A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context

Polycystic ovary syndrome and leukocyte telomere length : cross-sectional and longitudinal changes

Objective Telomeres are DNA-protein complexes that protect chromosome ends from DNA damage and are surrogate biomarkers of cellular ageing. Current evidence, almost entirely from cross-sectional observations, supports negative associations between leukocyte telomere length (LTL) and adverse lifestyle factors and cardio-metabolic risk factors. Polycystic ovary syndrome (PCOS), the most common gynecological endocrine disorder, is associated with inflammation and oxidative stress, both factors associated with accelerated telomere attrition. We therefore hypothesized that LTL would be shorter and decrease more rapidly in women with PCOS in comparison to a control population. Design Population-based cohort study: women of Northern Finland Birth Cohort 1966, with clinical examinations at ages 31 and 46. The sample included self-reported PCOS (PCOS) (age 31:N=190; age 46:N=207) and referent women (age 31:N=1054; age 46:N=1324) with data on LTL. Methods The association between LTL and PCOS at ages 31 and 46 was analyzed by linear regression models adjusted for BMI, smoking, alcohol consumption and socioeconomic status at the corresponding age. Results Women with PCOS had similar mean LTL at ages 31 and 46 (P>0.4 for both). The mean LTL change between ages 31 and 46 did not differ between groups (P=0.19). However, we observed a significant LTL attrition between ages 31 and 46 in the reference population (P<0.001), but not in women with PCOS (P=0.96). Conclusions This finding may suggest a difference in LTL attrition rate in women with PCOS, an unexpected finding that might affect their risk of age-related disease. Further research is needed to clarify the underlying mechanisms

Sustainable Forest Management Preferences of Interest Groups in Three Regions with Different Levels of Industrial Forestry: An Exploratory Attribute-Based Choice Experiment

The challenge of sustainable forest management is to integrate diverse and sometimes conflicting management objectives. In order to achieve this goal, we need a better understanding of the aspects influencing the preferences of diverse groups and how these groups make trade-offs between different attributes of SFM. We compare the SFM preferences of interest groups in regions with different forest use histories based on the reasoning that the condition of the forest reflects the forest use history of the area. The condition of the forest also shapes an individual’s forest values and attitudes. These held values and attitudes are thought to influence SFM preferences. We tested whether the SFM preferences vary amongst the different interest groups within and across regions. We collected data from 252 persons using a choice experiment approach, where participants chose multiple times among different options described by a combination of attributes that are assigned different levels. The novelty of our approach was the use of choice experiments in the assessment of regional preference differences. Given the complexity of interregional comparison and the small sample size, this was an exploratory study based on a purposive rather than random sample. Nevertheless, our results suggest that the aggregation of preferences of all individuals within a region does not reveal all information necessary for forest management planning since opposing viewpoints could cancel each other out and lead to an interpretation that does not reflect possibly polarised views. Although based on a small\ud sample size, the preferences of interest groups within a region are generally statistically significantly different from each other; however preferences of interest groups across regions are also significantly different. This illustrates the potential importance of assessing heterogeneity by region and by group

Epidural anesthesia and postoperative analgesia with ropivacaine and fentanyl in off-pump coronary artery bypass grafting: a randomized, controlled study

<p>Abstract</p> <p>Background</p> <p>Our aim was to assess the efficacy of thoracic epidural anesthesia (EA) followed by postoperative epidural infusion (EI) and patient-controlled epidural analgesia (PCEA) with ropivacaine/fentanyl in off-pump coronary artery bypass grafting (OPCAB).</p> <p>Methods</p> <p>In a prospective study, 93 patients were scheduled for OPCAB under propofol/fentanyl anesthesia and randomized to three postoperative analgesia regimens aiming at a visual analog scale (VAS) score < 30 mm at rest. The control group (n = 31) received intravenous fentanyl 10 μg/ml postoperatively 3-8 mL/h. After placement of an epidural catheter at the level of Th₂-Th₄before OPCAB, a thoracic EI group (n = 31) received EA intraoperatively with ropivacaine 0.75% 1 mg/kg and fentanyl 1 μg/kg followed by continuous EI of ropivacaine 0.2% 3-8 mL/h and fentanyl 2 μg/mL postoperatively. The PCEA group (n = 31), in addition to EA and EI, received PCEA (ropivacaine/fentanyl bolus 1 mL, lock-out interval 12 min) postoperatively. Hemodynamics and blood gases were measured throughout 24 h after OPCAB.</p> <p>Results</p> <p>During OPCAB, EA decreased arterial pressure transiently, counteracted changes in global ejection fraction and accumulation of extravascular lung water, and reduced the consumption of propofol by 15%, fentanyl by 50% and nitroglycerin by a 7-fold, but increased the requirements in colloids and vasopressors by 2- and 3-fold, respectively (<it>P </it>< 0.05). After OPCAB, PCEA increased PaO₂/FiO₂at 18 h and decreased the duration of mechanical ventilation by 32% compared with the control group (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>In OPCAB, EA with ropivacaine/fentanyl decreases arterial pressure transiently, optimizes myocardial performance and influences the perioperative fluid and vasoactive therapy. Postoperative EI combined with PCEA improves lung function and reduces time to extubation.</p> <p>Trial Registration</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT01384175">NCT01384175</a></p

Who are the users of national open access journals? : The case of the Finnish Journal.fi platform

In this paper we study the diversity of users of open access articles on the Finnish Journal.fi platform. This platform hosts around hundred open access journals from Finland publishing in different fields and mainly Finnish and English languages. The study is based on an online survey, conducted on 48 journals during Spring 2020, in which visitors were asked to indicate their background and allow their location and download behaviour be tracked. Among 668 survey participants, the two largest groups were students (40%) and researchers (36%), followed by private citizens (8%), other experts (7%) and teachers (5%). Other identified user categories include journalists, civil servants, entrepreneurs and politicians. While new publications attract a considerable share of the views, there is still a relatively large interest, especially among students, in older materials. Our findings indicate that Finnish language publications are particularly important for reaching students, citizens, experts and politicians. Thus, open access to publications in national languages is vital for the local relevance and outreach of research.

Effect of hepatic folic acid status on formic acid metabolism in blue foxes (Alopex lagopus)

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