Exploring cancer register data to find risk factors for recurrence of breast cancer – application of Canonical Correlation Analysis

BACKGROUND: A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. METHODS: Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built. RESULTS: The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor. CONCLUSION: In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones

The nature of unintended effects of Health Information Systems concerning patient safety: A systematic review with thematic synthesis

In order to understand the nature and causes through which Health Information Systems (HIS) can affect patient safety negatively, a systematic review with thematic synthesis of the qualitative studies was performed. 26 papers met our criteria and were included into content analysis. 40 error contributing factors in working with HIS were recognized. Upon which, 4 main categories of contributing factors were defined. Analysis of the semantic relation between contributing reasons and common types of errors in healthcare practice revealed 6 mechanisms that can function as secondary contributing reasons. Results of this study can support care providers, system designers, and system implementers to avoid unintended negative effects for patient safety

Linköping University E-Press: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15222BMC Medical Informatics and Decision Making

Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysi

Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis

Background: The guideline for postmastectomy radiotherapy (PMRT), which is prescribed to reduce recurrence of breast cancer in the chest wall and improve overall survival, is not always followed. Identifying and extracting important patterns of non-compliance are crucial in maintaining the quality of care in Oncology. Methods: Analysis of 759 patients with malignant breast cancer using decision tree induction (DTI) found patterns of non-compliance with the guideline. The PMRT guideline was used to separate cases according to the recommendation to receive or not receive PMRT. The two groups of patients were analyzed separately. Resulting patterns were transformed into rules that were then compared with the reasons that were extracted by manual inspection of records for the non-compliant cases. Results: Analyzing patients in the group who should receive PMRT according to the guideline did not result in a robust decision tree. However, classification of the other group, patients who should not receive PMRT treatment according to the guideline, resulted in a tree with nine leaves and three of them were representing non-compliance with the guideline. In a comparison between rules resulting from these three non-compliant patterns and manual inspection of patient records, the following was found: In the decision tree, presence of perigland growth is the most important variable followed by number of malignantly invaded lymph nodes and level of Progesterone receptor. DNA index, age, size of the tumor and level of Estrogen receptor are also involved but with less importance. From manual inspection of the cases, the most frequent pattern for non-compliance is age above the threshold followed by near cut-off values for risk factors and unknown reasons. Conclusion: Comparison of patterns of non-compliance acquired from data mining and manual inspection of patient records demonstrates that not all of the non-compliances are repetitive or important. There are some overlaps between important variables acquired from manual inspection of patient records and data mining but they are not identical. Data mining can highlight non-compliance patterns valuable for guideline authors and for medical audit. Improving guidelines by using feedback from data mining can improve the quality of care in oncology.Original publication: Amir R Razavi, Hans Gill, Hans Åhlfeldt and Nosrat Shahsavar, Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis, 2008, BMC Medical Informatics and Decision Making, (8), 41.http://dx.doi.org/10.1186/1472-6947-8-41. Copyright: The author

A decision support model for cost-effectiveness of radical prostatectomy in localized prostate cancer

Objective. This study aimed to develop a probabilistic decision support model to calculate the lifetime incremental cost-effectiveness ratio (ICER) between radical prostatectomy and watchful waiting for different patient groups. Material and methods. A randomized trial (SPCG-4) provided most data for this study. Data on survival, costs and quality of life were inputs in a decision analysis, and a decision support model was developed. The model can generate cost-effectiveness information on subgroups of patients with different characteristics. Results. Age was the most important independent factor explaining cost-effectiveness. The cost-effectiveness value varied from 21 026 Swedish kronor (SEK) to 858 703 SEK for those aged 65 to 75 years, depending on Gleason scores and prostate-specific antigen (PSA) values. Information from the decision support model can support decision makers in judging whether or not radical prostatectomy (RP) should be used to treat a specific patient group. Conclusions. The cost-effectiveness ratio for RP varies with age, Gleason scores, and PSA values. Assuming a threshold value of 200 000 SEK per quality-adjusted life-year (QALY) gained, for patients aged ≤70 years the treatment was always cost-effective, except at age 70, Gleason 0–4 and PSA ≤10. Using the same threshold value at age 75, Gleason 7–9 (regardless of PSA) and Gleason 5–6 (with PSA >20) were cost-effective. Hence, RP was not perceived to be cost-effective in men aged 75 years with low Gleason and low PSA. Higher threshold values for patients with clinically localized prostate cancer could be discussed

Exploring cancer register data to find risk factors for recurrence of breast cancer – application of Canonical Correlation Analysis

Abstract Background A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. Methods Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built. Results The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor. Conclusion In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones.</p

Mother knowledge about child development, parenting and perceveid social support after participation in parent training program „Encouraging Children’s Healthy Emotional Development”

Šajā pētījumā piedalījās 60 pirmsskolas vecuma bērnu mātes no, kurām 30 piedalījās vecāku apmācības programmā „Bērnu emocionālā audzināšana” un 30 mātes bija kontroles grupā. Veikti „Zināšanu par bērna attīstību” pirmskolas versijas (KIDI – P, MacPhee, 1981, Latvijā adaptējusi, Ukstiņa, 2011) un „Izjustā sociālā atbalsta” (MSPSS, Zimet, Dahlem, Zimet, & Farley,1988, Latvijā adaptējušas - Voitkāne, Miezīte, & Raščevska, 2004) aptauju pirms un pēc mērījumi abām grupām. Iegūtie rezultāti parādīja, ka statistiski nozīmīgi ir pieaugušas zināšanas mātēm apmācības programmas laikā, ko galvenokārt ir ietekmējusi mātes iegūtā pārliecība par savām zināšanām. Tika secināts, ka pastāv nozīmīgas sakarības zināšanu precizitātē un māšu izjustajā atbalstā no ģimenes un nozīmīgiem citiem cilvēkiem. Atslēgas vārdi: māte, zināšanas, bērna audzināšana, bērna attīstība, izjustais sociālais atbalstsThis study is based on a sample of 60 preschooler mothers. 30 of selected mothers participated in the parent training program "BEA" while other 30 served as a control group. Both groups were tested by employing surveys „Knowledge of child development” preschool version inventory (KIDI –P, MacPhee, 1981, in Latvia adapted by Ukstiņa, 2011) and „The multidimensional Scales of Perceived Social Support”(MSPSS, Zimet, Dahlem, Zimet,& Farley, 1988, in Latvia adapted by Voitkāne Miezīte, & Raščevska, 2004). All mothers were surveyed before and after the training programme. The evidence of this research shows a statistically significant increase in knowledge among the mothers that participated in the training programme. This was mainly a result of increased confidence about their knowledge. At the same time, itit was concluded that there exist statistically significant corelation between knowledge accuracy and perceived support from family and other important people

http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70933 A Decision Support Model for Cost Effectiveness of Radical Prostatectomy in Localized Prostate Cancer

A decision support model for cost-effectivenes

Hans Åhlfeldt1, Nosrat Shahsavar1,5 and the South-East Swedish Breast Cancer Study Group

N.B.: When citing this work, cite the original article