1,720,961 research outputs found
A naïve approach for deriving scoring systems to support clinical decision making
No full textScoring systems are frequently proposed in medicine to summarize a set of qualitative and quantitative items by means of a numeric score. Their design often requires modelling ability and subjective judgments. This can make it difficult to adapt a scoring system to a clinical setting different from that in which the system was developed. The objective of this study was to discuss an approach to derive scoring systems, which can be easily modified and matched to any scenario
A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients
Full text linkBackground: Length-of-stay prediction for cardiac surgery patients is a key point for medical management issues, such as optimization of resources in intensive care units and operating room scheduling. Scoring systems are a very attractive family of predictive models, but their retraining and updating are generally critical. The present approach to designing a scoring system for predicting length of stay in intensive care aims to overcome these difficulties, so that a model designed in a given scenario can easily be adjusted over time or for internal purposes.
Methods: A naive Bayes approach was used to develop a simple scoring system. A set of 36 preoperative, intraoperative and postoperative variables collected in a sample of 3256 consecutive adult patients undergoing heart surgery were considered as likely risk predictors. The number of variables was reduced by selecting an optimal subset of features. Scoring system performance was assessed by cross-validation.
Results: After the selection process, seven variables were entered in the prediction model, which showed excellent discrimination, good generalization power and suitable sensitivity and specificity. No significant difference was found between AUC of the training and testing sets. The 95% confidence interval for AUC estimated by the BCa bootstrap method was [0.841, 0.883] and [0.837, 0.880] in the training and testing sets, respectively. Chronic dialysis, low postoperative cardiac output and acute myocardial infarction proved to be the major risk factors.
Conclusions: The proposed approach produced a simple and trustworthy scoring system, which is easy to update regularly and to customize for other centers. This is a crucial point when scoring systems are used as predictive models in clinical practice
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning
Full text linkAbstract Background Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications. Methods Models based on Bayes rule, k-nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view. Results Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. k-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical. Conclusion Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p
Italian medical students quality of life: years 2005-2015
Full text linkBackground. Quality of Life (QoL) is a concept used to indicate the general wellness of persons or societies. University students report a low quality of life and a worse perception of their health status, because of a situation of greater discomfort in which they live during the course of the study, especially in faculties with an important emotional burden, such as medical schools. The aim of the study was to evaluate the perceived health status of first year medical students. Methods. We conducted a cross sectional study in the time span 2005-2015, administering the questionnaire Short Form 36 (SF-36) to first-year students of the School of Medicine of the University of Siena, Italy. In addition to demographic information such as gender and the age we investigated the region of residence, marital status, employment status, and smoking habits; height and weight were required to calculate the body mass index (BMI) to evaluate a possible physical discomfort connected with the perception of health status. The data from the questionnaires were organized and processed by software Stata® SE, version 12.1. Results. 1,104 questionnaires were collected. Medical students reported lower SF-36 scores, compared to the Italian population of the same age. Female gender and smoking habits influence negatively the score of several scales. Body Mass Index is positively correlated with the Physical Activity, while Age is negatively correlated with Social Activities. Conclusions. The perceived quality of life of the Italian medical students is lower when compared to the general population. This confirms that the condition of student implies additional problems, as other studies reports. It would be better to improve it, developing students' resilience. It would be interesting to extend this research to students of other years, from other faculties and other locations, to gain a broader view about the QoL of the Italian students
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example
Full text linkAbstract Background 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. Methods Eight models were developed: Bayes linear and quadratic models, k-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. Results Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-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, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Conclusion 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
Trend of accesses to the ED of Teaching Hospital of Tuscany for bronchiolitis 2018-2023: new emergency
No full textIntroduction: Acute bronchiolitis is the leading cause of lower respiratory tract infection and hospitalisation in children less than one year old worldwide. The aim of our study is to analyse paediatric accesses of children with bronchiolitis to the Emergency Department (ED) of Teaching Hospital (AOUS), Santa Maria alle Scotte of Siena, Tuscany
Methods: A retrospective observational study was conducted on the accesses performed at the ED of the AOUS of Siena by children under 18 years of age suffering from bronchiolitis from September 2018 to April 2023.
Results: There were 36031 patients between 0 and 18 years old in the Emergency Department, 383 of which presented bronchiolitis (age 4.8 months C.I.:3.5-6 months.; 54% male). Those who accessed the ED with a higher priority code were more likely to be subsequently admitted (O.R.:2.6; C.I.:1.3-5.1; p<0.01). Those who accessed the ED with symptoms of bronchiolitis during the weekend were less likely to have been sent from community medicine services or professionals (O.R:0.1; C.I:0.0-0.5; p<0.001). Children below 1 year old were more likely to access the ED with respiratory distress symptoms (O.R.:2.6; C.I.:1.5-4.3; p<0.001). Finally, those who accessed the ED with bronchiolitis were more likely to be admitted than those who accessed for other conditions (O.R:24.5; C.I.:19.4-31; p<0.001).
Conclusions: It is necessary to invest protocols integrating hospital services and community medicine in order to achieve a timely diagnosis and to reduce the accesses to the ED of children presenting mild, non-severe form of bronchiolitis in order to avoid the overload of hospital service
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
