| 1. |
- Ridderstolpe, Lisa, et al.
(författare)
-
Superficial and deep sternal wound complications : Incidence, risk factors and mortality
- 2001
-
Ingår i: European Journal of Cardio-Thoracic Surgery. - : Oxford Academic. - 1010-7940 .- 1873-734X. ; 20:6, s. 1168-1175
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives: Sternal wound complications often have a late onset and are detected after patients are discharged from the hospital. In an effort to catch all sternal wound complications, different postdischarge surveillance methods have to be used. Together with this long-term follow-up an analysis of risk factors may help to identify patients at risk and can lead to more effective preventive and control measures.Methods: This retrospective study of 3008 adult patients who underwent consecutive cardiac surgery from January 1996 through September 1999 at Link÷ping University Hospital, Sweden, evaluated 42 potential risk factors by univariate analysis followed by backward stepwise multivariate logistic regression analysis.Results: Two-thirds of the 291 (9.7%) sternal wound complications that occurred were identified after discharge. Of the 291 patients, 47 (1.6%) had deep sternal infections, 50 (1.7%) had postoperative mediastinitis, and 194 (6.4%) had superficial sternal wound complications. Twenty-three variables were selected by univariate analysis (P<0.15) and included in a multivariate analysis where eight variables emerged as significant (P<0.05). Preoperative risk factors for deep sternal infections/mediastinitis were obesity, insulin-dependent diabetes, smoking, peripheral vascular disease, and high New York Heart Association score. An intraoperative risk factor was bilateral use of internal mammary arteries, and a postoperative risk factor was prolonged ventilator support. Risk factors for superficial sternal wound complications were obesity, and an age of
|
|
| 2. |
- Ridderstolpe, Lisa, et al.
(författare)
-
Canonical correlation analysis of risk factors and clinical outcomes in cardiac surgery
- 2005
-
Ingår i: Journal of medical systems. - : Springer. - 0148-5598 .- 1573-689X. ; 29:4, s. 357-377
-
Tidskriftsartikel (refereegranskat)abstract
- Assessment of the association between risk factors and outcomes in cardiac surgery is a complex problem. The aim of this study was to explore the relationship between possible risk factors and several clinical outcomes in cardiac surgery by using canonical correlation analysis (CCA). This retrospective study of 2605 consecutive adult patients who underwent cardiac surgery, evaluated 74 potential risk factors and up to 12 outcomes by canonical correlation analysis. For three serious outcomes, sternal wound complications/mediastinitis, cerebral complications, and perioperative myocardial infarctions, CCA was preceded by univariate analyses and backward stepwise multivariate logistic regression analyses. The CCA suggests that the major risk factors for complications in these models are intraoperative and postoperative risk factors. The power of risk prediction models developed with multivariate regression analysis can be enhanced by application of canonical correlation analysis, thereby offering new ways of analyzing and interpreting sets of potential risk factors in relation to sets of clinical outcomes.
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Petersson, Håkan, 1972-, et al.
(författare)
-
A variance-based measure of inter-rater agreement in medical databases
- 2002
-
Ingår i: Journal of Biomedical Informatics. - 1532-0464 .- 1532-0480. ; 35:5-6, s. 331-342
-
Tidskriftsartikel (refereegranskat)abstract
- The increasing use of encoded medical data requires flexible tools for data quality assessment. Existing methods are not always adequate, and this paper proposes a new metric for inter-rater agreement of aggregated diagnostic data. The metric, which is applicable in prospective as well as retrospective coding studies, quantifies the variability in the coding scheme, and the variation can be differentiated in categories and in coders. Five alternative definitions were compared in a set of simulated coding situations and in the context of mortality statistics. Two of them were more effective, and the choice between them must be made according to the situation. The metric is more powerful for larger numbers of coded cases, and Type I errors are frequent when coding situations include different numbers of cases. We also show that it is difficult to interpret the meaning of variation when the structures of the compared coding schemes differ.
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Razavi, Amir Reza, 1973-, et al.
(författare)
-
An approach for generating fuzzy rules from decision trees
- 2006
-
Ingår i: Ubiquity. - : IOS Press. - 9781586036478 ; , s. 581-586
-
Konferensbidrag (refereegranskat)abstract
- Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree. After assigning membership functions for each variable in the crisp rules, they were converted into fuzzy rules and a mathematical model was constructed. One hundred randomly selected cases were examined by this model and compared with crisp rules predictions. The outcomes were examined by the area under the ROC curve (AUC). No significant difference was noticed between these two approaches for prediction of recurrence of breast cancer. By soft discretization of variables according to resulting rules from DTI, a predictive model, which is both more robust to noise and more comprehensible for clinicians, can be built.
|
|
| 9. |
- Razavi, Amir Reza, 1973-, et al.
(författare)
-
Canonical correlation analysis for data reduction in data mining applied to predictive models for breast cancer recurrence
- 2005
-
Ingår i: The XIXth International Congress of the European Federation for Medical Informatics,2005. - Amsterdam : IOSPress. ; , s. 175-180
-
Konferensbidrag (refereegranskat)abstract
- Data mining methods can be used for extracting specific medical knowledge such as important predictors for recurrence of breast cancer in pertinent data material. However, when there is a huge quantity of variables in the data material it is first necessary to identify and select important variables. In this study we present a preprocessing method for selecting important variables in a dataset prior to building a predictive model. In the dataset, data from 5787 female patients were, analysed. To cover more predictors and obtain a better assessment of the outcomes, data were retrieved from three different registers: the regional breast cancer, tumour markers, and cause of death registers. After retrieving information about selected predictors and outcomes from the different registers, the raw data were cleaned by running different logical rules. Thereafter, domain experts selected predictors assumed to be important regarding recurrence of breast cancer. After that, Canonical Correlation Analysis (CCA) was applied as a dimension reduction technique to preserve the character of the original data. Artificial Neural Network (ANN) was applied to the resulting dataset for two different analyses with the same settings. Performance of the predictive models was confirmed by ten-fold cross validation. The results showed an increase in the accuracy of the prediction and reduction of the mean absolute error.
|
|
| 10. |
- Razavi, Amir Reza, 1973-, et al.
(författare)
-
Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer
- 2007
-
Ingår i: MEDINFO 2007: PROCEEDINGS OF THE 12TH WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2. - : IOS Press. - 9781586037741 ; , s. 591-595
-
Konferensbidrag (refereegranskat)abstract
- Postmastectomy radiotherapy (PAMT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT However, this guideline is not always followed in practice. This study tries to find a method for revealing patterns of noncompliance between the actual treatment and the PMRT guideline.Data from breast cancer patients admitted to Linkoping University Hospital between 1990 and 2000 were analyzed in this study. Cases that were not treated in accordance with the guideline were selected and analyzed by decision tree induction (DTI). Thereafter, four resulting rules, as representations for groups of patients, were compared to the guideline.Finding patterns of non-compliance with guidelines by means of rules can be an appropriate alternative to manual methods, i.e. a case-by-case comparison when studying very large datasets. The resulting rules can be used in a knowledge base of a guideline-based decision support system to alert when inconsistencies with the guidelines may appear.
|
|
