps per analitzar el temps transcorregut entre dos esdeveniments. El que distingeix l'anàlisi de la supervivència d'altres àrees de l'estadística és que les dades normalment estan censurades. La censura en un interval apareix quan l'esdeveniment final d'interès no és directament observable i només se sap que el temps de fallada està en un interval concret. Un esquema de censura més complex encara apareix quan tant el temps inicial com el temps final estan censurats en un interval. Aquesta situació s'anomena doble censura. En aquest article donem una descripció formal d'un mètode bayesà paramètric per a l'anàlisi de dades censurades en un interval i dades doblement censurades així com unes indicacions clares de la seva utilització o pràctica. La metodologia proposada s'ilustra amb dades d'una cohort de pacients hemofílics que es varen infectar amb el virus VIH a principis dels anys 1980's.

Diagnosing pulmonary embolism in outpatients with clinical assessment, D-dimer measurement, venous ultrasound, and helical computed tomography: a multicenter management study.

PURPOSE: To evaluate a diagnostic strategy for pulmonary embolism that combined clinical assessment, plasma D-dimer measurement, lower limb venous ultrasonography, and helical computed tomography (CT). METHODS: A cohort of 965 consecutive patients presenting to the emergency departments of three general and teaching hospitals with clinically suspected pulmonary embolism underwent sequential noninvasive testing. Clinical probability was assessed by a prediction rule combined with implicit judgment. All patients were followed for 3 months. RESULTS: A normal D-dimer level (&lt;500 microg/L by a rapid enzyme-linked immunosorbent assay) ruled out venous thromboembolism in 280 patients (29%), and finding a deep vein thrombosis by ultrasonography established the diagnosis in 92 patients (9.5%). Helical CT was required in only 593 patients (61%) and showed pulmonary embolism in 124 patients (12.8%). Pulmonary embolism was considered ruled out in the 450 patients (46.6%) with a negative ultrasound and CT scan and a low-to-intermediate clinical probability. The 8 patients with a negative ultrasound and CT scan despite a high clinical probability proceeded to pulmonary angiography (positive: 2; negative: 6). Helical CT was inconclusive in 11 patients (pulmonary embolism: 4; no pulmonary embolism: 7). The overall prevalence of pulmonary embolism was 23%. Patients classified as not having pulmonary embolism were not anticoagulated during follow-up and had a 3-month thromboembolic risk of 1.0% (95% confidence interval: 0.5% to 2.1%). CONCLUSION: A noninvasive diagnostic strategy combining clinical assessment, D-dimer measurement, ultrasonography, and helical CT yielded a diagnosis in 99% of outpatients suspected of pulmonary embolism, and appeared to be safe, provided that CT was combined with ultrasonography to rule out the disease.

Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study.

AIMS/HYPOTHESIS: Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors. METHODS: We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals. RESULTS: The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles. CONCLUSIONS/INTERPRETATION: In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.

Scores to predict major bleeding risk during oral anticoagulation therapy: a prospective validation study.

BACKGROUND: Clinical scores may help physicians to better assess the individual risk/benefit of oral anticoagulant therapy. We aimed to externally validate and compare the prognostic performance of 7 clinical prediction scores for major bleeding events during oral anticoagulation therapy. METHODS: We followed 515 adult patients taking oral anticoagulants to measure the first major bleeding event over a 12-month follow-up period. The performance of each score to predict the risk of major bleeding and the physician's subjective assessment of bleeding risk were compared with the C statistic. RESULTS: The cumulative incidence of a first major bleeding event during follow-up was 6.8% (35/515). According to the 7 scoring systems, the proportions of major bleeding ranged from 3.0% to 5.7% for low-risk, 6.7% to 9.9% for intermediate-risk, and 7.4% to 15.4% for high-risk patients. The overall predictive accuracy of the scores was poor, with the C statistic ranging from 0.54 to 0.61 and not significantly different from each other (P=.84). Only the Anticoagulation and Risk Factors in Atrial Fibrillation score performed slightly better than would be expected by chance (C statistic, 0.61; 95% confidence interval, 0.52-0.70). The performance of the scores was not statistically better than physicians' subjective risk assessments (C statistic, 0.55; P=.94). CONCLUSION: The performance of 7 clinical scoring systems in predicting major bleeding events in patients receiving oral anticoagulation therapy was poor and not better than physicians' subjective assessments.

Efficient knowledge retrieval to calibrate input variables in forest fire prediction

Forest fires are a serious threat to humans and nature from an ecological, social and economic point of view. Predicting their behaviour by simulation still delivers unreliable results and remains a challenging task. Latest approaches try to calibrate input variables, often tainted with imprecision, using optimisation techniques like Genetic Algorithms. To converge faster towards fitter solutions, the GA is guided with knowledge obtained from historical or synthetical fires. We developed a robust and efficient knowledge storage and retrieval method. Nearest neighbour search is applied to find the fire configuration from knowledge base most similar to the current configuration. Therefore, a distance measure was elaborated and implemented in several ways. Experiments show the performance of the different implementations regarding occupied storage and retrieval time with overly satisfactory results.

Prognostication of neurologic outcome in cardiac arrest patients after mild therapeutic hypothermia: a meta-analysis of the current literature.

PURPOSE: To assess the sensitivity and false positive rate (FPR) of neurological examination and somatosensory evoked potentials (SSEPs) to predict poor outcome in adult patients treated with therapeutic hypothermia after cardiopulmonary resuscitation (CPR). METHODS: MEDLINE and EMBASE were searched for cohort studies describing the association of clinical neurological examination or SSEPs after return of spontaneous circulation with neurological outcome. Poor outcome was defined as severe disability, vegetative state and death. Sensitivity and FPR were determined. RESULTS: A total of 1,153 patients from ten studies were included. The FPR of a bilaterally absent cortical N20 response of the SSEP could be calculated from nine studies including 492 patients. The SSEP had an FPR of 0.007 (confidence interval, CI, 0.001-0.047) to predict poor outcome. The Glasgow coma score (GCS) motor response was assessed in 811 patients from nine studies. A GCS motor score of 1-2 at 72 h had a high FPR of 0.21 (CI 0.08-0.43). Corneal reflex and pupillary reactivity at 72 h after the arrest were available in 429 and 566 patients, respectively. Bilaterally absent corneal reflexes had an FPR of 0.02 (CI 0.002-0.13). Bilaterally absent pupillary reflexes had an FPR of 0.004 (CI 0.001-0.03). CONCLUSIONS: At 72 h after the arrest the motor response to painful stimuli and the corneal reflexes are not a reliable tool for the early prediction of poor outcome in patients treated with hypothermia. The reliability of the pupillary response to light and the SSEP is comparable to that in patients not treated with hypothermia.