| 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. |
- Petersson, Håkan, et al.
(författare)
-
Improving inter-rater reliability by coding scheme reorganization : managing signs and symptoms
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this paper is to study the potential for improving inter-rater reliability in general practice registries through the use of a semantic terminology model that enables diagnostic labels to be separated into symptoms and diseases, i.e. into different levels of diagnostic precision. Cases coded as symptoms according to the ICD-based coding system currently in use in Swedish general practice were reclassified with the help of the model, and inter-rater variability was measured through divergences of observed coding distributions from expected distributions. 40 percent of the symptom cases were candidates for reclassification; half of these could actually be reclassified. This decreased inter-rater variability, but the difference was not statistically significant. Diagnostic categories with large variation in utilization rates were foWld, which calls for careful selection of topics for medical audit. Although reclassification of symptoms may improve reliability, no straightforward association was found between a chapter's diagnostic precision and its contribution to overall variability. Nor could differences in diagnostic precision explain all variation within a chapter. Further research on other dimensions of the coding system is needed before symptom reclassification can be recommended as a general reliability-improving tool.
|
|
| 7. |
|
|
| 8. |
- Razavi, Amir Reza, et al.
(författare)
-
A Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer
- 2007
-
Ingår i: Studies in Health Technology and Informatics. - 0926-9630 .- 1879-8365. ; 129, s. 591-597
-
Tidskriftsartikel (refereegranskat)abstract
- Postmastectomy radiotherapy (PMRT) 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 non-compliance between the actual treatment and the PMRT guideline. Data from breast cancer patients admitted to Linköping 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.
|
|
| 9. |
|
|
| 10. |
- Razavi, Amir Reza, et al.
(författare)
-
A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining
- 2005
-
Ingår i: 10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540278313 ; , s. 434-443
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In medicine, data mining methods such as Decision Tree Induction (DTI) can be trained for extracting rules to predict the outcomes of new patients. However, incompleteness and high dimensionality of stored data are a problem. Canonical Correlation Analysis (CCA) can be used prior to DTI as a dimension reduction technique to preserve the character of the original data by omitting non-essential data. In this study, data from 3949 breast cancer patients were analysed. Raw data were cleaned by running a set of logical rules. Missing values were replaced using the Expectation Maximization algorithm. After dimension reduction with CCA, DTI was employed to analyse the resulting dataset. The validity of the predictive model was confirmed by ten-fold cross validation and the effect of pre-processing was analysed by applying DTI to data without pre-processing. Replacing missing values and using CCA for data reduction dramatically reduced the size of the resulting tree and increased the accuracy of the prediction of breast cancer recurrence.
|
|
